Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts
Identifieur interne : 000642 ( 2020/Analysis ); précédent : 000641; suivant : 000643Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts
Auteurs : Joel Hellewell [Royaume-Uni] ; Sam Abbott [Royaume-Uni] ; Amy Gimma [Royaume-Uni] ; Nikos I. Bosse [Royaume-Uni] ; Christopher I. Jarvis [Royaume-Uni] ; Timothy W. Russell [Royaume-Uni] ; James D. Munday [Royaume-Uni] ; Adam J. Kucharski [Royaume-Uni] ; W John Edmunds [Royaume-Uni] ; Sebastian Funk [Royaume-Uni] ; Rosalind M. Eggo [Royaume-Uni]Source :
- The Lancet. Global Health [ 2214-109X ] ; 2020.
Descripteurs français
- KwdFr :
- Coronavirus (pathogénicité), Flambées de maladies (), Humains, Infections à coronavirus (), Infections à coronavirus (diagnostic), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Isolement du patient, Pneumopathie virale (), Pneumopathie virale (diagnostic), Pneumopathie virale (transmission), Pneumopathie virale (épidémiologie), Traçage des contacts, Études de faisabilité.
- MESH :
- diagnostic : Infections à coronavirus, Pneumopathie virale.
- pathogénicité : Coronavirus.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Flambées de maladies, Humains, Infections à coronavirus, Isolement du patient, Pneumopathie virale, Traçage des contacts, Études de faisabilité.
English descriptors
- KwdEn :
- Betacoronavirus, Contact Tracing, Coronavirus (pathogenicity), Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Disease Outbreaks (prevention & control), Feasibility Studies, Humans, Patient Isolation, Pneumonia, Viral (diagnosis), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission).
- MESH :
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- pathogenicity : Coronavirus.
- prevention & control : Coronavirus Infections, Disease Outbreaks, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Betacoronavirus, Contact Tracing, Feasibility Studies, Humans, Patient Isolation.
Abstract
Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.
We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (
Simulated outbreaks starting with five initial cases, an
In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.
Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.
Url:
DOI: 10.1016/S2214-109X(20)30074-7
PubMed: 32119825
PubMed Central: 7097845
Affiliations:
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PMC:7097845Le document en format XML
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<term>Contact Tracing</term>
<term>Coronavirus (pathogenicity)</term>
<term>Coronavirus Infections (diagnosis)</term>
<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (prevention & control)</term>
<term>Coronavirus Infections (transmission)</term>
<term>Disease Outbreaks (prevention & control)</term>
<term>Feasibility Studies</term>
<term>Humans</term>
<term>Patient Isolation</term>
<term>Pneumonia, Viral (diagnosis)</term>
<term>Pneumonia, Viral (epidemiology)</term>
<term>Pneumonia, Viral (prevention & control)</term>
<term>Pneumonia, Viral (transmission)</term>
</keywords>
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<term>Flambées de maladies ()</term>
<term>Humains</term>
<term>Infections à coronavirus ()</term>
<term>Infections à coronavirus (diagnostic)</term>
<term>Infections à coronavirus (transmission)</term>
<term>Infections à coronavirus (épidémiologie)</term>
<term>Isolement du patient</term>
<term>Pneumopathie virale ()</term>
<term>Pneumopathie virale (diagnostic)</term>
<term>Pneumopathie virale (transmission)</term>
<term>Pneumopathie virale (épidémiologie)</term>
<term>Traçage des contacts</term>
<term>Études de faisabilité</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="pathogenicity" xml:lang="en"><term>Coronavirus</term>
</keywords>
<keywords scheme="MESH" qualifier="pathogénicité" xml:lang="fr"><term>Coronavirus</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Coronavirus Infections</term>
<term>Disease Outbreaks</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Betacoronavirus</term>
<term>Contact Tracing</term>
<term>Feasibility Studies</term>
<term>Humans</term>
<term>Patient Isolation</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Flambées de maladies</term>
<term>Humains</term>
<term>Infections à coronavirus</term>
<term>Isolement du patient</term>
<term>Pneumopathie virale</term>
<term>Traçage des contacts</term>
<term>Études de faisabilité</term>
</keywords>
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<front><div type="abstract" xml:lang="en"><title>Summary</title>
<sec><title>Background</title>
<p>Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.</p>
</sec>
<sec><title>Methods</title>
<p>We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (<italic>R</italic>
<sub>0</sub>
), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.</p>
</sec>
<sec><title>Findings</title>
<p>Simulated outbreaks starting with five initial cases, an <italic>R</italic>
<sub>0</sub>
of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when <italic>R</italic>
<sub>0</sub>
was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an <italic>R</italic>
<sub>0</sub>
of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for <italic>R</italic>
<sub>0</sub>
of 2·5 more than 70% of contacts had to be traced, and for an <italic>R</italic>
<sub>0</sub>
of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when <italic>R</italic>
<sub>0</sub>
was 1·5. For <italic>R</italic>
<sub>0</sub>
values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.</p>
</sec>
<sec><title>Interpretation</title>
<p>In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.</p>
</sec>
<sec><title>Funding</title>
<p>Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.</p>
</sec>
</div>
</front>
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