Advancing Early Warning Systems for Malaria

Malaria Early Warning Systems (EWS) are predictive tools that often use climatic and other environmental variables to forecast malaria risk and trigger timely interventions. Despite their potential benefits, the development and implementation of malaria EWS face significant challenges and limitations. We reviewed the current evidence on malaria EWS, including their settings, methods, performance, actions, and evaluation. We conducted a comprehensive literature search using keywords related to EWS and malaria in various databases and registers. We included primary research and programmatic reports on developing and implementing Malaria EWS. We extracted and synthesized data on the characteristics, outcomes, and experiences of Malaria EWS. We screened 6,233 records and identified 30 studies from 16 countries that met the inclusion criteria. The studies varied in their transmission settings, from pre-elimination to high burden, and their purposes, ranging from outbreak detection to resource allocation. The studies employed various statistical and machine-learning models to forecast malaria cases, often incorporating environmental covariates such as rainfall and temperature. The most common mode used is the time series model. The performance of the models was assessed using measures such as the Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), and adjusted R-squared (R 2 ). The studies reported actions and responses triggered by EWS predictions, such as vector control, case management, and health education. The lack of standardized criteria and methodologies limited the evaluation of EWS impact. Our review highlights the strengths and limitations of malaria early warning systems, emphasizing the need for methodological refinement, standardization of evaluation metrics, and real-time integration into public health workflows. While significant progress has been made, challenges remain in automating forecasting tools, ensuring scalability, and aligning predictions with actionable public health responses. Future efforts should enhance model precision, usability, and adaptability to improve malaria prevention and control strategies in endemic regions

Akaike information criterion · Early warning system · Environmental health · Machine learning · Malaria · Psychological intervention · Standardization · Warning system · Computer Science · Malaria Research and Control · Medicine · Mosquito-borne diseases and control · Species Distribution and Climate Change

• Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning

  Open Access•Patrick Martineau, Swadhin K Behera et al.•Frontiers in Public Health•2022

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• Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors

  Open Access•Julie D Pourtois, Krti Tallam et al.•PLOS Global Public Health•2023

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• Forecasting paediatric malaria admissions on the Kenya Coast using rainfall

  Open Access•Stella Wanjugu Karuri, Stella Karuri et al.•Global Health Action•2016

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• Forecasting Malaria Morbidity to 2036 Based on Geo-Climatic Factors in the Democratic Republic of Congo

  Open Access•Eric Panzi-Kalunda, Ngianga-Bakwin Kandala et al.•International Journal of…•2022

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• Approaches to the evaluation of outbreak detection methods

  Open Access•Rochelle E Watkins, Rochelle Watkins et al.•BMC Public Health•2006

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