The latest mathematical COVID-19 model released by Harvard University researchers predicts that recurrent winter outbreaks will probably occur after the first, most severe pandemic wave; prolonged or intermittent physical distancing may be necessary into 2022; and a resurgence is possible as late as 2024.
The report, published in Science, details how the researchers used estimates of seasonality, immunity, and cross-immunity of the HCoV-OC43 and HCoV-HKU1 human coronaviruses from U.S. time series data to predict the likely course of the pandemic in temperate regions through 2025. Cross-immunity reduces the rate at which someone who recovered from infection caused by one pathogen may be infected by another.
Predicting the likely pattern of the pandemic is important in projecting the required intensity, duration, and urgency of contact tracing, lockdowns, and physical distancing in the absence of effective drug treatments and a vaccine.
The authors said that COVID-19 could—but is not likely to—behave like its closest relative, SARS-CoV-1, the virus that causes severe acute respiratory syndrome (SARS), and be eradicated through strict public health measures after a brief, intense epidemic.
Or it could behave like pandemic flu, circulating seasonally after causing an initial global wave of infection, similar to that of other human coronaviruses originating in animals.
Key findings from the model are that COVID-19 can produce a significant outbreak, regardless of the time of year, although outbreaks starting in the winter or spring tended to produce lower peaks, while those starting in fall or winter were shorter and more severe.
Scientists do not know whether people who have recovered from COVID-19 have long-term immunity to the virus. If they don't, the authors said that the virus could circulate regularly, in tandem with HCoV-OC43 and HCoV-HKU1, which cause colds and other respiratory infections, causing yearly, biennial (every-other-year), or sporadic outbreaks for the next five years.
Because infections with the two other human coronaviruses used in the model provide immunity lasting about 10 months, COVID-19 is likely to cause annual outbreaks, the researchers said. But if COVID-19 infection confers longer-term immunity such as two years, the outbreaks could be biennial.
The virus's seasonal variation could depend on climate, as it does for flu, they said. If it behaves like flu, it could decline about 40 percent in the summer in a climate like that in New York or 20 percent in the warmer climate of Florida.
A 40 percent decline in R0 in the summer would lower the peak incidence of the first pandemic wave. "However, stronger seasonal forcing [variation] leads to a greater accumulation of susceptible individuals during periods of low transmission in the summer, leading to recurrent outbreaks with higher peaks in the post-pandemic period," the authors said.
If COVID-19 infection confers permanent immunity, the virus could disappear for five years or more after a major outbreak. And if COVID-19 confers 70 percent cross-immunity against HCoV-OC43 and HCoV-HKU1, all human coronaviruses could decline or disappear. This is the same level of cross-immunity that HCoV-OC43 induces against HCoV-HKU1, the authors said.
If COVID-19 doesn't fully disappear and immunity to it lasts only two years, cross-immunity from HCoV-OC43 and HCoV-HKU1 could stop the spread of the novel coronavirus for as long as three years before it re-emerges in 2024, they said.
To estimate how long physical (social) distancing measures need to be in place to slow COVID-19 transmission and how intense they need to be, the researchers used the SEIRS (susceptible, exposed, infectious, recovered, then susceptible again) transmission model to capture mild to moderate asymptomatic infections (95.6 percent of infections), illness requiring hospitalization but not critical care (3.08 percent), and illness requiring critical care (1.32 percent).
They found that, although one-time physical distancing measures lowered the epidemic peak, infections resurged when they were lifted. And longer and stricter physical distancing did not always correlate with greater peak flattening.
For example, given 20 weeks of physical distancing achieving a 60 percent reduction in R0 and no seasonal variation, the resurgence peak was nearly as high as the peak of the uncontrolled epidemic.
"The social distancing was so effective that virtually no population immunity was built," the authors wrote. "The greatest reductions in peak size come from social distancing intensity and duration that divide cases approximately equally between peaks."
But if seasonal variations occurred, simulations showed that the peak of a resurgence when physical distancing measures were lifted could be even higher than the one of an uncontrolled pandemic.
"Strong social distancing maintained a high proportion of susceptible individuals in the population, leading to an intense epidemic when R0 rises in the late autumn and winter," they said. "None of the one-time interventions was effective in maintaining the prevalence of critical cases below the critical care capacity."
Increasing the capacity of the healthcare system to provide critical care would allow more people to become immune faster, shortening the need for physical distancing. Under that scenario, physical distancing could end by early- to mid-2021, and the pandemic could be over by July 2022, according to the researchers.
"Intermittent social distancing might maintain critical care demand within current thresholds, but widespread surveillance will be required to time the distancing measures correctly and avoid overshooting critical care capacity," they wrote.
Until vaccines and effective treatments are available for COVID-19, the authors called for increasing critical care capacity, development of other interventions, viral and serologic testing to understand the durability of immunity, and widespread epidemiologic surveillance.
