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Estudio de modelización en 131 países-número de reproducción variable en el tiempo (R)
La asociación temporal de la introducción y el levantamiento de intervenciones no farmacéuticas con el número de reproducción variable en el tiempo (R) del SARS-CoV-2: un estudio de modelización en 131 países
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
Non-pharmaceutical
interventions (NPIs) were implemented by many countries to reduce the
transmission of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2), the causal agent of COVID-19. A resurgence in COVID-19
cases has been reported in some countries that lifted some of these
NPIs. We aimed to understand the association of introducing and lifting
NPIs with the level of transmission of SARS-CoV-2, as measured by the
time-varying reproduction number (R), from a broad perspective across 131 countries.
Muchos países aplicaron intervenciones no farmacéuticas para reducir la transmisión del coronavirus 2 del síndrome respiratorio agudo severo (SARS-CoV-2), el agente causal de COVID-19. Se ha informado de un resurgimiento de los casos de COVID-19 en algunos países que levantaron algunas de esas intervenciones no farmacéuticas. Nuestro objetivo era comprender la asociación de la introducción y el levantamiento de los NPI con el nivel de transmisión del SARS-CoV-2, medido por el número de reproducción variable en el tiempo (R), desde una perspectiva amplia en 131 países.
Methods
In this modelling study, we linked data on daily country-level estimates of R
from the London School of Hygiene & Tropical Medicine (London, UK)
with data on country-specific policies on NPIs from the Oxford COVID-19
Government Response Tracker, available between Jan 1 and July 20, 2020.
We defined a phase as a time period when all NPIs remained the same, and
we divided the timeline of each country into individual phases based on
the status of NPIs. We calculated the R ratio as the ratio between the daily R of each phase and the R
from the last day of the previous phase (ie, before the NPI status
changed) as a measure of the association between NPI status and
transmission of SARS-CoV-2. We then modelled the R ratio using a
log-linear regression with introduction and relaxation of each NPI as
independent variables for each day of the first 28 days after the change
in the corresponding NPI. In an ad-hoc analysis, we estimated the
effect of reintroducing multiple NPIs with the greatest effects, and in
the observed sequence, to tackle the possible resurgence of SARS-CoV-2.
Findings
790 phases from 131 countries were included in the analysis. A decreasing trend over time in the R
ratio was found following the introduction of school closure, workplace
closure, public events ban, requirements to stay at home, and internal
movement limits; the reduction in R ranged from 3% to 24% on
day 28 following the introduction compared with the last day before
introduction, although the reduction was significant only for public
events ban (R ratio 0·76, 95% CI 0·58–1·00); for all other
NPIs, the upper bound of the 95% CI was above 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; the increase in R ranged from 11% to 25% on day 28
following the relaxation compared with the last day before relaxation,
although the increase was significant only for school reopening (R
ratio 1·24, 95% CI 1·00–1·52) and lifting bans on public gatherings of
more than ten people (1·25, 1·03–1·51); for all other NPIs, the lower
bound of the 95% CI was below 1. 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 relaxation to observe 60% of the maximum increase in R.
In response to a possible resurgence of COVID-19, a control strategy of
banning public events and public gatherings of more than ten people was
estimated to reduce R, with an R ratio of 0·71 (95%
CI 0·55–0·93) on day 28, decreasing to 0·62 (0·47–0·82) on day 28 if
measures to close workplaces were added, 0·58 (0·41–0·81) if measures to
close workplaces and internal movement restrictions were added, and
0·48 (0·32–0·71) if measures to close workplaces, internal movement
restrictions, and requirements to stay at home were added.
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Se incluyeron en el análisis 790 fases de 131 países. Se encontró una tendencia decreciente a lo largo del tiempo en la proporción R tras la introducción del cierre de escuelas, el cierre de lugares de trabajo, la prohibición de eventos públicos, la exigencia de permanecer en casa y los límites de movimiento interno; la reducción de la proporción R osciló entre el 3% y el 24% en el día 28 posterior a la introducción en comparación con el último día antes de la introducción, aunque la reducción fue significativa sólo para la prohibición de eventos públicos (proporción R 0-76, IC del 95% 0-58-1-00); para todos los demás INP, el límite superior del IC del 95% fue superior a 1. Se encontró una tendencia al alza en el tiempo en la proporción R tras la relajación del cierre de escuelas, las prohibiciones de eventos públicos, las prohibiciones de reuniones públicas de más de diez personas, los requisitos para permanecer en casa y los límites de movimiento interno; el aumento de la R osciló entre el 11% y el 25% el día 28 después de la relajación en comparación con el último día antes de la relajación, aunque el aumento fue significativo sólo para la reapertura de escuelas (Razón R 1-24, IC del 95% 1-00-1-52) y el levantamiento de prohibiciones de reuniones públicas de más de diez personas (1-25, 1-03-1-51); para todos los demás NPI, el límite inferior del IC del 95% fue inferior a 1. Se necesitó una mediana de 8 días (IQR 6-9) después de la introducción de un NPI para observar el 60% de la reducción máxima de R y aún más tiempo (17 días [14-20]) después de la relajación para observar el 60% del aumento máximo de R. En respuesta a un posible resurgimiento de COVID-19, se estimó que una estrategia de control de la prohibición de eventos públicos y reuniones públicas de más de diez personas reduciría R, con un ratio R de 0-71 (95% CI 0-55-0-93) el día 28, disminuyendo a 0-62 (0-47-0-82) el día 28 si se añadían medidas para cerrar los lugares de trabajo, 0-58 (0-41-0-81) si se añadieron medidas para cerrar los lugares de trabajo y restricciones de movimiento interno, y 0-48 (0-32-0-71) si se añadieron medidas para cerrar los lugares de trabajo, restricciones de movimiento interno y requisitos para permanecer en casa.
Interpretation
Individual
NPIs, including school closure, workplace closure, public events ban,
ban on gatherings of more than ten people, requirements to stay at home,
and internal movement limits, are associated with reduced transmission
of SARS-CoV-2, but the effect of introducing and lifting these NPIs is
delayed by 1–3 weeks, with this delay being longer when lifting NPIs.
These findings provide additional evidence that can inform policy-maker
decisions on the timing of introducing and lifting different NPIs,
although R should be interpreted in the context of its known limitations.
Los
NPI individuales, incluyendo el cierre de escuelas, el cierre del lugar
de trabajo, la prohibición de eventos públicos, la prohibición de
reuniones de más de diez personas, requisitos para quedarse en casa, y
los límites de movimiento interno, se asocian con la transmisión
reducida de SARS-COV-2, pero la El efecto de introducir y levantar estos
NPIs se retrasa por 1-3 semanas, con este retraso más largo al levantar
los NPIs. Estas conclusiones proporcionan pruebas adicionales que
pueden informar sobre las decisiones normativas sobre el momento en que
se introduce y levanta diferentes NPIs, aunque R debe interpretarse en
el contexto de sus limitaciones conocidas.
Funding
Wellcome Trust Institutional Strategic Support Fund and Data-Driven Innovation initiative.
The
novel coronavirus severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) that was first reported in Wuhan (China) in December, 2019,
has since spread worldwide, and as of Oct 21, 2020, the resulting
COVID-19 pandemic had caused more than 40 million confirmed cases and
more than 1 million deaths (see COVID-19 Dashboard).
From early March, 2020, population-level non-pharmaceutical
interventions (NPIs) to reduce SARS-CoV-2 transmission were introduced
in many countries affected by COVID-19, and these have included school
closures, bans on public events, restrictions on gathering sizes, and
requirements to stay at home. Since the beginning of May, 2020, several
countries have started to lift some of these NPIs, and some countries
have witnessed a second surge in the number of reported COVID-19 cases.
In response to the resurgence, several countries have reintroduced NPIs
to reduce the transmission of SARS-CoV-2. It is important to understand
the impact of introducing and lifting these NPIs on the transmission of
SARS-CoV-2.
The time-varying reproduction number (R; also known as Rt) is defined by the expected number of secondary cases arising from a primary case infected at time t. R
is an important metric for measuring time-specific transmissibility and
could be used for assessing whether current interventions appear to be
effective, or whether additional interventions are required. If R remains below 1, then the epidemics will eventually die out; if R is above 1, sustained epidemics are expected.
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El nuevo coronavirus del síndrome respiratorio agudo severo coronavirus 2 (SARS-CoV-2) que se notificó por primera vez en Wuhan (China) en diciembre de 2019, se ha propagado desde entonces por todo el mundo, y hasta el 21 de octubre de 2020 la pandemia COVID-19 resultante había causado más de 40 millones de casos confirmados y más de 1 millón de muertes (véase el Tablero COVID-19). Desde principios de marzo de 2020, en muchos países afectados por COVID-19 se introdujeron intervenciones no farmacéuticas a nivel de la población para reducir la transmisión del SARS-CoV-2, que han incluido el cierre de escuelas, la prohibición de eventos públicos, restricciones en el tamaño de las reuniones y requisitos para permanecer en casa. Desde principios de mayo de 2020, varios países han comenzado a levantar algunos de estos NPI, y algunos países han sido testigos de un segundo aumento en el número de casos reportados de COVID-19. En respuesta al resurgimiento, varios países han reintroducido NPI para reducir la transmisión del SARS-CoV-2. Es importante comprender el impacto de la introducción y el levantamiento de estos NPI en la transmisión del SARS-CoV-2. El número de reproducción que varía con el tiempo (R; también conocido como Rt) se define por el número previsto de casos secundarios derivados de un caso primario infectado en el tiempo t. R es una medida importante para medir la transmisibilidad en el tiempo y podría utilizarse para evaluar si las intervenciones actuales parecen ser eficaces o si se requieren intervenciones adicionales. Si R permanece por debajo de 1, entonces las epidemias terminarán por extinguirse; si R está por encima de 1, se esperan epidemias sostenidas.
Studies
in China, Hong Kong, South Korea, Singapore, and many European
countries showed that several NPIs, including school closure, physical
distancing, and lockdown, could reduce R substantially to near or below 1.
However, scant data are available regarding the effects on R
following the relaxation of these NPIs. We aimed to assess the temporal
association between introducing and lifting different NPIs and levels
of SARS-CoV-2 transmission, as measured by R, across 131 countries.
Methods
Data sources
In this modelling study, we included data on country-level estimates of R from the EpiForecasts project by the London School of Hygiene & Tropical Medicine (London, UK).
Briefly, the instantaneous reproduction number is estimated based on
the daily counts of confirmed COVID-19 cases reported by the European
Centre for Disease Prevention and Control. The instantaneous
reproduction number represents the average number of secondary cases
that would arise from a primary case infected at a given time if the
conditions remained identical after that time, and thus measures the
instantaneous transmissibility.
The modelling framework accounts for reporting delay between symptom
onset and case notification, right truncation of notification dates, and
the delay between onset and infection based on empirical data to ensure
that temporal variations in R can be compared directly with the times at which NPIs were implemented.
OxCGRT was established by a dedicated team of public policy and
governance experts, who collect publicly available information on
indicators of government response. In OxCGRT, NPIs are grouped into the
following eight categories: closure of schools, closure of workplaces,
public events bans (eg, sports, festive, and religious events),
restrictions on the size of gatherings, closure of public transport,
stay at home orders, restrictions on internal movement, and restrictions
on international travel. Country-specific information on each of the
NPIs is available on a daily basis (since Jan 1, 2020). We also included
data on testing policy and contact tracing of each country from OxCGRT
for sensitivity analyses.
Data processing
We linked the R and NPI datasets by country and date to generate our working dataset, which contains a time series of daily R
estimates and the status of the eight NPIs for 131 countries between
Jan 1 and July 20, 2020. Details on the start and end dates of our
working dataset for each country are available in the appendix (pp 2–4).
The
original variables for NPIs in the OxCGRT dataset were ordinal, ranging
from “no intervention” (0 points), to “recommend intervention” (1
point), and then to “require intervention” (2 points). For this study,
we converted these NPI variables to a binary variable by merging the
variables “no intervention” and “recommend intervention” to increase the
statistical power of the analysis. Details of the conversion of each
NPI variable are available in the appendix (pp 5–6).
Data analysis
We
defined a phase as a time period when all of the eight NPIs remained
the same, and we divided the timeline of each country into individual
phases based on the status of NPIs. We first described the duration of
phases, the frequency of introducing and lifting each NPI, and the
temporal order of introducing and lifting each NPI. For each phase, we
defined Rday i as the R of the ith day of that phase (ie, since the NPI status changed) and defined Rday 0 as the R of the last day of its previous phase (ie, before the NPI status changed). As the effect of NPIs on transmission (measured as R) is expected to be relative to its original level, we calculated the R ratio between Rday i and Rday 0 as a measure of the degree of association of introducing and lifting an NPI (or NPIs) with the transmission of SARS-CoV-2 (figure 1). An R ratio of more than 1 indicates an increase in transmission since the change in the NPI (or NPIs), and an R
ratio of less than 1 indicates a decrease in transmission. On the basis
of the change of NPIs between two neighbouring phases and the
corresponding R ratio, we were able to assess the effect of introducing or lifting each of the NPIs.
Definimos una fase como un período de tiempo en el que los ocho NPI se mantuvieron iguales, y dividimos la línea de tiempo de cada país en fases individuales basadas en la situación de los NPI. Primero describimos la duración de las fases, la frecuencia de introducción y levantamiento de cada NPI, y el orden temporal de introducción y levantamiento de cada NPI. Para cada fase, definimos el día i como la R del día i de esa fase (es decir, desde que cambió el estado de los NPI) y definimos el día 0 como la R del último día de su fase anterior (es decir, antes de que cambiara el estado de los NPI). Como se espera que el efecto de los NPI en la transmisión (medido como R) sea relativo a su nivel original, calculamos la relación R entre el día i y el día 0 como medida del grado de asociación de la introducción y el levantamiento de un NPI (o NPI) con la transmisión del SARS-CoV-2 (figura 1). Una relación R superior a 1 indica un aumento de la transmisión desde el cambio del NPI (o NPI), y una relación R inferior a 1 indica una disminución de la transmisión. Sobre la base del cambio de NPI entre dos fases vecinas y la correspondiente relación R, pudimos evaluar el efecto de la introducción o el levantamiento de cada uno de los NPI
Figure 1Schematic presenting calculation of the R ratio
In the main analysis, we modelled the R
ratio using a log-linear regression, with the following equation, for
each day of the first 28 days following the change in the corresponding
NPI (ie, a total of 28 separate models):
where Yt represents the R ratio on day t (t=1, 2, …, 28);are binary indicators of whether each of the eight NPIs are introduced and lifted, respectively; andare binary indicators of whether multiple NPIs are introduced and lifted simultaneously, respectively. Hence,represents the baseline change in R on day t in the absence of changes in NPI status;represent the individual effects of introducing and lifting NPIs on day t, respectively; andrepresent
the interaction between introducing and lifting, respectively, multiple
NPIs as they are introduced and lifted simultaneously. No days beyond
the first 28 days following the change were included due to limited data
availability.
On the basis of the
model estimates, for each NPI, we calculated the time in days needed to
reach 60% of its maximum effect (measured by the R ratio, which
was required to be <0·95 or >1·05) in the first 28 days as a
measure of immediacy. Furthermore, we modelled the total visits to
workplaces and the total time spent in residential areas using Google
mobility data by applying the same regression model as for the main
analysis among 101 countries (details in appendix p 7). We compared the immediacy results of introducing and lifting workplace closure between using the R
ratio and using total visits to workplaces. We also compared the
immediacy results of introducing and lifting requirements to stay at
home between using the R ratio and using the total time spent in residential areas.
We
did a series of sensitivity analyses. First, we replaced the NPI of a
ban on gatherings of more than ten people with a ban on gatherings of
more than 100 people in the model to understand how limiting public
gatherings of different sizes could affect the transmission. Second, we
presented the effect of individual NPIs by only including phases in
which just one NPI was changed. Third, we used a different comparator,
the mean R for the 7 days before NPI status change (rather than R for the day before NPI status change), when calculating the R
ratio. Fourth, we excluded early phases in which the country's first
NPI was introduced. Fifth, we excluded large countries that could have
greater regional variability in NPI policies: Brazil, Canada, China,
India, Russia, and the USA. Sixth, we did 20 sets of analyses, each of
which randomly excluded ten countries from the dataset, to understand
how our estimates had been affected by possible outliers. Seventh, we
included only the phases with comprehensive testing (defined as the
requirement to test anyone with COVID-19 symptoms) in the analysis,
since testing practice could affect the estimate of R. Eighth,
we included only the phases with comprehensive contact tracing (defined
as the requirement to trace contacts for all COVID-19 cases) to
understand how contact tracing could modify the effect of NPIs in our
model.
In addition, based on the
modelled effect of individual NPIs from our main analysis, we did an
ad-hoc analysis to estimate the effect of reintroducing multiple NPIs
(those with the greatest effects and following the observed sequence of
introducing NPIs) to tackle the possible resurgence of SARS-CoV-2. We
considered four candidate strategies for the reintroduction: candidate 1
included a ban on public events and gatherings of more than ten people;
candidate 2 included workplace closure as well as a ban on public
events and gatherings of more than ten people; candidate 3 included
workplace closure, a ban on public events and gatherings of more than
ten people, and internal movement limits; and candidate 4 included
school and workplace closure, a ban on public events and gatherings of
more than ten people, internal movement limits, and requirements to stay
at home.
All data analyses and data visualisation were done in the R software (version 3.6.1). The R codes and the corresponding working dataset used for the analyses are available in GitHub.
Role of the funding source
The
funders of the study had no role in study design, data collection, data
analysis, data interpretation, writing of the manuscript, or the
decision to submit for publication. All authors had full access to all
the data in the study and were responsible for the decision to submit
the manuscript for publication.
Results
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).
Se incluyeron en el análisis 790 fases de 131 países (véase el apéndice, págs. 8 a 40, para obtener detalles sobre las estimaciones diarias de R y la situación del NPI en cada país). La duración media de las fases fue de 11 días (IQR 3-27), observándose la duración media más breve en las fases en que se introdujeron el cierre de escuelas (3 días [1-8]) y la prohibición de actos públicos (4 días [2-7]) (apéndice pág. 41). Los requisitos para permanecer en el hogar y las restricciones a los movimientos internos fueron los NPI más comunes que se introdujeron, y fueron introducidos y levantados con mayor frecuencia de manera simultánea (figura 2). En cuanto a la secuencia temporal de la introducción y el levantamiento de los NPI, el cierre de escuelas y las prohibiciones de eventos públicos fueron los dos primeros NPI introducidos y se levantaron más tarde que la mayoría de los NPI. Los requisitos de permanecer en el hogar y el cierre del transporte público fueron los dos últimos NPI introducidos y se levantaron antes que la mayoría de los NPI (figura 2).
Figure 2Frequency (A) and order (B) of introducing and lifting NPIs
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).
Figure 3Change over time in the R ratio following the introduction and relaxation of individual NPIs
Table 1Change in the R ratio over time on day 7, day 14, and day 28 after the introduction and relaxation of each NPI
Day 7
Day 14
Day 28
School closure
Introduction
0·89 (0·82–0·97)
0·86 (0·72–1·02)
0·85 (0·66–1·10)
Relaxation
1·05 (0·96–1·14)
1·18 (1·02–1·36)
1·24 (1·00–1·52)
Workplace closure
Introduction
0·89 (0·83–0·96)
0·89 (0·78–1·02)
0·87 (0·73–1·03)
Relaxation
1·04 (0·97–1·13)
1·10 (0·97–1·24)
1·01 (0·83–1·25)
Public events ban
Introduction
0·90 (0·82–0·99)
0·83 (0·68–1·00)
0·76 (0·58–1·00)
Relaxation
1·02 (0·93–1·11)
1·07 (0·92–1·24)
1·21 (0·97–1·50)
Ban on gatherings of more than ten people
Introduction
0·93 (0·87–0·99)
0·98 (0·87–1·10)
0·97 (0·83–1·14)
Relaxation
0·99 (0·93–1·06)
1·07 (0·96–1·20)
1·25 (1·03–1·51)
Public transport closure
Introduction
0·97 (0·91–1·04)
0·98 (0·87–1·11)
0·99 (0·84–1·18)
Relaxation
1·00 (0·93–1·07)
1·08 (0·96–1·22)
1·04 (0·85–1·27)
Requirements to stay at home
Introduction
0·90 (0·85–0·97)
0·89 (0·79–1·00)
0·97 (0·83–1·14)
Relaxation
0·97 (0·91–1·03)
1·02 (0·92–1·13)
1·11 (0·94–1·32)
Internal movement limits
Introduction
0·97 (0·90–1·03)
0·97 (0·87–1·10)
0·93 (0·79–1·10)
Relaxation
0·98 (0·92–1·04)
1·06 (0·95–1·18)
1·13 (0·94–1·37)
International travel limits
Introduction
0·89 (0·81–0·98)
0·97 (0·81–1·16)
1·08 (0·85–1·38)
Relaxation
0·95 (0·84–1·07)
1·02 (0·81–1·28)
0·98 (0·68–1·40)
Data are R ratio (95% CI). For each NPI, the reference period is the day before introduction or relaxation of that NPI. An R ratio of more than 1 indicates increased transmission, and an R ratio of less than 1 indicates decreased transmission. NPI=non-pharmaceutical intervention. R=time-varying reproduction number.
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).
Figure 4Change over time in the R ratio following the introduction and relaxation of a ban on public gatherings of different sizes
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).
Table 2Modelled change in the R ratio over time on day 7, day 14, and day 28 after the introduction of different composites of NPIs
Day 7
Day 14
Day 28
Candidate 1: ban on public events and gatherings of more than ten people
0·94 (0·85–1·03)
0·87 (0·73–1·05)
0·71 (0·55–0·93)
Candidate 2: workplace closure plus ban on public events and gatherings of more than ten people
0·84 (0·76–0·93)
0·78 (0·64–0·94)
0·62 (0·47–0·82)
Candidate 3: Workplace closure plus ban on public events and gatherings of more than ten people plus internal movement limits
0·81 (0·71–0·92)
0·76 (0·60–0·95)
0·58 (0·41–0·81)
Candidate
4: School and workplace closure plus ban on public events and
gatherings of more than ten people plus internal movement limits plus
stay at home requirement
0·65 (0·54–0·78)
0·58 (0·42–0·78)
0·48 (0·32–0·71)
Data are R ratio (95% CI). The reference period is the day before introduction of an NPI. An R ratio of more than 1 indicates increased transmission, and an R ratio of less than 1 indicates decreased transmission. NPI=non-pharmaceutical intervention. R=time-varying reproduction number.
To
the best of our knowledge, this study is the first to assess the
temporal association between changing the status of a range of NPIs and
the transmission of SARS-CoV-2, as measured by R, for all
countries for which data were available. On the basis of the empirical
data from 131 countries, we found that individual NPIs, including school
closure, workplace closure, public events bans, requirements to stay at
home, and internal movement limits, were associated with reductions in R
of 3–24% on day 28 after their introduction, compared with the day
before their introduction. Reopening schools, lifting bans on public
events, lifting bans on public gatherings of more than ten people,
lifting requirements to stay at home, and lifting internal movement
limits were associated with increases in R of 11–25% on day 28
after the relaxation. The effects of introducing and lifting NPIs were
not immediate; it took around 1 week following the introduction of an
NPI to observe 60% of the maximum reduction in R and even longer (almost 3 weeks) following the relaxation of an NPI to observe 60% of the maximum increase in R.
Our analysis suggests that, in the context of a resurgence of
SARS-CoV-2, a control strategy of banning public events and public
gatherings of more than ten people would be associated with a reduction
in R of 6% on day 7, 13% on day 14, and 29% on day 28; if this strategy also included closing workplaces, the overall reduction in R
would be 16% on day 7, 22% on day 14, and 38% on day 28. These findings
provide additional evidence that can inform policy makers' decisions on
the timing of introducing and lifting different NPIs.
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.
-
Hasta donde sabemos, este estudio es el primero en evaluar la asociación temporal entre el cambio de estado de una serie de NPI y la transmisión del SARS-CoV-2, medido por R, para todos los países de los que se disponía de datos. Sobre la base de los datos empíricos de 131 países, encontramos que los NPI individuales, incluido el cierre de escuelas, el cierre de lugares de trabajo, las prohibiciones de eventos públicos, los requisitos para permanecer en casa y los límites de movimiento interno, se asociaron con reducciones en R de 3 a 24% el día 28 después de su introducción, en comparación con el día anterior a su introducción. La reapertura de las escuelas, el levantamiento de las prohibiciones de eventos públicos, el levantamiento de las prohibiciones de reuniones públicas de más de diez personas, la exigencia de permanecer en casa y el levantamiento de los límites de movimiento interno se asociaron con aumentos en la R de 11-25% el día 28 después de la relajación. Los efectos de la introducción y el levantamiento de NPI no fueron inmediatos; tomó alrededor de 1 semana después de la introducción de un NPI para observar el 60% de la reducción máxima de R y aún más tiempo (casi 3 semanas) después de la relajación de un NPI para observar el 60% del aumento máximo de R. Nuestro análisis sugiere que, en el contexto de un resurgimiento del SARS-CoV-2, una estrategia de control de la prohibición de eventos públicos y reuniones públicas de más de diez personas se asociaría con una reducción de R del 6% en el día 7, del 13% en el día 14, y del 29% en el día 28; si esta estrategia también incluyera el cierre de los lugares de trabajo, la reducción general de R sería del 16% en el día 7, del 22% en el día 14, y del 38% en el día 28. Estos hallazgos proveen evidencia adicional que puede informar las decisiones de los políticos sobre el tiempo de introducción y levantamiento de diferentes NPI. Nuestros hallazgos sobre los efectos de la introducción de las NPI coinciden en líneas generales con los hallazgos del estudio multinacional de Flaxman y sus colegas que evaluó el impacto de diferentes NPI entre 11 países europeos.
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.
Our
analysis demonstrates that the effect of introducing and lifting NPIs
was not immediate and that the time required to reach certain levels of
effect differed by NPI. This finding provides important evidence to
policy makers on when to expect a notable effect from introducing or
lifting an NPI. The observed delay of effect could be explained by
behavioural inertia, which is supported by the similar immediacy results
of NPIs between using R and using Google mobility data.
School
closure was widely adopted previously to control influenza outbreaks
and pandemics, and was shown to reduce and delay peaks of epidemics.
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).
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.
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.
Our
findings also suggest that, within 28 days, lifting public events bans
could increase transmission by 21%, although the finding was not
significant, and lifting bans on gatherings of more than ten people
could increase transmission by 25%, which was the highest increase among
all NPIs. We did not observe a substantial reduction in transmission
after introduction of bans on gatherings of more than ten or more than
100 people, especially for more than 100 people, which showed an
increase in transmission after day 14; possible explanations for this
finding include low adherence and, for the ban on gatherings of more
than 100 people, an increase in smaller-scale gatherings. In addition,
it should be noted that for bans on physical gatherings, we were unable
to further stratify our analysis by indoor versus outdoor settings due
to scarcity of data.
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.
There are some advantages to our study. First, both the method for the R
calculation and the method for recording NPIs remained consistent over
time among different countries, which ensured comparability between
different phases in different countries in our analysis. Second, by
dividing the timeline into different phases according to the changes in
NPIs, we were able to assess the effect of individual NPIs. Third, we
were able to estimate the change in the effect of NPIs over time.
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,
We acknowledge several limitations of the methodology for the R
estimate used in our analysis. First, the adjustment for reporting
delays was only done globally and not specific to each country due to
the scarcity of available data on reporting delays. This could lead to
temporal inaccuracy of R, which could bias our findings on the immediacy of changes in R
associated with NPIs. Nonetheless, our findings on the immediacy of
changes associated with NPIs were consistent with the results of the
analysis using Google mobility data, indicating that the possible
temporal inaccuracy of R might have had little impact on the overall findings. Second, the R
estimate was subject to the specification of parameters (eg, incubation
period and generation time of SARS-CoV-2) in the model and could be
biased upwards or downwards. However, we believe it unlikely that this
bias affected the main findings of our study because we used the R ratio as the output metric (which cancels out all time-invariant elements related to the R estimate). Third, the modelling framework for R
was unable to account for the change over time in eligibility for
testing, method of testing, or case definition in different countries.
This could bias both the R estimate and the R ratio in
our analysis for the dates during which the changes were ongoing. For
example, we are likely to observe an artificial increase in R if a country increases the testing capacity within a short period. Last, the uncertainty range of the national R estimate was based on the number of national reported cases and therefore did not reflect any variations in R within the country.
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.
In
summary, our findings provide additional evidence that can inform
policy makers' decisions on the timing of introducing and lifting
different NPIs. The decisions to reintroduce and relax restrictions
should be informed by various factors, including the capacity and
resilience of the health-care system, and might be best made at
provincial or district rather than national levels in some countries.
Contributors
YL,
HC, and HN conceptualised the study. YL led data acquisition, analysis,
and visualisation. HN, HC, and YL led the data interpretation with
substantial contribution from DK, AH, MN, and XW. YL wrote the draft
report, and all other authors revised the report critically for
important intellectual content. All authors have read and approved the
final version of the report. YL and HN verified the data linkage of two
publicly available datasets and had full access to the linked data.
Declaration of interests
YL
reports grants from WHO, outside the submitted work. HC reports grants
from the Innovative Medicines Initiative, UK National Institute for
Health Research, and Bill & Melinda Gates Foundation, and grants and
personal fees from WHO and Sanofi, outside the submitted work. HN
reports grants from the Innovative Medicines Initiative, WHO, and the
National Institute for Health Research; personal fees from the Bill
& Melinda Gates Foundation, Janssen, and AbbVie; and grants and
personal fees from Sanofi and the Foundation for Influenza Epidemiology,
outside the submitted work. All other authors declare no competing
interests.
This
study was funded by the Wellcome Trust Institutional Strategic Support
Fund and Data-Driven Innovation initiative. We acknowledge Gerry Fowkes
(University of Edinburgh, Edinburgh, UK) from the UNCOVER group for his
comments on the draft report.
Effectiveness
of isolation, testing, contact tracing, and physical distancing on
reducing transmission of SARS-CoV-2 in different settings: a
mathematical modelling study.
Estimating
the time-varying reproduction number of SARS-CoV-2 using national and
subnational case counts [version 1; peer review: awaiting peer review].
Age-related
differences in nasopharyngeal severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) levels in patients with mild to moderate
coronavirus disease 2019 (COVID-19).
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