See this link for the dashboard.
Our multi-disciplinary team seeks to develop the critical ability to accurately forecast the arrival timeline of regional COVID-19 epidemic peaks. The most significant weakness exposed by the COVID-19 pandemic is that our current methods for predicting the location of regional outbreaks are woefully inadequate. This project seeks to address this problem by refining a compartmental, epidemiologic model and supplementing conventional case count
data. We will use novel data inputs: data from local hospitals, sewage sludge surveillance for assessing overall
SARS-CoV-2 viral load at the community level and other novel inputs, including data scraped from social media.
We hypothesize that together these enhancements will improve the robustness and predictive power of
epidemiologic modeling so that it can provide local hospitals with 8-10 days of warning in advance of impending
surges in COVID-19 caseloads. We anticipate complementing ongoing efforts to “flatten the curve” of COVID-
19 infection and assisting hospital systems focus resources where they are most needed in advance of caseload
surges by predicting where these surges will occur, and where they will not. If successful, we will establish a new
paradigm for accurately forecasting SARS-CoV-2 caseload and to provide more detailed and accurate public
health measure recommendations.