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1.
J Theor Biol ; 535: 110983, 2022 02 21.
Article in English | MEDLINE | ID: covidwho-1568886

ABSTRACT

During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) including school closures, workplace closures and social distancing policies have been employed worldwide to reduce transmission and prevent local outbreaks. However, transmission and the effectiveness of NPIs depend strongly on age-related factors including heterogeneities in contact patterns and pathophysiology. Here, using SARS-CoV-2 as a case study, we develop a branching process model for assessing the risk that an infectious case arriving in a new location will initiate a local outbreak, accounting for the age distribution of the host population. We show that the risk of a local outbreak depends on the age of the index case, and we explore the effects of NPIs targeting individuals of different ages. Social distancing policies that reduce contacts outside of schools and workplaces and target individuals of all ages are predicted to reduce local outbreak risks substantially, whereas school closures have a more limited impact. In the scenarios considered here, when different NPIs are used in combination the risk of local outbreaks can be eliminated. We also show that heightened surveillance of infectious individuals reduces the level of NPIs required to prevent local outbreaks, particularly if enhanced surveillance of symptomatic cases is combined with efforts to find and isolate nonsymptomatic infected individuals. Our results reflect real-world experience of the COVID-19 pandemic, during which combinations of intense NPIs have reduced transmission and the risk of local outbreaks. The general modelling framework that we present can be used to estimate local outbreak risks during future epidemics of a range of pathogens, accounting fully for age-related factors.


Subject(s)
COVID-19 , SARS-CoV-2 , Disease Outbreaks/prevention & control , Humans , Pandemics , Quarantine
2.
Am J Epidemiol ; 190(4): 611-620, 2021 04 06.
Article in English | MEDLINE | ID: covidwho-1447566

ABSTRACT

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.


Subject(s)
Disease Outbreaks/statistics & numerical data , Infections/epidemiology , Basic Reproduction Number , Global Health , Humans , Morbidity/trends , Software
3.
Stat Methods Med Res ; : 9622802211037079, 2021 Sep 27.
Article in English | MEDLINE | ID: covidwho-1438210

ABSTRACT

Since the beginning of the COVID-19 pandemic, the reproduction number R has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, R is defined as the average number of secondary infections caused by one primary infected individual. R seems convenient, because the epidemic is expanding if R>1 and contracting if R<1. The magnitude of R indicates by how much transmission needs to be reduced to control the epidemic. Using R in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of R but many, and the precise definition of R affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined R, there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate R vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when R is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of R, and the data and methods used to estimate it, can make R a more useful metric for future management of the epidemic.

4.
Int J Infect Dis ; 110: 15-20, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1340673

ABSTRACT

OBJECTIVES: A hospital-related cluster of 22 cases of coronavirus disease 2019 (COVID-19) occurred in Taiwan in January-February 2021. Rigorous control measures were introduced and could only be relaxed once the outbreak was declared over. Each day after the apparent outbreak end, we estimated the risk of future cases occurring in order to inform decision-making. METHODS: Probabilistic transmission networks were reconstructed, and transmission parameters (the reproduction number R and overdispersion parameter k) were estimated. The reporting delay during the outbreak was estimated (Scenario 1). In addition, a counterfactual scenario with less effective interventions characterized by a longer reporting delay was considered (Scenario 2). Each day, the risk of future cases was estimated under both scenarios. RESULTS: The values of R and k were estimated to be 1.30 ((95% credible interval (CI) 0.57-3.80) and 0.38 (95% CI 0.12-1.20), respectively. The mean reporting delays considered were 2.5 days (Scenario 1) and 7.8 days (Scenario 2). Following the final case, ttthe inferred probability of future cases occurring declined more quickly in Scenario 1 than Scenario 2. CONCLUSIONS: Rigorous control measures allowed the outbreak to be declared over quickly following outbreak containment. This highlights the need for effective interventions, not only to reduce cases during outbreaks but also to allow outbreaks to be declared over with confidence.


Subject(s)
COVID-19 , SARS-CoV-2 , Contact Tracing , Disease Outbreaks , Hospitals , Humans , Quarantine , Taiwan/epidemiology
5.
R Soc Open Sci ; 8(7): 210530, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1316856

ABSTRACT

As a countermeasure to the SARS-CoV-2 pandemic, there has been swift development and clinical trial assessment of candidate vaccines, with subsequent deployment as part of mass vaccination campaigns. However, the SARS-CoV-2 virus has demonstrated the ability to mutate and develop variants, which can modify epidemiological properties and potentially also the effectiveness of vaccines. The widespread deployment of highly effective vaccines may rapidly exert selection pressure on the SARS-CoV-2 virus directed towards mutations that escape the vaccine-induced immune response. This is particularly concerning while infection is widespread. By developing and analysing a mathematical model of two population groupings with differing vulnerability and contact rates, we explore the impact of the deployment of vaccines among the population on the reproduction ratio, cases, disease abundance and vaccine escape pressure. The results from this model illustrate two insights: (i) vaccination aimed at reducing prevalence could be more effective at reducing disease than directly vaccinating the vulnerable; (ii) the highest risk for vaccine escape can occur at intermediate levels of vaccination. This work demonstrates a key principle: the careful targeting of vaccines towards particular population groups could reduce disease as much as possible while limiting the risk of vaccine escape.

6.
PLoS One ; 16(7): e0253116, 2021.
Article in English | MEDLINE | ID: covidwho-1304454

ABSTRACT

We provide an assessment of the impact of government closure and containment measures on deaths from COVID-19 across sequential waves of the COVID-19 pandemic globally. Daily data was collected on a range of containment and closure policies for 186 countries from January 1, 2020 until March 11th, 2021. These data were combined into an aggregate stringency index (SI) score for each country on each day (range: 0-100). Countries were divided into successive waves via a mathematical algorithm to identify peaks and troughs of disease. Within our period of analysis, 63 countries experienced at least one wave, 40 countries experienced two waves, and 10 countries saw three waves, as defined by our approach. Within each wave, regression was used to assess the relationship between the strength of government stringency and subsequent deaths related to COVID-19 with a number of controls for time and country-specific demographic, health system, and economic characteristics. Across the full period of our analysis and 113 countries, an increase of 10 points on the SI was linked to 6 percentage points (P < 0.001, 95% CI = [5%, 7%]) lower average daily deaths. In the first wave, in countries that ultimately experiences 3 waves of the pandemic to date, ten additional points on the SI resulted in lower average daily deaths by 21 percentage points (P < .001, 95% CI = [8%, 16%]). This effect was sustained in the third wave with reductions in deaths of 28 percentage points (P < .001, 95% CI = [13%, 21%]). Moreover, interaction effects show that government policies were effective in reducing deaths in all waves in all groups of countries. These findings highlight the enduring importance of non-pharmaceutical responses to COVID-19 over time.


Subject(s)
COVID-19/mortality , Government , Pandemics/prevention & control , SARS-CoV-2 , COVID-19/therapy , COVID-19/transmission , Humans
7.
PLoS Med ; 18(7): e1003660, 2021 07.
Article in English | MEDLINE | ID: covidwho-1298077

ABSTRACT

BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.


Subject(s)
Antiviral Agents/therapeutic use , COVID-19/drug therapy , Randomized Controlled Trials as Topic , Sample Size , Humans , Models, Biological , SARS-CoV-2 , Treatment Outcome , Viral Load , Virus Replication , Virus Shedding
8.
BMC Med ; 19(1): 137, 2021 06 07.
Article in English | MEDLINE | ID: covidwho-1259195

ABSTRACT

BACKGROUND: The introduction of SARS-CoV-2, the virus that causes COVID-19 infection, in the UK in early 2020, resulted in the introduction of several control policies to reduce disease spread. As part of these restrictions, schools were closed to all pupils in March (except for vulnerable and key worker children), before re-opening to certain year groups in June. Finally, all school children returned to the classroom in September. METHODS: Here, we analyse data on school absences in late 2020 as a result of COVID-19 infection and how that varied through time as other measures in the community were introduced. We utilise data from the Department for Education Educational Settings database and examine how pupil and teacher absences change in both primary and secondary schools. RESULTS: Our results show that absences as a result of COVID-19 infection rose steadily following the re-opening of schools in September. Cases in teachers declined during the November lockdown, particularly in regions previously in tier 3, the highest level of control at the time. Cases in secondary school pupils increased for the first 2 weeks of the November lockdown, before decreasing. Since the introduction of the tier system, the number of absences with confirmed infection in primary schools was observed to be (markedly) lower than that in secondary schools. In December, we observed a large rise in the number of absences per school in secondary school settings in the South East and London, but such rises were not observed in other regions or in primary school settings. We conjecture that the increased transmissibility of the new variant in these regions may have contributed to this rise in secondary school cases. Finally, we observe a positive correlation between cases in the community and cases in schools in most regions, with weak evidence suggesting that cases in schools lag behind cases in the surrounding community. CONCLUSIONS: We conclude that there is no significant evidence to suggest that schools are playing a substantial role in driving spread in the community and that careful monitoring may be required as schools re-open to determine the effect associated with open schools upon community incidence.


Subject(s)
Absenteeism , COVID-19/epidemiology , Schools/statistics & numerical data , England/epidemiology , Female , Humans , Incidence , Male , Pandemics , SARS-CoV-2/isolation & purification
9.
J R Soc Interface ; 18(178): 20201014, 2021 05.
Article in English | MEDLINE | ID: covidwho-1234202

ABSTRACT

During infectious disease epidemics, an important question is whether cases travelling to new locations will trigger local outbreaks. The risk of this occurring depends on the transmissibility of the pathogen, the susceptibility of the host population and, crucially, the effectiveness of surveillance in detecting cases and preventing onward spread. For many pathogens, transmission from pre-symptomatic and/or asymptomatic (together referred to as non-symptomatic) infectious hosts can occur, making effective surveillance challenging. Here, by using SARS-CoV-2 as a case study, we show how the risk of local outbreaks can be assessed when non-symptomatic transmission can occur. We construct a branching process model that includes non-symptomatic transmission and explore the effects of interventions targeting non-symptomatic or symptomatic hosts when surveillance resources are limited. We consider whether the greatest reductions in local outbreak risks are achieved by increasing surveillance and control targeting non-symptomatic or symptomatic cases, or a combination of both. We find that seeking to increase surveillance of symptomatic hosts alone is typically not the optimal strategy for reducing outbreak risks. Adopting a strategy that combines an enhancement of surveillance of symptomatic cases with efforts to find and isolate non-symptomatic infected hosts leads to the largest reduction in the probability that imported cases will initiate a local outbreak.


Subject(s)
COVID-19 , SARS-CoV-2 , Disease Outbreaks/prevention & control , Humans
10.
Elife ; 102021 04 26.
Article in English | MEDLINE | ID: covidwho-1201638

ABSTRACT

Background: Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing. Methods: Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector-infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches. Results: The mechanistic method provides an improved fit to data from SARS-CoV-2 infector-infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms. Conclusions: High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced. Funding: Engineering and Physical Sciences Research Council (EPSRC).


The risk of a person with COVID-19 spreading the SARS-CoV-2 virus that causes it to others varies over the course of their infection. Transmission depends both on how much virus is in the infected person's airway and their behaviors, such as whether they wear a mask and how many people they have contact with. Learning more about when people are most infectious would help public health officials stop the spread of the virus. For example, officials can then introduce policies that ensure that people are isolated when they are most infectious. The majority of studies assessing when people with COVID-19 are most infectious so far have assumed that transmission is not linked to when symptoms appear. But that may not be true. After people develop symptoms, they may be more likely to stay home, avoid others, or take other measures that prevent transmission. Using computer modeling and data from previous studies of individuals who infected others with SARS-CoV-2, Hart et al. show that about 65% of virus transmission occurs before symptoms develop. In fact, the computational experiments show the risk of transmission is highest immediately before symptoms develop. This highlights the importance of identifying people exposed to someone infected with the virus and isolating potential recipients before they develop symptoms. This information may help public health officials develop more effective strategies to prevent the spread of SARS-CoV-2. It may also help scientists develop more accurate models to predict the spread of the virus. However, the computational experiments used data on infections early in the pandemic that may not reflect the current situation. Changes in public health policy, the behavior of individuals and the appearance of new strains of SARS-CoV-2, all affect the timing of transmission. As more recent data become available, Hart et al. plan to explore how characteristics of transmission have changed as the pandemic has progressed.


Subject(s)
COVID-19/transmission , Contact Tracing/methods , COVID-19/epidemiology , Carrier State/epidemiology , Carrier State/transmission , Health Policy , Humans , Models, Theoretical , Public Health , Risk Factors , SARS-CoV-2
11.
Lancet Infect Dis ; 21(7): 913-914, 2021 07.
Article in English | MEDLINE | ID: covidwho-1180126
12.
PLoS Biol ; 19(3): e3001128, 2021 03.
Article in English | MEDLINE | ID: covidwho-1145480

ABSTRACT

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.


Subject(s)
Betacoronavirus/physiology , COVID-19/therapy , COVID-19/virology , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/transmission , Coronavirus Infections/therapy , Coronavirus Infections/virology , Humans , Longitudinal Studies , Middle East Respiratory Syndrome Coronavirus/physiology , Models, Biological , SARS Virus/physiology , SARS-CoV-2/physiology , Viral Load/drug effects
13.
Sci Rep ; 11(1): 2547, 2021 01 28.
Article in English | MEDLINE | ID: covidwho-1052737

ABSTRACT

In the early stages of an outbreak, the term 'pandemic' can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of 'pandemic', an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters-such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions-on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term 'pandemic', and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.


Subject(s)
Communicable Diseases/epidemiology , Disease Outbreaks , Pandemics , Algorithms , Communicable Diseases/etiology , Evaluation Studies as Topic , Humans , Markov Chains , Models, Theoretical , Probability , Public Health Surveillance , Risk Assessment , Risk Factors , Travel
14.
PLoS Comput Biol ; 16(12): e1008409, 2020 12.
Article in English | MEDLINE | ID: covidwho-966830

ABSTRACT

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.


Subject(s)
Basic Reproduction Number , COVID-19 , COVID-19/epidemiology , COVID-19/transmission , Computational Biology , Humans , Models, Statistical , SARS-CoV-2
15.
PLoS Comput Biol ; 16(11): e1008478, 2020 11.
Article in English | MEDLINE | ID: covidwho-962641

ABSTRACT

We derive and validate a novel and analytic method for estimating the probability that an epidemic has been eliminated (i.e. that no future local cases will emerge) in real time. When this probability crosses 0.95 an outbreak can be declared over with 95% confidence. Our method is easy to compute, only requires knowledge of the incidence curve and the serial interval distribution, and evaluates the statistical lifetime of the outbreak of interest. Using this approach, we show how the time-varying under-reporting of infected cases will artificially inflate the inferred probability of elimination, leading to premature (false-positive) end-of-epidemic declarations. Contrastingly, we prove that incorrectly identifying imported cases as local will deceptively decrease this probability, resulting in delayed (false-negative) declarations. Failing to sustain intensive surveillance during the later phases of an epidemic can therefore substantially mislead policymakers on when it is safe to remove travel bans or relax quarantine and social distancing advisories. World Health Organisation guidelines recommend fixed (though disease-specific) waiting times for end-of-epidemic declarations that cannot accommodate these variations. Consequently, there is an unequivocal need for more active and specialised metrics for reliably identifying the conclusion of an epidemic.


Subject(s)
Epidemics , Communicable Diseases/transmission , Humans , Models, Theoretical , Probability , Reproducibility of Results , Social Isolation , Travel
16.
BMC Med ; 18(1): 152, 2020 05 25.
Article in English | MEDLINE | ID: covidwho-832144
17.
Preprint in English | ProQuest Central | ID: ppcovidwho-2077

ABSTRACT

Background: Interventions are now in place worldwide to reduce transmission of the novel coronavirus. Assessing temporal variations in transmission in different countries is essential for evaluating the effectiveness of public health interventions and the impact of changes in policy. Methods: We use case notification data to generate daily estimates of the time-dependent reproduction number in different regions and countries. Our modelling framework, based on open source tooling, accounts for reporting delays, so that temporal variations in reproduction number estimates can be compared directly with the times at which interventions are implemented. Results: We provide three example uses of our framework. First, we demonstrate how the toolset displays temporal changes in the reproduction number. Second, we show how the framework can be used to reconstruct case counts by date of infection from case counts by date of notification, as well as to estimate the reproduction number. Third, we show how maps can be generated to clearly show if case numbers are likely to decrease or increase in different regions. Results are shown for regions and countries worldwide on our website (https://epiforecasts.io/covid/) and are updated daily. Our tooling is provided as an open-source R package to allow replication by others. Conclusions: This decision-support tool can be used to assess changes in virus transmission in different regions and countries worldwide. This allows policymakers to assess the effectiveness of current interventions, and will be useful for inferring whether or not transmission will increase when interventions are lifted. As well as providing daily updates on our website, we also provide adaptable computing code so that our approach can be used directly by researchers and policymakers on confidential datasets. We hope that our tool will be used to support decisions in countries worldwide throughout the ongoing COVID-19 pandemic.

18.
Proc Biol Sci ; 287(1932): 20201405, 2020 08 12.
Article in English | MEDLINE | ID: covidwho-711780

ABSTRACT

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Immunity, Herd , Models, Theoretical , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , COVID-19 , Child , Coronavirus Infections/immunology , Coronavirus Infections/prevention & control , Disease Eradication , Family Characteristics , Humans , Pandemics/prevention & control , Pneumonia, Viral/immunology , Pneumonia, Viral/prevention & control , Schools , Seroepidemiologic Studies
19.
J Travel Med ; 27(5)2020 Aug 20.
Article in English | MEDLINE | ID: covidwho-529772

ABSTRACT

BACKGROUND: Substantial limitations have been imposed on passenger air travel to reduce transmission of severe acute respiratory syndrome coronavirus 2 between regions and countries. However, as case numbers decrease, air travel will gradually resume. We considered a future scenario in which case numbers are low and air travel returns to normal. Under that scenario, there will be a risk of outbreaks in locations worldwide due to imported cases. We estimated the risk of different locations acting as sources of future coronavirus disease 2019 outbreaks elsewhere. METHODS: We use modelled global air travel data and population density estimates from locations worldwide to analyse the risk that 1364 airports are sources of future coronavirus disease 2019 outbreaks. We use a probabilistic, branching-process-based approach that considers the volume of air travelers between airports and the reproduction number at each location, accounting for local population density. RESULTS: Under the scenario we model, we identify airports in East Asia as having the highest risk of acting as sources of future outbreaks. Moreover, we investigate the locations most likely to cause outbreaks due to air travel in regions that are large and potentially vulnerable to outbreaks: India, Brazil and Africa. We find that outbreaks in India and Brazil are most likely to be seeded by individuals travelling from within those regions. We find that this is also true for less vulnerable regions, such as the United States, Europe and China. However, outbreaks in Africa due to imported cases are instead most likely to be initiated by passengers travelling from outside the continent. CONCLUSIONS: Variation in flight volumes and destination population densities creates a non-uniform distribution of the risk that different airports pose of acting as the source of an outbreak. Accurate quantification of the spatial distribution of outbreak risk can therefore facilitate optimal allocation of resources for effective targeting of public health interventions.


Subject(s)
Air Travel , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Risk Assessment , Africa/epidemiology , Airports , Betacoronavirus , COVID-19 , China/epidemiology , Communicable Diseases, Imported , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Europe/epidemiology , Global Health , Humans , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Population Surveillance , SARS-CoV-2 , South America/epidemiology , Travel Medicine , United States/epidemiology
20.
J Clin Med ; 9(5)2020 May 01.
Article in English | MEDLINE | ID: covidwho-154862

ABSTRACT

Interventions targeting symptomatic hosts and their contacts were successful in bringing the 2003 SARS pandemic under control. In contrast, the COVID-19 pandemic has been harder to contain, partly because of its wide spectrum of symptoms in infectious hosts. Current evidence suggests that individuals can transmit the novel coronavirus while displaying few symptoms. Here, we show that the proportion of infections arising from hosts with few symptoms at the start of an outbreak can, in combination with the basic reproduction number, indicate whether or not interventions targeting symptomatic hosts are likely to be effective. However, as an outbreak continues, the proportion of infections arising from hosts with few symptoms changes in response to control measures. A high proportion of infections from hosts with few symptoms after the initial stages of an outbreak is only problematic if the rate of new infections remains high. Otherwise, it can simply indicate that symptomatic transmissions are being prevented successfully. This should be considered when interpreting estimates of the extent of transmission from hosts with few COVID-19 symptoms.

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