Algorithms and Software Tools for Testing and Control of COVID-19

Extensive and ongoing testing of populations for viremia and antibodies is expected to play a major role in shaping and managing the process of reopening the states. The goal of this interdisciplinary project is to bring together epidemiologists, systems theorists, and data scientists to develop models, algorithms, and software tools to support the state-level PCR (polymerase chain reaction) and serological testing efforts. Specifically, the team will develop: 1) algorithms to assimilate real-time testing data into networked epidemiological models; and 2) mean-field type control strategies to inform and evaluate the effect of social distancing and other control measures on the progression of the disease. A successful completion of this project will result in epidemiological models that are better and more realistic along two dimensions: 1) they are able to assimilate noisy data from ongoing population level testing; and 2) they include the effect of population level feedback that may result as a consequence of control measures. Such models are expected to be useful to inform testing guidelines, such as what groups to sample, with which tests, and with what frequency, and to better evaluate the effects of deploying control measures. The algorithms will be implemented as efficient, scalable, and open-source software and made available to policy makers and the public via an interactive website, assimilating daily observational data to generate real-time disease maps (with quantified uncertainty) and tools to allow simulation under different control policies. This will require substantial backend computation, with simulation and learning running on the C3.ai/Azure platform.