COVID-19 infections may be much higher, but even moderate interventions can help reduce spread. "This suggests that the opportunity window to contain the epidemic of COVID-19 in its early stage is closing," researchers say. Cedars-Sinai Medical Centre investigators, who led the study, said they chose "very conservative" methods to estimate the number of coronavirus cases. "This makes our current estimation likely to be an underestimation of the true number of infected individuals in the UD," they wrote.
Dr Shlomo Melmed, executive vice president of academic affairs and dean of the medical faculty at Cedars-Sinai, added: "Cedars-Sinai is committed to the global efforts to combat COVID-19 and we believe that early dissemination of this study and the free sharing of the code that underlies the model will help in those efforts."
To arrive at infection estimates for their new study, the researchers modelled only COVID-19 coronavirus cases "imported" directly to the US from the area of Wuhan, China, before 23 January, when the Chinese government locked down the city, and they assumed the lockdown stopped all outbound traffic. Potential cases arriving in the US from other parts of China, or other heavily affected countries such as South Korea, Italy or Iran, were not included in the estimate.
The scale of the COVID-19 epidemic in the US was calculated based on: air traffic data between Wuhan and the US, totals of confirmed cases publicly released by the CDC and transmission dynamics as estimated from previous research. The study took into account the identification and quarantine of individual domestic cases in the US. Among other considerations, the research team assumed the imported cases were no longer spreading infection.
Based on all these assumptions and methods, the investigators estimated the total number of people in the US infected with coronavirus as of 1 March to be between 1,043 and 9,484. The first figure assumed current preventive procedures – such as quarantines and screening international travellers at airports – had reduced as much as 25% of the transmissibility in unidentified cases. The second figure assumed no intervention procedures had been undertaken to reduce the transmissibility.
The disparity between the lowest and highest estimates has important implications for controlling the COVID-19 epidemic, said Dr Dermot P McGovern, professor of medicine and biomedical Sciences at Cedars-Sinai and senior author of the new study. "Our model suggests that even moderately effective population interventions to reduce transmission can have a profound impact on the scale of the epidemic," McGovern explained. "This finding supports the role of public health interventions in controlling this disease."
But slowing transmission is not an easy task, given that most COVID-19 cases appear to be mild or even asymptomatic, the researchers noted, which can make it difficult to identify infected individuals who may be spreading the virus.
Despite these obstacles, McGovern said, it may still be possible to mitigate the COVID-19 outbreak through steps already suggested by public health experts.vThese include promoting social distancing and personal hygiene and restricting large-scale gatherings for occasions such as sporting events.
Additionally, performing mass screening for infected individuals, while expensive and logistically challenging, also would potentially help to control the epidemic, said McGovern, who also is director of Translational Research in the Inflammatory Bowel and Immunobiology Research Institute at Cedars-Sinai.
Dr Dalin Li, the study's first author and co-corresponding author with McGovern, said the research team is releasing the just-completed study data online before the full study has been accepted in a journal due to the urgency of the COVID-19 outbreak.
"We are making the results public before peer review as it will be important for timely and informed public health decision-making. We are also making the model available to the research community so that others can build upon it." said Li, a research scientist in the Inflammatory Bowel and Immunobiology Research Institute.
Funding: Research reported in this publication was supported by the National Institutes of Health, the Helmsley Charitable Trust and the F Widjaja Foundation.
Abstract (not peer reviewed)
Introduction: Coronavirus Disease 2019 (COVID-19) infection has been characterized by rapid spread and unusually large case clusters. It is important to have an estimate of the current state of COVID-19 epidemic in the U.S. to develop informed public health strategies. Methods: We estimated the potential scale of the COVID-19 epidemic (as of 03/01/2020) in the U.S. from cases imported directly from Wuhan area. We used simulations based on transmission dynamics parameters estimated from previous studies and air traffic data from Wuhan to the U.S and deliberately built our model based on conservative assumptions. Detection and quarantine of individual COVID-19 cases in the U.S before 03/01/2020 were also taken into account. We. A SEIR model was used to simulate the growth of the number of infected individuals in Wuhan area and in the U.S. Results: With the most likely model, we estimated that there would be 9,484 infected cases (90%CI 2,054-24,241) as of 03/01/2020 if no intervention procedure had been taken to reduce the transmissibility in unidentified cases. Assuming current preventive procedures have reduced 25% of the transmissibility in unidentified cases, the number of infected cases would be 1,043 (90%CI 107-2,474). Conclusion: Our research indicates that, as of 03/01/2020., it is likely that there are already thousands of individuals in the US infected with SARS-CoV-2. Our model is dynamic and is available to the research community to further evaluate as the situation becomes clearer.
Authors
Dalin Li, Jun Lv, Gregory Botwin, Jonathan Braun, Weihua Cao, Liming Li, Dermot PB McGovern
[link url="https://www.cedars-sinai.org/newsroom/study-estimates-covid-19-may-have-infected-over-9000-in-us/"]Cedars-Sinai Medical Centre material[/link]
[link url="https://www.medrxiv.org/content/10.1101/2020.03.06.20031880v1"]MedRxiv abstract[/link]