How relevant is the basic reproductive number computed during the coronavirus disease 2019 (COVID-19) pandemic, especially during lockdowns?
[...]in (b), the fourth secondary infection in (a), say, \({y_{24}}\) by primary infected \({y_2}\) becomes a primary infected that generates 3 secondary infections out of which only 2 were traced and diagnosed. [...]the mean number of secondary infections during (\({t_i}\), \({t_i+4}\)) is given by...
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Published in: | Infection control and hospital epidemiology Vol. 43; no. 1; pp. 125 - 127 |
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Abstract | [...]in (b), the fourth secondary infection in (a), say, \({y_{24}}\) by primary infected \({y_2}\) becomes a primary infected that generates 3 secondary infections out of which only 2 were traced and diagnosed. [...]the mean number of secondary infections during (\({t_i}\), \({t_i+4}\)) is given by(2)\(\root 4 \of {\mathop \prod \limits_{k = 0}^3 \left( {1\, { + \, \gamma_{i + k}}\% } \right)}.\) Similarly, the trend in eq. Even if the testing numbers and testing patterns are constant over a period, the proportion of underreported cases may not be constant. [...]the estimation of \({R_0}\) is likely to be highly variable in any given situation. When the ratios \({Y_{i + k + 1}}{\rm{\;}}/{Y_{i + k}}\) for \(k = 0, 1, \ldots n\) are considered, then the geometric mean of these growth rates would be(4)\[\root n \of {\mathop \prod \limits_{k = 0}^n {{{Y_{i + k + 1}}} \over {{Y_{i + k}}} = \root n \of {{{Y_{i + n + 1}}} \over {{Y_i}}}.\] However, \[{\widehat R_0}\] or \[{\widehat R_t}\], (the estimated basic and time-varying reproductive numbers at the start or ongoing through an epidemic, respectively) may not be at all close to \({R_0}\) or \({R_t}\) even if the \({Y_i}\) values are generated from a mathematical model for a period \(i > 0\) that uses data on susceptible, exposed, infected, and recovered in which the underlying epidemiological processes are time varying. |
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AbstractList | [...]in (b), the fourth secondary infection in (a), say, \({y_{24}}\) by primary infected \({y_2}\) becomes a primary infected that generates 3 secondary infections out of which only 2 were traced and diagnosed. [...]the mean number of secondary infections during (\({t_i}\), \({t_i+4}\)) is given by(2)\(\root 4 \of {\mathop \prod \limits_{k = 0}^3 \left( {1\, { + \, \gamma_{i + k}}\% } \right)}.\) Similarly, the trend in eq. Even if the testing numbers and testing patterns are constant over a period, the proportion of underreported cases may not be constant. [...]the estimation of \({R_0}\) is likely to be highly variable in any given situation. When the ratios \({Y_{i + k + 1}}{\rm{\;}}/{Y_{i + k}}\) for \(k = 0, 1, \ldots n\) are considered, then the geometric mean of these growth rates would be(4)\[\root n \of {\mathop \prod \limits_{k = 0}^n {{{Y_{i + k + 1}}} \over {{Y_{i + k}}} = \root n \of {{{Y_{i + n + 1}}} \over {{Y_i}}}.\] However, \[{\widehat R_0}\] or \[{\widehat R_t}\], (the estimated basic and time-varying reproductive numbers at the start or ongoing through an epidemic, respectively) may not be at all close to \({R_0}\) or \({R_t}\) even if the \({Y_i}\) values are generated from a mathematical model for a period \(i > 0\) that uses data on susceptible, exposed, infected, and recovered in which the underlying epidemiological processes are time varying. |
Author | Srinivasa Rao, Arni S R Kurien, Thomas Bhat, Ramesh Sudhakar, Kurapati Swanson, David A Byrareddy, Siddappa N Krantz, Steven G Bonsall, Michael B |
AuthorAffiliation | 7 Department of Sociology, University of California–Riverside , Riverside , California 9 (formerly with) Centers for Disease Control and Prevention, World Bank, and United States Agency for International Development 2 Laboratory for Theory and Mathematical Modeling, Department of Medicine - Division of Infectious Diseases, Medical College of Georgia , Augusta University, Augusta , Georgia 8 NMIMS University , Mumbai , India 5 Department of Medicine, Pondicherry Institute of Medical Sciences , Puducherry , India 6 Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center , Omaha , Nebraska 4 Mathematical Ecology Research Group, Department of Zoology, University of Oxford , Oxford , United Kingdom 3 Department of Mathematics, Washington University , St Louis , Missouri 1 Medical College of Georgia , Augusta , Georgia |
AuthorAffiliation_xml | – name: 1 Medical College of Georgia , Augusta , Georgia – name: 3 Department of Mathematics, Washington University , St Louis , Missouri – name: 7 Department of Sociology, University of California–Riverside , Riverside , California – name: 6 Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center , Omaha , Nebraska – name: 5 Department of Medicine, Pondicherry Institute of Medical Sciences , Puducherry , India – name: 4 Mathematical Ecology Research Group, Department of Zoology, University of Oxford , Oxford , United Kingdom – name: 8 NMIMS University , Mumbai , India – name: 2 Laboratory for Theory and Mathematical Modeling, Department of Medicine - Division of Infectious Diseases, Medical College of Georgia , Augusta University, Augusta , Georgia – name: 9 (formerly with) Centers for Disease Control and Prevention, World Bank, and United States Agency for International Development |
Author_xml | – sequence: 1 givenname: Arni S R surname: Srinivasa Rao fullname: Srinivasa Rao, Arni S R organization: Laboratory for Theory and Mathematical Modeling, Department of Medicine - Division of Infectious Diseases, Medical College of Georgia, Augusta University, Augusta, Georgia – sequence: 2 givenname: Steven G surname: Krantz fullname: Krantz, Steven G organization: Department of Mathematics, Washington University, St Louis, Missouri – sequence: 3 givenname: Michael B surname: Bonsall fullname: Bonsall, Michael B organization: Mathematical Ecology Research Group, Department of Zoology, University of Oxford, Oxford, United Kingdom – sequence: 4 givenname: Thomas surname: Kurien fullname: Kurien, Thomas organization: Department of Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India – sequence: 5 givenname: Siddappa N surname: Byrareddy fullname: Byrareddy, Siddappa N organization: Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, Nebraska – sequence: 6 givenname: David A surname: Swanson fullname: Swanson, David A organization: Department of Sociology, University of California-Riverside, Riverside, California – sequence: 7 givenname: Ramesh surname: Bhat fullname: Bhat, Ramesh organization: NMIMS University, Mumbai, India – sequence: 8 givenname: Kurapati surname: Sudhakar fullname: Sudhakar, Kurapati organization: (formerly with) Centers for Disease Control and Prevention, World Bank, and United States Agency for International Development |
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CitedBy_id | crossref_primary_10_1016_j_dsx_2020_06_014 crossref_primary_10_3390_axioms10030204 crossref_primary_10_3390_jcm10061256 crossref_primary_10_1007_s11356_023_26738_3 crossref_primary_10_1038_s41598_021_90118_5 |
Cites_doi | 10.1017/ice.2020.116 10.1016/j.jtbi.2020.110243 10.1186/1471-2458-14-147 10.1126/science.abd7672 10.1371/journal.pone.0236464 10.1145/5666.5673 |
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References | S0899823X20013768_ref1 S0899823X20013768_ref2 Abbott (S0899823X20013768_ref4) 2020; 5 S0899823X20013768_ref10 S0899823X20013768_ref9 S0899823X20013768_ref7 S0899823X20013768_ref8 S0899823X20013768_ref5 S0899823X20013768_ref6 S0899823X20013768_ref3 |
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SubjectTerms | Basic Reproduction Number Communicable Disease Control Coronaviruses COVID-19 Epidemics Epidemiology Humans Infections Letter to the Editor Pandemics SARS-CoV-2 Vitamin E |
Title | How relevant is the basic reproductive number computed during the coronavirus disease 2019 (COVID-19) pandemic, especially during lockdowns? |
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