A Platform for Disease Intervention Planning

Abstract

The research and development of new tools and strategies for disease intervention planning requires resources, data, and computation spread across multiple institutions and individuals. Whether this is towards an objective such as drug discovery or informing intervention policy, it should be possible for these tools to be flexibly deployed to meet the decision support needs of the Global Health community. In this work we introduce a new platform to demonstrate the utility of a scalable computational infrastructure, blockchain based validation and machine learning (ML) algorithms, to assist in the generation of validated novel policies for malaria control. We have conducted preliminary tests in the generation of simulation-based evidence to guide policy level decision making. Specifically to assess the performance of; the scalable infrastructure under different simulation complexities and distributed compute; Hyperledger Fabric to provide validation of shared information within our application; and ML approaches to generate novel policy insights. Finally the components of the platform may be leveraged via a non-Technical user or policy-maker through an Interactive Dashboard, bridging the gap between research and immediate needs in Disease Intervention Planning.