Forecasting the spread of COVID-19 under different reopening strategies.
Sci Rep
; 10(1): 20367, 2020 11 23.
Article
in English
| MEDLINE | ID: covidwho-940865
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Models, Statistical
/
Disease Susceptibility
/
Pandemics
/
SARS-CoV-2
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Sci Rep
Year:
2020
Document Type:
Article
Affiliation country:
S41598-020-77292-8
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