In a new study posted to the Research Square* preprint server, researchers demonstrated the daily variations in time-varying transmission heterogeneity (kt) through three coronavirus disease 2019 (COVID-19) epidemic waves of sustained local transmission in Hong Kong.
Super-spreading seems like a distinct feature of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing COVID-19 pandemic. Epidemiological measures, such as the effective reproductive number (Re), and time-varying effective reproduction number (Ret), could quantify SARS-CoV-2 transmission rates and predict the pandemic progression.
However, these measures often overlook heterogeneity in the individual-level transmission and super-spreading events. Moreover, the Poisson distribution model that measures transmission variance (as Re) is inappropriate to model datasets that feature super-spreading.
On the other hand, the negative binomial distribution is a fitting model to measure transmission heterogeneity variations over time for COVID-19. For low values of dispersion parameter k (0 < k < 1), the corresponding distribution concentrated around zero. Such overdispersion indicated the likelihood of super-spreading events with the potential to alter the COVID-19-pandemic dynamics.
Unlike Re and Ret, scientific studies have interpreted k as a fixed parameter unaffected by non-pharmaceutical interventions (NPIs) or time, and few studies have investigated temporal variations in k and the relative effect of NPIs on time-varying transmission heterogeneity.
About the study
In the current study, researchers used a novel approach to compare the epidemiology and kt of two beta-coronavirus datasets from Hong Kong between January 23rd, 2020, and April 5th, 2021. Notably, Hong Kong had witnessed two beta-coronavirus epidemics: SARS in 2003 and the currently ongoing COVID-19-pandemic.
The researchers presented the fluctuations in kt of COVID-19 on a continuous scale (daily) with no international introductions to confound local transmission results and in correlation with super-spreading events, defined as more than six secondary cases per primary case.
They used the k values to compute the proportion of cases responsible for 80% of onwards transmissions (Prop80). Further, the researchers used sensitivity analyses on the hypothetical worst-case scenarios to assess the potential impact of under and over observation (imperfect) of COVID-19 cases on estimates of kt.
The research team generated additional evidence that favored greater levels of transmission heterogeneity for COVID-19 than previous estimates by increasing the number of transmission pairs, 4,697 pairs vs. 169 pairs (used previously).
The authors found that the measures of transmission heterogeneity for SARS and COVID-19 fluctuated temporally and were partially associated with super-spreading events, with SARS exhibiting more heterogeneity and less temporal variability than SARS-CoV-2.
The values for kt and Prop80t decreased throughout three epidemic waves of COVID-19 in Hong Kong, and these reductions correlated with the NPIs that prevented potential super-spreading events. They also observed a correlation between kt, Prop80t with Ret for COVID-19; however, for SARS, this correlation was inconclusive.
The most plausible explanation is that implementing stringent NPIs, such as social distancing and mandatory masking during the COVID-19-pandemic reduced random contacts at the community level, thereby increasing the proportion of cases that terminated the infection chain. It prevented super-spreading events during the COVID-19-pandemic, which is a hallmark feature of transmission heterogeneity.
These findings elucidate why the interpretation of kt and measures of transmission heterogeneity over time, including response to NPIs, could help devise pandemic mitigation strategies and prevent super-spreading events and widespread SARS-CoV-2 transmission, much before it occurs.
The global estimates showed less than 10% of COVID-19 cases were responsible for 80% of onward transmissions. The estimates of the current study were slightly higher, with a kt of 14.2%, indicating a unique pathogen-population dynamic in Hong Kong. An underdetection of COVID-19 cases in Hong Kong might have led to a moderate overestimation of kt.
In a worst-case scenario, where half of the COVID-19-cases remained undetected in Hong Kong, the observed marginal distribution of kt for COVID-19 was not higher than SARS. However, applying the same rate of underdetection to the overall k estimates showed higher heterogeneity for SARS (k = 0.04) than for COVID-19 (k = 0.1).
Overall, the study estimated temporal variations in transmission heterogeneity on a continuous scale for COVID-19 and SARS. Notably, 14.2% of cases were responsible for 80% of all onwards transmissions of COVID-19 in Hong Kong, and ~70% did not cause any onward COVID-19 transmission. To conclude, time-varying estimates of transmission heterogeneity could be potential indicators for all the emerging pandemics and used robustly for epidemic surveillance.
Research Square publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.