Study population and setting
Authors used data on commonly circulating beta coronaviruses HCoV-OC43 and HCoV-HK1 to estimate the effects of seasonal forcing (i.e., seasonality) and waning immunity on potential future transmission dynamics of the currently circulating novel SARS-CoV-2. Using the weekly percentage of positive HCoV-OC43 and HCoV-HKU1 laboratory tests and the proportion of physician visits for influenza-like illness (ILI) in the United States from the 2014-15 through 2018-19 influenza seasons, authors estimated R0 (reproduction number), duration of immunity, and the degree of cross-immunity between HCoV-OC43 and HCoV-HKU1 using a two-strain Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) model. An SEIRS model extends beyond the traditional SEIR model by assuming that post-infection immunity is not lifelong, and that persons will eventually become susceptible to disease again after they have recovered. Then, using the same model, authors incorporated SARS-CoV-2 into the model to estimate the same parameters while accounting for the two commonly circulating strains. Finally, authors used a one-strain model to estimate the qualitative effectiveness of social distancing interventions on the transmission of SARS-CoV-2.
Summary of Main Findings
Assuming immunity from HCoV-OC43 and HCoV-HKU1 lasts approximately 45 weeks and some degree of cross-immunity occurs between the two, authors estimated the R0 for HCoV-OC43 and HCoV-HKU1 would vary seasonally, ranging from 1.7 in summer months to 2.2 in winter months, with a peak R0 observed around mid-January. Assuming no mitigation activities were implemented during the first year of the COVID-19 pandemic, the dynamics of SARS-CoV-2 post-pandemic showed a highly variable number of scenarios, from annual or biennial peaks, to complete elimination or apparent elimination for several years followed by a resurgence that can occur as late as 2025. These different scenarios were highly sensitive to the duration of immunity, degree of cross-immunity with other circulating beta coronaviruses, and the strength of seasonal variation in transmission assumed in the model. Authors also estimated the long-term dynamics of SARS-CoV-2 under one-time or intermittent social-distancing measures. Intermittent social distancing was shown to prevent exceeding critical care capacity. The worst-case scenario for one-time social-distancing (i.e., with no cross-immunity from currently circulating coronaviruses) resulted in an epidemic wave in autumn bigger than that avoided by the measures in spring.
Using one-strain, two-strain, and three-strain SEIRS models, this study was able to assess the effects of seasonal forcing (i.e., seasonality), cross-immunity, and duration of immunity on transmission dynamics of SARS-CoV-2. Authors developed the models to capture varying levels of severity of COVID-19, including moderate, mild, and asymptomatic cases, infections that lead to hospitalization but not critical care, and those that required critical care.
This study used a deterministic model, meaning that model outcomes are completely predetermined by the model parameters, and do not account for the randomness inherent in the disease transmission processes. Therefore, these models were unable to account for possibilities such as SARS-CoV-2 or other HCoV fade-outs. Furthermore, a large degree of uncertainty in the results exists due to the lack of knowledge regarding the true degree of cross-immunity between the three virus strains. Models only used five years of historic data, limiting future projections. Authors also assumed seasonal forcing was constant year to year, did not account for the effects of schools opening in the fall, did not differentiate between locations, and did not stratify calculations by age – all of which may have significant implications on model results.
This study is among the first to assess the effects of seasonal forcing (i.e., seasonality), waning immunity, and cross-immunity with other beta coronaviruses on SARS-CoV-2 transmission dynamics, and highlights the importance of measuring these parameters. The effectiveness of interventions, such as social distancing, could be largely impacted by these factors, and the timing of implementing and lifting such mitigation strategies should account for this. Lifting social-distancing measures by the end of summer could result in a big second wave that could potentially coincide with the flu season, thus resulting in an even more overwhelmed health system.
This review was posted on: 21 July 2020