ArticleGeoHealth2019
Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead.
Article in GeoHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of sample size estimation accuracy on power in malaria cluster randomised trials measuring epidemiological outcomes.BMC medical research methodology · 2024Pooled it
- Modelling the impact of climate variability on malaria morbidity in the Tamale Metropolitan Area: a time series analysis.BMC infectious diseases · 2026Article
- Using data science to identify climate change and health adverse impacts and solutions in Africa: a scoping review.npj health systems · 2026Article
- Epidemiological trends of malaria in Sennar State, Sudan.Malaria journal · 2025Article
- Time of Emergence and Future Projections of Extremes of Malaria Infections in Africa.GeoHealth · 2025Article
- The effect of climate change and temperature extremes onJournal of the Royal Society, Interface · 2024Article
- Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond.The Lancet regional health. Europe · 2023Review
- Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles.PLoS medicine · 2021Article
- Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania.Malaria journal · 2020Article
- Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead.GeoHealth · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria forecasts from dynamical systems have never been attempted at the health district or local clinic catchment scale, and so their usefulness for public health preparedness and response at the local level is fundamentally unknown. A pilot preoperational forecasting system is introduced in which the European Centre for Medium Range Weather Forecasts ensemble prediction system and seasonal climate forecasts of temperature and rainfall are used to drive the uncalibrated dynamical malaria model VECTRI to predict anomalies in transmission intensity 4 months ahead. It is demonstrated that the system has statistically significant skill at a number of sentinel sites in Uganda with high-quality data. Skill is also found at approximately 50% of the Ugandan health districts despite inherent uncertainties of unconfirmed health reports. A cost-loss economic analysis at three example sentinel sites indicates that the forecast system can have a positive economic benefit across a broad range of intermediate cost-loss ratios and frequency of transmission anomalies. We argue that such an analysis is a necessary first step in the attempt to translate climate-driven malaria information to policy-relevant decisions.
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