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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: web application, production service resource, software resource, analysis service resource, service resource
Keywords: COVID-19, SARS-CoV-2, stochastic growth model, stochastic SIR model, Bayesian inference,
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Source: SciCrunch Registry
Data set from Kravitz, Owen and Kretizer's 2012 paper "Optogenetic identification of striatal projection neuron subtypes during in vivo recordings."
