Forecasting dengue cases: Vietnam as a case study (360G-Wellcome-208267_Z_17_Z)

Vector-borne infectious diseases continue to be a burden to Vietnam’s economy and population health. Understanding the spatiotemporal patterns of the disease transmission is thus vital for planning resources and targeting control measures. In this work, we propose a large-scale study on how different factors interact and contribute to the dynamics of dengue in Vietnam. We will employ multiple modelling techniques to analyse the dynamics of the disease and how to best predict future cases. We hypothesise that urbanisation has played a significant role in deriving the distribution of the Aedes mosquito and on dengue transmission, and will therefore incorporate factors about urbanisation gained from satellite images into predictive models. These models will use three main methods: statistical, mechanistic and machine learning. The key goal is to predict the number of new dengue cases as far as possible, and we will work in collaboration with those who use the forecasts to assess the most useful timeframe and accuracy. A particular aim will be the prediction of upcoming hotspots of dengue transmission so that hospitals can plan their resources. We will work to present the results clearly so they can best be used to help to reduce dengue burden in Vietnam.