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URL: https://qiwei.shinyapps.io/PredictCOVID19/
Proper Citation: BayesEpiModels Web App (RRID:SCR_019292)
Description: Web app to help assess both short- and long-term forecasts of COVID-19 across the United States at multiple levels. Done by implementing one time-series model (ARIMA), one compartmental models (basic SIR), and six classical growth models, which all yield satisfactory prediction results in the past and current pandemics at early stage.
Resource Type: analysis service resource, production service resource, service resource, software resource, web application
Keywords: COVID-19, SARS-CoV-2, stochastic growth model, stochastic SIR model, Bayesian inference,
Availability: Free, Freely available
Resource Name: BayesEpiModels Web App
Resource ID: SCR_019292
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Source: SciCrunch Registry (RRID:SCR_005400)
Description: Interactive portal for finding and submitting biomedical resources. Resources within SciCrunch have assigned RRIDs which are used to cite resources in scientific manuscripts. SciCrunch Registry, formerly NIF Registry, provides resources catalog. Allows to add new resources. Allows edit existing resources after registration. Curators are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping contents current.
URL: https://rrid.site/rin/sources/SCR_005400
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