Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England

Abstract

As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.3 (95% credible interval (CrI) 7.7-8.8). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of immunity in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.9%, 95% CrI 2.7-3.2), followed by Delta (2.2%, 95% CrI 2.0–2.4), Wildtype (1.2%, 95% CrI 1.1–1.2), and Omicron (0.7%, 95% CrI 0.6-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.

The impact of health inequity on regional variation of COVID-19 transmission in England

“…Each new variant was associated with a sequential increase in the reproduction number, in alignment with similar studies into the transmission potential of each variant 10 .…”

Inference of epidemic dynamics in the COVID-19 era and beyond

“…During epidemics such as Ebola in West Africa, there was also evidence that transmission declined as populations can change their behaviour independently of top-down measures, in response to directly observed community infections and/or indirectly observed information [156] and similar changes in behaviour independent of interventions were observed for SARS-CoV-2 [157,54]. The extent to which behavioural feedback can influence the dynamics of a growing epidemic will depend on the relative timescale of epidemic growth versus delays from infection to outcome.…”

“…As a result, they did not present the balance of risk integrating over the likely future of COVID-19 (i.e. eventual reopening with exit waves of infection [157]), and hence would have underestimated the benefits of vaccination. Subsequent work found that, given the severity of the Delta variant that caused waves in mid-2021, earlier vaccination with imperfect vaccine candidates (in this case because of reduced efficacy) was preferable in terms of hospitalisations and deaths averted to waiting months for other vaccine options [165].…”

“…Based on the estimated relationship between titre and protection from symptomatic and severe disase from a meta analysis of available studies [171], scenario models used the inferred exposure structure of the UK population to explore different scenarios about potential epidemic impact [139]. However, the growing complexity of the relationship between variant characteristics, population exposure, individual titre and subsequent protection for COVID-19 poses an ongoing challenge to such real-time analysis [172,157].…”

Comparative Analysis of Clinical Outcomes Using Propensity Score Matching: Coronavirus Disease 2019 vs. Seasonal Influenza in Korea

“… 4 5 6 7 8 Additionally, although omicron, the most recent variant, exhibits increased transmissibility than previous variants, it tends to result in milder disease severity. 9 10 Consequently, the mortality rates have significantly decreased recently. 3 …”