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Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys.

ABSTRACT: There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available data sources. It is based on a discrete-time Susceptible-Infected-Removed (SIR) model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data on deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random-sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the United States. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress toward herd immunity.

SUBMITTER: Irons NJ

PROVIDER: S-EPMC8346866 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn December 2020, Public Health England with NHS Test and Trace initiated a pilot study in which close contacts of people with confirmed COVID-19 were given the option to carryout lateral flow device antigen tests at home, as an alternative to self-isolation for 10-14 days. In this study, we evaluated engagement with daily testing, and assessed levels of adherence to the rules relating to behaviour following positive or negative test results.MethodsWe conducted a service evaluation of the pilot study, examining survey responses from a subset of those who responded to an evaluation questionnaire. We used an online cross-sectional survey offered to adult contacts of confirmed COVID-19 cases who consented to daily testing. We used a comparison group of contacts who were not offered testing and instead self-isolated.ResultsAcceptability of daily testing was lower among survey respondents who were not offered the option of testing and among people from ethnic minority groups. Overall, 52% of respondents reported being more likely to share details of people that they had been in contact with following a positive test result, if they knew that their contacts would be offered the option of daily testing. Only 2% reported that they would be less likely to provide details of their contacts. On the days that they were trying to self-isolate, 19% of participants reported that they left the house, with no significant group differences. Following a negative test, 13% of respondents reported that they increased their contacts, but most (58%) reported having fewer risky contacts.ConclusionsOur data suggest that daily testing is potentially acceptable, may facilitate sharing contact details of close contacts among those who test positive for COVID-19, and promote adherence to self-isolation. A better understanding is needed of how to make this option more acceptable for all households. The impact of receiving a negative test on behaviour remains a risk that needs to be monitored and mitigated by appropriate messaging. Future research should examine attitudes and behaviour in a context where infection levels are lower, testing is more familiar, and restrictions on activity have been reduced.

Dataset Information

Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys.

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