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1.
medRxiv ; 2021 Mar 31.
Artigo em Inglês | MEDLINE | ID: mdl-33791738

RESUMO

INTRODUCTION: Vaccination programs aim to control the COVID-19 pandemic. However, the relative impacts of vaccine coverage, effectiveness, and capacity in the context of nonpharmaceutical interventions such as mask use and physical distancing on the spread of SARS-CoV-2 are unclear. Our objective was to examine the impact of vaccination on the control of SARS-CoV-2 using our previously developed agent-based simulation model. METHODS: We applied our agent-based model to replicate COVID-19-related events in 1) Dane County, Wisconsin; 2) Milwaukee metropolitan area, Wisconsin; 3) New York City (NYC). We evaluated the impact of vaccination considering the proportion of the population vaccinated, probability that a vaccinated individual gains immunity, vaccination capacity, and adherence to nonpharmaceutical interventions. The primary outcomes were the number of confirmed COVID-19 cases and the timing of pandemic control, defined as the date after which only a small number of new cases occur. We also estimated the number of cases without vaccination. RESULTS: The timing of pandemic control depends highly on vaccination coverage, effectiveness, and adherence to nonpharmaceutical interventions. In Dane County and Milwaukee, if 50% of the population is vaccinated with a daily vaccination capacity of 0.1% of the population, vaccine effectiveness of 90%, and the adherence to nonpharmaceutical interventions is 65%, controlled spread could be achieved by July 2021 and August 2021, respectively versus in March 2022 in both regions without vaccine. If adherence to nonpharmaceutical interventions increases to 70%, controlled spread could be achieved by May 2021 and April 2021 in Dane County and Milwaukee, respectively. DISCUSSION: In controlling the spread of SARS-CoV-2, the impact of vaccination varies widely depending not only on effectiveness and coverage, but also concurrent adherence to nonpharmaceutical interventions. The effect of SARS-CoV-2 variants was not considered. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.

2.
PLoS One ; 16(7): e0254456, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34260633

RESUMO

INTRODUCTION: Vaccination programs aim to control the COVID-19 pandemic. However, the relative impacts of vaccine coverage, effectiveness, and capacity in the context of nonpharmaceutical interventions such as mask use and physical distancing on the spread of SARS-CoV-2 are unclear. Our objective was to examine the impact of vaccination on the control of SARS-CoV-2 using our previously developed agent-based simulation model. METHODS: We applied our agent-based model to replicate COVID-19-related events in 1) Dane County, Wisconsin; 2) Milwaukee metropolitan area, Wisconsin; 3) New York City (NYC). We evaluated the impact of vaccination considering the proportion of the population vaccinated, probability that a vaccinated individual gains immunity, vaccination capacity, and adherence to nonpharmaceutical interventions. We estimated the timing of pandemic control, defined as the date after which only a small number of new cases occur. RESULTS: The timing of pandemic control depends highly on vaccination coverage, effectiveness, and adherence to nonpharmaceutical interventions. In Dane County and Milwaukee, if 50% of the population is vaccinated with a daily vaccination capacity of 0.25% of the population, vaccine effectiveness of 90%, and the adherence to nonpharmaceutical interventions is 60%, controlled spread could be achieved by June 2021 versus October 2021 in Dane County and November 2021 in Milwaukee without vaccine. DISCUSSION: In controlling the spread of SARS-CoV-2, the impact of vaccination varies widely depending not only on effectiveness and coverage, but also concurrent adherence to nonpharmaceutical interventions.


Assuntos
Vacinas contra COVID-19/uso terapêutico , COVID-19/prevenção & controle , Cooperação do Paciente/estatística & dados numéricos , Cobertura Vacinal/estatística & dados numéricos , Simulação por Computador , Humanos , Máscaras , Distanciamento Físico , Dispositivos de Proteção Respiratória/estatística & dados numéricos , Estados Unidos , Saúde da População Urbana
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