Characterization of Israeli COVID-19 Outbreak Drivers and Forecasting Using a Versatile Web App

SummaryBackgroundNo versatile web app exists that allows epidemiologists and managers around the world to fully analyze the impacts of COVID-19 mitigation. TheNMB-DASAweb app presented here fills this gap.MethodsOur web app uses a model that explicitly identifies a contact class of individuals, symptomatic and asymptomatic classes and a parallel set of response class, subject to lower contact pathogen contact rates. The user inputs a CSV file containing incidence and mortality time series. A default set of parameters is available that can be overwritten through input or online entry, and a subset of these can be fitted to the model using an MLE algorithm. The end of model-fitting and forecasting intervals are specifiable and changes to parameters allows counterfactual and forecasted scenarios to be explored.FindingsWe illustrate the app in the context of the current COVID-19 outbreak in Israel, which can be divided into four distinct phases: an initial outbreak; a social distancing, a social relaxation, and a second wave mitigation phase. Our projections beyond the relaxation phase indicate that an 85% drop in social relaxation rates are needed just to stabilize the current incidence rate and that at least a 95% drop is needed to quell the outbreak.InterpretationOur analysis uses only incidence and mortality rates. In the hands of policy makers and health officers, we believe our web app provides an invaluable tool for evaluating the impacts of different outbreak mitigation policies and measures.FundingThis research was funded by NSF Grant 2032264.