Pub Date : 2024-02-08DOI: 10.1016/j.epidem.2024.100748
Clara Bay , Guillaume St-Onge , Jessica T. Davis , Matteo Chinazzi , Emily Howerton , Justin Lessler , Michael C. Runge , Katriona Shea , Shaun Truelove , Cecile Viboud , Alessandro Vespignani
Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different plausible states-of-the-world that may or may not be realized and that depend on policy interventions, unpredictable changes in the epidemic outlook, etc. As a consequence, long-term scenario projections require different evaluation criteria than the ones used for traditional short-term epidemic forecasts. Here, we propose a novel ensemble procedure for assessing pandemic scenario projections using the results of the Scenario Modeling Hub (SMH) for COVID-19 in the United States (US). By defining a “scenario ensemble” for each model and the ensemble of models, termed “Ensemble”, we provide a synthesis of potential epidemic outcomes, which we use to assess projections’ performance, bypassing the identification of the most plausible scenario. We find that overall the Ensemble models are well-calibrated and provide better performance than the scenario ensemble of individual models. The ensemble procedure accounts for the full range of plausible outcomes and highlights the importance of scenario design and effective communication. The scenario ensembling approach can be extended to any scenario design strategy, with potential refinements including weighting scenarios and allowing the ensembling process to evolve over time.
{"title":"Ensemble2: Scenarios ensembling for communication and performance analysis","authors":"Clara Bay , Guillaume St-Onge , Jessica T. Davis , Matteo Chinazzi , Emily Howerton , Justin Lessler , Michael C. Runge , Katriona Shea , Shaun Truelove , Cecile Viboud , Alessandro Vespignani","doi":"10.1016/j.epidem.2024.100748","DOIUrl":"10.1016/j.epidem.2024.100748","url":null,"abstract":"<div><p>Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different plausible <em>states-of-the-world</em> that may or may not be realized and that depend on policy interventions, unpredictable changes in the epidemic outlook, etc. As a consequence, long-term scenario projections require different evaluation criteria than the ones used for traditional short-term epidemic forecasts. Here, we propose a novel ensemble procedure for assessing pandemic scenario projections using the results of the Scenario Modeling Hub (SMH) for COVID-19 in the United States (US). By defining a “scenario ensemble” for each model and the ensemble of models, termed “Ensemble<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>”, we provide a synthesis of potential epidemic outcomes, which we use to assess projections’ performance, bypassing the identification of the most plausible scenario. We find that overall the Ensemble<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> models are well-calibrated and provide better performance than the scenario ensemble of individual models. The ensemble procedure accounts for the full range of plausible outcomes and highlights the importance of scenario design and effective communication. The scenario ensembling approach can be extended to any scenario design strategy, with potential refinements including weighting scenarios and allowing the ensembling process to evolve over time.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100748"},"PeriodicalIF":3.8,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000094/pdfft?md5=686ea29ba90293da9eb64efceccf51d5&pid=1-s2.0-S1755436524000094-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1016/j.epidem.2024.100747
C. Champagne , M. Gerhards , J.T. Lana , A. Le Menach , E. Pothin
In order to evaluate the impact of various intervention strategies on Plasmodium vivax dynamics in low endemicity settings without significant seasonal pattern, we introduce a simple mathematical model that can be easily adapted to reported case numbers similar to that collected by surveillance systems in various countries. The model includes case management, vector control, mass drug administration and reactive case detection interventions and is implemented in both deterministic and stochastic frameworks. It is available as an R package to enable users to calibrate and simulate it with their own data. Although we only illustrate its use on fictitious data, by simulating and comparing the impact of various intervention combinations on malaria risk and burden, this model could be a useful tool for strategic planning, implementation and resource mobilization.
为了评估各种干预策略对低流行率环境中无显着季节性模式的间日疟原虫动态的影响,我们引入了一个简单的数学模型,该模型可轻松适用于与各国监测系统收集的病例数类似的报告病例数。该模型包括病例管理、病媒控制、大规模用药和反应性病例检测等干预措施,可在确定性和随机性框架内实施。该模型以 R 软件包的形式提供,用户可以使用自己的数据对其进行校准和模拟。虽然我们仅在虚构数据上说明了其用途,但通过模拟和比较各种干预组合对疟疾风险和负担的影响,该模型可以成为战略规划、实施和资源调动的有用工具。
{"title":"Quantifying the impact of interventions against Plasmodium vivax: A model for country-specific use","authors":"C. Champagne , M. Gerhards , J.T. Lana , A. Le Menach , E. Pothin","doi":"10.1016/j.epidem.2024.100747","DOIUrl":"10.1016/j.epidem.2024.100747","url":null,"abstract":"<div><p>In order to evaluate the impact of various intervention strategies on <em>Plasmodium vivax</em> dynamics in low endemicity settings without significant seasonal pattern, we introduce a simple mathematical model that can be easily adapted to reported case numbers similar to that collected by surveillance systems in various countries. The model includes case management, vector control, mass drug administration and reactive case detection interventions and is implemented in both deterministic and stochastic frameworks. It is available as an R package to enable users to calibrate and simulate it with their own data. Although we only illustrate its use on fictitious data, by simulating and comparing the impact of various intervention combinations on malaria risk and burden, this model could be a useful tool for strategic planning, implementation and resource mobilization.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100747"},"PeriodicalIF":3.8,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000082/pdfft?md5=6e50e285d69bb56a36285b9913b9e041&pid=1-s2.0-S1755436524000082-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139689021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-02DOI: 10.1016/j.epidem.2024.100744
Iris Ganser , David L. Buckeridge , Jane Heffernan , Mélanie Prague , Rodolphe Thiébaut
Background
Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.
Methods
To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.
Results
The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83–85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69–77) and 11 % (9–18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66–69) vs. 48 % (45–49) reduction), while school closures reduced transmission by 15 % (12–18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507–204,249) and 384,000 (88,579–1,020,386) hospitalizations could have been averted.
Conclusion
Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.
{"title":"Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study","authors":"Iris Ganser , David L. Buckeridge , Jane Heffernan , Mélanie Prague , Rodolphe Thiébaut","doi":"10.1016/j.epidem.2024.100744","DOIUrl":"10.1016/j.epidem.2024.100744","url":null,"abstract":"<div><h3>Background</h3><p>Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.</p></div><div><h3>Methods</h3><p>To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.</p></div><div><h3>Results</h3><p>The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83–85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69–77) and 11 % (9–18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66–69) vs. 48 % (45–49) reduction), while school closures reduced transmission by 15 % (12–18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507–204,249) and 384,000 (88,579–1,020,386) hospitalizations could have been averted.</p></div><div><h3>Conclusion</h3><p>Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100744"},"PeriodicalIF":3.8,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000057/pdfft?md5=d3f74a44758a72b272e6e9dbdf0accab&pid=1-s2.0-S1755436524000057-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139668180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-23DOI: 10.1016/j.epidem.2024.100743
Alec S. Henderson , Roslyn I. Hickson , Morgan Furlong , Emma S. McBryde , Michael T. Meehan
Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus’ transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed.
{"title":"Reproducibility of COVID-era infectious disease models","authors":"Alec S. Henderson , Roslyn I. Hickson , Morgan Furlong , Emma S. McBryde , Michael T. Meehan","doi":"10.1016/j.epidem.2024.100743","DOIUrl":"10.1016/j.epidem.2024.100743","url":null,"abstract":"<div><p>Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus’ transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100743"},"PeriodicalIF":3.8,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000045/pdfft?md5=81964f7b1a598ee07c500b91aefa3c7f&pid=1-s2.0-S1755436524000045-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-13DOI: 10.1016/j.epidem.2024.100742
Oliver Eales , Steven Riley
The time-varying reproduction number measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common implicit assumption, when estimating from an epidemic time series, is that has the same relationship with these downstream outcomes as it does with the time series of incidence. However, this assumption is unlikely to be valid given that most epidemic time series are not perfect proxies of incidence. Rather they represent convolutions of incidence with uncertain delay distributions. Here we define the apparent time-varying reproduction number, , the reproduction number calculated from a downstream epidemic time series and demonstrate how differences between and depend on the convolution function. The mean of the convolution function sets a time offset between the two signals, whilst the variance of the convolution function introduces a relative distortion between them. We present the convolution functions of epidemic time series that were available during the SARS-CoV-2 pandemic. Infection prevalence, measured by random sampling studies, presents fewer biases than other epidemic time series. Here we show that additionally the mean and variance of its convolution function were similar to that obtained from traditional surveillance based on mass-testing and could be reduced using more frequent testing, or by using stricter thresholds for positivity. Infection prevalence studies continue to be a versatile tool for tracking the temporal trends of , and with additional refinements to their study protocol, will be of even greater utility during any future epidemics or pandemics.
{"title":"Differences between the true reproduction number and the apparent reproduction number of an epidemic time series","authors":"Oliver Eales , Steven Riley","doi":"10.1016/j.epidem.2024.100742","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100742","url":null,"abstract":"<div><p>The time-varying reproduction number <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common implicit assumption, when estimating <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> from an epidemic time series, is that <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> has the same relationship with these downstream outcomes as it does with the time series of incidence. However, this assumption is unlikely to be valid given that most epidemic time series are not perfect proxies of incidence. Rather they represent convolutions of incidence with uncertain delay distributions. Here we define the apparent time-varying reproduction number, <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>A</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>, the reproduction number calculated from a downstream epidemic time series and demonstrate how differences between <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>A</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> and <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> depend on the convolution function. The mean of the convolution function sets a time offset between the two signals, whilst the variance of the convolution function introduces a relative distortion between them. We present the convolution functions of epidemic time series that were available during the SARS-CoV-2 pandemic. Infection prevalence, measured by random sampling studies, presents fewer biases than other epidemic time series. Here we show that additionally the mean and variance of its convolution function were similar to that obtained from traditional surveillance based on mass-testing and could be reduced using more frequent testing, or by using stricter thresholds for positivity. Infection prevalence studies continue to be a versatile tool for tracking the temporal trends of <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>, and with additional refinements to their study protocol, will be of even greater utility during any future epidemics or pandemics.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100742"},"PeriodicalIF":3.8,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000033/pdfft?md5=53225f2009ee336d61b5ff4d2797f64e&pid=1-s2.0-S1755436524000033-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-09DOI: 10.1016/j.epidem.2024.100741
Madhav Chaturvedi , Denise Köster , Nicole Rübsamen , Veronika K Jaeger , Antonia Zapf , André Karch
The parametrisation of infectious disease models is often done based on epidemiological studies that use diagnostic and serology tests to establish disease prevalence or seroprevalence in the population being modelled. During outbreaks of an emerging infectious disease, tests are often used, both for disease control and epidemiological studies, before studies evaluating their accuracy in the population have concluded, with assumptions made about accuracy parameters like sensitivity and specificity. In this simulation study, we simulated such an outbreak, based on the case study of COVID-19, and found that inaccurate parametrisation of infectious disease models due to assumptions about antibody test accuracy in a seroprevalence study can cause modelling results that inform public health decisions to be inaccurate; for example, in our simulation setup, assuming that antibody test specificity was 0.99 instead of 0.90 when it was in fact 0.90 led to an average relative difference of 0.78 in model-projected peak hospitalisations, even when test sensitivity and all other parameters were accurately characterised. We therefore suggest that methods to speed up test evaluation studies are vitally important in the public health response to an emerging outbreak.
{"title":"The impact of inaccurate assumptions about antibody test accuracy on the parametrisation and results of infectious disease models of epidemics","authors":"Madhav Chaturvedi , Denise Köster , Nicole Rübsamen , Veronika K Jaeger , Antonia Zapf , André Karch","doi":"10.1016/j.epidem.2024.100741","DOIUrl":"10.1016/j.epidem.2024.100741","url":null,"abstract":"<div><p>The parametrisation of infectious disease models is often done based on epidemiological studies that use diagnostic and serology tests to establish disease prevalence or seroprevalence in the population being modelled. During outbreaks of an emerging infectious disease, tests are often used, both for disease control and epidemiological studies, before studies evaluating their accuracy in the population have concluded, with assumptions made about accuracy parameters like sensitivity and specificity. In this simulation study, we simulated such an outbreak, based on the case study of COVID-19, and found that inaccurate parametrisation of infectious disease models due to assumptions about antibody test accuracy in a seroprevalence study can cause modelling results that inform public health decisions to be inaccurate; for example, in our simulation setup, assuming that antibody test specificity was 0.99 instead of 0.90 when it was in fact 0.90 led to an average relative difference of 0.78 in model-projected peak hospitalisations, even when test sensitivity and all other parameters were accurately characterised. We therefore suggest that methods to speed up test evaluation studies are vitally important in the public health response to an emerging outbreak.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100741"},"PeriodicalIF":3.8,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000021/pdfft?md5=75f9c801534d848e2f185e2738974aa9&pid=1-s2.0-S1755436524000021-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139409434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-08DOI: 10.1016/j.epidem.2024.100740
John Ellis , Emma Brown, Claire Colenutt, David Schley , Simon Gubbins
To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.
{"title":"Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation","authors":"John Ellis , Emma Brown, Claire Colenutt, David Schley , Simon Gubbins","doi":"10.1016/j.epidem.2024.100740","DOIUrl":"10.1016/j.epidem.2024.100740","url":null,"abstract":"<div><p>To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100740"},"PeriodicalIF":3.8,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175543652400001X/pdfft?md5=0b487ddc9370c198baf00059992893a6&pid=1-s2.0-S175543652400001X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139396591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-06DOI: 10.1016/j.epidem.2023.100739
L. Hounsome , D. Herr , R. Bryant , R. Smith , L. Loman , J. Harris , U. Youhan , E. Dzene , P. Hadjipantelis , H. Long , T. Laurence , S. Riley , F. Cumming
During September and October 2021, a substantial number of Polymerase Chain Reaction (PCR) tests in England processed at a single laboratory were incorrectly reported as negative. We estimate the number of false negative test results issued and investigate the epidemiological impact of this incident. We estimate the number of COVID-19 cases that would have been reported had the sensitivity of the laboratory test procedure not dropped for the period 2 September to 12 October. In addition, by making comparisons between the most affected local areas and comparator populations, we estimate the number of additional infections, cases, hospitalisations and deaths that could have occurred as a result of increased transmission due to false negative test results.We estimate that around 39,000 tests may have been false negatives during this period and, as a direct result of this incident, the most affected areas in the South-West of England could have experienced between 6000 and 34,000 additional reportable cases, with a central estimate of around 24,000 additional reportable cases. Using modelled relationships between key variables, we estimate that this central estimate could have translated to approximately 55,000 additional infections.Each false negative likely led to around 1.5 additional infections. The incident is likely to have had a measurable impact on cases and infections in the affected areas in the South-West of England.
Impact statement
These results indicate the significant negative impact of incorrect testing on COVID outcomes; and make a substantial contribution to understanding the impact of testing systems and the need to ensure high accuracy in testing and reporting of results.
{"title":"Epidemiological impact of a large number of false negative SARS-CoV-2 test results in South West England during September and October 2021","authors":"L. Hounsome , D. Herr , R. Bryant , R. Smith , L. Loman , J. Harris , U. Youhan , E. Dzene , P. Hadjipantelis , H. Long , T. Laurence , S. Riley , F. Cumming","doi":"10.1016/j.epidem.2023.100739","DOIUrl":"10.1016/j.epidem.2023.100739","url":null,"abstract":"<div><p>During September and October 2021, a substantial number of Polymerase Chain Reaction (PCR) tests in England processed at a single laboratory were incorrectly reported as negative. We estimate the number of false negative test results issued and investigate the epidemiological impact of this incident. We estimate the number of COVID-19 cases that would have been reported had the sensitivity of the laboratory test procedure not dropped for the period 2 September to 12 October. In addition, by making comparisons between the most affected local areas and comparator populations, we estimate the number of additional infections, cases, hospitalisations and deaths that could have occurred as a result of increased transmission due to false negative test results.We estimate that around 39,000 tests may have been false negatives during this period and, as a direct result of this incident, the most affected areas in the South-West of England could have experienced between 6000 and 34,000 additional reportable cases, with a central estimate of around 24,000 additional reportable cases. Using modelled relationships between key variables, we estimate that this central estimate could have translated to approximately 55,000 additional infections.Each false negative likely led to around 1.5 additional infections. The incident is likely to have had a measurable impact on cases and infections in the affected areas in the South-West of England.</p></div><div><h3>Impact statement</h3><p>These results indicate the significant negative impact of incorrect testing on COVID outcomes; and make a substantial contribution to understanding the impact of testing systems and the need to ensure high accuracy in testing and reporting of results.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100739"},"PeriodicalIF":3.8,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000750/pdfft?md5=216300aca77e4e3309d1e026aef25e7f&pid=1-s2.0-S1755436523000750-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139376169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-29DOI: 10.1016/j.epidem.2023.100738
Sara L. Loo , Emily Howerton , Lucie Contamin , Claire P. Smith , Rebecca K. Borchering , Luke C. Mullany , Samantha Bents , Erica Carcelen , Sung-mok Jung , Tiffany Bogich , Willem G. van Panhuis , Jessica Kerr , Jessi Espino , Katie Yan , Harry Hochheiser , Michael C. Runge , Katriona Shea , Justin Lessler , Cécile Viboud , Shaun Truelove
Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.
{"title":"The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy","authors":"Sara L. Loo , Emily Howerton , Lucie Contamin , Claire P. Smith , Rebecca K. Borchering , Luke C. Mullany , Samantha Bents , Erica Carcelen , Sung-mok Jung , Tiffany Bogich , Willem G. van Panhuis , Jessica Kerr , Jessi Espino , Katie Yan , Harry Hochheiser , Michael C. Runge , Katriona Shea , Justin Lessler , Cécile Viboud , Shaun Truelove","doi":"10.1016/j.epidem.2023.100738","DOIUrl":"10.1016/j.epidem.2023.100738","url":null,"abstract":"<div><p>Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100738"},"PeriodicalIF":3.8,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000749/pdfft?md5=5aec925efd209aadbaed8387f3492c49&pid=1-s2.0-S1755436523000749-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139062209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1016/j.epidem.2023.100736
Sang Woo Park , Kevin Messacar , Daniel C. Douek , Alicen B. Spaulding , C. Jessica E. Metcalf , Bryan T. Grenfell
Recent outbreaks of enterovirus D68 (EV-D68) infections, and their causal linkage with acute flaccid myelitis (AFM), continue to pose a serious public health concern. During 2020 and 2021, the dynamics of EV-D68 and other pathogens have been significantly perturbed by non-pharmaceutical interventions against COVID-19; this perturbation presents a powerful natural experiment for exploring the dynamics of these endemic infections. In this study, we analyzed publicly available data on EV-D68 infections, originally collected through the New Vaccine Surveillance Network, to predict their short- and long-term dynamics following the COVID-19 interventions. Although long-term predictions are sensitive to our assumptions about underlying dynamics and changes in contact rates during the NPI periods, the likelihood of a large outbreak in 2023 appears to be low. Comprehensive surveillance data are needed to accurately characterize future dynamics of EV-D68. The limited incidence of AFM cases in 2022, despite large EV-D68 outbreaks, poses further questions for the timing of the next AFM outbreaks.
{"title":"Predicting the impact of COVID-19 non-pharmaceutical intervention on short- and medium-term dynamics of enterovirus D68 in the US","authors":"Sang Woo Park , Kevin Messacar , Daniel C. Douek , Alicen B. Spaulding , C. Jessica E. Metcalf , Bryan T. Grenfell","doi":"10.1016/j.epidem.2023.100736","DOIUrl":"10.1016/j.epidem.2023.100736","url":null,"abstract":"<div><p>Recent outbreaks of enterovirus D68 (EV-D68) infections, and their causal linkage with acute flaccid myelitis (AFM), continue to pose a serious public health concern. During 2020 and 2021, the dynamics of EV-D68 and other pathogens have been significantly perturbed by non-pharmaceutical interventions against COVID-19; this perturbation presents a powerful natural experiment for exploring the dynamics of these endemic infections. In this study, we analyzed publicly available data on EV-D68 infections, originally collected through the New Vaccine Surveillance Network, to predict their short- and long-term dynamics following the COVID-19 interventions. Although long-term predictions are sensitive to our assumptions about underlying dynamics and changes in contact rates during the NPI periods, the likelihood of a large outbreak in 2023 appears to be low. Comprehensive surveillance data are needed to accurately characterize future dynamics of EV-D68. The limited incidence of AFM cases in 2022, despite large EV-D68 outbreaks, poses further questions for the timing of the next AFM outbreaks.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100736"},"PeriodicalIF":3.8,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000725/pdfft?md5=cb4e69ff64f60b0e8cd7d3fd6ccac356&pid=1-s2.0-S1755436523000725-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138744925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}