A versatile web app for identifying the drivers of COVID-19 epidemics.

ABSTRACT:

Background

No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap.

Methods

Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasti

SUBMITTER: Getz WM 

PROVIDER: S-EPMC7962635 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature