corona virus-Covid19

Large Majority of COVID-19 Patients Do Not Develop Symptoms Interview with:

Rahul Subramanian PhD candidate Department of Ecology and Evolution Biological Sciences Division University of Chicago Chicago, IL 60637

Rahul Subramanian

Rahul Subramanian PhD candidate
Department of Ecology and Evolution
Biological Sciences Division
University of Chicago
Chicago, IL 60637 What is the background for this study? What are the main findings? 

Response: Understanding the proportion of COVID-19 cases that become symptomatic, as well as the extent to which people without symptoms contribute to COVID-19 transmission, has important public health implications.

However, changes in PCR testing capacity over time have made these quantities hard to estimate precisely.

We used a model that incorporates daily changes in PCR testing capacity, cases, and serology to precisely estimate the proportion of cases that were symptomatic in New York City during the initial wave of the outbreak.

Only 1 in 7 to 1 in 5 cases were symptomatic.

Furthermore, non-symptomatic cases of the virus (this includes people who are either pre-symptomatic or asymptomatic) substantially contribute to community transmission, making up at least 50% of the driving force of SARS-CoV-2 infection. What should readers take away from your report?

Response: A large majority of COVID-19 cases do not develop symptoms.

People without symptoms contribute substantially (at least 50%) to community transmission.

Any public health measures to deal with the pandemic (mass testing, masking, social distancing, etc.) should be applied to non-symptomatic people as well as symptomatic people. What recommendations do you have for future research as a result of this work?

Response: The model framework that we provide can be extended to consider heterogeneity in COVID-19 transmission within different neighborhoods of large cities and updated to take into account ne variants of COVID-19.

Future models of COVID-19 transmission should take into account changes in testing capacity.

Making daily testing data readily available in addition to reported cases will greatly facilitate this. Is there anything else you would like to add?

Response: While there are a number of existing models that use epidemiological data to estimate undetected case numbers and transmission rates, this is the first peer-reviewed model to incorporate data about daily testing capacity and changes in testing rates over time to provide a more accurate picture of what proportion of SARS-CoV-2 infections are symptomatic in a large U.S. city.

Several prior models have suggested that the number of undetected cases is large. What differentiates our model is that we can distinguish between cases that are undetected because of a lack of testing and cases that are actually asymptomatic. This has important public health implications.

The study, “Quantifying Asymptomatic Infection and Transmission of COVID-19 in New York City using Observed Cases, Serology and Testing Capacity”, was supported by the National Science Foundation (1735359).


Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity

Rahul Subramanian, Qixin He, Mercedes Pascual
Proceedings of the National Academy of Sciences Mar 2021, 118 (9) e2019716118; DOI: 10.1073/pnas.2019716118

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Last Updated on February 16, 2021 by Marie Benz MD FAAD