Development of a new model for evaluating malaria risk In Chimoio, Mozambique

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceMalaria is a life-threatening disease that continues to pose serious economic, social and health burdens. Climate plays an important role in the dynamics and distribution of malaria. In particular, temperature and precipitation appear to be critical in perpetuating malaria transmission. In the last decades, there has been an increased interest in the use of weather forecasts for predicting malaria epidemics and setting up early warning systems. In 2017, there have been almost 9 million reported cases of malaria in Mozambique. Malaria is considered one of the deadliest diseases in the country. Previous studies have established that temperature, rainfall and humidity were determinant for malaria transmission and intensity in this region. The purpose of this study is to apply time series analysis and regression modelling to analyse the relationship between malaria incidence and these climatic variables in Chimoio, a municipality located in central Mozambique, and possibly develop a model that can accurately predict the occurrence of malaria outbreaks across this region. With a combination of two (15-week lagged maximum temperature and 3-week lagged precipitation) to three (15-week lagged maximum temperature, 12-week lagged relative humidity and 3-week lagged precipitation) climatic variables, added to the number of malaria cases reported in the previous week, we were able to explain more than 70% of the variability in weekly malaria incidence. These models also quite accurately represent the observed trends of malaria incidence in Chimoio, during the study period. This simple and economical approach, supported by meteorological and epidemiologic data that are readily available, could potentially be applied by local health authorities in order to predict malaria outbreaks. With this information, adequate preventative interventions and resource allocation could be planned and deployed within a more reasonable time frame. Further studies are required in order to determine if this methodology can be successfully applied to other regions of the globe