Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead. Issue 3 (22nd March 2019)

• Record Type: Journal Article
• Authors: Tompkins, Adrian M.; Colón‐González, Felipe J.; Di Giuseppe, Francesca; Namanya, Didacus B.
• Is Part Of: GeoHealth. Volume 3:Issue 3(2019)
• Page Start: 58
• Page End: 66
• Publication Date: 2019-03-22
• Subjects: health -- early warning system -- Africa -- seasonal forecast; Environmental health -- Periodicals; Electronic journals; Periodicals; 616.98
• DOI: 10.1029/2018GH000157
• Languages: English
• ISSNs: 2471-1403

Abstract

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. Plain Language Summary: The malaria parasite and its mosquito vector are both weather sensitive, with temperature impacting development rates and mortality, and rainfall providing mosquito breeding sites. We take advantage of these effects to provide forecasts of malaria transmission intensity up to 4 months ahead, using a state‐of‐the‐art climate prediction system to drive a mathematical model of the parasite/vector cycle. The system is evaluated to have skill in Uganda at the district and local clinic scale, a first for subnational forecasts. We then perform an economic analysis to show at which range of decision entry points and anomalous transmission events the system has positive benefit, necessary for integration into decision‐making processes. Key Points: A climate‐driven malaria early warning system is skillful at the subnational scale in Uganda A cost‐loss economic analysis allows a user to determine for which interventions and event frequency the system has value Short health data records and lack of high‐quality sites hamper early warning system evaluation and improvement