Why the coronavirus might not be exponentially spreading Watch Now

For those wondering how long they'll have to be in quarantine because of the COVID-19 respiratory disease, the question for those in the US may not be how long, but how many times.

Work posted on the medRxiv pre-print server Tuesday by a group of researchers at Harvard's T.H. Chan School of Public Health in Boston suggests that multiple targeted periods of "social distancing" of various kinds will likely be necessary for the US before any vaccine is found for the disease.

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There's a tension in fighting COVID-19: So-called herd immunity needs to be built up, which requires that the disease be allowed to spread to some extent, for without exposure, that immunity will never be built. But the disease must not spread so much that it overwhelms the US's medical resources.

In "Social distancing strategies for curbing the COVID-19 epidemic," authors Stephen Iissler, Christine Tedijanto, Marc Lipsitch, and Yonatan Grad of the Chan School write that "a single period of social distancing will not be sufficient."

Paradoxically, going into an intense quarantine with nothing to follow it can actually be counter-productive.

Without repeated intervals of distancing, "there was a resurgence of infection when the simulated social distancing measures were lifted" in the model scenarios they ran.

The authors found that a resurgence could happen even after especially arduous periods of distancing, such as a 20-week period of social distancing. "The social distancing is so effective that virtually no population immunity is built."

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Instead, the authors argue interventions need to be made multiple times over a period of time, called "intermittent distancing," at intervals that depend upon the state of the health care infrastructure at any moment in time, meaning, how much load it can absorb of critical care cases of the disease.

"Intermittent social distancing can maintain critical care demand within current thresholds," they advise.

Kissler et al.

The authors suggest a threshold to be maintained is no more than 37.5 cases of the disease per 10,000 adult people in the population. That should be the "on" switch to re-commence social distancing, they argue. That threshold, they estimate, would keep the number of patients needing critical care at 0.89 persons for every 10,000 people in the population, which should be adequate to not overwhelm the health care system.

The predictions made by the researchers are constructed via a familiar, long-standing epidemiological model known as "SEIR," an acronym that stands for the "susceptible," "exposed," "infectious," and "recovered" individuals in a community. The approach uses differential equations to compute how fast a disease may spread based on how many people are in a community and how many are already sick or who have gotten better or died. It's a statistical technique, and so it's important to remember that it's not a guarantee of future trends, it's a way to model what might happen. Bear in mind, the paper is not yet peer-reviewed, and so fellow researchers have not yet vetted the work.

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But the model is based upon knowledge gathered over the years about the quality of epidemics, and one notion, in particular, is important to the Harvard team, that of "seasonal forcing," the premise that diseases tend to follow the course of temperature and humidity.

"The transmission of many respiratory pathogens, including the human coronaviruses that cause mild common cold-like syndromes, is seasonal in temperate regions, peaking in the winter months," the authors write. Seasonal effects complicate matters because they mean that COVID-19 could abate in warm weather, in the US, only to return in force in the fall.



US President Donald Trump has referenced a theory that COVID-19 will abate in April, but it's not certain that COVID-19 will follow a seasonal pattern, as Science Magazine's Jon Cohen pointed out last week. "No one knows whether SARS-CoV-2, the virus that causes COVID-19, will change its behavior come spring," he writes.

The issue, then, becomes one of closely monitoring infection rates in the population. For that, the Harvard authors note, blood tests are necessary to tell who in the population has developed antibodies for the disease. That would provide a broader indicator of who is sick, including the people who are "asymptomatic." It would also be a way to measure how many people in the society have already been through an infection and recovered, thereby reducing the susceptible population. Numerous efforts are ongoing to design the antibody tests, which are different from the main genetic tests used to tell if people have COVID-19.

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"Widespread surveillance will be required to time the distancing measures correctly and avoid overshooting critical care capacity," the authors observe.

They note, too, that the lower the amount of medical infrastructure, the longer it takes to build immunity. If immunity is a function of letting the virus run wild, then immunity is interrupted every time cases go over a threshold and lead to more quarantine.

Building up medical infrastructure could solve that, they argue.

"Increasing critical care capacity allows population immunity to be accumulated more rapidly, reducing the overall duration of the epidemic and the total length of social distancing measures."

Unless capacity is built up, social distancing could likely be required into 2022, the authors write.

Hence, the most intriguing lesson of the paper may be that US society has a choice: build up the healthcare system and get back to normal, or else spend a long time going in and out of quarantine.