A new method of monitoring outbreaks like COVID-19 gives an accurate, real-time estimate of the growth rate of an outbreak by carefully assessing the relationship between the amount of virus in the bodies of infected people, called viral load, and speed at which the number of cases increases or decreases.
“This new method, which effectively links what we know about the growth of the virus in the body to the dynamics of the virus spreading in a population, provides a whole new measure that public health officials, policy makers and epidemiologists can use it to get up-to-date real-time information about the epidemic, “said Michael Mina, assistant professor of epidemiology at Harvard TH Chan School of Public Health and senior member of the Center for Communicable Disease Dynamics.
Mina is the lead author of an article describing the method, published on June 3, 2021 in the journal Science.
Outbreak surveillance is critical to the public health response to understand how well interventions such as masks, containments or vaccines are working, and to know where to distribute additional resources when cases increase.
Current approaches to epidemic surveillance rely almost entirely on tracking the number of cases or hospitalization rates over time, and examining rates of test positivity and death. Throughout the COVID-19 pandemic, for example, daily case data like that published by The New York Times has been crucial for public health officials and researchers to assess how well states and countries control the spread of the SARS-CoV-2 virus that causes COVID-19. However, these types of data can often be of limited use due to varying testing practices or poor reporting. For example, a growing epidemic may appear to stabilize if testing capacity is at peak or notification is delayed because resources are concentrated elsewhere. These pitfalls of tracking case reports over time can negatively impact appropriate public health responses.
As epidemics increase or decrease exponentially, when cases increase, most people positive at any time will have been recently infected and therefore will have higher viral loads – as measured by PCR (chain reaction) tests. polymerase) – at the time they are tested. This is because the virus is at its peak in the body soon after infection and then drops to very low but still detectable levels in PCR tests for weeks or even months after infection. When the epidemic slows down and cases decline, the average person found to be positive in surveillance tests will potentially have been infected weeks before the test and therefore have a lower viral load by the time of the test.
To better track pandemic hotspots, researchers at Harvard Chan School have developed a mathematical tool that carefully assesses the relationship between viral load – measured from the PCR test in a value called the cycle threshold (Ct value) – and the rate at which cases increase or decrease. Using even the relatively small number of 30 SARS-CoV-2 positive samples taken during surveillance testing in a single day can give an accurate real-time estimate of the rate of the epidemic’s growth. When Ct values are available from multiple times, researchers have found that they can use even a very limited amount of positive results to reconstruct the epidemic curve and estimate the number of people infected over time.
Even the viral amounts detected in positive PCR test samples collected from one location at a single time can help estimate the rate of growth or decline of an epidemic in a population, the researchers found.
In the United States and much of the world, PCR Ct values - values that show the amount of virus collected on a swab from someone’s nose – are often rejected and the PCR test results returned with a simple “positive” or “negative” result.
“Our work shows how valuable Ct values are and why we should not only stop our current practice of throwing them away, but why we should instead make them key data to collect for our response to the pandemic,” Mina said, who has previously published on the use of PCR Ct values to aid clinical decision making and who has been a leader in developing new approaches for using COVID-19 testing to limit the spread of disease.
James Hay, who co-led the research as a postdoctoral researcher in Mina’s lab, stressed that the new technique is not specific to COVID-19 but it is a method that will be valuable to monitor epidemics and epidemics of other viruses in the future. “This tool is not only for COVID, but rather provides a new approach to estimate the epidemic trajectories of many types of virus, and is an approach that does not rely on potentially biased approaches like counting cases over time. and will not depend on specific case reports or hospitalizations, ”he said.
Other Harvard Chan School researchers who contributed to the study include Lee Kennedy-Shaffer and Marc Lipsitch.
“Estimation of epidemiological dynamics from transverse viral load distributions”, James A. Hay, Lee Kennedy-Shaffer, Sanjat Kanjilal, Niall J. Lennon, Stacey B. Gabriel, Marc Lipsitch, Michael J. Mina, Science, online 2 June 2021.
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The Harvard TH Chan School of Public Health brings together dedicated experts from many disciplines to educate new generations of global health leaders and produce powerful ideas that improve the lives and health of people everywhere. As a community of leading scientists, educators and students, we work together to bring innovative ideas from the lab to people’s lives – not only by making scientific breakthroughs, but also by working to change individual behaviors, public policies and health care practices. Each year, more than 400 faculty members at Harvard Chan School teach more than 1,000 full-time students around the world and train thousands more through online courses and executive training. Founded in 1913 as the Harvard-MIT School of Health Officers, the school is recognized as the oldest public health professional training program in the United States.
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