New model predicts malaria epidemics in highlands of East Africa
Updated: Feb 20
Malaria epidemics remain a serious threat to human populations living in the highlands of East Africa. Malaria transmission is unstable and climate-sensitive in these areas, making it difficult to predict and prevent outbreaks. Early detection and prevention of malaria epidemics can save countless lives, but until recently, there was no reliable way to predict them.
A team of researchers has developed a new, validated, and automated model that can predict malaria epidemics up to four months in advance. The model is based on temperature and rainfall data, making it a powerful tool for early detection and prevention of malaria outbreaks.
The researchers collected confirmed inpatient malaria data from eight sites in Kenya, Tanzania, and Uganda for the period of 1995-2009. They also collected temperature and rainfall data from meteorological stations closest to the source of the malaria data from the period of 1960-2009. The team then constructed process-based models for computing the risk of an epidemic in two general highland ecosystems using the collected data.
The models were validated using sensitivity, specificity, and positive predictive power. Depending on the availability and quality of the malaria and meteorological data, the models indicated good functionality at all sites. The additive model was found most suited for the poorly drained U-shaped valley ecosystems while the multiplicative model was most suited for the well-drained V-shaped valley ecosystem. The +18°C model was adaptable to any of the ecosystems and was designed for conditions where climatology data were not available.
The validated models would be of great use in the early detection and prevention of malaria epidemics. The additive and multiplicative models were shown to be robust and with high climate-based, early epidemic predictive power. They are designed for use in the common, well- and poorly drained valley ecosystems in the highlands of East Africa.
In conclusion, the new climate-based, validated, and automated models for predicting malaria epidemics in the highlands of East Africa are powerful tools for early detection and prevention of malaria outbreaks. By using temperature and rainfall data, the models can provide up to four months of lead-time for detecting and preventing epidemics. The researchers hope that their work will help save countless lives in the future.
Read the full paper here: