In a recent study published in the PLOS ONE Journal, researchers developed a model framework to estimate the probability of an undetected epidemic using the number of suspected and confirmed cases and the epidemiological characteristics of the pathogen.
Study: Estimating the undetected emergence of COVID-19 in the US. Image Credit: Maridav/Shutterstock.com
Background
During the early stages of the coronavirus disease 2019 (COVID-19) pandemic in the United States (US), when the number of confirmed cases was relatively low, the decision to enforce travel restrictions or disease mitigation measures was left to the state governments and local authorities.
With limited data on the number of cases and a lack of clarity on the characteristics of the virus, the local governments faced the challenge of implementing safety measures that could be socially and economically detrimental.
Furthermore, since many of the COVID-19 cases were asymptomatic or mildly symptomatic, most of the COVID-19 cases went unreported.
As with the Zika Virus outbreak in 2016, it was necessary to determine whether the sporadic number of reported infections represented cases within specific clusters in the population or whether it suggested a community transmission that could lead to an epidemic.
About the study
In the present study, the researchers accounted for factors such as transmission risk, asymptomatic infections, potential events of super spreading, and epidemiological characteristics of the disease (specific for each county) while modeling the stochastic emergence severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
This modeling framework was designed to develop situational awareness about the Zika Virus and was adopted in the early months of the COVID-19 pandemic to help improve awareness about SARS-CoV-2 among the decision-making bodies and the public.
The first set of estimates from this model was published in early April 2020 by the New York Times as a national risk map, which is believed to have been viewed by close to 700 million people.
The susceptible-exposed-infected-recovered (SEIR) model used the number of reported cases to estimate the probability of an outbreak in a county or region expanding into an epidemic based on the assumption that no public health interventions or disease mitigation measures were implemented.
The stochastic simulations for the outbreak were run along various scenarios beginning with one undetected case of infection and progressing to a situation where the total number of cases reached 2,000 or the outbreak subsided.
Results
The model had estimated at least a 50% chance of an epidemic by the time the first case in the county was reported.
In the retrospective analysis that included a reproduction number specific for each county, the estimates suggested counties with one confirmed COVID-19 by March 16, 2020, were at a mean epidemic risk of 71%.
Furthermore, to validate the model, the risk estimates were compared to the counties that reported an increase in the number of cases between March 16 and 23, 2020, and the estimates produced by the model were significantly correlated to the actual increase in the number of cases.
The researchers reported that the results of their model were consistent with what is currently known about the early transmission of SARS-CoV-2 in the US.
Considerable undocumented transmission of COVID-19 was detected by phylodynamic and epidemiological models until the social-distancing, lockdowns, and other non-pharmaceutical measures were implemented to reduce the transmission of SARS-CoV-2.
The findings suggested that if the aim is to rapidly contain a viral outbreak in the emergent stages, public health interventions should be implemented as soon as the first case is reported. Since by the time the first case is confirmed, the risk of an epidemic is already at 50%.
A proactive approach to the COVID-19 pandemic has been shown to reduce the need for economically detrimental health measures in the later stages.
Conclusions
Overall, the results indicated that the model successfully predicted the risk of a potential COVID-19 epidemic in each region based on the early numbers of reported cases and limited data on the epidemiological characteristics of the virus.
The validation of the estimates suggested that it is essential to proactively implement disease mitigation measures at the first report of a confirmed case to prevent a significant subsequent economic and health burden.
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