Design and implementation of an online platform for integration and analyzing multivariate multisource malaria data

Abstract

One of the common public health problems reported by the World Health Organization (WHO) in the African Region is malaria, where the burden of the disease is highest globally. The greatest challenge experienced in the fight against Malaria is, surveillance, which leads to early detection and treatment, and is crucial for reducing transmission and preventing deaths. Malaria surveillance includes gathering, analyzing, and interpreting malaria-related data. Though there exist many facilities with Malaria data, the collection and integration of data from different sources has been a major challenge that needs to be addressed. The proposed solution is aimed at the development of an online solution that can be used to collect malaria data from multiple sources including hospitals, drug stores and weather stations in various formats and aggregated into a format that can further be used in the prediction of malaria outbreak. From the results, the system collects data from hospitals and drug stores, which is then integrated with weather data. The generated data was used to train a machine learning model, as a proof of concept to validate that it can it be used to predict malaria outbreaks. This solution does not only solve the problem of data collection and integration but also ensures timely actions are taken in cases of out breaks. The implementation of this solution therefore significantly improves on the current practices by ensuring that hospital records and over the counter sale of drugs are reported electronically, daily and in real-time as opposed to manually and weekly. The solution also introduces the use of multi-source data in the analysis of malaria outbreaks rather than only focusing on hospital records as the only source of information for outbreak detection. Further to this, the project has the potential to contribute to the WHO Global Malaria Technical Strategy 2016-2030, as early detection and treatment of malaria are essential for reducing the burden of the disease. The methodology and system produced in this study can be used in other regions to improve malaria surveillance and outbreak prediction.