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Analysis of serological surveys of antibodies to SARS-CoV-2 in the United States to estimate parameters needed for transmission modeling and to evaluate and improve the accuracy of predictions.
Glasser, John W; Feng, Zhilan; Vo, MyVan; Jones, Jefferson N; Clarke, Kristie E N.
  • Glasser JW; National Center for Immunization and Respiratory Diseases, CDC, USA. Electronic address: jglasser@cdc.gov.
  • Feng Z; Department of Mathematics, Purdue University, USA; Division of Mathematical Sciences, NSF, USA.
  • Vo M; Department of Mathematics, Purdue University, USA.
  • Jones JN; National Center for Immunization and Respiratory Diseases, CDC, USA.
  • Clarke KEN; Center For Surveillance, Epidemiology, and Laboratory Services, CDC, USA.
J Theor Biol ; 556: 111296, 2023 01 07.
Article in English | MEDLINE | ID: covidwho-2260758
ABSTRACT
Seroprevalence studies can estimate proportions of the population that have been infected or vaccinated, including infections that were not reported because of the lack of symptoms or testing. Based on information from studies in the United States from mid-summer 2020 through the end of 2021, we describe proportions of the population with antibodies to SARS-CoV-2 as functions of age and time. Slices through these surfaces at arbitrary times provide initial and target conditions for simulation modeling. They also provide the information needed to calculate age-specific forces of infection, attack rates, and - together with contact rates - age-specific probabilities of infection on contact between susceptible and infectious people. We modified the familiar Susceptible-Exposed-Infectious-Removed (SEIR) model to include features of the biology of COVID-19 that might affect transmission of SARS-CoV-2 and stratified by age and location. We consulted the primary literature or subject matter experts for contact rates and other parameter values. Using time-varying Oxford COVID-19 Government Response Tracker assessments of US state and DC efforts to mitigate the pandemic and compliance with non-pharmaceutical interventions (NPIs) from a YouGov survey fielded in the US during 2020, we estimate that the efficacy of social-distancing when possible and mask-wearing otherwise at reducing susceptibility or infectiousness was 31% during the fall of 2020. Initialized from seroprevalence among people having commercial laboratory tests for purposes other than SARS-CoV-2 infection assessments on 7 September 2020, our age- and location-stratified SEIR population model reproduces seroprevalence among members of the same population on 25 December 2020 quite well. Introducing vaccination mid-December 2020, first of healthcare and other essential workers, followed by older adults, people who were otherwise immunocompromised, and then progressively younger people, our metapopulation model reproduces seroprevalence among blood donors on 4 April 2021 less well, but we believe that the discrepancy is due to vaccinations being under-reported or blood donors being disproportionately vaccinated, if not both. As experimenting with reliable transmission models is the best way to assess the indirect effects of mitigation measures, we determined the impact of vaccination, conditional on NPIs. Results indicate that, during this period, vaccination substantially reduced infections, hospitalizations and deaths. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics."
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Aged / Humans Country/Region as subject: North America Language: English Journal: J Theor Biol Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Aged / Humans Country/Region as subject: North America Language: English Journal: J Theor Biol Year: 2023 Document Type: Article