Study develops new model to estimate undetected COVID-19 emergence

In a recent study published in pro swan In the Journal, researchers developed a model framework to estimate the likelihood of an undetected epidemic using the number of suspected and confirmed cases and the epidemiological characteristics of pathogens.

study: Estimation of unknown emergence of COVID-19 in the United States. Image Credit: Maridav/Shutterstock.com

Background

In the early stages of the 2019 coronavirus disease (COVID-19) pandemic, in the United States (US), where the number of confirmed cases was relatively low, decisions to implement travel restrictions and disease mitigation measures were left to state governments. was local government.

With limited data on the number of cases and an unclear characterization of the virus, local governments faced the challenge of implementing socially and economically harmful safety measures.

Additionally, most COVID-19 cases were not reported, as many were asymptomatic or mild.

Similar to the 2016 Zika virus outbreak, do the reported sporadic infections represent cases within specific clusters of the population or suggest community transmission that could lead to epidemics? had to decide.

About research

In the current study, researchers tested the stochastic emergence severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

This modeling framework, designed to increase situational awareness about the Zika virus, was adopted early in the COVID-19 pandemic to improve awareness of SARS-CoV-2 among decision-making bodies and the public. rice field.

The model’s first set of estimates was published as the National Risk Map by The New York Times in early April 2020 and is believed to have been viewed by nearly 700 million people.

The Susceptibility-Exposure-Infection-Recovery (SEIR) model uses reported case numbers to develop an epidemic in a county or region based on the assumption that no public health interventions or disease mitigation measures have been implemented. estimated the likelihood of

Probabilistic simulations of outbreaks were run along different scenarios, starting with one undetected case of infection, reaching a total case count of 2,000, or ending with the outbreak subsiding.

result

The model estimated an epidemic chance of at least 50% by the time the county reported its first case.

A retrospective analysis that included county-specific reproduction numbers estimated the average epidemic risk for counties with confirmed COVID-19 in one county by March 16, 2020, at 71%.

Additionally, to validate the model, risk estimates were compared with counties reporting increased cases between March 16 and 23, 2020. significantly correlated with an increase in the number of Case.

The researchers reported that the results of their model are consistent with what is currently known about early SARS-CoV-2 infections in the United States.

considerable undocumented Infection with COVID-19 It was detected by phylogenetic and epidemiological models until social distancing, lockdowns, and other non-pharmaceutical measures were implemented to reduce transmission of SARS-CoV-2.

Findings suggest that public health interventions should be implemented as soon as the first cases are reported if the aim is to quickly contain the virus outbreak in the emergency phase. By the time the first case is confirmed, the epidemic risk has already reached 50%.

A proactive approach to the COVID-19 pandemic has been shown to reduce the need for economically harmful health measures at later stages.

Conclusion

Overall, the results indicate that the model successfully predicted the risk of potential COVID-19 epidemics in each region, based on the limited data on reported initial case numbers and epidemiological features of the virus. I was.

Validation of estimates suggested that aggressive implementation of disease mitigation measures is essential to prevent significant subsequent economic and health burdens in the first reports of confirmed cases. .