Modeling the influence of COVID superspreading events

In a recent study posted to the medRxiv* preprint server, University of Kansas researchers assessed the effect of superspreading events (SSEs) on the United States (US) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak dynamics.

Study: Exploring the Role of Superspreading Events in SARS-CoV-2 Outbreaks. Image Credit: StockTom / ShutterstockStudy: Exploring the Role of Superspreading Events in SARS-CoV-2 Outbreaks. Image Credit: StockTom / Shutterstock

Background

SARS-CoV-2, the novel coronavirus that emerged in late December 2019, has quickly swept over the globe, resulting in over 546 million illnesses and more than 6.3 million fatalities thus far. Coronavirus disease 2019 (COVID-19) has strained the US healthcare network, with several hospitals exceeding or nearing capacity and few limiting services. 

Governments at the state and national levels have responded by issuing guidelines and regulations for decreasing SARS-CoV-2 transmission, including social-distancing directives, mask mandates, stay-at-home instructions, and restrictions on big gatherings. However, insufficient adherence and compliance by the population have affected the efficiency of these laws and regulations, encouraging SSEs, which have assisted the SARS-CoV-2 transmission.

About the study

In the present study, the researchers developed a continuous-time Markov chain (CTMC) model to examine the impact of SSEs on the dynamics of the SARS-CoV-2 outbreak in the US. The authors defined SSEs as social or public events that lead to numerous SARS-CoV-2 infections over a short period.

The current research sought to determine the effect of SSEs compared to non-SSEs on COVID-19 outbreak dynamics, the efficacy of hospitalization and quarantine as containment methods for SSE relative to non-SSE-dominated outbreaks, and the impact of quarantine violation on the efficacy of quarantine for SSE compared to non-SSE-dominated outbreaks.

The investigators simulated a CTMC model for SARS-CoV-2 spread utilizing Gillespie's direct algorithm under three distinct scenarios: 1) neither hospitalization nor quarantine; 2) quarantine, hospitalization, premature hospital discharge, and quarantine violation; and 3) hospitalization and quarantine but not premature hospital discharge or quarantine violation. They also alter the rate of quarantine violations under realistic hospitalization and quarantine (RHQ) scenarios.

Results

The study results demonstrated that the SARS-CoV-2 outbreaks with SSE dominance were often more variable yet less severe and more prone to extinction than outbreaks without SSE dominance. The authors observed this after eliminating hospitalization and quarantine conditions or upon the inclusion of hospitalization, quarantine, early hospital discharge, and quarantine breach. 

However, the severity of the most catastrophic SSE-dominated outbreaks was higher than the most severe outbreaks without SSE dominance, despite most SSE-dominated outbreaks being less severe. Upon the inclusion of quarantine and hospitalization, while excluding quarantine breach and premature hospital discharge, SARS-CoV-2 outbreaks dominated by SSE were more susceptible to extinction than outbreaks without SSE dominance but were more severe and less variable.

Upon the inclusion of quarantine, hospitalization, premature hospital discharge, and halved quarantine breach, outbreaks dominated by SSE were comparable to when quarantine and hospitalization were included, but quarantine breach and premature hospital discharge were excluded. Besides, when quarantine breach was doubled outbreaks were similar to when quarantine and hospitalization were excluded.

Quarantine and hospitalization were more potent at regulating outbreaks dominated by SSE than those without SSE dominance in all scenarios. Similarly, quarantine breaches and premature hospital discharge were significant for outbreaks dominated by SSE.

SSE-dominated outbreaks were extremely improbable to become extinct when quarantine and hospitalization were excluded. They were moderately unlikely to become extinct when quarantine, hospitalization, premature hospital discharge, and quarantine violation were included. Furthermore, they were highly plausible to become extinct when hospitalization and quarantine were included, but quarantine breach and premature hospital discharge were excluded. 

Moreover, SSE-dominated outbreaks were more likely to become extinct when quarantine violations were halved. However, outbreaks dominated by SSE were less likely to become extinct when quarantine breaches were doubled.

Conclusions

Altogether, the study findings showed that COVID-19 outbreaks dominated by SSE differ noticeably from non-SSE-dominated outbreaks in their severity, variability, and chances of extinction. They also vary, albeit more low-key, from outbreaks dominated by superspreading individuals (SI). The possibility of hospitalization or quarantine and the likelihood of premature hospital discharge or violation of quarantine significantly impact the dynamics of SSE-dominated outbreaks.

Hospitalization and quarantine were substantially effective preventative interventions for COVID-19 outbreaks dominated by SSE. Nevertheless, premature hospital discharge and breach of the quarantine drastically diminished their efficacy. Besides, the team assessed control techniques using the probability of extinction.

The present findings have significant public health consequences, necessitating SARS-CoV-2 modelers must: 1) assess the contribution of SSEs or SIs to COVID-19 spread; and 2) differentiate between SSEs, SIs, and non-SIs/non-SSEs in their models. More research into the combined and individual effects of SSEs and SIs on SARS-CoV-2 outbreak dynamics and the efficacy of control strategies for various kinds of outbreaks were required to guide eradication and containment initiatives.

*Important notice

medRxiv 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.

Journal reference:
Shanet Susan Alex

Written by

Shanet Susan Alex

Shanet Susan Alex, a medical writer, based in Kerala, India, is a Doctor of Pharmacy graduate from Kerala University of Health Sciences. Her academic background is in clinical pharmacy and research, and she is passionate about medical writing. Shanet has published papers in the International Journal of Medical Science and Current Research (IJMSCR), the International Journal of Pharmacy (IJP), and the International Journal of Medical Science and Applied Research (IJMSAR). Apart from work, she enjoys listening to music and watching movies.

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