Model: UChicago-CovidIL

Model: University of Chicago - UChicago-CovidIL

Name:	CovidIL
Abbreviation:	UChicago-CovidIL
Owner:	covid19hub
Team name:	University of Chicago
Description:	This is a compartmental, age-structured SEIR model that infers past SARS-CoV-2 transmission rates and forecasts mortality under current and hypothetical public health interventions.
Contributors:	Sarah Cobey <cobey@uchicago.edu>, Philip Arevalo, Ed Baskerville, Spencer Carran, Katelyn Gostic, Laugen McGough, Sylvia Ranjeva, Frank Wen
License:	Creative Commons Attribution Non-Commercial 4.0
Notes:	primary
Citation:	Missing
Methods:	The model is age-structured to capture both age-specific contact patterns and age-specific differences in the biological dynamics, such as the probability of asymptomatic infection. Each model compartment is sub-divided into nine different age categories. To model the state of Illinois, we divide the state into four regions based on the Restore Illinois plan. We infer a common statewide pre-intervention transmission rate, and a separate post-intervention transmission rate for each of the four regions. We also infer a region-specific initial number of infected individuals. Other model parameters are informed by Illinois-specific data or best estimates from existing literature. We fit the model to incident in-hospital deaths and confirmed COVID-19 ICU cases prior to May 15, 2020. For inference, we use maximum likelihood inference via sequential Monte Carlo for partially observed Markov process models (POMP). We assume that hospitalized deaths are reported with probability 0.8075 and we fitted the reporting rate of non-hospitalized deaths. The model can be adapted to investigate different types of interventions. We currently model shelter-in-place as a reduction in the overall transmission rate, which is shared by all infected individuals, and reductions in age- and location-specific contact rates. To model the relaxing of shelter-in-place interventions, we project three different scenarios. 1) The inferred post-intervention transmission rate remains unchanged. We assume that this is maximum reduction in transmission and call this model CovidIL_100. 2) The post-SIP transmission rate increases 10% of the way back to pre-SIP transmission according to a logistic function centered on June 1. 3) The post-SIP transmission rate increases 30% of the way back to pre-SIP transmission according to a logistic function centered on June 1.
Home:	https://github.com/cobeylab/covid_IL
Auxiliary data:	Missing

Forecasts (3)

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2020-05-05	2020-05-05-UChicago-CovidIL.csv	2020-05-26 00:01:06 UTC	2020-05-26 12:00:00 UTC
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2020-05-11	2020-05-11-UChicago-CovidIL.csv	2020-05-26 00:01:11 UTC	2020-05-26 12:00:00 UTC
2020-05-18	2020-05-18-UChicago-CovidIL.csv	2020-05-26 00:01:14 UTC	2020-05-26 12:00:00 UTC
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