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Dynamics of nosocomial SARS-CoV-2 transmissions: Facing the challenge of variants of concern in a Brazilian reference hospital.

ABSTRACT: The hospital environment can be considered a high risk for the occurrence of SARS-CoV-2 transmission outbreaks, either for health professionals who are directly involved in the care of suspected or confirmed cases of the disease, or for patients, for being in an environment more vulnerable to the acquisition of nosocomial infections. In this molecular epidemiology study, we aimed to analyze the occurrence and transmission dynamics of SARS-CoV-2 in outbreaks and local chains of transmission in a large tertiary teaching hospital in southern Brazil, in addition to verifying circulating strains and their epidemiological relation in the local context, from September 21, 2020 to October 5, 2021. Positive samples involved in COVID-19 clusters or outbreaks were analyzed using clinical, epidemiological and genomic data. Different lineages and sublineages among patients in the same room were observed. Most patients had their first clinical manifestation, evidence of suspicion, and diagnostic confirmation within 7-14 days or >14 days after hospital admission. The patients who have contact with confirmed cases of COVID-19 spent, on average, 6.28 days in the same environment until the positive test. There was a significant association between the outcome and the number of vaccine doses (p < 0.05), where those who received two doses presented a lower occurrence of death. There was a total replacement of variant of concern (VOC) Gamma by VOC Delta from August 2021 at the study site. Although the epidemiological analysis indicates nosocomial infections, through genomic sequencing, it was established that most of the hospital outbreaks had different origins. These findings highlight the utility of integrating epidemiological and genomic data to identify possible routes of viral entry and dissemination.

SUBMITTER: Hoffmann AT

PROVIDER: S-EPMC9880750 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Dynamics of nosocomial SARS-CoV-2 transmissions: Facing the challenge of variants of concern in a Brazilian reference hospital.

Hoffmann Andressa Taíz AT da Silva Mariana Soares MS Gularte Juliana Schons JS Pasqualotto Alessandro Comaru AC Proença Módena José Luiz JL Hansen Alana Witt AW Stadñik Claudio Marcel Berdún CMB Sukienik Teresa Cristina Teixeira TCT Demoliner Meriane M Heldt Fágner Henrique FH Filippi Micheli M Pereira Vyctoria Malayhka de Abreu Góes VMAG de Marques Cristiani Gomes CG Kohler Ionara Ines II Quevedo Daniela Muller De DM Spilki Fernando Rosado FR

Journal of medical virology 20230201 2

The hospital environment can be considered a high risk for the occurrence of SARS-CoV-2 transmission outbreaks, either for health professionals who are directly involved in the care of suspected or confirmed cases of the disease, or for patients, for being in an environment more vulnerable to the acquisition of nosocomial infections. In this molecular epidemiology study, we aimed to analyze the occurrence and transmission dynamics of SARS-CoV-2 in outbreaks and local chains of transmission in a ...[more]

PMID: 36579775

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Project description:The events of the recent SARS-CoV-2 epidemics have shown the importance of social factors, especially given the large number of asymptomatic cases that effectively spread the virus, which can cause a medical emergency to very susceptible individuals. Besides, the SARS-CoV-2 virus survives for several hours on different surfaces, where a new host can contract it with a delay. These passive modes of infection transmission remain an unexplored area for traditional mean-field epidemic models. Here, we design an agent-based model for simulations of infection transmission in an open system driven by the dynamics of social activity; the model takes into account the personal characteristics of individuals, as well as the survival time of the virus and its potential mutations. A growing bipartite graph embodies this biosocial process, consisting of active carriers (host) nodes that produce viral nodes during their infectious period. With its directed edges passing through viral nodes between two successive hosts, this graph contains complete information about the routes leading to each infected individual. We determine temporal fluctuations of the number of exposed and the number of infected individuals, the number of active carriers and active viruses at hourly resolution. The simulated processes underpin the latent infection transmissions, contributing significantly to the spread of the virus within a large time window. More precisely, being brought by social dynamics and exposed to the currently existing infection, an individual passes through the infectious state until eventually spontaneously recovers or otherwise is moves to a controlled hospital environment. Our results reveal complex feedback mechanisms that shape the dependence of the infection curve on the intensity of social dynamics and other sociobiological factors. In particular, the results show how the lockdown effectively reduces the spread of infection and how it increases again after the lockdown is removed. Furthermore, a reduced level of social activity but prolonged exposure of susceptible individuals have adverse effects. On the other hand, virus mutations that can gradually reduce the transmission rate by hopping to each new host along the infection path can significantly reduce the extent of the infection, but can not stop the spreading without additional social strategies. Our stochastic processes, based on graphs at the interface of biology and social dynamics, provide a new mathematical framework for simulations of various epidemic control strategies with high temporal resolution and virus traceability.

Dataset Information

Dynamics of nosocomial SARS-CoV-2 transmissions: Facing the challenge of variants of concern in a Brazilian reference hospital.

Publications

Dynamics of nosocomial SARS-CoV-2 transmissions: Facing the challenge of variants of concern in a Brazilian reference hospital.

Similar Datasets

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