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Relationship of Test Positivity Rates with COVID-19 Epidemic Dynamics.

ABSTRACT: Detection and isolation of infected people are believed to play an important role in the control of the COVID-19 pandemic. Some countries conduct large-scale screenings for testing, whereas others test mainly people with high prior probability of infection such as showing severe symptoms and/or having an epidemiological link with a known or suspected case or cluster of cases. However, what a good testing strategy is and whether the difference in testing strategy shows a meaningful, measurable impact on the COVID-19 epidemic remain unknown. Here, we showed that patterns of association between effective reproduction number (Rt) and test positivity rate can illuminate differences in testing situation among different areas, using global and local data from Japan. This association can also evaluate the adequacy of current testing systems and what information is captured in COVID-19 surveillance. The differences in testing systems alone cannot predict the results of epidemic containment efforts. Furthermore, monitoring test positivity rates and severe case proportions among the nonelderly can predict imminent case count increases. Monitoring test positivity rates in conjunction with the concurrent Rt could be useful to assess and strengthen public health management and testing systems and deepen understanding of COVID-19 epidemic dynamics.

SUBMITTER: Furuse Y

PROVIDER: S-EPMC8125747 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe potential for work to be a risk factor for coronavirus disease 2019 (COVID-19) was recognized early in the pandemic based on the likelihood of work-related differences in exposures to COVID-19 in different occupations. Due to intense demands of the pandemic, implementation of recommendations to collect information on occupation in relation to COVID-19 has been uneven across the United States. The objective of this study was to investigate COVID-19 test positivity by occupation.MethodsWe analyzed data collected from September 8 to November 30, 2020, by the Delphi Group at Carnegie Mellon University US COVID-19 Trends and Impact Survey, offered daily to a random sample of US-based Facebook users aged 18 years or older, who were invited via a banner in their news feed. Our focus was ever testing positive for COVID-19 in respondents working outside the home for pay in the past 4 weeks.ResultsThe major occupational groups of "Production", "Building and grounds cleaning and maintenance," "Construction and extraction," "Healthcare support," and "Food preparation and serving" had the five highest test positivity percentages (16.7%-14.4%). Highest detailed occupational categories (28.6%-19.1%) were "Massage therapist," "Food processing worker," "Bailiff, correctional officer, or jailer," "Funeral service worker," "First-line supervisor of production and operating workers," and "Nursing assistant or psychiatric aide." Differences in test positivity by occupation remained after adjustment for age, gender, and pre-existing medical conditions.ConclusionInformation on differences in test positivity by occupation can aid targeting of messaging for vaccination and testing and mitigation strategies for the current and future respiratory infection epidemics and pandemics. These results, obtained before availability of COVID-19 vaccines, can form a basis for comparison to evaluate impacts of vaccination and subsequent emergence of viral variants.

Project description:BackgroundIn Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination.MethodsTo tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This enabled us to reconstruct the temporal evolution of the transmission rate of COVID-19 with the non-specific hypothesis that it follows a basic stochastic process constrained by the available data. This model is coupled with Bayesian inference (particle Markov Chain Monte Carlo method) for parameter estimation and utilized mainly well-documented Irish hospital data.ResultsIn Ireland, mitigation measures provided a 78-86% reduction in transmission during the first wave between March and May 2020. For the second wave in October 2020, our reduction estimation was around 20% while it was 70% for the third wave in January 2021. This third wave was partly due to the UK variant appearing in Ireland. In June 2020 we estimated that sero-prevalence was 2.0% (95% CI: 1.2-3.5%) in complete accordance with a sero-prevalence survey. By the end of April 2021, the sero-prevalence was greater than 17% due in part to the vaccination campaign. Finally we demonstrate that the available observed confirmed cases are not reliable for analysis owing to the fact that their reporting rate has as expected greatly evolved.ConclusionWe provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission during and after mitigation for the three waves. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as we await for a full roll out of a vaccination programme and as new variants potentially more transmissible and/or more infectious could continue to emerge and mitigation measures change silent transmission, challenges remain.

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Relationship of Test Positivity Rates with COVID-19 Epidemic Dynamics.

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