A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model
| Autor: | Worku Awoke, Dawn M. Nekorchuk, T. Gebrehiwot, Michael C. Wimberly, Mastewal Worku, Justin K. Davis, Abere Mihretie |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Mosquito-borne disease Environmental Engineering Land surface temperature biology Ecological Modeling 030231 tropical medicine Evolutionary algorithm Plasmodium falciparum medicine.disease biology.organism_classification Article 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Geography parasitic diseases Statistics Genetic algorithm medicine Life history Time series Software Malaria |
| Zdroj: | Environ Model Softw |
| ISSN: | 1364-8152 |
| DOI: | 10.1016/j.envsoft.2019.06.010 |
| Popis: | Time series models of malaria cases can be applied to forecast epidemics and support proactive interventions. Mosquito life history and parasite development are sensitive to environmental factors such as temperature and precipitation, and these variables are often used as predictors in malaria models. However, malaria-environment relationships can vary with ecological and social context. We used a genetic algorithm to optimize a spatiotemporal malaria model by aggregating locations into clusters with similar environmental sensitivities. We tested the algorithm in the Amhara Region of Ethiopia using seven years of weekly Plasmodium falciparum data from 47 districts and remotely-sensed land surface temperature, precipitation, and spectral indices as predictors. The best model identified six clusters, and the districts in each cluster had distinctive responses to the environmental predictors. We conclude that spatial stratification can improve the fit of environmentally-driven disease models, and genetic algorithms provide a practical and effective approach for identifying these clusters. |
| Databáze: | OpenAIRE |
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