COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020-2021.
BMC Public Health
; 23(1): 988, 2023 05 27.
Artículo
en Inglés
| MEDLINE | ID: covidwho-20242605
ABSTRACT
BACKGROUND:
Policy responses to COVID-19 in Victoria, Australia over 2020-2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victorian Department of Health COVID-19 response team during this period.METHODS:
An agent-based model, Covasim, was used to simulate the impact of policy interventions on COVID-19 outbreaks and epidemic waves. The model was continually adapted to enable scenario analysis of settings or policies being considered at the time (e.g. elimination of community transmission versus disease control). Model scenarios were co-designed with government, to fill evidence gaps prior to key decisions.RESULTS:
Understanding outbreak risk following incursions was critical to eliminating community COVID-19 transmission. Analyses showed risk depended on whether the first detected case was the index case, a primary contact of the index case, or a 'mystery case'. There were benefits of early lockdown on first case detection and gradual easing of restrictions to minimise resurgence risk from undetected cases. As vaccination coverage increased and the focus shifted to controlling rather than eliminating community transmission, understanding health system demand was critical. Analyses showed that vaccines alone could not protect health systems and need to be complemented with other public health measures.CONCLUSIONS:
Model evidence offered the greatest value when decisions needed to be made pre-emptively, or for questions that could not be answered with empiric data and data analysis alone. Co-designing scenarios with policy-makers ensured relevance and increased policy translation.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Estudio observacional
/
Estudio pronóstico
Tópicos:
Vacunas
Límite:
Humanos
País/Región como asunto:
Oceanía
Idioma:
Inglés
Revista:
BMC Public Health
Asunto de la revista:
Salud Pública
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
S12889-023-15936-w
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