ArticleEnvironmental modelling & software : with environment data news2019
A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model.
Article in Environmental modelling & software : with environment data news, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models.Scientific reports · 2025Article
- Advancing climate change health adaptation through implementation science.The Lancet. Planetary health · 2022Review
- Cloud-based applications for accessing satellite Earth observations to support malaria early warning.Scientific data · 2022Article
- Article
- Remote sensing of environmental risk factors for malaria in different geographic contexts.International journal of health geographics · 2021Article
- Satellite Observations and Malaria: New Opportunities for Research and Applications.Trends in parasitology · 2021Review
- Spatial prediction of COVID-19 epidemic using ARIMA techniques in India.Modeling earth systems and environment · 2021Article
- EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases.IEEE transactions on instrumentation and measurement · 2021Article
- Applying Deep Learning in the Training of Communication Design Talents Under University-Industrial Research Collaboration.Frontiers in psychology · 2021Article
- Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil.Chaos, solitons, and fractals · 2020Article
- Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations Over the Amhara Region, Ethiopia.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
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
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