वुहान, चीन में COVID-19 महामारी के परिणामों पर सामाजिक मिश्रण को कम करने के लिए नियंत्रण रणनीतियों का प्रभाव: एक मॉडलिंग अध्ययन – द लांसेट

वुहान, चीन में COVID-19 महामारी के परिणामों पर सामाजिक मिश्रण को कम करने के लिए नियंत्रण रणनीतियों का प्रभाव: एक मॉडलिंग अध्ययन – द लांसेट

Translating…

Summary

Background

In December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world.

Methods

To examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April).

Findings

Our projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66–97) and 24% (13–90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic.

Interpretation

Restrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R0 and the duration of infectiousness.

Funding

Bill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in the city of Wuhan, Hubei, China, in early December, 2019.

  • Li Q
  • Guan X
  • Wu P
  • et al.

Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia.

  • Zhu N
  • Zhang D
  • Wang W
  • et al.

A novel coronavirus from patients with pneumonia in China, 2019.

Since then, the local and national governments have taken unprecedented measures in response to the coronavirus disease 2019 (COVID-19) outbreak caused by SARS-CoV-2.

  • Chen S
  • Yang J
  • Yang W
  • Wang C
  • Bärnighausen T

COVID-19 control in China during mass population movements at New Year.

Exit screening of passengers was shortly followed by travel restrictions in Wuhan on Jan 23, 2020, halting all means of unauthorised travel into and out of the city. Similar control measures were extended to the entire province of Hubei by Jan 26, 2020.

  • Chen S
  • Yang J
  • Yang W
  • Wang C
  • Bärnighausen T

COVID-19 control in China during mass population movements at New Year.

Non-pharmaceutical physical distancing interventions, such as extended school closures and workplace distancing, were introduced to reduce the impact of the COVID-19 outbreak in Wuhan.

  • Fong MW
  • Gao H
  • Wong JY
  • et al.

Nonpharmaceutical measures for pandemic influenza in nonhealthcare settings—social distancing measures.

Within the city, schools remained closed, Lunar New Year holidays were extended so that people stayed away from their workplaces, and the local government promoted physical distancing and encouraged residents to avoid crowded places. These measures greatly changed age-specific mixing patterns within the population in previous outbreak response efforts for other respiratory infectious diseases.

  • Hens N
  • Ayele GM
  • Goeyvaerts N
  • et al.

Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries.

  • Ahmed F
  • Zviedrite N
  • Uzicanin A

Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review.

Although travel restrictions undoubtedly had a role in reducing exportations of infections outside Wuhan and delayed the onset of outbreaks in other regions,

  • Quilty BJ
  • Clifford S
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  • Eggo RM

Effectiveness of airport screening at detecting travellers infected with novel coronavirus (2019-nCoV).

  • Tian H
  • Li Y
  • Liu Y
  • et al.

Early evaluation of the Wuhan City travel restrictions in response to the 2019 novel coronavirus outbreak.

changes in mixing patterns affected the trajectory of the outbreak within Wuhan itself. To estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, we look at Wuhan, hoping to provide some insights for the rest of the world.

Research in context

Evidence before this study

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China in late 2019. In mid-January, 2020, schools and workplaces closed as part of the Lunar New Year holidays. These closures were then extended to prevent SARS-CoV-2 spread. The intended effect of such physical distancing measures was to reduce person-to-person contact, which spreads infectious diseases. Epidemic parameters, such as time-dependent reproduction numbers governing SARS-CoV-2 transmission in Wuhan, have been estimated based on local and internationally exported cases. The frequency of contacts in different age groups and locations (schools, workplaces, households, and others) in China has also been previously estimated. We searched PubMed and medRxiv for studies published in English up to March 7, 2020, with the terms “coronavirus AND (school OR work) AND (Wuhan OR Hubei)” and identified 108 and 130 results, respectively. However, to our knowledge, no published article has reported use of location-specific transmission models that consider the impacts of school or workplace closures to study the spread of SARS-CoV-2 in Wuhan.

Added value of this study

We built an age-specific and location-specific transmission model to assess progression of the Wuhan outbreak under different scenarios of school and workplace closure. We found that changes to contact patterns are likely to have substantially delayed the epidemic peak and reduced the number of coronavirus disease 2019 (COVID-19) cases in Wuhan. If these restrictions are lifted in March, 2020, a second peak of cases might occur in late August, 2020. Such a peak could be delayed by 2 months if the restrictions were relaxed a month later, in April, 2020.

Implications of all the available evidence

The measures put in place to reduce contacts in school and work are helping to control the COVID-19 outbreak by affording health-care systems time to expand and respond. Authorities need to carefully consider epidemiological and modelling evidence before lifting these measures to mitigate the impact of a second peak in cases.

Person-to-person transmission is mostly driven by who interacts with whom,

  • Riou J
  • Althaus CL

Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020.

  • Chan JFW
  • Yuan S
  • Kok KH
  • et al.

A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster.

which can vary by age and location of the contact (ie, school, work, home, and community). Under the context of a large-scale ongoing outbreak, contact patterns would drastically shift from their baseline conditions. In the COVID-19 outbreak in Wuhan, physical distancing measures, including but not limited to school and workplace closures and health promotions that encourage the general public to avoid crowded places, are designed to drastically shift social mixing patterns and are often used in epidemic settings.

  • Fong MW
  • Gao H
  • Wong JY
  • et al.

Nonpharmaceutical measures for pandemic influenza in nonhealthcare settings—social distancing measures.

Although contact patterns can be inferred from reported social contact data that include information on which setting the contact took place in, such studies are often focused on high-income countries,

  • Mossong J
  • Hens N
  • Jit M
  • et al.

Social contacts and mixing patterns relevant to the spread of infectious diseases.

or particular high-density areas.

  • Zhang J
  • Klepac P
  • Read JM
  • et al.

Patterns of human social contact and contact with animals in Shanghai, China.

This limitation can be addressed by quantifying contact patterns in the home, school, work, and other locations across a range of countries based on available information from household-level data and local population demographic structures.

  • Prem K
  • Cook AR
  • Jit M

Projecting social contact matrices in 152 countries using contact surveys and demographic data.

To examine how these changes in population mixing have affected the outbreak progression, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak,

  • Li Q
  • Guan X
  • Wu P
  • et al.

Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia.

  • Riou J
  • Althaus CL

Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020.

  • Kucharski AJ
  • Russell TW
  • Diamond C
  • et al.

Early dynamics of transmission and control of COVID-19: a mathematical modelling study.

  • Abbott S
  • Hellewell J
  • Munday J
  • Funk S

The transmissibility of novel Coronavirus in the early stages of the 2019–20 outbreak in Wuhan: exploring initial point-source exposure sizes and durations using scenario analysis.

  • Backer JA
  • Klinkenberg D
  • Wallinga J

Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20–28 January 2020.

we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model

  • Klepac P
  • Pomeroy LW
  • Bjørnstad ON
  • Kuiken T
  • Osterhaus ADME
  • Rijks JM

Stage-structured transmission of phocine distemper virus in the Dutch 2002 outbreak.

  • Klepac P
  • Caswell H

The stage-structured epidemic: linking disease and demography with a multi-state matrix approach model.

for several physical distancing measures.

Methods

 SEIR model

We simulated the outbreak in Wuhan using a deterministic stage-structured SEIR model over a 1 year period, during which the modelled outbreak peters out. An implication of this approach is that all demographic changes in the population (ie, births, deaths, and ageing) are ignored.

We divided the population according to the infection status into susceptible (S), exposed (E), infected (I), and removed (R) individuals, and according to age into 5-year bands until age 70 years and a single category aged 75 and older (resulting in 16 age categories). Susceptible individuals might acquire the infection at a given rate when they come in contact with an infectious person and enter the exposed disease state before they become infectious and later either recover or die. We assumed Wuhan to be a closed system with a constant population size of 11 million (ie, S + E + I + R=11 million) throughout the course of this epidemic. We used the SEIR model presented in

figure 1

. The age-specific mixing patterns of individuals in age group

i

alter their likelihood of being exposed to the virus given a certain number of infectious people in the population. Additionally, we incorporated contributions of asymptomatic and subclinical cases; however, the question of whether such individuals are able to transmit infection remains unresolved at the time of writing, although evidence suggests that they are likely to.

  • Liu Y
  • Funk S
  • Flasche S

The contribution of pre-symptomatic transmission to the COVID-19 outbreak. Centre for Mathematical Modelling of Infectious Disease Repository.

We also considered a scenario in which we assumed that younger individuals are more likely to be asymptomatic (or subclinical) and less infectious than older individuals.

  • Bi Q
  • Wu Y
  • Mei S
  • et al.

Epidemiology and transmission of COVID-19 in Shenzhen China: analysis of 391 cases and 1286 of their close contacts.

  • Davies N

nicholasdavies/ncov-age-dist.

Figure thumbnail gr1

Figure 1Age-structured SEIR model and details of the modelled physical distancing interventions

According to infection status, we divided the population into susceptible (S), exposed (E), infected (I), and removed (R) individuals. An infected individual in an age group can be clinical (Ic) or subclinical (Isc), and ρi refers to the probability that an individual is symptomatic or clinical. The age-specific mixing patterns of individuals in age group i, Ci,j, alter their likelihood of being exposed to the virus given a certain number of infected individuals in the population. Younger individuals are more likely to be asymptomatic and less infectious, ie, subclinical. When ρi=0 for all i, the model simplifies to a standard SEIR. The force of infection φi,t is given by (βΣjCi,jICj,t+αβΣjCi,jISCj,t, where β is the transmission rate and α is the proportion of transmission that resulted from a subclinical individual. SEIR= susceptible-exposed-infected-removed.

For a given age group i, epidemic transitions can be described by

Si,t+1=Si,tβSi,tj=1nCi,jIj,tcαβi,tj=1nCi,jIj,tsc

Ei,t+1=βj=1nCi,jIj,tc+αβi,tj=1nCi,jIj,tsc(1κ)Ei,t

Ij,t+1=ρiκEi,t+(1γ)Ij,tc

Ij,t+1=(1ρi)κEi,t+(1γ)Ij,tsc

Ri,t+1=Ri,t+γIj,t+1c+γIj,t+1sc

Where β is the transmission rate (scaled to the right value of R0), Ci,j describe the contacts of age group j made by age group i, κ=1-exp(–1/dL) is the daily probability of an exposed individual becoming infectious (with dL being the average incubation period), and γ=1–exp(–1/dI) is the daily probability that an infected individual recovers when the average duration of infection is dI. We also incorporated contributions of asymptomatic and subclinical cases, 1–ρi denotes the probability of an infected case being asymptomatic or subclinical. We assumed that younger individuals are more likely to be asymptomatic (or subclinical) and less infectious (proportion of infectiousness compared to Ic, α).

Using parameters from the literature as presented in the

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