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These maps compare vulnerability averaged at the state-level (A) with dengue rates per 1000 persons (B), in Malaysia. Average dengue rates during February for even years from 2001 to 2011 are shown as this dataset was used to validate the WADI-Dengue case study. Dengue rates are shown for February because the WADI-Dengue uses a 2 month lag for climate data, based on the exposure thresholds identified in the methods section. Although averaged vulnerability values hide features such as urban areas and mountainous regions, the lower rates of cases observed on the east coast of Malaysia during the monsoon season (November – January) are consistent with the vulnerability profile.
Source publication
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-assoc...
Citations
... This specialist group was made up of sociologists, geographers, civil engineers, emergency service workers, institutional authorities responsible for risk management and non-governmental organisations (NGOs) working in disaster management (see Table 1). Expert consultation is commonly used in applied studies on social vulnerability to natural hazards (Dickin et al., 2013;Hagenlocher et al., 2018;Tucker et al., 2015), and enabled us to address the high specificity and complexity of the causal processes of social vulnerability to flood risk, in the absence of secondary data and sufficient specialised bibliography at local level. Stratified purposeful sampling (Patton, 2002) was chosen in order to select interviewees of sufficiently heterogeneous areas of expertise. ...
The mainstream approach of social vulnerability to flood risk faces some challenges regarding its ability to address the complexity of its causal processes. The objective of this study was to analyse the causal processes of social vulnerability to flood impacts from a relational perspective. To this end, a social network analysis was performed that identified the conditions and causes of social vulnerability and systematically articulated the relationships between them. This analysis was tested on the specific case of flood risk on the coast of the province of Alicante (SE Spain). To ascertain the conditions and causes of social vulnerability to flood risk, a multidisciplinary group of local experts was consulted, and the resulting data processed in a relational way using Atlas.ti and Gephi softwares. The result was a social vulnerability network comprising 84 nodes and 189 edges distributed into four dimensions: the adaptive capacity of tourists, socio-economic structure, land use planning and risk management. The information was ranked for betweenness centrality, revealing the components with highest causal power of social vulnerability to social impacts in flooding events: low flood risk awareness, economic growth based on real estate boom, property speculation and lack of political interest in flood risk management. This proposal places emphasis on the driving forces of social vulnerability and not exclusively on the specific adaptive conditions of the population, which allows a strategic identification and management of generative forces that ultimately induce the social impacts of floods.
... The peer-reviewed articles used data ranging from one to twenty years. Eleven studies (1,5,6,9,10,11,16,18,28,32,37) used data sets with a duration of fewer than three years, while twenty-four of the 45 studies used data sets with a duration of three years or longer (2,3,4,7,12,13,17,19,20,22,25,26,27,29,30,31,33,35,38,40,41,42,43,44) (Table 1) (Table 1). These data were derived primarily from national censuses. ...
... Three studies used precipitation, temperature, and humidity (26, 29,35). Predominantly used predictors were precipitation and temperature (2,6,7,9,16,22,27,31,33,34,39,40,41,43), and eleven studies used only one rain parameter, namely 1,13,16,19,23,24,28,30,32,34,36. Thirteen studies used climatological stations to interpolate the spatial distribution of temperature and eight studies used remote sensing to analyze Land Surface Temperature (LST) (13,19,23,24,28,30,31,34). They used remotely sensed data on climatic variables to address the lack of routinely collected data from meteorological stations. ...
... A Bayesian spatial model was used to generate a map of DHF relative risk in an area and investigate the relationship between socioenvironmental factors and DHF risk using spatial autocorrelation [15]. Five studies (7,9,31,34,40) used The Water Associated Disease Index (WADI) to identify and visualize vulnerability to different water-associated diseases by integrating a range of social and biophysical determinants in map format [22], [24], [45], [48], [54]. It aims to assess vulnerability by integrating disease-speci c measures of environmental exposure (i.e., temperature, precipitation, land use, etc.) with disease-speci c measures of social susceptibility (i.e., life expectancy, educational attainment, access to healthcare, etc.) to provide a holistic picture of risk to disease. ...
This review aims to provide a comprehensive overview of the important predictors, and additionally spatial modeling tools capable of producing Dengue Hemorrhagic Fever (DHF) risk maps. A literature search was conducted in PubMed, Scopus, Science Direct, and Google Scholar for studies reporting DHF risk factors. The Preferred Reporting Items for Systematic Reviews (PRISMA) 2020 statement is used to report this scoping review. It lasted from January 2011 to August of 2022. Initially 1329 articles were found, after inclusion and exclusion criteria, 45 manuscripts were selected. A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and Machine Learning technique. We found that There was no pattern of predictor use associated with particular approaches; instead, a wide range of predictors was used to create DHF risk maps. Predictors are various variables or factors that are considered when assessing the likelihood or intensity of DHF outbreaks in a specific area in the context of DHF risk mapping. These predictors can include climatology factors (e.g., temperature, rainfall, humidity), socio-economic indicators (e.g., population density, urbanization level), environmental factors (land-use, elevation) and other relevant factors (e.g., mosquito abundance, previous DHF cases). The spatial model of DHF risk is a valuable tool for public health authorities, policymakers, and communities to identify areas at higher risk of dengue transmission, but its limitations underscore the importance of complementing it with other approaches and considering contextual factors for a more holistic assessment of DHF outbreaks. It enables targeted interventions, such as vector control measures and public awareness campaigns, to be implemented in high-risk areas, ultimately helping to mitigate the impact of dengue outbreaks and protect public health.
... Este grupo de expertos estuvo compuesto por (tabla 1): sociólogos, geógrafos, ingenieros civiles, personal de los servicios de emergencia, autoridades institucionales encargadas de la gestión del riesgo y organizaciones no gubernamentales relacionadas con la gestión de desastres. La consulta a expertos, comúnmente utilizada en estudios aplicados sobre vulnerabilidad social ante amenazas naturales (Dickin et al., 2013;Tucker et al., 2015;Hagenlocher et al., 2018), permitió hacer frente a la elevada especificidad y complejidad de los procesos generativos de vulnerabilidad social ante riesgo de inundación, sobre los cuales no existen datos secundarios ni la suficiente bibliografía es-pecializada a escala local. En relación con las características de la muestra, optamos por una muestra intencional estratificada (Patton, 2002) a fin de hacer operativa una selección de entrevistados lo suficientemente heterogénea entre disciplinas de conocimiento. ...
Estudo a retratar o processo de Urbanização e segregação no litoral turístico
... Such multicomponent approaches for dengue risk mapping can be based on indices or on models [27]. For the former, vulnerability indices have been proposed based on exposure and susceptibility indicators [32], which typically integrate multiple indicators in a single measure. They generally rely on the aggregation of data of different indicators using a weighted summation. ...
... In Brazil, the Health Vulnerability Index (HVI) developed by Geren-cia de Epidemiologia Informaçao (GEEPI, 2013) Belo Horizonte and the ArboAlvo model have been used for the study of dengue and other arboviral diseases [34,35]. At a global scale, maps of vulnerability to infectious diseases have been created using methodologies such as the Infectious Disease Vulnerability Index (IDVI) [36] and the Water-Associated Disease Index (WADI) [32]. The WADI has been used for dengue vulnerability estimation and mapping at different spatial scales in several countries [32,[37][38][39][40]. ...
... At a global scale, maps of vulnerability to infectious diseases have been created using methodologies such as the Infectious Disease Vulnerability Index (IDVI) [36] and the Water-Associated Disease Index (WADI) [32]. The WADI has been used for dengue vulnerability estimation and mapping at different spatial scales in several countries [32,[37][38][39][40]. This index has been widely used because of its ease of implementing and because it is constructed using freely available data such as living conditions, population characteristics, climate, land use and land cover [32]. ...
To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010–2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.
... Due to the uneven distribution associated with exposure to SARS-CoV-2 a spatial dimension is crucial [29]. In this perspective, estimating the spatial susceptibility and vulnerability in health-related subjects is essential to prevent disease spread [30][31][32] since knowledge of the distribution of susceptible individuals allows for the assessment of multiple susceptibility levels [33]. ...
Background
COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.
Methods
We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.
Results
Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.
Conclusions
This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.
... Esta abordagem assumia homogeneidade territorial da propensão à infeção, contrariando evidências de alguns estudos (Alves, 2022;Sá Marques et al., 2020). A classificação de unidades territoriais à suscetibilidade é comum na literatura de riscos naturais (United Nations Office for Disaster Risk Reduction [UNISDR], 2004), mas também em temas de saúde (Andrew et al., 2008;Dickin et al., 2013), porque o seu conhecimento é crucial em contextos epidémicos. A análise de suscetibilidade a doenças tem sido facilitada pela existência de um volume crescente de informação epidemiológica (Leso et al., 2021). ...
Medidas de contenção da transmissão da COVID-19 foram adotadas por todo o mundo. Em Portugal estas baseavam-se exclusivamente na monitorização de indicadores epidemiológicos, ignorando a variação espacial da suscetibilidade à infeção. Utilizando uma regressão linear múltipla passo-a-passo e um método bivariado de análise de pontos de transição derivou-se um índice espacial de suscetibilidade à infeção de COVID-19. Os resultados demonstraram que a propagação da COVID-19 em Portugal Continental associa-se com vários fatores decorrentes de especificidades territoriais, nomeadamente sociodemográficas, económicas e de mobilidade. Com 36% da área de estudo identificaram-se municípios com mais de 80% dos casos confirmados e o cruzamento do índice de suscetibilidade com a taxa de incidência confirma que em 74% dos casos se classificaram corretamente os municípios para medidas ajustadas aos contextos geográficos. Este estudo propõe o conhecimento da suscetibilidade à infeção de COVID-19 como critério para aplicação de medidas de contenção alinhadas com a monitorização epidemiológica.
... The assessment of the environmental and socioeconomic suitability (hereafter "suitability") for human leptospirosis offers an alternative way to identify spatial patterns of the disease in areas where epidemiological data may be biased. While not a predictive approach, the suitability analysis can synthesize social and biophysical information to describe different conditions which may lead to the occurrence of the disease [16]. Spatial patterns of suitable conditions for the occurrence of infectious diseases have been previously assessed using a wide range of analytical tools (e.g. ...
Background:
Leptospirosis is among the leading zoonotic causes of morbidity and mortality worldwide. Knowledge about spatial patterns of diseases and their underlying processes have the potential to guide intervention efforts. However, leptospirosis is often an underreported and misdiagnosed disease and consequently, spatial patterns of the disease remain unclear. In the absence of accurate epidemiological data in the urban agglomeration of Santa Fe, we used a knowledge-based index and cluster analysis to identify spatial patterns of environmental and socioeconomic suitability for the disease and potential underlying processes that shape them.
Methods:
We geocoded human leptospirosis cases derived from the Argentinian surveillance system during the period 2010 to 2019. Environmental and socioeconomic databases were obtained from satellite images and publicly available platforms on the web. Two sets of human leptospirosis determinants were considered according to the level of their support by the literature and expert knowledge. We used the Zonation algorithm to build a knowledge-based index and a clustering approach to identify distinct potential sets of determinants. Spatial similarity and correlations between index, clusters, and incidence rates were evaluated.
Results:
We were able to geocode 56.36% of the human leptospirosis cases reported in the national epidemiological database. The knowledge-based index showed the suitability for human leptospirosis in the UA Santa Fe increased from downtown areas of the largest cities towards peri-urban and suburban areas. Cluster analysis revealed downtown areas were characterized by higher levels of socioeconomic conditions. Peri-urban and suburban areas encompassed two clusters which differed in terms of environmental determinants. The highest incidence rates overlapped areas with the highest suitability scores, the strength of association was low though (CSc r = 0.21, P < 0.001 and ESc r = 0.19, P < 0.001).
Conclusions:
We present a method to analyze the environmental and socioeconomic suitability for human leptospirosis based on literature and expert knowledge. The methodology can be thought as an evolutive and perfectible scheme as more studies are performed in the area and novel information regarding determinants of the disease become available. Our approach can be a valuable tool for decision-makers since it can serve as a baseline to plan intervention measures.
... Previous studies have also demonstrated the relationship between rainfall and the time interval for the emergence of dengue (Stewart et al., 2013;Ngugi et al., 2017;Santos et al., 2019). The duration and seasonality of the dengue season can be directly related to precipitation, relative humidity, and air temperature (Dickin et al., 2013;Wangdi et al., 2018;Astuti et al., 2019;Tsheten et al., 2020). A study in Bhutan, which has a tropical climate between January 2016 and June 2019, identified the dengue season between June and August. ...
Dengue is an endemic disease in more than 100 countries, but there are few studies about the effects of hydroclimatic variability on dengue incidence (DI) in tropical dryland areas. This study investigates the association between hydroclimatic variability and DI (2008-2018) in a large tropical dryland area. The area studied comprehends seven municipalities with populations ranging from 32,879 to 2,545,419 inhabitants. First, the precipitation and temperature impacts on interannual and seasonal DI were investigated. Then, the monthly association between DI and hydroclimatic variables was analyzed using generalized least squares (GLS) regression. The model's capability to reproduce DI given the current hydroclimatic conditions and DI seasonality over the entire time period studied were assessed. No association between the interannual variation of precipitation and DI was found. However, seasonal variation of DI was shaped by precipitation and temperature. February-July was the main dengue season period. A precipitation threshold, usually above 100 mm, triggers the rapid DI rising. Precipitation and minimum air temperature were the main explanatory variables. A two-month-lagged predictor was relevant for modeling, occurring in all regressions, followed by a non-lagged predictor. The climate predictors differed among the regression models, revealing the high spatial DI variability driven by hydroclimatic variability. GLS regressions were able to reproduce the beginning, development, and end of the dengue season, although we found underestimation of DI peaks and overestimation of low DI. These model limitations are not an issue for climate change impact assessment on DI at the municipality scale since historical DI seasonality was well simulated. However, they may not allow seasonal DI forecasting for some municipalities. These findings may help not only public health policies in the studied municipalities but also have the potential to be reproducible for other dryland regions with similar data availability.
... Dickin and colleagues[60] review the use of index approaches to measure and communicate complex information about water vulnerability. Criticisms of indices include their reductionist nature (simplifying inherently complex information and causal processes) and the choice of components and their weighting (equal weight, stakeholder elicitation, expert judgment, and regression modeling). ...
... Zhang and Schwartz (2020) applied a global spatial regression model to investigate the spread of virus in both metropolitan and non-metropolitan regions. Spatial modelling is considered as an effective tool for statistically and geographically analysing the relationship between disease transmission rate and some explanatory variables (Dickin et al. 2013;Neil, 2006;Pickle, 2002 ). In this research, three global spatial models are examined to investigate how well the variations of COVID-19 can be explained based on demographic, socioeconomic, meteorological and health-related factors as explanatory variables affecting disease infection rate in Canada. ...
The COVID-19 was first declared by World Health Organization (WHO) as global pandemic on March 11th 2020. While most of COVID-related studies have focused on epidemiological perspective, the spatial analysis of disease outbreak is also important to provide perceptions of transmission rates. Therefore, this paper attempts to identify the potential factors contributing to the COVID-19 incidence rate at provincial-level in Canada. Three statistical regression models, ordinary least squares (OLS), spatial error model, and spatial lag model (SLM) were applied to 14 independent variables including socio-demographic, economic, weather, health and facilities related factors. The results indicated that three factors including median income, diabetes and unemployment significantly affected the COVID-19 rates in Canada. Among three global models, the SLM performed the best to explain the key variables and spatial variability of disease incidence with a R2 value of 61%. However, in this study, the application of local regression models such as geographically weighted regression (GWR) and multiscale GWR (MGWR) have not been considered and this could be a scope for the future research.
