Information Systems Research
Modelling
Towards the development of epidemic forecasting models
Accurate forecasting models are extremely important for disease surveillance for monitoring, forecasting, detection, prevention and control of malaria epidemics. However, more work needs to be done for developing operational and reliable forecasting models and very few published studies have been conducted in Southern Africa. A range of different models will be fitted taking into account temporal correlations, trends, seasonality and effect of climatic factors.
Spatio-temporal modelling of Malaria in Southern Africa
This project aims to map the geographical patterns and estimating the disease burden which is critical for malaria control programme design, implementation and evaluation. Furthermore, the project aims to develop malaria risk maps for Southern Africa by track temporal changes in malaria transmission across the region using different datasets and statistical approaches. This work is part of the development of the third generation of MARA maps.
Development of empirical models of malaria seasonality for Africa
Seasonality affects the dynamic relationship between vector mosquito densities, inoculation rate, parasite prevalence and disease outcome. Quantitative description and mapping of malaria seasonality is therefore important for modelling malaria transmission dynamics. The aim of this work is to develop disease and transmission based seasonality models using clinical and entomological indices, respectively.Progress includes development of a seasonality model through cooperation with the ecology and epidemiology group at Oxford University and the STI modelling team.