Modelling infectious diseases is a complex and multi‐disciplinary problem that necessitates the combined use of multicriteria decision analysis (MCDA) and machine learning (ML) in a spatial framework. This research attempts to demonstrate the extensive applications of MCDA in the field of public health and to illustrate its utility with the combined use of spatial models and machine learning. The study investigates the risk factors for communicable diseases with a focus on vector‐borne infectious diseases, such as West Nile Virus (WNV), malaria, dengue, etc. It aims to quantify vector‐borne disease risk by examining the geographic contextual effects of socio‐economic, climatic, and environmental factors using the objective‐weighting technique adopted from MCDA and machine learning in a geographic information systems (GIS) framework. The authors attempted to minimize subjective bias from the decision space by utilizing an objective‐weighted technique to quantify the risk. The study adopted Shannon's entropy to derive weights for each factor and its classes. The derived weighted layers are fed to an artificial neural network to obtain a final map of risk susceptibility. This final risk map allows policymakers to examine vulnerable areas and identify the factors pivotal to the contribution of risk. Findings show the traffic volume as the most influential variable, and terrain slope as the least one in the disease spread for the study area. The risk appears to be concentrated and distributed along vegetation, wetlands, and around water bodies. The results produced by ensemble learning show great promise with more than 94% accuracy. The accuracy of the results was determined by the confusion matrix and the kappa index of agreement (KIA). The vector control programmes need to adapt to better manage the dynamic changes in patterns involving vector‐borne infectious diseases.
An understanding of how infected-susceptible populations interact is critical to identify underlying causal factors and disease transmission patterns of infectious diseases. Disease transmission patterns are dynamic, non-linear, and spatially complex. This anisotropic characteristic of disease spread necessitates the ideal solution to be sensitive to the geographic context. A Spatial Diffusion Model (SDM) to predict interaction potential and COVID-19 risk probability is developed by adapting the Newtonian gravity model. This novel approach overcomes the limitations of existing epidemiological studies by characterizing the behavioral patterns of the infected population to model the spatiotemporal transmission of disease across the geographic space. The proposed model is robust as it couples a multicriteria behavioral pattern to enhance predictive capability. The model shows an 83.74% correlation with the observational COVID-19 case data. The highest risk patterns for COVID-19 are predicted in the neighborhoods of New York City (NYC), exhibiting clustered socioeconomic disparities along with racial and ethnic heterogeneity. Policymakers can use these results to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions. This study emphasizes the need for improved spatial epidemiological models including enhanced depictions of human activity patterns and the need to integrate spatial data with advanced mathematical models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.