Researchers at Umeå University have developed a model that uses seasonal weather data from satellite images to accurately predict outbreak of malaria with a one-month lead time. With a so-called GAMBOOST model, a host of weather information gathered from satellite images can be used as a cost-effective disease forecasting model, allowing health officials to get ahead of the malaria infection curve by allocating resources and mobilizing public health responses. The model was recently described in the journal Scientific Reports, a Nature Research publication.
Researchers at Umeå University have developed a model that uses seasonal weather data from satellite images to accurately predict outbreak of malaria with a one-month lead time. With a so-called GAMBOOST model, a host of weather information gathered from satellite images can be used as a cost-effective disease forecasting model, allowing health officials to get ahead of the malaria infection curve by allocating resources and mobilizing public health responses. The model was recently described in the journal Scientific Reports, a Nature Research publication.
In the forecasting model, information about land surface temperature, rainfall, evaporation and plant perspiration is used to establish links between observable weather patterns and future patterns of malaria outbreaks. Using hospital and weather data from a rural district in Western Kenya, the researchers have been able to show with a high level of accuracy that conducive environmental conditions occur before a corresponding increase in hospital admissions and mortality due to malaria.
Read more at Umeå University
Image: This is Maquins Sewe, Department of Public Health and Clinical Medicine, Epidemiology and Global Health Unit, Umeå University. (Credit: Umeå University)
