School of Public Health
Twin Cities
Increases in frequency and severity of severe weather events (e.g. cyclones, tropical storms, and severe storms) are a hallmark of climate change and impact established programs to control and eliminate malaria. Mozambique is already experiencing these and currently does not have the capacity to respond to all of the infectious disease challenges that co-occur. Plasmodium falciparum malaria is endemic and the National Malaria Control Program (PNCM) is managing prevention, control, and elimination strategies throughout the country under varying environmental conditions. The progress of these programs is at risk when infrastructure is damaged due to severe storms because emergency response is usually limited to immediate risks and to highest impacted areas. Using digital technology, climate and malaria data can be integrated to identify all areas at risk of malaria infection and disease severity in the aftermath of these severe weather events.
Malaria case and severity data is routinely collected by the PNCM surveillance system systematically through the DHS and dhis2 systems. Rural health centers (RHCs) and hospitals report data to their respective District Health Office, which aggregates these data to report to the Provincial and National Health Offices. These researchers will collaborate with the PNCM to use these data to investigate spatial and temporal trends in weekly malaria case and severity data at the District level for all of Mozambique since the implementation of the surveillance system in 2016.
Climate and weather data are freely and publicly available with a 4-hour lag from NASA satellite imagery. While named storms fit a specific criterion and are well documented by landfall, their spatial and temporal extent is generally not estimated. Un-named storms are not well documented, but can be accounted for using satellite imagery.
The researchers will integrate malaria surveillance data and climate data to determine geographic and temporal areas of increased malaria risk following severe weather events. They will quantify these areas using a Baysian time-series model. This model will provide the basis of a software platform that will predict areas of high malaria risk for delivery of prevention and control measures following severe weather events.
The software platform will be developed using data already being collected by the PNCM for surveillance and will directly link information about environmental and infrastructural risk to public health programs. The long-term objective of this project is to provide an open source technology that can provide a rapid response for malaria and other infectious disease prevention and control programs following natural disasters.