• Giulia Pedrielli

Simulation and Optimization to help in Pandemics Response

COVID-19 has deeply changed our lives, and several challenges have come to light as a result of the pandemic. Examples are the delay from drug design to manufacturing (therapeutics and vaccines), understanding the spread of the virus in large scale settings, considering different controls (e.g., testing, vaccination, quarantine). Optimize the use of testing as a means for data collection, but also to reduce spread. Design policies for the synchronous use of testing and vaccination to fight the spread of the disease.

On 04/15/2020 we were awarded the grant "Collaborative Research: RAPID: RTEM: Rapid Testing as Multi-fidelity Data Collection for Epidemic Modeling" [Thanks NSF!!!]

In this Rapid Response Research (RAPID) project, the team will examine the problem of establishing optimal practices for rapid testing for the novel coronavirus. The result will be the Rapid Testing for Epidemic Modeling (RTEM), which will translate into science-based predictions of the COVID-19 epidemic's characteristics, including the duration and overall size, and help the global efforts to combat the disease. The RTEM will fill an important gap in data-driven decision making during the COVID-19 epidemic and, thus, will enable services with significant national economic and health impact. The educational impact of the project will be on mentoring of post-doctoral and PhD researchers and on curricula by incorporating research challenges and outcomes into existing undergraduate and graduate classes.

Computational models for the spatio-temporal dynamics of emerging infectious diseases and data- and model-driven computer simulations for disease spreading are increasingly critical in predicting geo-temporal evolution of epidemics as well as designing, activating, and adapting practices for controlling epidemics. In this project, the researchers tackle a Rapid Testing for Epidemic Modeling (RTEM) problem: Given a partially known target disease model and a set of testing modalities (from surveys to surveillance testing at known disease hotspots), with varying costs, accuracies, and observational delays, what is the best rapid testing strategy that would help recover the underlying disease model? Several scientific questions arise: What is the value of testing? Should only sick people be tested for virus detection? What level of resources should be devoted to the development of highly accurate tests (low false positives, low false negatives)? Is it better to use only one type of test aiming at the best cost/effectiveness trade off, or a non-homogeneous testing policy? Naturally these questions need to be investigated at the interface of epidemiology, computer science, machine learning, mathematical modeling and statistics. As part of the work, the team will develop a model of transmission dynamics and control, tailored to COVID-19 in a way that accommodates diagnostic testing with varying fidelities and delays underlying a rapid testing regimen. The investigators will further integrate the resulting RTEM-SEIR model with EpiDMS and DataStorm for executing continuous coupled simulations.

For more info, check out our project website!

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