Impact of delaying the lifting of SARS-CoV-2 lockdown restrictions

In a recent study posted to the medRxiv* pre-print server, researchers used statistical and mathematical models to explore the progressive transmissibilities of B.1.177, Alpha, and Delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) circulating in England between September 2020 and July 2021. They modeled the coronavirus disease 2019 (COVID-19) epidemic during this period to project the epidemic trajectories in the first half of 2021 in England.

Several past studies have used statistical or mathematical modeling to understand available SARS-CoV-2 data and forecast different pandemic scenarios, but these two have rarely been used in conjunction.

About the study

In the present study, researchers used both these tools to simulate the spread of different SARS-CoV-2 variants in England between September 2020 and July 2021, evaluate their relative transmissibility, and model the impact of delaying Step 4 of the roadmap in the presence of the Delta variant and with continual vaccination.

Step 4 on the reopening roadmap, which would have fully relaxed the social distancing measures back to normal, was planned for June 21, 2021, in England. The Delta variant began spreading in England from the middle of April 2021, increasing the COVID-19 daily cases from 1,734 to 9,371 and daily hospitalizations from 114 to 242 ahead of the planned date for Step 4 reopening: between May 23, 2021, and June 17, 2021. Subsequently, the UK government was concerned over the safety of relaxing restrictions. This study, performed in June 2021, helped policymakers decide whether Step 4 on the roadmap should proceed as planned or postponed.

The researchers used statistical analysis of genomic surveillance data of SARS-CoV-2 and illustrated the application of the calibrated Covasim model across different COVID-19 epidemic scenarios to quantify the impact of the vaccination strategy and the Delta variant on planning cases, hospitalizations, and deaths due to COVID-19 alongside the four steps of the roadmap in England in the first half of 2021.

This study used the existing Covasim model at the median of 100 simulations. Covasim, a stochastic model, also introduced uncertainty in predicted outcomes; thus, researchers could project epidemic trajectories only four weeks into the future. 

Results

Both statistical and mathematical modeling results confirmed that emerging SARS-CoV-2 variants were progressively more transmissible. While the transmissibility of the B.1.177 strain was 20% more than the previously predominant variant, the transmissibility of the Alpha variant was 50-80% more than B.1.177 strain. The Delta variant was the most transmissible with 65-90% more transmissibility than the Alpha variant, so it replaced the Alpha variant in England in May 2021. 

Although the statistical estimations of relative advantage in the transmissibility of Alpha over B.1.177 and of Delta over Alpha was spatially heterogeneous, the results were consistent with the values determined by Covasim, and with previous estimates of the transmissibility of Alpha relative to previously circulating variants by Davies et al. and Volz et al. Similarly, the study results showing the transmissibility of the Delta variant vs. previous prevalent variants were close to the reported range of 69% to 83% by Sonabend et al. Overall, the study results were in agreement with other modeling results further confirming that Step 4 of the roadmap for relaxing restrictions should be delayed amid the emergence of the highly transmissible Delta variant.

While the third national lockdown in early 2021 in England successfully suppressed the spread of the Alpha variant, the study results projected a fatal third wave of COVID-19 infections following Step 4 of the roadmap in the absence of vaccination due to the uncontrolled spread of the Delta variant. The study analysis suggested a one-month delay of Step 4 of England’s COVID-19 roadmap out of lockdown was adequate to substantially reduce the resurgence in COVID-19 infections, hospitalizations, and deaths amid the emergence of a highly infectious Delta variant in late spring 2021. Also, the simulations of June 2021 showed that the delay drastically dampened the projected resurgence due to Delta, which was further suppressed due to the effect of the vaccination program that began in December 2020.

Conclusions

The study is among the pioneering works modeling the sequential competition of more than two SARS-CoV-2 variants over different periods and quantifying progressive transmissibility using statistical analysis and agent-based model, Covasim. Since the analysis used both statistical and mathematical modeling for estimations, this provided a robustness check within the same study besides generating results aligned with previous studies.

The study findings provided critical insights into competitive behavior and the relative transmissibility of SARS-CoV-2 variants, which could be used for planning responses to future emerging variants and exploring co-infections with different strains of SARS-CoV-2 and influenza virus.

This analysis illustrated the crucial role of appropriate timing of easing lockdown-induced restrictions and its impact in preventing large surges in COVID-19 infections in the English epidemic. Other countries could also benefit by taking cues from such lockdown exit strategies.

Most importantly, the study results, combined with findings of other modeling studies, were used to scientifically advise the UK Government to delay Step 4 reopening until July 19 to avert adverse outcomes due to the spread of the Delta variant.

*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.                                                                                       

Written by

Neha Mathur

Neha Mathur has a Master’s degree in Biotechnology and extensive experience in digital marketing. She is passionate about reading and music. When she is not working, Neha likes to cook and travel.