A network-based explanation of why most COVID-19 infection curves are linear.
Proc Natl Acad Sci U S A
; 117(37): 22684-22689, 2020 09 15.
Article
in English
| MEDLINE | ID: covidwho-729025
ABSTRACT
Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of nonpharmaceutical interventions pushing the growth rate below the recovery rate. In this phase of the pandemic many countries showed an almost linear growth of confirmed cases for extended time periods. This new containment regime is hard to explain by traditional models where either infection numbers grow explosively until herd immunity is reached or the epidemic is completely suppressed. Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, [Formula see text], below which linear growth and low infection prevalence must occur. Above [Formula see text] traditional epidemiological dynamics take place, e.g., as in susceptible-infected-recovered (SIR) models. When calibrating our model to empirical estimates of the transmission rate and the number of days being contagious, we find [Formula see text] Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household size (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fine-tuning of parameters. In particular, we compare the United States and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Models, Statistical
/
Coronavirus Infections
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Proc Natl Acad Sci U S A
Year:
2020
Document Type:
Article
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