@article{Attaway2016,
abstract = {Background: Risk maps identifying suitable locations for infection transmission are important for public health planning. Data on dengue infection rates are not readily available in most places where the disease is known to occur. Methods: A newly available add-in to Esri's ArcGIS software package, the ArcGIS Predictive Analysis Toolset (PA Tools), was used to identify locations within Africa with environmental characteristics likely to be suitable for transmission of dengue virus. Results: A more accurate, robust, and localized (1 km × 1 km) dengue risk map for Africa was created based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that a search of the peer-reviewed literature showed to be associated with dengue risk. Variables related to temperature, precipitation, elevation, and population density were identified as good predictors of dengue suitability. Areas of high dengue suitability occur primarily within West Africa and parts of Central Africa and East Africa, but even in these regions the suitability is not homogenous. Conclusion: This risk mapping technique for an infection transmitted by Aedes mosquitoes draws on entomological, epidemiological, and geographic data. The method could be applied to other infectious diseases (such as Zika) in order to provide new insights for public health officials and others making decisions about where to increase disease surveillance activities and implement infection prevention and control efforts. The ability to map threats to human and animal health is important for tracking vectorborne and other emerging infectious diseases and modeling the likely impacts of climate change.},
author = {Attaway, David F. and Jacobsen, Kathryn H. and Falconer, Allan and Manca, Germana and Waters, Nigel M.},
doi = {10.1016/j.actatropica.2016.02.018},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Sisa Terblok Putih/attaway2016.pdf:pdf},
issn = {18736254},
journal = {Acta Tropica},
keywords = {Africa,Dengue,Developing countries,Geographic information systems,Medical geography,Risk mapping},
pages = {248--257},
pmid = {26945482},
publisher = {Elsevier B.V.},
title = {{Risk analysis for dengue suitability in Africa using the ArcGIS predictive analysis tools (PA tools)}},
url = {http://dx.doi.org/10.1016/j.actatropica.2016.02.018},
volume = {158},
year = {2016}
}
@article{Zheng2019,
abstract = {Background: Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people's health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics. Methods: A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed. Results: DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidityerature factors for the PRD and the ULR-Road density-NDVIerature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics. Conclusions: The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics.},
author = {Zheng, Lan and Ren, Hong Yan and Shi, Run He and Lu, Liang},
doi = {10.1186/s40249-019-0533-9},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Sisa Terblok Putih/Zheng 2019.pdf:pdf},
issn = {20499957},
journal = {Infectious Diseases of Poverty},
keywords = {China,Dengue fever,Environmental factor,Generalized additive model,Socioeconomic factor,Spatiotemporal pattern},
month = {mar},
number = {24},
pages = {1--12},
pmid = {30922405},
title = {{Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China}},
volume = {8},
year = {2019}
}
@article{Yajid2020,
abstract = {Data on annual cumulative DF cases in Melaka Tengah from 2013 to 2016 was used to determine the distribution pattern of DF cases. The spatial distribution was examined using average nearest neighbour (ANN) techniques and kernel density estimation (KDE) to detect the hotspot areas. The spatial distribution of dengue incidence within Melaka Tengah area showed that the spatial pattern of DF cases was dispersed at multiple distances. Also, this study simulated DF hotspot model by extrapolating the pattern of dengue activity in the previous interepidemic period. It was found that the DF epidemic in Melaka Tengah was spread throughout study area during the end of the years between July and December, and the wide spatial distribution was conserved during the peak of the epidemic in September. Overall, this analysis demonstrated spatial clustering of DF activity which can facilitate prediction of the magnitude, timing, and location of future DF epidemic.},
author = {Yajid, Mohd Zulhafiz Mohd and {Che Dom}, Nazri and Camalxaman, Siti Nazrina and Nasir, Rabiatul Adawiyah},
doi = {10.1080/10106049.2019.1581265},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Yajid SpatialtemporalanalysisforidentificationofdengueriskareainMelakaTengahdistrict1.pdf:pdf},
issn = {10106049},
journal = {Geocarto International},
keywords = {Dengue,GIS,epidemiology,spatial analysis},
number = {14},
pages = {1570--1579},
publisher = {Taylor & Francis},
title = {{Spatial-temporal analysis for identification of dengue risk area in Melaka Tengah district}},
url = {https://doi.org/10.1080/10106049.2019.1581265},
volume = {35},
year = {2020}
}
@article{Henry2020,
abstract = {Over the years, Jamaica has experienced sporadic cases of dengue fever. Even though the island is vulnerable to dengue, there is paucity in the spatio‐temporal analysis of the disease using Geographic Information Systems (GIS) and remote sensing tools. Further, access to time series dengue data at the community level is a major challenge on the island. This study therefore applies the Water‐Associated Disease Index (WADI) framework to analyze vulnerability to dengue in Jamaica based on past, current and future climate change conditions using three scenarios: (1) WorldClim rainfall and temperature dataset from 1970 to 2000; (2) Climate Hazard Group InfraRed Precipitation with Station data (CHIRPS) rainfall and land surface temperature (LST) as proxy for air temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2002 to 2016, and (3) maximum temperature and rainfall under the Representative Concentration Pathway (RCP) 8.5 climate change scenario for 2030. downscaled at 25 km based on the Regional Climate Model, RegCM4.3.5. Although vulnerability to dengue varies spatially and temporally, a higher vulnerability was depicted in urban areas in comparison to rural areas. The results also demonstrate the possibility for expansion in the geographical range of dengue in higher altitudes under climate change conditions based on scenario 3. This study provides an insight into the use of data with different temporal and spatial resolution in the analysis of dengue vulnerability.},
author = {Henry, Sheika and Mendon{\c{c}}a, Francisco de Assis},
doi = {10.3390/ijerph17093156},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Henry 2020 Past, Present, and Future Vulnerability to Dengue in Jamaica A Spatial Analysis of Monthly Variations.pdf:pdf},
issn = {16604601},
journal = {International Journal of Environmental Research and Public Health},
keywords = {Climate change,Dengue,Jamaica,Vulnerability assessment,WADI framework},
number = {9},
pages = {1--14},
pmid = {32369951},
title = {{Past, present, and future vulnerability to dengue in jamaica: A spatial analysis of monthly variations}},
volume = {17},
year = {2020}
}
@article{Hagenlocher2013,
abstract = {Background: As a result of changes in climatic conditions and greater resistance to insecticides, many regions across the globe, including Colombia, have been facing a resurgence of vector-borne diseases, and dengue fever in particular. Timely information on both (1) the spatial distribution of the disease, and (2) prevailing vulnerabilities of the population are needed to adequately plan targeted preventive intervention. We propose a methodology for the spatial assessment of current socioeconomic vulnerabilities to dengue fever in Cali, a tropical urban environment of Colombia.Methods: Based on a set of socioeconomic and demographic indicators derived from census data and ancillary geospatial datasets, we develop a spatial approach for both expert-based and purely statistical-based modeling of current vulnerability levels across 340 neighborhoods of the city using a Geographic Information System (GIS). The results of both approaches are comparatively evaluated by means of spatial statistics. A web-based approach is proposed to facilitate the visualization and the dissemination of the output vulnerability index to the community.Results: The statistical and the expert-based modeling approach exhibit a high concordance, globally, and spatially. The expert-based approach indicates a slightly higher vulnerability mean (0.53) and vulnerability median (0.56) across all neighborhoods, compared to the purely statistical approach (mean = 0.48; median = 0.49). Both approaches reveal that high values of vulnerability tend to cluster in the eastern, north-eastern, and western part of the city. These are poor neighborhoods with high percentages of young (i.e., < 15 years) and illiterate residents, as well as a high proportion of individuals being either unemployed or doing housework.Conclusions: Both modeling approaches reveal similar outputs, indicating that in the absence of local expertise, statistical approaches could be used, with caution. By decomposing identified vulnerability " hotspots" into their underlying factors, our approach provides valuable information on both (1) the location of neighborhoods, and (2) vulnerability factors that should be given priority in the context of targeted intervention strategies. The results support decision makers to allocate resources in a manner that may reduce existing susceptibilities and strengthen resilience, and thus help to reduce the burden of vector-borne diseases. {\textcopyright} 2013 Hagenlocher et al.; licensee BioMed Central Ltd.},
author = {Hagenlocher, Michael and Delmelle, Eric and Casas, Irene and Kienberger, Stefan},
doi = {10.1186/1476-072X-12-36},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/hagenlocher 2013 Assessing socioeconomic vulnerability to dengue fever in Cali, Columbia.pdf:pdf},
issn = {1476072X},
journal = {International Journal of Health Geographics},
keywords = {Colombia,Composite indicators,Dengue fever,Expert-based vs. statistical modeling,GIS,Geovisualization,Vector-borne diseases,Vulnerability},
number = {36},
pages = {1--14},
pmid = {23945265},
title = {{Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: Statistical vs expert-based modeling}},
volume = {12},
year = {2013}
}
@article{Pastrana2014,
abstract = {Dengue fever is among the most important emerging infectious diseases in the world, and in recent years it has been a source of public concern for the public health control systems of many tropical and subtropical countries. Thus, the purpose of this study was to apply spatial and statistical methodologies to analyze the geographic distribution of dengue and to relate its incidence to the Health Vulnerability Index (HVI), an indicator that integrates different socioeconomic variables to estimate the degree of health vulnerability in different Brazilian cities. The cases of dengue, incidence rates and Bayesian incidence rates were determined using census tracts covering a period of 3. years in a city with socioeconomic and administrative characteristics typical of Brazilian municipalities. Distribution plots, descriptive statistics, kernel density maps, test of global and local spatial autocorrelation and Spearman correlation were used. No association was found between the incidence of dengue and the HVI. Conversely, statistically significant high-incidence clusters were found over the 3. years in an area identified as having lower health vulnerability. The finding that HVI was not a good indicator of dengue in the city studied may be explained by the complexity of the disease. Administrative and financial problems in the municipalities, environmental factors, cultural changes and the emergence of new serotypes are other factors that hinder the understanding and control of the disease. However, the spatial and statistical methodologies used here are suitable and useful tools for the accurate understanding of dengue and other infectious epidemiological processes.},
author = {Pastrana, M.E.O. and Brito, R.L. and Nicolino, R.R. and de Oliveira, C.S.F. and Haddad, J.P.A.},
doi = {10.1016/j.sste.2014.04.001},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/pastrana 2014 Spatial and statistical methodologies to determine the distribution of dengue in Brazilian municipalities and relate incidence with the Health Vulnerability Index.pdf:pdf},
issn = {18775853},
journal = {Spatial and Spatio-temporal Epidemiology},
keywords = {Dengue fever,Epidemiology,Health vulnerability index,Spatial distribution,Spatial statistics},
pages = {143--151},
pmid = {25457603},
publisher = {Elsevier Ltd},
title = {{Spatial and statistical methodologies to determine the distribution of dengue in Brazilian municipalities and relate incidence with the health vulnerability index}},
url = {http://dx.doi.org/10.1016/j.sste.2014.04.001},
volume = {11},
year = {2014}
}
@article{Hu2012,
abstract = {Background: Understanding how socioecological factors affect the transmission of dengue fever (DF) may help to develop an early warning system of DF. Objectives: We examined the impact of socioecological factors on the transmission of DF and assessed potential predictors of locally acquired and overseas-acquired cases of DF in Queensland, Australia. Methods: We obtained data from Queensland Health on the numbers of notified DF cases by local government area (LGA) in Queensland for the period 1 January 2002 through 31 December 2005. Data on weather and the socioeconomic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive model was fitted at the LGA level to quantify the relationship between DF and socioecological factors. Results: Our estimates suggest an increase in locally acquired DF of 6% [95% credible interval (CI): 2%, 11%] and 61% (95% CI: 2%, 241%) in association with a 1-mm increase in average monthly rainfall and a 1°C increase in average monthly maximum temperature between 2002 and 2005, respectively. By contrast, overseas-acquired DF cases increased by 1% (95% CI: 0%, 3%) and by 1% (95% CI: 0%, 2%) in association with a 1-mm increase in average monthly rainfall and a 1-unit increase in average socioeconomic index, respectively. Conclusions: Socioecological factors appear to influence the transmission of DF in Queensland, but the drivers of locally acquired and overseas-acquired DF may differ. DF risk is spatially clustered with different patterns for locally acquired and overseas-acquired cases.},
author = {Hu, W. and Clements, A. and Tong, S. and Williams, G. and Mengersen, K.},
doi = {10.1289/ehp.1003270},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Hu 2012 Spatial Patterns and socioecological Drivers of dengue fever transmission.pdf:pdf},
issn = {15529924},
journal = {Environmental Health Perspectives},
keywords = {Bayesian spatial analysis,Dengue,Socioecological factors. environ health},
number = {2},
pages = {260--266},
title = {{Spatial patterns and socioecological drivers of dengue fever transmission in queensland, Australia}},
volume = {120},
year = {2012}
}
@article{Faridah2021,
abstract = {Background: Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases. Methods: Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area's developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases. Results: The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city. Conclusions: This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs.},
author = {Faridah, L. and Mindra, I.G.N. and Putra, R.E. and Fauziah, N. and Agustian, D. and Natalia, Y.A. and Watanabe, K.},
doi = {10.1186/s41182-021-00329-9},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Faridah 2021 Spatial and temporal analysis of hospotalized dengue patients in Bandung.pdf:pdf},
issn = {13494147},
journal = {Tropical Medicine and Health},
keywords = {Bandung,Dengue infection,Spatial pattern},
number = {1},
pages = {1--9},
publisher = {Tropical Medicine and Health},
title = {{Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk}},
volume = {49},
year = {2021}
}
@article{Hafeez2017,
abstract = {Background Dengue is identified as serious vector born infectious disease by WHO, threating around 2.5 billion people around the globe. Pakistan is facing dengue epidemic since 1994 but 2010 and 2011 dengue outbreaks were worst. During 2011 dengue outbreak 22,562 cases were reported and 363 died due to this fatal infection in Pakistan. In this study, Lahore District was chosen as it was severely affected in 2011 dengue outbreak with 14,000 reported cases and 300 deaths. There is no vaccine developed yet for the disease control, so only effective early warning, prevention and control measures can reduce the potential disease risk. Methods This study proposes a method for detecting spatial autocorrelation of temporal dynamics of disease using Local Index of Spatial Autocorrelation (LISA) using three temporal indices: (a) how often the dengue cases occur, frequency index; (b) how long the epidemic wave prevails, duration index; (c) how significant dengue cases occur in successive periods, severity index. Overlay analysis of LISA value for each temporal index resulted in eight risk types. Results The mapping of spatio-temporal risk indices and their overlay analysis identified that 10.6% area of Lahore (184.3 km2 and population density 119,110 persons/km2) had high values for frequency, duration, and severity index (p < 0.05) and 16% area (having 25% population) is at potential risk of dengue. Conclusion Spatial risk identification by using local spatial-autocorrelation helps in identifying other possible causes of disease risk and further strategic planning for prevention and control measures.},
author = {Hafeez, Si. and Amin, M. and Munir, B.A.},
doi = {10.1016/j.sste.2017.04.001},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/hafeez 2017 Spatial mapping of temporal risk to improve prevention measures A case study of dengue epidemic in Lahore.pdf:pdf},
issn = {18775853},
journal = {Spatial and Spatio-temporal Epidemiology},
keywords = {GIS,Infectious diseases,Risk clusters,Spatial autocorrelation,Spatial epidemiology},
pages = {77--85},
pmid = {28552190},
publisher = {Elsevier Ltd},
title = {{Spatial mapping of temporal risk to improve prevention measures: A case study of dengue epidemic in Lahore}},
volume = {21},
year = {2017}
}
@article{Ghosh2019,
abstract = {Dengue fever becomes one of the serious vector-borne diseases in the world, particularly in tropical countries. Mosquitoes borne disease transmission are mainly transpired by physical, environmental and socio-economic variables. Therefore, disease mapping is essential for monitoring, prediction, and prevention of dengue. Accordingly, the aims of this research are to find out the dengue outbreak, spatial pattern, understand the factors and construct a risk map to accurately predict and control of dengue. For this purpose, the Kharagpur city of West Bengal has been selected which is frequently encountered with the dengue outbreak. Epidemiological data were collected from the district health department. The spatial scan statistics of the Poisson model and Local Moran's I is used for the mapping of spatial patterns and concentration of dengue and Monte Carlo hypothesis testing was applied to test the significance of the result. Therefore, multiple logistic regression analysis has been applied with selected parameters to produce a risk map for the entire study area. Finally, this study has proposed suitable site-specific management strategies. However, this study provides an example of disease risk estimation which will be remarkable for spatial analysis of epidemiological research as well as health planning and disease surveillance.},
author = {Ghosh, S. and Dinda, S. and Chatterjee, D.N and Das, K. and Mahata, R.},
doi = {10.1007/s41324-018-0224-9},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Ghos 2019 The spatial clustering of dengue disease and risk susceptibility mapping an approach towards sustainable health management in Kharagpur city, India.pdf:pdf},
issn = {23663294},
journal = {Spatial Information Research},
keywords = {Dengue disease,Dengue risk mapping,Kharagpur city,Poisson model,SaTScan™,Spatial clustering},
number = {2},
pages = {187--204},
publisher = {Springer Singapore},
title = {{The spatial clustering of dengue disease and risk susceptibility mapping: an approach towards sustainable health management in Kharagpur city, India}},
volume = {27},
year = {2019}
}
@article{Wijayanti2016,
abstract = {Background: Dengue is a major mosquito-borne viral disease and an important public health problem. Identifying which factors are important determinants in the risk of dengue infection is critical in supporting and guiding preventive measures. In South-East Asia, half of all reported fatal infections are recorded in Indonesia, yet little is known about the epidemiology of dengue in this country. Methodology/Principal findings: Hospital-reported dengue cases in Banyumas regency, Central Java were examined to build Bayesian spatial and spatio-temporal models assessing the influence of climatic, demographic and socio-economic factors on the risk of dengue infection. A socio-economic factor linking employment type and economic status was the most influential on the risk of dengue infection in the Regency. Other factors such as access to healthcare facilities and night-time temperature were also found to be associated with higher risk of reported dengue infection but had limited explanatory power. Conclusions/Significance: Our data suggest that dengue infections are triggered by indoor transmission events linked to socio-economic factors (employment type, economic status). Preventive measures in this area should therefore target also specific environments such as schools and work areas to attempt and reduce dengue burden in this community. Although our analysis did not account for factors such as variations in immunity which need further investigation, this study can advise preventive measures in areas with similar patterns of reported dengue cases and environment.},
author = {Wijayanti, S.P.M. and Porphyre, T. and Chase-Topping, M. and Rainey, S.M. and McFarlane, M. and Schnettler, E. and Biek, R. and Kohl, A.},
doi = {10.1371/journal.pntd.0004964},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Wijayanti 2016 the importance of socio-economic versus environmental risk factors for reported.pdf:pdf},
issn = {19352735},
journal = {PLoS Neglected Tropical Diseases},
number = {9},
pages = {1--15},
pmid = {27603137},
title = {{The Importance of Socio-Economic Versus Environmental Risk Factors for Reported Dengue Cases in Java, Indonesia}},
volume = {10},
year = {2016}
}
@article{Udayanga2020,
abstract = {Background: Assessing the vulnerability of an infectious disease such as dengue among endemic population is an important requirement to design proactive programmes in order to improve resilience capacity of vulnerable communities. The current study aimed to evaluate the climate change induced socio-economic vulnerability of local communities to dengue in Colombo and Kandy districts of Sri Lanka. Methods: A total of 42 variables (entomological, epidemiological, meteorological parameters, land-use practices and socio-demographic data) of all the 38 Medical Officer of Health (MOH) areas in the districts of Colombo and Kandy were considered as candidate variables for a composite index based vulnerability assessment. The Principal Component Analysis (PCA) was used in selecting and setting the weight for each indicator. Exposure, Sensitivity, Adaptive Capacity and Vulnerability of all MOH areas for dengue were calculated using the composite index approach recommended by the Intergovernmental Panel on Climate Change. Results: Out of 42 candidate variables, only 23 parameters (Exposure Index: six variables; Sensitivity Index: 11 variables; Adaptive Capacity Index: six variables) were selected as indicators to assess climate change vulnerability to dengue. Colombo Municipal Council (CMC) MOH area denoted the highest values for exposure (0.89: exceptionally high exposure), sensitivity (0.86: exceptionally high sensitivity) in Colombo, while Kandy Municipal Council (KMC) area reported the highest exposure (0.79: high exposure) and sensitivity (0.77: high sensitivity) in Kandy. Piliyandala MOH area denoted the highest level of adaptive capacity (0.66) in Colombo followed by Menikhinna (0.68) in Kandy. The highest vulnerability (0.45: moderate vulnerability) to dengue was indicated from CMC and the lowest indicated from Galaha MOH (0.15; very low vulnerability) in Kandy. Interestingly the KMC MOH area had a notable vulnerability of 0.41 (moderate vulnerability), which was the highest within Kandy. Conclusions: In general, vulnerability for dengue was relatively higher within the MOH areas of Colombo, than in Kandy, suggesting a higher degree of potential susceptibility to dengue within and among local communities of Colombo. Vector Controlling Entities are recommended to consider the spatial variations in vulnerability of local communities to dengue for decision making, especially in allocation of limited financial, human and mechanical resources for dengue epidemic management.},
author = {Udayanga, L. and Gunathilaka, N. and Iqbal, M. C.M. and Abeyewickreme, W.},
doi = {10.1186/s40249-020-00717-z},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Udayanga 2020 Climate change induced vulnerability and adaptation for dengue incidence.pdf:pdf},
issn = {20499957},
journal = {Infectious Diseases of Poverty},
keywords = {Climate change,Dengue,Sri Lanka,Vulnerability},
number = {1},
pages = {1--17},
pmid = {32703273},
publisher = {Infectious Diseases of Poverty},
title = {{Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy districts: The detailed investigation in Sri Lanka}},
volume = {9},
year = {2020}
}
@article{Sahdev2020,
abstract = {The identification and mapping ofdengue epidemics is one of the critical issues in providing better health services and policy development. In this process, multiple physiographic and socio-economic criteria have been taken into consideration. The proposed approach of this paper is to provide a framework for integrating the strengths of Geographic Information System (GIS) based MultiCriteria Decision-Making (MCDM) to reach the most appropriate spatial solutions for the decision-makers. In this paper, the National Capital Territory (NCT) of Delhi was considered for mapping the potential sites for the development of dengue epidemics. For this purpose, various thematic layers were prepared using QGIS software. Five parameters (spatial analysis of Dengue Cases, land surface temperature, landfill sites, water logging, land use/land cover) were examined for mapping dengue epidemics vulnerable zones. The final dengue epidemics vulnerability assessment map reveals that the study area was divided into three different vulnerability regions namely high vulnerable regions-326.24 sq.km (20.55%), moderate vulnerable regions-674.25 sq.km (42.48%) and low vulnerable regions-586.56 sq.km (36.95%). In this paper, the integrated approach of GIS based MCDM is showcased as a major contribution towards the development of effective health care management system (HCMS). This study also provides a new approach for decisionmakers in order to decrease the spatial extent of this chronic disease and also reduce the human health hazard.},
author = {Sahdev, S. and Kumar, M.},
doi = {10.24193/JSSPSI.2020.6.07},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Sahdev 2020 Identification and Mapping of Dengue Epidemics using GISBased Multi-Criteria Decision Making1.pdf:pdf},
issn = {22482199},
journal = {Journal of Settlements and Spatial Planning},
keywords = {Analytic Hierarchy Process,Dengue epidemics,Geographic Information System,Health Care Management System,Pairwise Comparison Matrix},
number = {6},
pages = {61--69},
title = {{Identification and mapping of dengue epidemics using gisbased multi-criteria decision making. The case of delhi, india}},
year = {2020}
}
@article{Dom2017,
abstract = {This study aims to fully integrated and validated spatial temporal statistical model using epidemiological data as a predictive model for surveillance and control of DF cases. Kernel-density estimation (KDE) method was carried out by using spatial union analysis in order to predict and visualize the DF hotspot area by monthly basis in the Subang Jaya area. The generated maps were then verified using Receiver operating characteristics (ROC) was performed to validate the DF hotspot simulation model. Spatial analysis showed that the dengue epidemics in Subang Jaya were spatially dependent. This analysis demonstrated spatial clustering of dengue activity which can facilitate prediction of the magnitude, timing and location of future dengue epidemic. The model developed highlights the adaptation capabilities of the approach where the accuracy assessment result showed accuracy about 60% agreements between the hotspot map and the actual DF location data. It can thus be suggested that any future population increase will be associated with increased DF risk in areas which already accommodate this disease environmentally, climatically and socioeconomically. Future risk could be modelled using the same methods. This would help decision maker in choosing which areas should be under intensive treatment to counter mosquito breeding and reduce prevalence of DF.},
author = {Dom, N.C. and Ahmad, A.H. and Latif, Z.A. and Ismail, R.},
doi = {10.14456/ea.2017.29},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Dom 2017 Integration of GIS-based model with epidemiological data as a tool for dengue surveillance.pdf:pdf},
issn = {19061714},
journal = {EnvironmentAsia},
keywords = {Dengue,Epidemiological data,GIS,Malaysia,Surveillance},
number = {2},
pages = {135--146},
title = {{Integration of GIS-based model with epidemiological data as a tool for dengue surveillance}},
volume = {10},
year = {2017}
}
@article{Delmelle2016,
abstract = {Dengue fever has gradually re-emerged across the global South, particularly affecting urban areas of the tropics and sub-tropics. The dynamics of dengue fever transmission are sensitive to changes in environmental conditions, as well as local demographic and socioeconomic factors. In 2010, the municipality of Cali, Colombia, experienced one of its worst outbreaks, however the outbreak was not spatially homogeneous across the city. In this paper, we evaluate the role of socioeconomic and environmental factors associated with this outbreak at the neighborhood level, using a Geographically Weighted Regression model. Key socioeconomic factors include population density and socioeconomic stratum, whereas environmental factors are proximity to both tire shops and plant nurseries and the presence of a sewage system (R2 = 0.64). The strength of the association between these factors and the incidence of dengue fever is spatially heterogeneous at the neighborhood level. The findings provide evidence to support public health strategies in allocating resources locally, which will enable a better detection of high risk areas, a reduction of the risk of infection and to strengthen the resilience of the population.},
author = {Delmelle, E. and Hagenlocher, M. and Kienberger, S. and Casas, I.},
doi = {10.1016/j.actatropica.2016.08.028},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Delmelle 2016 A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia.pdf:pdf},
issn = {18736254},
journal = {Acta Tropica},
keywords = {Colombia,Dengue fever,Environmental and socioeconomic determinants,GIS,Geographically weighted regression (GWR)},
pages = {169--176},
pmid = {27619189},
publisher = {Elsevier B.V.},
title = {{A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia}},
url = {http://dx.doi.org/10.1016/j.actatropica.2016.08.028},
volume = {164},
year = {2016}
}
@article{Dickin2014,
abstract = {Water management practices and access to safe water supplies have major implications for human health. While a range of assessments has been developed to assess water vulnerability, limited work has extended these concepts to health and wellbeing. Water-associated disease cycles are characterized by complex linkages between social and ecological determinants, thus conceptualizing vulnerability in the context of health offers a useful framework for analysis. This paper applies a water associated disease index (WADI) as a tool to deepen understanding of changing vulnerability to dengue, comparing conditions in 2000 and 2010 in Pernambuco, Northeast Brazil. Multi-dimensional data were integrated into indicators of exposure and susceptibility using the WADI approach, including water access, land cover, climate, and solid waste collection, and outputs were validated and visualized in map form. The findings illustrate heterogeneous patterns of vulnerability to dengue in the region, and highlight trends of seasonal and long-term changes. Highest vulnerability was observed in densely populated Recife and the surrounding coastal region in both time periods, with climate conditions creating seasonal trends in exposure to dengue. While more remote areas in the semi-arid Sert{\~{a}}o showed low vulnerability overall, increases were observed in some areas between 2000 and 2010 due to land use intensification and growing population densities. These findings suggest that interventions should consider the dynamic nature of social and ecological factors that contribute to health outcomes and address current as well as future populations vulnerable to dengue transmission. This vulnerability mapping approach can be applied to other water-associated diseases impacted by global environmental change to highlight priority areas for further investigation and contribute towards improving interventions.},
author = {Dickin, S.K. and Schuster-Wallace, C.J.},
doi = {10.1016/j.gloenvcha.2014.09.007},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/dickin 2014 Assessing changing vulnerability to dengue in northeastern Brazil using a water-associated disease index approach.pdf:pdf},
issn = {09593780},
journal = {Global Environmental Change},
keywords = {Brazil,Dengue,Global environmental change,Health,Vulnerability,Water-associated disease},
pages = {155--164},
publisher = {Elsevier Ltd},
title = {{Assessing changing vulnerability to dengue in northeastern Brazil using a water-associated disease index approach}},
url = {http://dx.doi.org/10.1016/j.gloenvcha.2014.09.007},
volume = {29},
year = {2014}
}
@article{Shafie2011,
abstract = {In Malaysia, the incidence of Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) have risen dramatically in the last twenty years. With the use of Geographical Information System an explanation for the spread and control of these diseases can be obtained. This study aims to develop a spatial modeling that can predict the risks for DF and DHF based on environmental factors such as physical surroundings, land use, rainfall, temperature and GIS application using logistic regression. A total of 16 variables were used in the process of spatial modeling development. At the significant level of 0.05, the results of logistic regression showed that only 10 out of 16 significant variables in the modeling process. The accuracy of the resulting model is 70.3%. A crucial feature of this study is a risk area map for incidence of DF and DHF in the study area. This study also highlights the application of spatial analysis in planning and implementing the process for the prevention and control activities of DF and DHF in Malaysia.},
author = {Shafie, A.},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Aziz Shafie 2011 Evaluation of the spatial Factors for high Incidence of Dengue Fever.pdf:pdf},
issn = {01266039},
journal = {Sains Malaysiana},
keywords = {Dengue Fever,Dengue Hemorrhagic Fever,Geographic Information System (GIS),Risk mapping,Spatial modeling},
number = {8},
pages = {937--943},
title = {{Evaluation of the spatial risk factors for high incidence of Dengue Fever and Dengue Hemorrhagic Fever using GIS application}},
volume = {40},
year = {2011}
}
@article{Acharya2018a,
abstract = {Both the number of cases of dengue fever and the areas of outbreaks within Nepal have increased significantly in recent years. Further expansion and range shift is expected in the future due to global climate change and other associated factors. However, due to limited spatially-explicit research in Nepal, there is poor understanding about the present spatial distribution patterns of dengue risk areas and the potential range shift due to future climate change. In this context, it is crucial to assess and map dengue fever risk areas in Nepal. Here, we used reported dengue cases and a set of bioclimatic variables on the MaxEnt ecological niche modeling approach to model the climatic niche and map present and future (2050s and 2070s) climatically suitable areas under different representative concentration pathways (RCP2.6, RCP6.0 and RCP8.5). Simulation-based estimates suggest that climatically suitable areas for dengue fever are presently distributed throughout the lowland Tarai from east to west and in river valleys at lower elevations. Under the different climate change scenarios, these areas will be slightly shifted towards higher elevation with varied magnitude and spatial patterns. Population exposed to climatically suitable areas of dengue fever in Nepal is anticipated to further increase in both 2050s and 2070s on all the assumed emission scenarios. These findings could be instrumental to plan and execute the strategic interventions for controlling dengue fever in Nepal.},
author = {Acharya, B.K. and Cao, C. and Xu, M. and Khanal, L. and Naeem, S. and Pandit, S.},
doi = {10.3390/ijerph15020187},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Acharya et al. - 2018 - Present and future of dengue fever in nepal Mapping climatic suitability by ecological niche model(2).pdf:pdf},
issn = {16604601},
journal = {International Journal of Environmental Research and Public Health},
keywords = {Climate change,Dengue mapping,MaxEnt,Nepal},
number = {187},
pages = {1--15},
pmid = {29360797},
title = {{Present and future of dengue fever in nepal: Mapping climatic suitability by ecological niche model}},
volume = {15},
year = {2018}
}
@article{Pakaya2022,
abstract = {One method of reducing the spread of dengue hemorrhagic fever (DHF) is to provide a map of DHF-prone locations based on spatial analysis. The major way to prevent the spread of DHF is to manage and control its vector by focussing on specific regions of localisation and removing appropriate breeding circumstances. Spatial analysis can detect DHF clusters that are larger than expected based on the underlying data. This research aimed to identify and map DHF vulnerability zones based on many parameters within the scope of the analytical hierarchy and geographical information systems (GIS). We found that the consistency ratio of 0.079 for analytic hierarchy process (AHP) factor weights was judged to be satisfactory. The population density, distance to the road, and distance to health facilities were shown to be the most relevant factors in determining DHF vulnerability. Gorontalo Regency is dominated by low vulnerability classes with an area of 139,493.5 ha or 65.08% of the total area. The GIS-AHP process could be used to assess transmissible DHF vulnerability zonation, which would aid in improving surveillance strategies for DHF and other vector-borne diseases in order to encourage prevention and control actions.},
author = {Pakaya, Ririn and Hano, Yanti Hz. and Olii, Muhammad Ramdhan},
doi = {10.11591/ijphs.v11i1.21084},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Pakaya, Hano, Olii - 2022 - Dengue hemorrhagic fever vulnerability assessment in Gorontalo Regency using analytic hierarchy process and.pdf:pdf},
issn = {2252-8806},
journal = {International Journal of Public Health Science (IJPHS)},
number = {1},
pages = {138},
title = {{Dengue hemorrhagic fever vulnerability assessment in Gorontalo Regency using analytic hierarchy process and geoinformation techniques}},
volume = {11},
year = {2022}
}
@article{Barbosa2014,
abstract = {Dengue fever is a major public health problem worldwide, caused by any of four virus (DENV-1, DENV-2, DENV-3 and DENV-4; Flaviviridae: Flavivirus), transmitted by Aedes aegypti mosquito. Reducing the levels of infestation by A. aegypti is one of the few current strategies to control dengue fever. Entomological indicators are used by dengue national control program to measure the infestation of A. aegypti, but little is known about predictive power of these indicators to measure dengue risk. In this spatial case-control study, we analyzed the spatial distribution of the risk of dengue and the influence of entomological indicators of A. aegypti in its egg, larva-pupa and adult stages occurring in a mid-size city in the state of S{\~{a}}o Paulo. The dengue cases were those confirmed by the city's epidemiological surveillance system and the controls were obtained through random selection of points within the perimeter of the inhabited area. The values of the entomological indicators were extrapolated for the entire study area through the geostatistical ordinary kriging technique. For each case and control, the respective indicator values were obtained, according with its geographical coordinates and analyzed by using a generalized additive model. Dengue incidence demonstrated a seasonal behavior, as well as the entomological indicators of all mosquito's evolutionary stages. The infestation did not present a significant variation in intensity and was not a limiting or determining factor of the occurrence of cases in the municipality. The risk maps of the disease from crude and adjusted generalized additive models did not present differences, suggesting that areas with the highest values of entomological indicators were not associated with the incidence of dengue. The inclusion of other variables in the generalized additive models may reveal the modulatory effect for the risk of the disease, which is not found in this study. {\textcopyright} 2014 Barbosa et al.},
author = {Barbosa, G.L. and Donal{\'{i}}sio, M.R. and Stephan, C. and Louren{\c{c}}o, R.W. and Andrade, V.R. and Arduino, M.B. and Lima, V.L.C.},
doi = {10.1371/journal.pntd.0002873},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/file.pdf:pdf},
issn = {19352735},
journal = {PLoS Neglected Tropical Diseases},
number = {5},
pmid = {24831806},
title = {{Spatial Distribution of the Risk of Dengue and the Entomological Indicators in Sumar{\'{e}}, State of S{\~{a}}o Paulo, Brazil}},
volume = {8},
year = {2014}
}
@article{Zafar2021,
abstract = {Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi‐criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon's Entropy (SE) and the Water‐Associated Disease Index (WADI), and one subjective approach, the Best‐Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson's correlation coefficient (r) larger than 0.5 and a p‐value less than 0.05 implied a good spatial and temporal performance. Spatially, DVIWADI was significantly correlated on average in 19% (4–40%) of districts in Laos (mean r = 0.5) and 27% (15–53%) of subdistricts in Thailand (mean r = 0.85). The DVISE was validated in 22% (12–40%) of districts in Laos and in 13% (3–38%) of subdistricts in Thailand. The DVIBWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0–28%) of Lao districts. The DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI can also be applied to other water‐associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.},
author = {Zafar, S. and Shipin, O. and Paul, R.E. and Rockl{\"{o}}v, J. and Haque, U. and Rahman, M.S. and Mayxay, M. and Pientong, C. and Aromseree, Si. and Poolphol, P. and Pongvongsa, T. and Vannavong, N. and Overgaard, H.J.},
doi = {10.3390/ijerph18179421},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/12. Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand.pdf:pdf},
issn = {16604601},
journal = {International Journal of Environmental Research and Public Health},
keywords = {Epidemiology,Exposure,Health status indicators,Spatial analysis,Susceptibility},
number = {17},
pages = {1--25},
pmid = {34502007},
title = {{Development and comparison of dengue vulnerability indices using gis‐based multi‐criteria decision analysis in lao pdr and Thailand}},
volume = {18},
year = {2021}
}
@article{Pham2020,
abstract = {Dengue fever has continuously been a disease burden in Vietnam during the last 20 years, particularly in the Mekong Delta region (MDR), which is one of the most vulnerable to climate change. Variations in temperature and precipitation are likely to alter the incidence and distribution of vector-borne diseases such as dengue. This study focuses on assessing dengue risk via the vulnerability concept, which is composed of exposure and susceptibility using a combined approach of mapping and modelling for the MDR of Vietnam during the period between 2001 and 2016. Multisource remote sensing data from Global Satellite Mapping of Precipitation (GSMaP) and Moderate Resolution Imaging Spectrophotometer (MODIS) was used for presenting climate and environment variables in mapping and modelling vulnerability. Monthly and yearly maps of vulnerability to dengue in the MDR, produced for 15-year period, aided analysis of the temporal and spatial patterns of vulnerability to dengue in the study region and were used for constructing time-series modelling of vulnerability for the following year. The results showed that there is a clear seasonal variation in the vulnerability due to variability of the climate factor and its strong dispersion across the study region, with higher vulnerability in the scattered areas of urban and mixed horticulture land and lower vulnerability in areas covered by forest and bare soil lands. The Pearson's correlation was applied to evaluate the association between dengue rates and vulnerability values aggregated at the provincial level. Reasonable linear association, with correlation coefficients of 0.41–0.63, was found in two-thirds of the provinces. The predicted vulnerabilities to dengue during 2016 were comparable with the estimated values and trends for most provinces of the MDR. Our demonstrated approach with integrated geospatial data seems to be a promising tool in supporting the public health sector in assessing potential space and time of a subsequent increase in vulnerability to dengue, particularly in the context of climate change.},
author = {Pham, N.T.T. and Nguyen, C.T. and Vu, H.H.},
doi = {10.1016/j.envres.2020.109545},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/13. Assessing and modelling vulnerability to dengue in the Mekong Delta of Vietnam by geospatial and time-series approaches.pdf:pdf},
issn = {10960953},
journal = {Environmental Research},
keywords = {Dengue,Time-series modelling,Vulnerability mapping,Vulnerability to dengue},
pages = {1--10},
pmid = {32361079},
publisher = {Elsevier Inc.},
title = {{Assessing and modelling vulnerability to dengue in the Mekong Delta of Vietnam by geospatial and time-series approaches}},
url = {https://doi.org/10.1016/j.envres.2020.109545},
volume = {186},
year = {2020}
}
@article{Acharya2018,
abstract = {Dengue fever is expanding rapidly in many tropical and subtropical countries since the last few decades. However, due to limited research, little is known about the spatial patterns and associated risk factors on a local scale particularly in the newly emerged areas. In this study, we explored spatial patterns and evaluated associated potential environmental and socioeconomic risk factors in the distribution of dengue fever incidence in Jhapa district, Nepal. Global and local Moran's I were used to assess global and local clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semi-parametric geographically weighted regression (s-GWR) models were compared to describe spatial relationship of potential environmental and socioeconomic risk factors with dengue incidence. Our result revealed heterogeneous and highly clustered distribution of dengue incidence in Jhapa district during the study period. The s-GWR model best explained the spatial association of potential risk factors with dengue incidence and was used to produce the predictive map. The statistical relationship between dengue incidence and proportion of urban area, proximity to road, and population density varied significantly among the wards while the associations of land surface temperature (LST) and normalized difference vegetation index (NDVI) remained constant spatially showing importance of mixed geographical modeling approach (s-GWR) in the spatial distribution of dengue fever. This finding could be used in the formulation and execution of evidence-based dengue control and management program to allocate scare resources locally.},
author = {Acharya, B.K. and Cao, C.X. and Lakes, T. and Chen, W. and Naeem, S. and Pandit, S.},
doi = {10.1007/s00484-018-1601-8},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/15. Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model.pdf:pdf},
issn = {00207128},
journal = {International Journal of Biometeorology},
keywords = {Dengue fever,Geographically weighted regression,Nepal,Risk factors,Spatial heterogeneity},
number = {11},
pages = {1973--1986},
pmid = {30182200},
publisher = {International Journal of Biometeorology},
title = {{Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model}},
volume = {62},
year = {2018}
}
@article{Puggioni2020,
abstract = {Dengue Fever (DF) is a mosquito vector transmitted flavivirus and a reemerging global public health threat. Although several studies have addressed the relation between climatic and environmental factors and the epidemiology of DF, or looked at purely spatial or time series analysis, this article presents a joint spatio-temporal epidemiological analysis. Our approach accounts for both temporal and spatial autocorrelation in DF incidence and the effect of temperatures and precipitation by using a hierarchical Bayesian approach. We fitted several space-time areal models to predict relative risk at the municipality level and for each month from 1990 to 2014. Model selection was performed according to several criteria: the preferred models detected significant effects for temperature at time lags of up to four months and for precipitation up to three months. A boundary detection analysis is incorporated in the modeling approach, and it was successful in detecting municipalities with historically anomalous risk.},
author = {Puggioni, G. and Couret, J. and Serman, E. and Akanda, A.S. and Ginsberg, H.S.},
doi = {10.1016/j.sste.2020.100375},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Puggioni et al. - 2020 - Spatiotemporal modeling of dengue fever risk in Puerto Rico.pdf:pdf},
issn = {18775853},
journal = {Spatial and Spatio-temporal Epidemiology},
keywords = {Conditionally autoregressive models,Misalignment,Space time epidemiology,Vector-borne diseases},
pmid = {33138945},
publisher = {Elsevier Ltd},
title = {{Spatiotemporal modeling of dengue fever risk in Puerto Rico}},
url = {https://doi.org/10.1016/j.sste.2020.100375},
volume = {35},
year = {2020}
}
@article{Dickin2013,
abstract = {The Water-associated Disease Index (WADI) was developed to identify and visualize vulnerability to different water-associated diseases by integrating a range of social and biophysical determinants in map format. In this study vulnerability is used to encompass conditions of exposure, susceptibility, and differential coping capacity to a water-associated health hazard. By assessing these conditions, the tool is designed to provide stakeholders with an integrated and long-term understanding of subnational vulnerabilities to water-associated disease and contribute to intervention strategies to reduce the burden of illness. The objective of this paper is to describe and validate the WADI tool by applying it to dengue. A systemic ecohealth framework that considers links between people, the environment and health was applied to identify secondary datasets, populating the index with components including climate conditions, land cover, education status and water use practices. Data were aggregated to create composite indicators of exposure and of susceptibility in a Geographic Information System (GIS). These indicators were weighted by their contribution to dengue vulnerability, and the output consisted of an overall index visualized in map format. The WADI was validated in this Malaysia case study, demonstrating a significant association with dengue rates at a sub-national level, and illustrating a range of factors that drive vulnerability to the disease within the country. The index output indicated high vulnerability to dengue in urban areas, especially in the capital Kuala Lumpur and surrounding region. However, in other regions, vulnerability to dengue varied throughout the year due to the influence of seasonal climate conditions, such as monsoon patterns. The WADI tool complements early warning models for water-associated disease by providing upstream information for planning prevention and control approaches, which increasingly require a comprehensive and geographically broad understanding of vulnerability for implementation. {\textcopyright} 2013 Dickin et al.},
author = {Dickin, S.K. and Schuster-Wallace, C.J. and Elliott, S.J.},
doi = {10.1371/journal.pone.0063584},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/4. Developing a Vulnerability Mapping Methodology Applying the Water-Associated Disease Index to Dengue in Malaysia.pdf:pdf},
issn = {19326203},
journal = {PLoS ONE},
number = {5},
pages = {1--11},
pmid = {23667642},
title = {{Developing a Vulnerability Mapping Methodology: Applying the Water-Associated Disease Index to Dengue in Malaysia}},
volume = {8},
year = {2013}
}
@article{Jeefoo2011,
abstract = {Geographic Information System (GIS) techniques are used to develop the risk zone map of Dengue Fever (DF)/Dengue Haemorrhagic Fever (DIIF) in Chachoengsao province, Thailand. The riskzonation index is derived using the Analytical Hierarchy Process (AHP). The medical database considered for the study was referenced to die geographical and meteorological data layers. Based on this analysis, potential areas were categorized as very high, high, moderately high, moderately low, low, and very low risk categories. The model used in /ftis study provided valuable information to prepare DF/DHF risk zones for decision support to mitigate the epidemic. Factor weights used in AHP were evaluated and found to be acceptable as the consistency ratio (CR) was 0.031, which was < 0.1. Spatial modelling included a huge database about physical-environment, climatic, and demographic factors. Approximately 74.96% people lived m very high, high, and moderately high risk zones. The most at risk zone was shown to be Mueang Chachoengsao district with a population of 142,485 or 2234% of total population. The results from AHP based DF/DHF risk zonation gave useful information on different levels of disease risk areas. The methodology is general and can be applied in other application fields, such as disease outbreak during natural disasters.},
author = {Jeefoo, P. and Tripathi, N.K.},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/16. Dengue risk zone index (DRZI) for mapping dengue risk areas.pdf:pdf},
journal = {International Journal Geoinformatics},
number = {1},
pages = {53--62},
title = {{Dengue risk zone index (DRZI) for mapping dengue risk areas}},
volume = {7},
year = {2011}
}
@article{Withanage2021,
abstract = {Dengue is one of the most important vector-borne infection in Sri Lanka currently leading to vast economic and social burden. Neither a vaccine nor drug is still not being practiced, vector controlling is the best approach to control disease transmission in the country. Therefore, early warning systems are imminent requirement. The aim of the study was to develop Geographic Information System (GIS)-based multivariate analysis model to detect risk hotspots of dengue in the Gampaha District, Sri Lanka to control diseases transmission. A risk model and spatial Poisson point process model were developed using separate layers for patient incidence locations, positive breeding containers, roads, total buildings, public places, land use maps and elevation in four high risk areas in the district. Spatial correlations of each study layer with patient incidences was identified using Kernel density and Euclidean distance functions with minimum allowed distance parameter. Output files of risk model indicate that high risk localities are in close proximity to roads and coincide with vegetation coverage while the Poisson model highlighted the proximity of high intensity localities to public places and possibility of artificial reservoirs of dengue. The latter model further indicate that clustering of dengue cases in a radius of approximately 150 m in high risk areas indicating areas need intensive attention in future vector surveillances.},
author = {Withanage, G.P. and Gunawardana, M. and Viswakula, S.D. and Samaraweera, K. and Gunawardena, N.S. and Hapugoda, M.D.},
doi = {10.1038/s41598-021-83204-1},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Withanage et al. - 2021 - Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic.pdf:pdf},
isbn = {0123456789},
issn = {20452322},
journal = {Scientific Reports},
number = {1},
pages = {1--11},
pmid = {33602959},
publisher = {Nature Publishing Group UK},
title = {{Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)}},
url = {https://doi.org/10.1038/s41598-021-83204-1},
volume = {11},
year = {2021}
}
@article{Tsheten2021,
abstract = {Background Dengue is the most rapidly spreading vector-borne disease globally, with a 30-fold increase in global incidence over the last 50 years. In Bhutan, dengue incidence has been on the rise since 2004, with numerous outbreaks reported across the country. The aim of this study was to identify and map areas that are vulnerable to dengue in Bhutan. Methodology/Principal findings We conducted a multicriteria decision analysis (MCDA) using a weighted linear combination (WLC) to obtain a vulnerability map of dengue. Risk factors (criteria) were identified and assigned with membership values for vulnerability according to the available literature. Sensitivity analysis and validation of the model was conducted to improve the robustness and predictive ability of the map. Our study revealed marked differences in geographical vulnerability to dengue by location and season. Low-lying areas and those located along the southern border were consistently found to be at higher risk of dengue. The vulnerability extended to higher elevation areas including some areas in the Capital city Thimphu during the summer season. The higher risk was mostly associated with relatively high population density, agricultural and built-up landscapes and relatively good road connectivity. Conclusions Using MCDA, our study identified vulnerable areas in Bhutan during specific seasons when and where the transmission of dengue is most likely to occur. This study provides evidence for the National Vector-borne Disease Control programme to optimize the use of limited public health resources for surveillance and vector control, to mitigate the public health threat of dengue.},
author = {Tsheten, T. and Clements, A.A. and Gray, D.J. and Wangdi, K.},
doi = {10.1371/journal.pntd.0009021},
file = {:H\:/File Baru/Daftar Pustaka ICVIAS/journal.pntd.0009021.pdf:pdf},
isbn = {1111111111},
issn = {19352735},
journal = {PLoS Neglected Tropical Diseases},
number = {2},
pages = {1--17},
pmid = {33566797},
title = {{Dengue risk assessment using multicriteria decision analysis: A case study of Bhutan}},
url = {http://dx.doi.org/10.1371/journal.pntd.0009021},
volume = {15},
year = {2021}
}
@article{Schmidt2011,
abstract = {Background: Aedes aegypti, the major vector of dengue viruses, often breeds in water storage containers used by households without tap water supply, and occurs in high numbers even in dense urban areas. We analysed the interaction between human population density and lack of tap water as a cause of dengue fever outbreaks with the aim of identifying geographic areas at highest risk. Methods and Findings: We conducted an individual-level cohort study in a population of 75,000 geo-referenced households in Vietnam over the course of two epidemics, on the basis of dengue hospital admissions (n = 3,013). We applied space-time scan statistics and mathematical models to confirm the findings. We identified a surprisingly narrow range of critical human population densities between around 3,000 to 7,000 people/km2 prone to dengue outbreaks. In the study area, this population density was typical of villages and some peri-urban areas. Scan statistics showed that areas with a high population density or adequate water supply did not experience severe outbreaks. The risk of dengue was higher in rural than in urban areas, largely explained by lack of piped water supply, and in human population densities more often falling within the critical range. Mathematical modeling suggests that simple assumptions regarding area-level vector/host ratios may explain the occurrence of outbreaks. Conclusions: Rural areas may contribute at least as much to the dissemination of dengue fever as cities. Improving water supply and vector control in areas with a human population density critical for dengue transmission could increase the efficiency of control efforts. Please see later in the article for the Editors' Summary. {\textcopyright} 2011 Schmidt et al.},
author = {Schmidt, W.P. and Suzuki, M. and Thiem, V. and White, R.G. and Tsuzuki, A. and Yoshida, L.M. and Yanai, H. and Haque, U. and Tho, L. and Anh, D.D. and Ariyoshi, K.},
doi = {10.1371/journal.pmed.1001082},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/file (3).pdf:pdf},
issn = {15491277},
journal = {PLoS Medicine},
number = {8},
pages = {1--10},
pmid = {21918642},
title = {{Population density, water supply, and the risk of dengue fever in vietnam: Cohort study and spatial analysis}},
volume = {8},
year = {2011}
}
@article{Vincenti-Gonzalez2017,
abstract = {Background: Dengue virus (DENV) transmission is spatially heterogeneous. Hence, to stratify dengue prevalence in space may be an efficacious strategy to target surveillance and control efforts in a cost-effective manner particularly in Venezuela where dengue is hyperendemic and public health resources are scarce. Here, we determine hot spots of dengue seroprevalence and the risk factors associated with these clusters using local spatial statistics and a regression modeling approach. Methodology/Principal Findings: From August 2010 to January 2011, a community-based cross-sectional study of 2012 individuals in 840 households was performed in high incidence neighborhoods of a dengue hyperendemic city in Venezuela. Local spatial statistics conducted at household- and block-level identified clusters of recent dengue seroprevalence (39 hot spot households and 9 hot spot blocks) in all neighborhoods. However, no clusters were found for past dengue seroprevalence. Clustering of infection was detected at a very small scale (20-110m) suggesting a high disease focal aggregation. Factors associated with living in a hot spot household were occupation (being a domestic worker/housewife (P = 0.002), lower socio-economic status (living in a shack (P<0.001), sharing a household with <7 people (P = 0.004), promoting potential vector breeding sites (storing water in containers (P = 0.024), having litter outdoors (P = 0.002) and mosquito preventive measures (such as using repellent, P = 0.011). Similarly, low socio-economic status (living in crowded conditions, P<0.001), having an occupation of domestic worker/housewife (P = 0.012) and not using certain preventive measures against mosquitoes (P<0.05) were directly associated with living in a hot spot block. Conclusions/Significance: Our findings contribute to a better comprehension of the spatial dynamics of dengue by assessing the relationship between disease clusters and their risk factors. These results can inform health authorities in the design of surveillance and control activities. Focalizing dengue control measures during epidemic and inter-epidemic periods to disease high risk zones at household and neighborhood-level may significantly reduce virus transmission in comparison to random interventions.},
author = {Vincenti-Gonzalez, M.F. and Grillet, M.E. and Velasco-Salas, Z.I. and Lizarazo, E. F. and Amarista, M.A. and Sierra, G.M. and Comach, G. and Tami, A.},
doi = {10.1371/journal.pntd.0005317},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vincenti-Gonzalez et al. - 2017 - Spatial Analysis of Dengue Seroprevalence and Modeling of Transmission Risk Factors in a Dengue Hypere.pdf:pdf},
isbn = {1111111111},
issn = {19352735},
journal = {PLoS Neglected Tropical Diseases},
number = {1},
pages = {1--21},
pmid = {28114342},
title = {{Spatial Analysis of Dengue Seroprevalence and Modeling of Transmission Risk Factors in a Dengue Hyperendemic City of Venezuela}},
volume = {11},
year = {2017}
}
@article{Riad2021,
abstract = {Vector-borne disease risk assessment is crucial to optimize surveillance, preventative measures (vector control), and resource allocation (medical supplies). High arthropod abundance and host interaction strongly correlate to vector-borne pathogen transmission. Increasing host density and movement increases the possibility of local and long-distance pathogen transmission. Therefore, we developed a risk-assessment framework using climate (average temperature and rainfall) and host demographic (host density and movement) data, particularly suitable for regions with unreported or underreported incidence data. This framework consisted of a spatiotemporal network-based approach coupled with a compartmental disease model and nonhomogeneous Gillespie algorithm. The correlation of climate data with vector abundance and host–vector interactions is expressed as vectorial capacity—a parameter that governs the spreading of infection from an infected host to a susceptible one via vectors. As an example, the framework is applied for dengue in Bangladesh. Vectorial capacity is inferred for each week throughout a year using average monthly temperature and rainfall data. Long-distance pathogen transmission is expressed with human movement data in the spatiotemporal network. We have identified the spatiotemporal suitability of dengue spreading in Bangladesh as well as the significant-incidence window and peak-incidence period. Analysis of yearly dengue data variation suggests the possibility of a significant outbreak with a new serotype introduction. The outcome of the framework comprised spatiotemporal suitability maps and probabilistic risk maps for spatial infection spreading. This framework is capable of vector-borne disease risk assessment without historical incidence data and can be a useful tool for preparedness with accurate human movement data.},
author = {Riad, Mahbubul H. and Cohnstaedt, Lee W. and Scoglio, Caterina M.},
doi = {10.4269/ajtmh.20-0444},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Riad, Cohnstaedt, Scoglio - 2021 - Risk assessment of dengue transmission in Bangladesh using a spatiotemporal network model and climate.pdf:pdf},
issn = {14761645},
journal = {American Journal of Tropical Medicine and Hygiene},
number = {4},
pages = {1444--1455},
pmid = {33534755},
title = {{Risk assessment of dengue transmission in Bangladesh using a spatiotemporal network model and climate data}},
volume = {104},
year = {2021}
}
@article{Cordeiro2011,
abstract = {Background: Many factors have been associated with circulation of the dengue fever virus and vector, although the dynamics of transmission are not yet fully understood. The aim of this work is to estimate the spatial distribution of the risk of dengue fever in an area of continuous dengue occurrence. Methods. This is a spatial population-based case-control study that analyzed 538 cases and 727 controls in one district of the municipality of Campinas, S{\~{a}}o Paulo, Brazil, from 2006-2007, considering socio-demographic, ecological, case severity, and household infestation variables. Information was collected by in-home interviews and inspection of living conditions in and around the homes studied. Cases were classified as mild or severe according to clinical data, and they were compared with controls through a multinomial logistic model. A generalized additive model was used in order to include space in a non-parametric fashion with cubic smoothing splines. Results: Variables associated with increased incidence of all dengue cases in the multiple binomial regression model were: higher larval density (odds ratio (OR) = 2.3 (95%CI: 2.0-2.7)), reports of mosquito bites during the day (OR = 1.8 (95%CI: 1.4-2.4)), the practice of water storage at home (OR = 2.5 (95%CI: 1.4, 4.3)), low frequency of garbage collection (OR = 2.6 (95%CI: 1.6-4.5)) and lack of basic sanitation (OR = 2.9 (95%CI: 1.8-4.9)). Staying at home during the day was protective against the disease (OR = 0.5 (95%CI: 0.3-0.6)). When cases were analyzed by categories (mild and severe) in the multinomial model, age and number of breeding sites more than 10 were significant only for the occurrence of severe cases (OR = 0.97, (95%CI: 0.96-0.99) and OR = 2.1 (95%CI: 1.2-3.5), respectively. Spatial distribution of risks of mild and severe dengue fever differed from each other in the 2006/2007 epidemic, in the study area. Conclusions: Age and presence of more than 10 breeding sites were significant only for severe cases. Other predictors of mild and severe cases were similar in the multiple models. The analyses of multinomial models and spatial distribution maps of dengue fever probabilities suggest an area-specific epidemic with varying clinical and demographic characteristics. {\textcopyright} 2011 Cordeiro et al; licensee BioMed Central Ltd.},
author = {Cordeiro, Ricardo and Donalisio, Maria R. and Andrade, Valmir R. and Mafra, Ana C.N. and Nucci, Luciana B. and Brown, John C. and Stephan, Celso},
doi = {10.1186/1471-2458-11-355},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Cordeiro et al. - 2011 - Spatial distribution of the risk of dengue fever in southeast Brazil, 2006-2007.pdf:pdf},
issn = {14712458},
journal = {BMC Public Health},
pmid = {21599980},
title = {{Spatial distribution of the risk of dengue fever in southeast Brazil, 2006-2007}},
volume = {11},
year = {2011}
}
@article{Ong2018,
abstract = {Background: Singapore experiences endemic dengue, with 2013 being the largest outbreak year known to date, culminating in 22,170 cases. Given the limited resources available, and that vector control is the key approach for prevention in Singapore, it is important that public health professionals know where resources should be invested in. This study aims to stratify the spatial risk of dengue transmission in Singapore for effective deployment of resources. Methodology/principal findings: Random Forest was used to predict the risk rank of dengue transmission in 1km2grids, with dengue, population, entomological and environmental data. The predicted risk ranks are categorized and mapped to four color-coded risk groups for easy operation application. The risk maps were evaluated with dengue case and cluster data. Risk maps produced by Random Forest have high accuracy. More than 80% of the observed risk ranks fell within the 80% prediction interval. The observed and predicted risk ranks were highly correlated ($\rho$ ≥0.86, P <0.01). Furthermore, the predicted risk levels were in excellent agreement with case density, a weighted Kappa coefficient of more than 0.80 (P <0.01). Close to 90% of the dengue clusters occur in high risk areas, and the odds of cluster forming in high risk areas were higher than in low risk areas. Conclusions: This study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good surveillance tool to guide vector control operations.},
author = {Ong, J. and Liu, X. and Rajarethinam, J. and Kok, S.Y. and Liang, S. and Tang, C.S. and Cook, A.R. and Ng, L.C. and Yap, G.},
doi = {10.1371/journal.pntd.0006587},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ong et al. - 2018 - Mapping dengue risk in Singapore using Random Forest.pdf:pdf},
isbn = {1111111111},
issn = {19352735},
journal = {PLoS Neglected Tropical Diseases},
number = {6},
pages = {1--12},
pmid = {29912940},
title = {{Mapping dengue risk in Singapore using Random Forest}},
volume = {12},
year = {2018}
}
@article{Souza2020,
author = {Souza, M. L.A. and Andrade, L. M.B. and Spyrides, M. H.C. and Tinoco, I. C.M.},
doi = {10.1016/j.uclim.2020.100712},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/Souza 2020 Profile Eestimates for the analysis of climatic and socio-sanitary vulnerability to dengue in municipalities in Northeast Brazil.pdf:pdf},
issn = {22120955},
journal = {Urban Climate},
pages = {1--12},
title = {{Profile Eestimates for the analysis of climatic and socio-sanitary vulnerability to dengue in municipalities in Northeast Brazil}},
volume = {34},
year = {2020}
}
@article{Chiu2014,
abstract = {Understanding the spatial characteristics of dengue fever (DF) incidences is crucial for governmental agencies to implement effective disease control strategies. We investigated the associations between environmental and socioeconomic factors and DF geographic distribution, are proposed a probabilistic risk assessment approach that uses threshold-based quantile regression to identify the significant risk factors for DF transmission and estimate the spatial distribution of DF risk regarding full probability distributions. To interpret risk, return period was also included to characterize the frequency pattern of DF geographic occurrences. The study area included old Kaohsiung City and Fongshan District, two areas in Taiwan that have been affected by severe DF infections in recent decades. Results indicated that water-related facilities, including canals and ditches, and various types of residential area, as well as the interactions between them, were significant factors that elevated DF risk. By contrast, the increase of per capita income and its associated interactions with residential areas mitigated the DF risk in the study area. Nonlinear associations between these factors and DF risk were present in various quantiles, implying that water-related factors characterized the underlying spatial patterns of DF, and high-density residential areas indicated the potential for high DF incidence (e.g., clustered infections). The spatial distributions of DF risks were assessed in terms of three distinct map presentations: expected incidence rates, incidence rates in various return periods, and return periods at distinct incidence rates. These probability-based spatial risk maps exhibited distinct DF risks associated with environmental factors, expressed as various DF magnitudes and occurrence probabilities across Kaohsiung, and can serve as a reference for local governmental agencies.},
author = {Chiu, C.H. and Wen, T.H. and Chien, L.C. and Yu, H.L.},
doi = {10.1371/journal.pone.0106334},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Chiu et al. - 2014 - A probabilistic spatial dengue fever risk assessment by a threshold-based-quantile regression method.pdf:pdf},
issn = {19326203},
journal = {PLoS ONE},
number = {10},
pages = {1--13},
pmid = {25302582},
title = {{A probabilistic spatial dengue fever risk assessment by a threshold-based-quantile regression method}},
volume = {9},
year = {2014}
}
@article{Panhwer2017,
abstract = {There is compelling evidence that changes in the climate of the earth occur which cannot be explained without taking into account human influence. Many vulnerability studies anticipate climate change will lead to increased occurrence of transmittable diseases, including Vector Borne Diseases (VBDs). VBDs are spread by the bite of infected mosquitoes and other insects (vectors). The breeding of species of mosquitoes is strongly associated with demographic factors. Mosquito populations and mosquito density is considered to be the key signs that determine and evaluates risk of dengue in any particular area. Measuring the population of mosquitos requires enormous exertions and use of the latest information ICT techniques. The incidents of dengue can be prevented by timely decision to implement vector control activities. Recent advances in Geographic Information System (GIS) and mapping new technologies have shaped novel opportunities for public health managers to improve, plan, monitor and assess health systems. GIS represents better explanation and analysis for the spread and control of such vector borne diseases. This study used GIS application to identify areas prone tosuch diseases. The results show that Latifabad is a major area prone to VBD followed by Bhitainagar, Qasimabad and Heerabad. The ability to identify likely areas of dengue incidence helps for quick and efficient disease supervision and prevention practices.},
author = {Panhwer, M.A. and Pirzada, N. and Khahro, S.H.},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Panhwer, Pirzada, Khahro - 2017 - Spatial Risk Mapping for Dengue Fever Using GIS A Case Study of Hyderabad.pdf:pdf},
journal = {Sindh University Research Journal},
keywords = {dengue,hyderabad,vector borne disease},
number = {1},
pages = {93--96},
title = {{Spatial Risk Mapping for Dengue Fever Using GIS: A Case Study of Hyderabad}},
volume = {49},
year = {2017}
}
@article{Dom2016,
abstract = {Objective To highlight the use of analytical hierarchy process (AHP) in geographical information system that incorporates environmental indices to generate dengue risk zonation area. Methods The medical database considered for the study was referenced to the environmental data layers. Factors related to the risk of dengue fever (DF) were selected throughout previous research and were arranged in a hierarchical structure. The relative weights of factors were calculated, which were within acceptable range with the consistency ratio being less than 0.1. The outcomes from AHP based DF risk zonation area produced useful information on different levels of risks. Results As a result, factor weights used in AHP were evaluated and found to be acceptable as the consistency ratio of 0.05, which was < 0.1. The most influential factors were found to be housing types, population density, land-use and elevation. Findings from this study provided valuable insights that could potentially enhance public health initiatives. The geographical information system and spatial analytical method could be applied to augment surveillance strategies of DF and other communicable diseases in an effort to promote actions of prevention and control. The disease surveillance data obtained could be integrated with environmental database in a synergistic way, which will in turn provide additional input towards the development of epidemic forecasting models. Conclusions This attempt, if successful, will have significant implications that could strengthen public health interventions and offers priorities in designing the most optimum and sustainable control program to combat dengue in Malaysia.},
author = {Dom, N.C. and Ahmad, A.H. and Latif, Z.A. and Ismail, R.},
doi = {10.1016/S2222-1808(16)61158-1},
file = {:H\:/File Baru/Daftar Pustaka ICVIAS/S2222-1808%2816%2961158-1.pdf:pdf},
issn = {22221808},
journal = {Asian Pacific Journal of Tropical Disease},
keywords = {Analytical hierarchy process,Dengue,Geographical information system,Malaysia},
number = {12},
pages = {928--935},
publisher = {Asian Pacific Tropical Medicine Press},
title = {{Application of geographical information system-based analytical hierarchy process as a tool for dengue risk assessment}},
url = {http://dx.doi.org/10.1016/S2222-1808(16)61158-1},
volume = {6},
year = {2016}
}
@article{Dhewantara2019,
abstract = {Background: Dengue fever control in the tropical island of Bali in Indonesia carries important significance both nationally and globally, as it is one of the most endemic islands in Indonesia and a worldwide popular travel destination. Despite its importance, the spatial and temporal heterogeneity in dengue risk and factors associated with its variation in risk across the island has not been not well explored. This study was aimed to analyze for the first time the geographical and temporal patterns of the incidence of dengue and to quantify the role of environmental and social factors on the spatial heterogeneity of dengue incidence in Bali. Methods: We analyzed retrospective dengue notification data at the sub-district level (Kecamatan) from January 2012 to December 2017 which obtained from the Indonesian Ministry of Health. Seasonality in notified dengue incidence was assessed by seasonal trend decomposition analysis with Loess (STL) smoothing. Crude standardized morbidity rates (SMRs) of dengue were calculated. Moran's I and local indicators of spatial autocorrelation (LISA) analysis were employed to assess spatial clustering and high-risk areas over the period studied. Bayesian spatial and temporal conditional autoregressive (CAR) modeling was performed to quantify the effects of rainfall, temperature, elevation, and population density on the spatial distribution of risk of dengue in Bali. Results: Strong seasonality of dengue incidence was observed with most cases notified during January to May. Dengue incidence was spatially clustered during the period studied with high-risk kecamatans concentrated in the south of the island, but since 2014, the high-risk areas expanded toward the eastern part of the island. The best-fitted CAR model showed increased dengue risk in kecamatans with high total annual rainfall (relative risk (RR): 1.16 for each 1-mm increase in rainfall; 95% Credible interval (CrI): 1.03–1.31) and high population density (RR: 7.90 per 1000 people/sq.km increase; 95% CrI: 3.01–20.40). The RR of dengue was decreased in kecamatans with higher elevation (RR: 0.73 for each 1-m increase in elevation; 95% CrI: 0.55–0.98). No significant association was observed between dengue RR and year except in 2014, where the dengue RR was significantly lower (RR: 0.53; 95% CrI: 0.30–0.92) relative to 2012. Conclusions: Dengue incidence was strongly seasonal and spatially clustered in Bali. High-risk areas were spread from kecamatans in Badung and Denpasar toward Karangasem and Klungkung. The spatial heterogeneity of dengue risk across Bali was influenced by rainfall, elevation, and population density. Surveillance and targeted intervention strategies should be prioritized in the high-risk kecamatans identified in this study to better control dengue transmission in this most touristic island in Indonesia. Local health authorities should recommend travelers to use personal protective measures, especially during the peak epidemic period, before visiting Bali.},
author = {Dhewantara, P.W. and Marina, R. and Puspita, T. and Ariati, Y. and Purwanto, E. and Hananto, M. and Hu, W. and Magalhaes, R. J.S.},
doi = {10.1016/j.tmaid.2019.06.008},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/Data Base SR/DIpakai/dhewantara 2019 Spatial and temporal variation of dengue incidence in the island of Bali, Indonesia An ecological study.pdf:pdf},
issn = {18730442},
journal = {Travel Medicine and Infectious Disease},
keywords = {Bali,Conditional autoregression model,Dengue,Risk factors,Spatial analysis,Travel medicine},
pages = {101437},
pmid = {31362115},
publisher = {Elsevier},
title = {{Spatial and temporal variation of dengue incidence in the island of Bali, Indonesia: An ecological study}},
url = {https://doi.org/10.1016/j.tmaid.2019.06.008},
volume = {32},
year = {2019}
}
@article{Wongpituk2020,
abstract = {Abstract Canada's cattle/beef sector has already weathered a shock after a 2003 case of BSE resulted in closed borders and industry restructuring. Now the sector has to adjust to similar shocks due to COVID-19. This paper examines the supply chain from the consumer up to the cow-calf producer by considering consumer reactions, labour market constraints, and supply response. A quarterly market model of North American cattle and beef markets is used to examine price and revenue impacts associated with the market disruptions. Depending on the scenario, there is considerable price and revenue suppression at all levels of the market. This article is protected by copyright. All rights reserved},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Wongpituk, K. and Kalayanarooj, S. and Nithikathkul, C.},
eprint = {arXiv:1011.1669v3},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wongpituk, Kalayanarooj, Nithikathkul - 2020 - Geospatial Analysis of DHF Surveillance Model in Si Sa Ket Province, Thailand using Geogr.pdf:pdf},
isbn = {9789896540821},
issn = {0038092X},
journal = {International Journal of Geoinformatics},
number = {3},
pages = {1--8},
pmid = {25246403},
title = {{Geospatial Analysis of DHF Surveillance Model in Si Sa Ket Province, Thailand using Geographic Information System}},
url = {https://doi.org/10.1016/j.tmaid.2020.101607%0Ahttps://doi.org/10.1016/j.ijsu.2020.02.034%0Ahttps://onlinelibrary.wiley.com/doi/abs/10.1111/cjag.12228%0Ahttps://doi.org/10.1016/j.ssci.2020.104773%0Ahttps://doi.org/10.1016/j.jinf.2020.04.011%0Ahttps://doi.o},
volume = {16},
year = {2020}
}
@article{Martinez-Bello2017,
abstract = {Background: Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach. Methods: We estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1-20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008-2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen's Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation. Results: We found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair. Conclusion: The study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.},
author = {Mart{\'{i}}nez-Bello, D.A. and L{\'{o}}pez-Qu{\'{i}}lez, A. and {Torres Prieto}, A.},
doi = {10.1186/s12942-017-0104-x},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/s12942-017-0104-x.pdf:pdf},
issn = {1476072X},
journal = {International Journal of Health Geographics},
keywords = {Bayesian modeling,Cohen's Kappa,Disease mapping,Satellite images},
number = {1},
pages = {1--15},
pmid = {28810908},
publisher = {BioMed Central},
title = {{Relative risk estimation of dengue disease at small spatial scale}},
volume = {16},
year = {2017}
}
@article{Ordonez-Sierra2019,
abstract = {Dengue is the most important viral disease transmitted by mosquitoes, predominantly Aedes (Stegomyia) aegypti (L.) (Diptera:Culicidae). Forty percent of the world's population is at risk of contracting the disease, and a large area of Mexico presents suitable environmental conditions for the life cycle of Ae. aegypti. In particular, the Central Mexican Highlands have a high population density, increasing the risk of transmission and propagation of dengue. In the present study, the potential distribution of Ae. aegypti was modeled under an ecological niche approach using the maximum entropy technique with the aim of determining the spatial risk distribution of dengue. The final model of five variables (minimum temperature of the coldest month |Bio6|, precipitation of the wettest month |Bio13|, precipitation seasonality |Bio15|, the normalized difference vegetation index (NDVI), and relative humidity) contributed to more than 90% of the model's performance. The results of the potential distribution model were then compared with the number of dengue cases per locality during the 2009-2015 period considering four suitability of presence categories. Category 4 corresponded with the highest suitability of presence (0.747 to 1) and the greatest risk of dengue (odds ratio [OR] = 103.27; P < 0.001). In conclusion, the present ecological niche model represents an important tool for the monitoring of dengue and the identification of high-risk areas.},
author = {Ordo{\~{n}}ez-Sierra, R. and Mastachi-Loza, C.A. and D{\'{i}}az-Delgado, C. and Cuervo-Robayo, A.P. and Ortiz, C.R.F. and G{\'{o}}mez-Albores, M.A. and Torres, IM.},
doi = {10.1093/jme/tjz244},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/10.1093@jme@tjz244.pdf:pdf},
issn = {19382928},
journal = {Journal of Medical Entomology},
keywords = {GIS,dengue,modeling,mosquito-borne disease,risk assessment},
number = {3},
pages = {728--737},
pmid = {31880769},
title = {{Spatial Risk Distribution of Dengue Based on the Ecological Niche Model of Aedes aegypti (Diptera: Culicidae) in the Central Mexican Highlands}},
volume = {57},
year = {2019}
}
@article{AjimAli2018,
abstract = {Detecting and mapping of Dengue risk areas is a complex, tiring, multifaceted and protracted task requiring evaluation of many criteria. It is not sure that always one single factor is liable for Dengue Fever transmission in all areas, but it differs with changing geographical location. This paper presents the application of analytic hierarchy process alongside with geospatial analysis for detecting Dengue risk areas in Kolkata Municipal Corporation by integrating environmental parameters. It employs two stage analyses synergistically to form a Spatial Decision Support System. The first stage analysis makes use of the thematic layers in Geographical Information System in combination with environmental factors leading to support the second stage analysis using the analytic hierarchy process as a tool. Moreover, weighted overlay analysis was used for detecting potential risk areas. The research result shows that the calculated weights of criteria are within the range of Consistency Ratio being > 0.1. The chosen decision criteria are consistent because the calculated Consistency Ratio is 0.0551 which is < 0.1 and considered as acceptable for decision making. The most influential factors are found the Household Density, Water Logged Areas, Land Surface Temperature, Population Density, Land Elevation and Land Use Land Cover. The present study shows that the spatial relationship can help in understanding the pattern and distribution of dengue outbreak and zonation of potential risk areas.},
author = {{Ajim Ali}, Sk and Ahmad, Ateeque},
doi = {10.1007/s41324-018-0187-x},
file = {:H\:/File Baru/Daftar Pustaka ICVIAS/s41324-018-0187-x.pdf:pdf},
issn = {23663294},
journal = {Spatial Information Research},
keywords = {Analytic hierarchy process,Decision criteria,Dengue Fever,Dengue risk zonation,Geographical Information System},
number = {4},
pages = {449--469},
publisher = {Springer Singapore},
title = {{Using analytic hierarchy process with GIS for Dengue risk mapping in Kolkata Municipal Corporation, West Bengal, India}},
url = {https://doi.org/10.1007/s41324-018-0187-x},
volume = {26},
year = {2018}
}
@article{Khormi2011,
abstract = {Dengue fever (DF) and its impacts are growing environmental, economic, and health concerns in Saudi Arabia. In this study, we have attempted to model areas with humans at risk of dengue fever prevalence, depending on the spatial relationship between dengue fever cases and different socioeconomic parameters. We have developed new methods to verify the quality of neighborhoods from high resolution satellite images based on several factors such as density of houses in each neighborhood in each district, width of streets, and roof area of houses. In the absence of detailed neighborhood quality information being available for each district, we felt this factor would best approximate the reality on the ground at local scales. Socioeconomic parameters, such as population numbers, population density, and neighborhood quality were analyzed using Geographically Weighted Regression (GWR) to create a prediction model identifying levels of risk of dengue and to describe the association between DF cases and the related socio-economic factors. Descriptive analysis was used to characterize dengue fever victims among Saudis and non-Saudis in various age groups. The results show that there was a strong positive association between dengue fever cases and socioeconomic factors (R 2 = 0.80). The prevalence among Saudis was higher compared to non-Saudis in 2006 and 2007, while the prevalence among non-Saudis was higher in 2008, 2009 and 2010. For age groups, DF was more prevalent in adults between the ages of 16 and 60, accounting for approximately 74% of all reported cases in 2006, 67% in 2007, 81% in 2008, 87% in 2009, and 81% in 2010. {\textcopyright} 2011 Elsevier B.V.},
author = {Khormi, H.M. and Kumar, L.},
doi = {10.1016/j.scitotenv.2011.08.028},
file = {:H\:/File Baru/Daftar Pustaka ICVIAS/j.scitotenv.2011.08.028.pdf:pdf},
issn = {00489697},
journal = {Science of the Total Environment},
keywords = {Dengue Fever (DF),Geographic Information Systems (GIS),Geographically Weighted Regression,High resolution satellite images,Socioeconomic parameters},
number = {22},
pages = {4713--4719},
pmid = {21906782},
publisher = {Elsevier B.V.},
title = {{Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study}},
url = {http://dx.doi.org/10.1016/j.scitotenv.2011.08.028},
volume = {409},
year = {2011}
}
@article{Wu2021,
abstract = {Background In recent years, frequent outbreaks of dengue fever (DF) have become an increasingly serious public health issue in China, especially in the Pearl River Delta (PRD) with fast socioeconomic developments. Previous studies mainly focused on the historic DF epidemics, their influencing factors, and the prediction of DF risks. However, the future risks of this disease under both different socioeconomic development and representative concentration pathways (RCPs) scenarios remain little understood. Methodology and principal findings In this study, a spatial dataset of gross domestic product (GDP), population density, and land use and land coverage (LULC) in 2050 and 2070 was obtained by simulation based on the different shared socioeconomic pathways (SSPs), and the future climatic data derived from the RCP scenarios were integrated into the Maxent models for predicting the future DF risk in the PRD region. Among all the variables included in this study, socioeconomics factors made the dominant contribution (83% or so) during simulating the current spatial distribution of the DF epidemics in the PRD region. Moreover, the spatial distribution of future DF risk identified by the climatic and socioeconomic (C&S) variables models was more detailed than that of the climatic variables models. Along with global warming and socioeconomic development, the zones with DF high and moderate risk will continue to increase, and the population at high and moderate risk will reach a maximum of 48.47 million (i.e., 63.78% of the whole PRD) under the RCP 4.5/SSP2 in 2070. Conclusions The increasing DF risk may be an inevitable public health threat in the PRD region with rapid socioeconomic developments and global warming in the future. Our results suggest that curbs in emissions and more sustainable socioeconomic growth targets offer hope for limiting the future impact of dengue, and effective prevention and control need to continue to be strengthened at the junction of Guangzhou-Foshan, north-central Zhongshan city, and central-western Dongguan city. Our study provides useful clues for relevant hygienic authorities making targeted adapting strategies for this disease.},
author = {Wu, Wei and Ren, Hongyan and Lu, Liang},
doi = {10.1371/journal.pntd.0009745},
file = {:C\:/Users/ASUS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wu, Ren, Lu - 2021 - Increasingly expanded future risk of dengue fever in the Pearl River Delta, China.pdf:pdf},
isbn = {1111111111},
issn = {19352735},
journal = {PLoS Neglected Tropical Diseases},
number = {9},
pages = {1--18},
pmid = {34559817},
title = {{Increasingly expanded future risk of dengue fever in the Pearl River Delta, China}},
url = {http://dx.doi.org/10.1371/journal.pntd.0009745},
volume = {15},
year = {2021}
}
@article{Hnusuwan2020,
abstract = {Dengue fever is a major public health problem and has been an epidemic in Thailand for a long time. Therefore, there is a need to find a way to prevent the disease. This research aimed to explore the important factors of dengue fever, to study the factors affecting dengue hemorrhagic fever in Surat Thani Province, and to map the potential outbreak of dengue fever. Collecting patient information was done including, Rainfall, Digital Elevation Model (DEM), Land Use and Land Cover (LULC), Population Density, and Patients in Surat Thani Province, which was analyzed using data mining techniques involving analysis using 3 algorithms comprising Random Forest, J48, and Random Tree. The correct result is Random Forest since the accuracy of the data is 96.7 percent followed by J48 with accuracy of 95.9 percent. The final sequence is Random Tree with accuracy of 93.5 percent. Then, using the information can be displayed through ArcGIS program to see the risk points that are compared to the risk areas that have been previously done. The results can be very risky in Mueang District, Kanchanadit District, and Don Sak District, corresponding to the information obtained from the Public Health Office and the risk map created from the patient information.},
author = {Hnusuwan, Benjawan and Kajornkasirat, Siriwan and Puttinaovarat, Supattra},
doi = {10.3991/ijoe.v16i11.16455},
file = {:H\:/File Baru/Disertasi Istri/Rujukan SR/auer,+57_Dengue+Risk+Mapping+from+Geospatial+Data+Using+GIS+and+Data+Mining+Techniques.pdf:pdf},
issn = {26268493},
journal = {International journal of online and biomedical engineering},
keywords = {Data mining,Dengue risk mapping,GIS,Geospatial data},
number = {11},
pages = {57--79},
title = {{Dengue Risk Mapping from Geospatial Data Using GIS and Data Mining Techniques}},
volume = {16},
year = {2020}
}