In this presentation, I’d like to talk about How Can Scientific Methods Help People Manage Vector-borne Infectious Diseases in Africa. At the beginning of the 21st century, infectious diseases caused at least 10 million deaths each year, which consists of nearly a quarter of deaths worldwide. Vector borne diseases is one of the most typical type of infectious diseases that can be transmitted through parasites, bacteria or viruses. Vector-borne diseases are also one of the main causes of emerging diseases: they are responsible for more than 17% of all infectious diseases and cause more than 700,000 deaths annually. (WHO, 2020) Even though attentions have been paid to the problems caused by these diseases, it is very difficult or even impossible to completely eliminate such infections. For one thing, it is hard to determine where the initial vector or pathogens come from, even when health bureaus attempt to keep certain disease under control, given the seasonal climate changes and constant human mobility, these diseases are very likely to reappear. Africa, due to factors like changing ecosystems, migration, breakdown of public health measures, poverty and social inequality, etc., happen to be the origin of many pathogens. In addition, most attention to this health-related-problem is focus on tropical and subtropical regions in previous relevant studies, those poorest places that are also or even more vulnerable to infection transmission received insufficient attention. Therefore, I feel that it is pretty necessary to apply scientific methods to help people manage vector-borne infections for an un-biased Africa. The two models I selected are logistic regression and K-NearestNeighbor. In the future, the first thing to do is to collect data for previously ignored areas. Since traditional human mobility data are proprietary, expensive and time consuming to collect and process, we can apply general human movement estimates instead. We should also replace the missing data with best guesses like mean and median, and try to resolve the challenges in previous studies, like solving the imbalanced data and missing information problems and figuring out the dynamic nature of predictors.