Study |
Study design |
Objectives |
Population, n, Country |
Interventions |
Risk
of bias |
Cohort studies |
Cohort studies |
Cohort studies |
Cohort studies |
Cohort studies |
Cohort
studies |
Hsieh et al., 2005 29
|
Retrospective cohort study
|
To evaluate the effectiveness of quarantine in reducing the time from
onset to diagnosis and the time from diagnosis to classification
|
SARS positive patients, previously quarantined or not quarantined during
the 2003 outbreak
n=819
Taiwan
|
Quarantine of individuals who were in close contact with infected
persons
|
Moderate (no control for confounding)
|
Pang et al., 2003 26
|
Retrospective cohort study
|
To describe and evaluate measures undertaken to control the SARS
outbreak: quarantine among other things
|
Individual with close contact to a SARS patients who were quarantined
n= 30,000
Beijing
|
Quarantine of individuals who were in close contact with infected
persons
|
Moderate (no control for confounding)
|
Park et al., 2020 27
|
Prospective cohort study
|
To evaluate the effects of different quarantine strategies on the
prevention and rate of secondary viral transmission
|
Patients from 3 hemodialysis units exposed to MERS, during the 2015
outbreak
n=116
Korea
|
Quarantine of exposed individuals
|
Serious (no control for confounding; selection bias)
|
Wang et al., 2007 28
|
Retrospective cohort study
|
To identify risk factors for the development of SARS among quarantined
persons
|
Individuals with known or suspected (travelers coming from SARS-affected
areas) exposure to infected people during the 2003 outbreak
n=147,526
Taiwan
|
Quarantine of known or suspected exposed individuals
|
Moderate (no control for confounding)
|
Modelling studies |
Modelling studies |
Modelling studies |
Modelling studies |
Modelling studies |
Modelling
studies |
Study |
Type of model |
Objectives |
Data source, setting, n |
Interventions |
Quality |
Becker et al., 2005 30
|
Transmission model
|
To determine to which extent the interventions reduce the effective
reproduction number and which intervention requirements are necessary to
achieve elimination of the disease
|
Data from SARS outbreak 2003 in Singapore and Hong Kong and the
Australian census 2001
n=NR
|
closing schools
contact tracing
isolation
quarantine
measures to avoid exposure (e.g., wearing masks, reducing
hand-to-mouth contacts)
|
Minor concerns
|
Chau et al., 2003 31
|
Back-projection method
|
To estimate the SARS infection curve and assess the effectiveness of
interventions
|
Data from the SARS outbreak in Hong Kong;
March 1, 2003 to June 24, 2003
n=NR
|
disinfection of infected areas
isolation
quarantine
protective equipment in hospitals
|
Major concerns
|
Day et al., 2006 30,32
|
Probabilistic model
|
To determine which factors make quarantine an effective control measure
for some diseases but not for others
|
Data based on other mathematical models and epidemiological studies of
SARS
n=NR
|
quarantine
|
No concerns
|
Fraser et al., 2004
33
|
Model of infectious disease outbreak dynamics
|
To identify the general properties of emerging infectious agents that
determine the likely success of isolating symptomatic individuals and
tracing and quarantining their contacts
|
Data based on other mathematical models, the analysis of clinical
patient records and case studies of 4 known pathogens: SARS, HIV,
pandemic influenza, smallpox;
n=NR
|
100% effective isolation of symptomatic patients
90% effective isolation
75% effective isolation
100% effective isolation with 100% effective contact tracing
90% effective isolation with 100% effective contact tracing
75% effective isolation with 100% effective contact tracing
|
Major concerns
|
Gumel et al., 2004 34
|
Deterministic model
|
To examine the impact of isolation and quarantine on the control of SARS
and cumulative deaths
|
Data from WHO and epidemiological studies (outbreaks in Toronto,
Beijing, Hong Kong, Singapore)
n=NR
|
isolation
average quarantine
|
Minor concerns
|
Gupta et al., 2005 35
|
Mathematic and health economic model
|
To estimate the economic effects of an epidemic, the number of averted
infections, the direct and indirect costs of quarantine, and the total
savings
|
Data from other researchers, the popular press, and interviews about the
SARS outbreak in Toronto 2003
n=NR
|
isolation and treatment of infected people without quarantine
quarantine implemented early on
|
Major concerns
|
Hsieh et al., 2007 36
|
Susceptible–infective–removal model with additional
compart-ments
|
To assess the impact of quarantine on prevented additional SARS cases
and additional deaths
|
Data from Taiwan Center for Disease Control, SARS database (SARS
outbreak in Taiwan 2003)
n=151,460
|
Quarantine of individuals who were in close contact with infected
persons
Quarantine of travelers coming from SARS-affected areas
|
Minor concerns
|
Lloyd-Smith et al., 2003 37
|
Stochastic model
|
To address the relative benefits of case isolation, quarantine,
hospital-wide contact precautions and reduced HCW (health care
workes)-community mixing
|
Data source = NR
n= 100,000 individuals and a hospital of 3000 individuals
|
contact tracing
isolation
quarantine
|
Minor concerns
|
Mubayi et al., 2010 38
|
Dynamical model, cost-effectiveness model
|
To compare 3 different quarantine strategies implemented alongside a
single isolation strategy, with resource allocation modeled in terms of
simple cost functions
|
Data from SARS outbreaks in Hong Kong (census data from 2001–2004 in
Hong Kong City) and related studies
n=NR
|
three contact-tracing strategies
isolation
|
No concerns
|
Nishiura et al., 2004 39
|
Deterministic mathematical model
|
To predict the epidemiological outcomes and assess the effect of any
specified control strategy on SARS
|
Data from SARS outbreak in Hong Kong and epidemiological data from other
countries
n=NR
|
isolation
quarantine
precautionary public health measures
|
Major concerns
|
Peak et al., 2017 40
|
Agent-based branching model
|
To identify which disease characteristics and intervention attributes
are most critical in deciding between quarantine and symptom monitoring
and to provide a general framework for understanding the consequences of
isolation policies during emerging epidemics
|
Data from other case studies
n=NR
|
contact tracing
isolation
quarantine
symptom monitoring
|
Minor concerns
|
Pourbohloul et al., 2005 41
|
Urban contact network model
|
To assess a population’s vulnerability to an infectious disease based on
the structure of the network and on the average transmissibility of the
disease
|
Publicly available data from sources such as Statistics Canada.
n=10,308 (2,000 households)
|
face masks
closing public venues
isolation
quarantine
vaccination
|
No concerns
|
Wang et al., 2004 42,
|
General, deterministic model
|
To predict future incidence and simulate the impact of additional
control strategies by studying the transmission dynamics of the spread
of SARS in Beijing
|
Daily reported cases by the Ministry of Health of the People’s Republic
of China.
n=NR
|
6 subpopulations
susceptible
exposed
quarantine
suspect
probable
removed
|
Minor concerns
|
Wen-Tao et al., 2020 45
|
Susceptible–infected–recovered model
|
To predict the outcome of prevention and control measures of diverse
intensity in Wuhan
|
Official data from COVID-19 outbreak in Wuhan.
n=1·5 million inhabitants of Wuhan
|
Combination and different intensity of
the cessation of public transportation
the recommendation to citizens to stay at home
the isolation of confirmed and suspected patients
|
Minor concerns
|
Yip et al., 2007 43
|
Back-projection method
|
To reconstruct the infection curve for the 2003 SARS epidemic in Taiwan
and ascertain the temporal changes in the mean daily number of
infections that occurred during the course of the outbreak
|
Taiwan Center for Disease Control and the World Health Organization
n=NR
|
Quarantine of people who potentially had contact with infectious
individuals
Quarantine of travelers coming from SARS-affected areas
|
Major concerns
|
Yue et al., 2020 46
|
Dynamic infectious disease model
|
To develop a model to predict the future trend of the epidemic,
introducing a quarantine rate parameter to the model
|
Numbers of confirmed cases and cures published by the Chinese National
Health Committee
n=NR
|
Different extent of combined control measures
|
Minor concerns
|
Zhang et al., 2017 44
|
Transmission dynamics mode
|
To estimate the transmissibility of MERS and identify the effective
countermeasures that stopped its spread
|
Outbreak data released by Korea Centers for Disease Control and
Prevention.
n=NR
|
isolation
quarantine
|
No concerns
|