{"database":"biostudies-other","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["117"],"submitter":["Lucian Smith"],"journal":["Proceedings of the National Academy of Sciences of the United States of America"],"pagination":["16732-16738"],"species":["Severe acute respiratory syndrome coronavirus 2"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/MODEL2008070001"],"repository":["biostudies-other"],"additional_accession":["32616574"],"pubmed_authors":["Lucian Smith","Kausthubh Ramachandran"]},"is_claimable":false,"name":"Bertozzi2020 - SIR model of scenarios of COVID-19 spread in CA and NY","description":"The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.","dates":{"release":"2020-08-07T00:00:00Z","modification":"2025-07-15T09:46:38.087Z","creation":"2025-03-29T22:19:50.062Z"},"accession":"MODEL2008070001","cross_references":{"biomodels___db":["BIOMD0000000956"],"pubmed":["32616574"],"ncit":["C128320","NCIT:C43509","NCIT:C171133","C25746","NCIT:C43468"],"mamo":["MAMO_0000028"],"ido":["0000503","0000514","0000511","0000621"],"doid":["DOID:0080600"],"taxonomy":["9606","2697049"]}}