The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries – The Lancet Infectious Diseases

790 phases from 131 countries were included in the analysis (see appendix pp 8–40 for details on daily R estimates and NPI status for each country). The median duration of phases was 11 days (IQR 3–27), with the shortest median duration observed in phases in which closure of schools (3 days [1–8]) and public events bans (4 days [2–7]) were introduced (appendix p 41). Requirements to stay at home and restrictions on internal movements were the most common NPIs introduced, and were most often introduced and lifted simultaneously (figure 2). With regard to the temporal sequence of introducing and lifting NPIs, closure of schools and public events bans were the first two NPIs introduced and were lifted later than most NPIs. Requirements to stay at home and closure of public transport were the last two NPIs introduced and were lifted earlier than most NPIs (figure 2).

According to the results from the main analysis, a decreasing trend over time in the R ratio was found in the first 14 days following the introduction of school closure, workplace closure, public events bans, requirements to stay at home, and internal movement limits (figure 3); the reduction in R ranged from 3% to 24% on day 28 following the introduction (table 1). The introduction of a public events ban was associated with the highest reduction in R; the R ratio was 0·90 (95% CI 0·82–0·99) on day 7, 0·83 (0·68–1·00) on day 14, and 0·76 (0·58–1·00) on day 28 (table 1). An increasing trend over time in the R ratio was found following the relaxation of school closure, bans on public events, bans on public gatherings of more than ten people, requirements to stay at home, and internal movement limits, especially after the first week after relaxation; the increase in R ranged from 11% to 25% on day 28 following the relaxation (figure 3). The relaxation of school closure was associated with the greatest increase in R on day 7 (R ratio 1·05, 95% CI 0·96–1·14) and day 14 (1·18, 1·02–1·36). The relaxation of a ban on gatherings of more than ten people was associated with the greatest increase in R on day 28, with an R ratio of 1·25 (95% CI 1·03–1·51) on day 28. Negative interaction––ie, towards an R ratio of 1—was identified when multiple NPIs were introduced or lifted simultaneously (appendix p 42).

The immediacy of effect by introducing and lifting NPIs differed. The effects of introducing and lifting NPIs were not immediate; it took a median of 8 days (IQR 6–9) following the introduction of an NPI to observe 60% of the maximum reduction in R and even longer (17 days [14–20]) following its relaxation to observe 60% of the maximum increase in R (appendix p 43). Similar delays were noted for workplace closure and requirements to stay at home when using R as well as when using Google mobility data (appendix p 44). With Google mobility data it took an estimated 6 days and 12 days (compared with 6 days and 9 days when using R) following the introduction and relaxation of workplace closure, respectively, to observe 60% of the maximum change in the total visits to workplace, and it took an estimated 6 days and 17 days (compared with 6 days and 23 days when using R) following the introduction and relaxation of requirements to stay at home, respectively, to observe 60% of the maximum change in the total time spent at residential areas.

When comparing the effect of a ban on gatherings of more than ten people with that of a ban on gatherings of more than 100 people, we found that both bans were associated with a decrease in the R ratio in the first week, followed by an increase in the R ratio starting from the second week, but the increase was more pronounced for the ban on gatherings of more than 100 people, with R ratios above 1 after day 14 (figure 4). When lifting these two bans, we observed a delayed increase in R for the ban on gatherings of more than ten people (appendix p 45); on day 14, the R ratio was 1·07 (95% CI 0·96–1·20) for lifting the ban on gatherings of more than ten people and 1·23 (1·07–1·42) for lifting the ban on gatherings of more than 100 people (figure 4).

Similar results in terms of the trend in R over time following introduction and relaxation of NPIs (although with wider CIs due to data scarcity) were observed from sensitivity analyses that included only phases during which only one NPI was changed (appendix p 46), used the mean R of the last 7 days (rather than the last day) in the previous phase for calculating the R ratio (appendix p 47), excluded early phases when the country’s first NPI was introduced (appendix p 48), excluded seven large countries that could have greater regional variability in NPI policies (appendix p 49), excluded ten countries randomly (appendix p 50), included only phases with comprehensive testing (appendix p 51), and included only phases with comprehensive contact tracing (appendix p 52). Contrary to the main analysis, we found that if a public events ban was not introduced as the first intervention, it showed a non-significant reduction in R on day 28, with an R ratio of 0·80 (95% CI 0·57–1·11).

On the basis of the results from the main analysis, we estimated the effects of four candidates of composite NPIs (table 2; appendix p 53). The greatest reductions in R were seen for candidate 4 (school and workplace closure, plus a ban on public events and gatherings of more than ten people, internal movement limits, and a requirement to stay at home) at all timepoints (table 2).

Our findings on the effects of introducing NPIs were broadly in line with the findings from Flaxman and colleagues’ multicountry study that assessed the impact of different NPIs among 11 European countries.

Flaxman and colleagues reported that several NPIs (eg, school closure and public events ban), and lockdown in particular, had a large effect (81%) on reducing transmission.

However, Flaxman and colleagues did not assess changes over time in the effect of lockdown and assumed that the effect was immediate. In this study, we estimated that an extreme intervention similar to lockdown, consisting of school and workplace closure, bans on public events and gatherings, requirements to stay at home, and limits on internal movement, could reduce R by 35% on day 7, 42% on day 14, and 52% on day 28. Our findings on the effects of introducing NPIs were also qualitatively similar to those from a study by Islam and colleagues that modelled the incidence rate ratio of COVID-19 with OxCGRT NPI data,

although that study did not assess the effects of lifting NPIs.

School closure was widely adopted previously to control influenza outbreaks and pandemics, and was shown to reduce and delay peaks of epidemics.

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For SARS-CoV-2, the role of children in its transmission is still unclear. A modelling study from China showed that school closure alone could not interrupt transmission, but it could potentially reduce peak incidence by 40–60% and delay the epidemic of COVID-19.

In this study, we showed that closing schools alone could decrease transmission by 15% (R ratio 0·85, 95% CI 0·66–1·10) on day 28 and reopening schools could increase transmission by 24% (1·24, 1·00–1·52) on day 28. It should be acknowledged that in our analysis, we were unable to account for different precautions regarding school reopening that were adopted by some countries, such as physical distancing within classrooms (eg, limiting class sizes and placing transparent dividers between students) and outside classrooms (eg, physical distancing during meal times, recreation, and transportation), enhanced hygiene (eg, routine deep cleaning and personal handwashing and face masks), and others (eg, thermal temperature checks on arrival).

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Such precautions are imperative for safer school reopening. A COVID-19 outbreak was reported in a high school in Israel 10 days after its reopening; students were in crowded classrooms and were not instructed to wear face masks due to high temperatures.

In addition, it should be noted that we did not consider the normal school holidays in some countries. We were also unable to assess the effect of reopening different levels of school (eg, elementary vs middle schools) since the effect might differ by finer age bands within school-age children and adolescents.

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A report found that children younger than 5 years with mild to moderate COVID-19 had high viral loads in their nasopharynx compared with older children and adults, and thus could potentially be important drivers of transmission in the general population.

Our findings suggest that, as a single NPI, banning public events resulted in the greatest reduction in R, with an R ratio on day 28 of 0·76 (95% CI 0·58–1·00). This finding is unsurprising because a ban on crowded activities could prevent superspreading events, which were commonly reported at the beginning of the COVID-19 pandemic.

Another explanation for the high reduction is that a ban on public events was often the first NPI to be introduced in countries; our sensitivity analysis that excluded NPIs that were introduced first showed a non-significant reduction of transmission with banning public events, with an R ratio of 0·80 (95% CI 0·57–1·11) on day 28.

Notably, we did not observe a substantial difference in our results when including in a sensitivity analysis only phases with comprehensive contact tracing in place. This was not as expected since contact tracing was believed to reduce transmission through early identification of infectious cases. This finding could be due to the lack of representativeness, since only 18% of our data were included in this sensitivity analysis. Nevertheless, a modelling study, which might explain our results, suggested that a contact-tracing strategy will contribute to containment of COVID-19 only if it can be organised in a timely manner that minimises testing and tracing delays.

However, our data lacked the necessary granularity to further explore timeliness of testing and tracing. Additionally, similar to the findings by Islam and colleagues,

we did not observe substantial effects of public transport closure on the R ratio.

We acknowledge several challenges and limitations regarding our analysis. First, our analysis was based on data on control policy rather than on actual population behaviour. In particular, we were unable to account for the growing awareness of personal hygiene (including wearing face coverings) among the public in response to the pandemic. These behavioural changes lead to a further reduction of transmission and are likely to vary over time. We were also unable to examine compliance with these NPIs due to the scarcity of suitable data that were reliable across countries over time. Second, some NPIs (eg, school closure and public events ban) were often introduced earlier than other NPIs (eg, requirements to stay at home); therefore, we were unable to assess the effect of different rank orders of changes in NPI status. NPIs that were introduced earlier might have had a longer-term effect on R and thus might bias the estimates for later NPIs. Third, our data on R and NPIs were at the national level, whereas both R and NPIs could vary among different parts of a country. An increase in national-level R could be due to a clustered outbreak in some areas or due to several scattered cases nationwide. Fourth, we acknowledged the potentially high heterogeneity across different countries in terms of both NPIs and COVID-19 case ascertainment. Our findings should be regarded as a broad summary across the full dataset, and we did not intend to draw any separate conclusions for individual countries. Our sensitivity analyses indicated that our main findings were not sensitive to the removals of different lists of countries. Fifth, individual awareness and personal hygiene have been changing over time since the pandemic started, which could contribute greatly to the change in transmission of SARS-CoV-2 (eg, wearing face masks was uncommon before the COVID-19 pandemic); therefore, the impact on R by future reintroduction and re-relaxation of NPIs might be substantially different. Sixth, we did not consider the role of underlying seasonality or meteorological factors (eg, temperature and humidity) in SARS-CoV-2 transmission. A modelling study found that introduction of NPIs was strongly associated with growth of COVID-19 cases and, by comparison, humidity was only weakly associated with the growth; no association was found for latitude or temperature.

Seventh, we only assessed the effect of introducing and lifting NPIs for the first 28 days after introduction and relaxation, and the findings (including the trend) should not be generalised to beyond 28 days. Finally, although our study could essentially be regarded as a natural experiment study,

our findings do not necessarily imply causation.

We also acknowledge the innate limitations of R as a measure of transmission of SARS-CoV-2. First, although R is often assumed to have straightforward interpretations in practice, estimating R during an ongoing outbreak is complicated and associated with substantial uncertainty. Second, the estimates of R become unreliable with wider uncertainty range if the number of cases is low, which reduces its applicability at the very local level or when the number of cases in a large region is low. Third, R can be sensitive to a surge in the number of cases in certain settings (eg, care homes, schools, factories, and hospitals) and does not fully represent transmission in the general population. Fourth, R is an average population-level measure of transmission and does not reflect the individual-level transmission of SARS-CoV-2. The potential of SARS-CoV-2 transmission varies among individuals and is reflected by the reported superspreading events.

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