ArticleScientific reports2017
Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Differences of Rainfall-Malaria Associations in Lowland and Highland in Western Kenya.International journal of environmental research and public health · 2019Pooled it
- Influence of climate variability and seasonal trends on malaria incidence in Dar es Salaam, Tanzania using generalized additive models.Epidemiology and infection · 2026Article
- Climate, Interventions, and Malaria Outcomes in a Warming World: Towards Climate-Smart Malaria Control in Kenya.Tropical medicine and infectious disease · 2025Article
- Advance Warning and Response Systems in Kenya: A Scoping Review.medRxiv : the preprint server for health sciences · 2025Article
- Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018.Scientific reports · 2025Article
- Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.PLOS global public health · 2025Article
- Impact of Climate Variability and Interventions on Malaria Incidence and Forecasting in Burkina Faso.International journal of environmental research and public health · 2024Article
- Exploring malaria prediction models in Togo: a time series forecasting by health district and target group.BMJ open · 2024Article
- The relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya: A time-series analysis of monthly incidence data from 2008 to 2019.Parasite epidemiology and control · 2023Article
- Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning.Frontiers in public health · 2022Article
- Remote sensing of environmental risk factors for malaria in different geographic contexts.International journal of health geographics · 2021Article
- A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles.Journal of the Royal Society, Interface · 2021Article
- Satellite Observations and Malaria: New Opportunities for Research and Applications.Trends in parasitology · 2021Review
- Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore.PLoS neglected tropical diseases · 2020Article
- Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps.Current environmental health reports · 2020Review
- Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model.Scientific reports · 2019Article
- Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria.Scientific reports · 2019Article
- Quantifying Seasonal Variation in Insecticide-Treated Net Use among Those with Access.The American journal of tropical medicine and hygiene · 2019Article
- Vulnerability of farming communities to malaria in the Bole district, Ghana.Parasite epidemiology and control · 2018Article
- Spatio-temporal modelling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique.Scientific reports · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.
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