Funding source: USC Zumberge Grant

Funding amount: $85,000

Performance period: 08/01/2020 to 09/31/2021

There is an urgent need for performing widespread COVID-19 testing to control disease spread, as many infected individuals are asymptomatic or have mild flu-like symptoms, yet are still transmissive. These patients may not seek care and therefore not be diagnosed or undergo quarantine, resulting in subsequent fatal downstream infections. Officials have called for increasing testing as a critical step needed to reopen the country.

However, complete population testing across the entire US population, or even over a complete metropolitan area, is prohibitively challenging as testing supplies are limited and require trained health staff which could be better put to use in caring for those confirmed to be infected. It is therefore critical to focus testing in high-priority areas, where tests are likely to capture positive cases. While this includes high risk individuals (contacts of positive cases, elderly in nursing homes, etc.), identifying infected individuals more generally as tests become more widely available will provide crucial information on overall disease prevalence and spread to inform future disease control efforts. Synthesizing and using traffic patterns as transportation patterns change will shed light on possible transmission patterns in populated urban areas such as Los Angeles County.

We therefore propose using the USC Archived Data Management System (ADMS), which collects and synthe- sizes traffic data, to create an epidemic model informed by up-to-date origin-destination traffic data. We will use the model to identify which of the 26 health districts in Los Angeles (LA) county are at highest risk for unidentified cases and optimally locate testing sites within these regions. This allows our recommendations to incorporate change in transportation patterns due to disease mitigation policies (e.g., social distancing recommendations, etc.). Specifically, we will partner with the LA County Department of Public Health (see Letter of Support) to:

Intellectual Merit. The results of the proposed research include:

• Using methodology from infectious disease transmission models, network data, and facility location models together in a novel way.
• Creating a network model with realistic, time-varying travel patterns in a large metropolitan area to contrast with other dynamic models of disease in the LA area, which will further understanding of the impacts of structural modeling assumptions on disease prediction.
• Provide much needed insight using empirical data into population flow dynamics in the context of social recommendations to limit social contact.

BroaderImpacts. The results of this proposal will:

• Inform public health efforts to roll out COVID-19 testing sites in a high-risk urban area (LA), an urgent need
• given the likelihood of widespread disease.
• Facilitate the maintenance of social distancing recommendations while promoting disease testing by locat- ing testing facilities to maximize patient coverage.