RAPID: A fast and scalable method to improve epidemiological models for COVID-19

Computer and Information Science and Engineering - The current COVID-19 pandemic is prompting the scientific community to improve the epidemiological models currently employed to understand the spread of infectious diseases. Current models divide a population of individuals into compartments, such as, susceptible, exposed but non-infectious, asymptomatic but infectious, symptomatic and infectious, recovered, and deceased. To simplify the mathematical modeling of infectious diseases, the prevailing assumption is that individuals in the same compartment behave identically. One way to add sophistication to these so-called compartmental models is by categorizing individuals in a more fine-grained manner, thus adding more compartments. This results in more parameters added to a model. Estimating these parameters requires more data, and more data increases the computational cost of estimating the parameters. In addition, as this pandemic is showing, our access to data is varied. Within each country and municipality, different sampling strategies are being pursued. This project lowers the computational cost of setting up and updating complex, compartmental epidemiological models as more data becomes available. By doing so, the project improves the ability of the scientific community to make more accurate predictions on the spread of the virus and inform on the effectiveness of local policy decisions on mitigation strategies.

The investigators adopt a category of model fitting that has seen recent success in molecular dynamics simulations in molecular modeling ? maximum entropy biasing methods. These methods replace model parameter optimization with a minimal biasing term that is independent of the model parameters. This makes the runtime complexity of model optimization linear with the amount of data and independent of the unknown number of parameters. The activities will enable rapid optimization of complex models that additionally consider spatial resolution and sampling biasing. The improved cost of the optimization process will permit frequent updates of compartmental models without the need for full parameter optimization each time new data is observed. The investigators will collaborate closely with others in the rapidly coalescing COVID-19 research community by releasing code, data, and findings.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.