Cargando…
A comprehensive estimation of country-level basic reproduction numbers R(0) for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data
In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R(0) on its own. After the exponential phase, dynamic complexities like societal resp...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277067/ https://www.ncbi.nlm.nih.gov/pubmed/34255772 http://dx.doi.org/10.1371/journal.pone.0254145 |
_version_ | 1783722010377977856 |
---|---|
author | Spouge, John L. |
author_facet | Spouge, John L. |
author_sort | Spouge, John L. |
collection | PubMed |
description | In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R(0) on its own. After the exponential phase, dynamic complexities like societal responses muddy the practical interpretation of many estimated parameters. The computer program ARRP, already available from sequence alignment applications, automatically estimated the end of the exponential phase in COVID-19 and extracted the exponential growth rate r for 160 countries. By positing a gamma-distributed generation time, the exponential growth method then yielded R(0) estimates for COVID-19 in 160 countries. The use of ARRP ensured that the R(0) estimates were largely freed from any dependency outside the exponential phase. The Prem matrices quantify rates of effective contact for infectious disease. Without using any age-stratified COVID-19 data, but under strong assumptions about the homogeneity of susceptibility, infectiousness, etc., across different age-groups, the Prem contact matrices also yielded theoretical R(0) estimates for COVID-19 in 152 countries, generally in quantitative conflict with the R(0) estimates derived from the exponential growth method. An exploratory analysis manipulating only the Prem contact matrices reduced the conflict, suggesting that age-groups under 20 years did not promote the initial exponential growth of COVID-19 as much as other age-groups. The analysis therefore supports tentatively and tardily, but independently of age-stratified COVID-19 data, the low priority given to vaccinating younger age groups. It also supports the judicious reopening of schools. The exploratory analysis also supports the possibility of suspecting differences in epidemic spread among different age-groups, even before substantial amounts of age-stratified data become available. |
format | Online Article Text |
id | pubmed-8277067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82770672021-07-20 A comprehensive estimation of country-level basic reproduction numbers R(0) for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data Spouge, John L. PLoS One Research Article In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R(0) on its own. After the exponential phase, dynamic complexities like societal responses muddy the practical interpretation of many estimated parameters. The computer program ARRP, already available from sequence alignment applications, automatically estimated the end of the exponential phase in COVID-19 and extracted the exponential growth rate r for 160 countries. By positing a gamma-distributed generation time, the exponential growth method then yielded R(0) estimates for COVID-19 in 160 countries. The use of ARRP ensured that the R(0) estimates were largely freed from any dependency outside the exponential phase. The Prem matrices quantify rates of effective contact for infectious disease. Without using any age-stratified COVID-19 data, but under strong assumptions about the homogeneity of susceptibility, infectiousness, etc., across different age-groups, the Prem contact matrices also yielded theoretical R(0) estimates for COVID-19 in 152 countries, generally in quantitative conflict with the R(0) estimates derived from the exponential growth method. An exploratory analysis manipulating only the Prem contact matrices reduced the conflict, suggesting that age-groups under 20 years did not promote the initial exponential growth of COVID-19 as much as other age-groups. The analysis therefore supports tentatively and tardily, but independently of age-stratified COVID-19 data, the low priority given to vaccinating younger age groups. It also supports the judicious reopening of schools. The exploratory analysis also supports the possibility of suspecting differences in epidemic spread among different age-groups, even before substantial amounts of age-stratified data become available. Public Library of Science 2021-07-13 /pmc/articles/PMC8277067/ /pubmed/34255772 http://dx.doi.org/10.1371/journal.pone.0254145 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Spouge, John L. A comprehensive estimation of country-level basic reproduction numbers R(0) for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data |
title | A comprehensive estimation of country-level basic reproduction numbers R(0) for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data |
title_full | A comprehensive estimation of country-level basic reproduction numbers R(0) for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data |
title_fullStr | A comprehensive estimation of country-level basic reproduction numbers R(0) for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data |
title_full_unstemmed | A comprehensive estimation of country-level basic reproduction numbers R(0) for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data |
title_short | A comprehensive estimation of country-level basic reproduction numbers R(0) for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data |
title_sort | comprehensive estimation of country-level basic reproduction numbers r(0) for covid-19: regime regression can automatically estimate the end of the exponential phase in epidemic data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277067/ https://www.ncbi.nlm.nih.gov/pubmed/34255772 http://dx.doi.org/10.1371/journal.pone.0254145 |
work_keys_str_mv | AT spougejohnl acomprehensiveestimationofcountrylevelbasicreproductionnumbersr0forcovid19regimeregressioncanautomaticallyestimatetheendoftheexponentialphaseinepidemicdata AT spougejohnl comprehensiveestimationofcountrylevelbasicreproductionnumbersr0forcovid19regimeregressioncanautomaticallyestimatetheendoftheexponentialphaseinepidemicdata |