Epidemics最新文献

{"title":"Optimal environmental testing frequency for outbreak surveillance","authors":"Jason W. Olejarz ,&nbsp;Kirstin I. Oliveira Roster ,&nbsp;Stephen M. Kissler ,&nbsp;Marc Lipsitch ,&nbsp;Yonatan H. Grad","doi":"10.1016/j.epidem.2024.100750","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100750","url":null,"abstract":"<div><p>Public health surveillance for pathogens presents an optimization problem: we require enough sampling to identify intervention-triggering shifts in pathogen epidemiology, such as new introductions or sudden increases in prevalence, but not so much that costs due to surveillance itself outweigh those from pathogen-associated illness. To determine this optimal sampling frequency, we developed a general mathematical model for the introduction of a new pathogen that, once introduced, increases in prevalence exponentially. Given the relative cost of infection <em>vs.</em> sampling, we derived equations for the expected combined cost per unit time of disease burden and surveillance for a specified sampling frequency, and thus the sampling frequency for which the expected total cost per unit time is lowest.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100750"},"PeriodicalIF":3.8,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000112/pdfft?md5=6e7f8d2f3d29a314a0b91fe6f7970335&pid=1-s2.0-S1755436524000112-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139936138","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}

{"title":"Projecting Omicron scenarios in the US while tracking population-level immunity","authors":"Anass Bouchnita ,&nbsp;Kaiming Bi ,&nbsp;Spencer J. Fox ,&nbsp;Lauren Ancel Meyers","doi":"10.1016/j.epidem.2024.100746","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100746","url":null,"abstract":"<div><p>Throughout the COVID-19 pandemic, changes in policy, shifts in behavior, and the emergence of new SARS-CoV-2 variants spurred multiple waves of transmission. Accurate assessments of the changing risks were vital for ensuring adequate healthcare capacity, designing mitigation strategies, and communicating effectively with the public. Here, we introduce a model of COVID-19 transmission and vaccination that provided rapid and reliable projections as the BA.1, BA.4 and BA.5 variants emerged and spread across the US. For example, our three-week ahead national projection of the early 2021 peak in COVID-19 hospitalizations was only one day later and 11.6–13.3% higher than the actual peak, while our projected peak in mortality was two days earlier and 0.22–4.7% higher than reported. We track population-level immunity from prior infections and vaccination in terms of the percent reduction in overall susceptibility relative to a completely naive population. As of October 1, 2022, we estimate that the US population had a 36.52% reduction in overall susceptibility to the BA.4/BA.5 variants, with 61.8%, 15.06%, and 23.54% of immunity attributable to infections, primary series vaccination, and booster vaccination, respectively. We retrospectively projected the potential impact of expanding booster coverage starting on July 15, 2022, and found that a five-fold increase in weekly boosting rates would have resulted in 70% of people over 65 vaccinated by Oct 10, 2022 and averted 25,000 (95% CI: 14,400–35,700) deaths during the BA.4/BA.5 surge. Our model provides coherent variables for tracking population-level immunity in the increasingly complex landscape of variants and vaccines and enables robust simulations of plausible scenarios for the emergence and mitigation of novel COVID variants.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100746"},"PeriodicalIF":3.8,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000070/pdfft?md5=2ac003d96b33d8376d3d3a22eb52805d&pid=1-s2.0-S1755436524000070-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749114","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}

{"title":"Acquisition and clearance dynamics of Campylobacter spp. in children in low- and middle-income countries","authors":"Dehao Chen ,&nbsp;Arie H. Havelaar ,&nbsp;James A. Platts-Mills ,&nbsp;Yang Yang","doi":"10.1016/j.epidem.2024.100749","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100749","url":null,"abstract":"<div><p>The prevalence of <em>Campylobacter</em> infection is generally high among children in low- and middle-income countries (LMIC), but the dynamics of its acquisition and clearance are understudied. We aim to quantify this process among children under two years old in eight LMIC using a statistical modeling approach, leveraging enzyme-immunoassay-based <em>Campylobacter</em> genus data and quantitative-PCR<em>-</em>based <em>Campylobacter jejuni/coli</em> data from the MAL-ED study. We developed a Markov model to compare the dynamics of acquisition and clearance of <em>Campylobacter</em> across countries and to explore the effect of antibiotic usage on <em>Campylobacter</em> clearance. Clearance rates were generally higher than acquisition rates, but their magnitude and temporal pattern varied across countries. For <em>C. jejuni/coli</em>, clearance was faster than acquisition throughout the two years at all sites. For <em>Campylobacter</em> spp., the acquisition rate either exceeded or stayed very close to the clearance rate after the first half year in Bangladesh, Pakistan and Tanzania, leading to high prevalence. Bangladesh had the shortest (28 and 57 days) while Brazil had the longest (328 and 306 days) mean times from last clearance to acquisition for <em>Campylobacter</em> spp. and <em>C. jejuni/coli</em>, respectively. South Africa had the shortest (10 and 8 days) while Tanzania had the longest (53 and 41 days) mean times to clearance for <em>Campylobacter</em> spp. and <em>C. jejuni/col</em>, respectively. The use of Macrolide accelerated clearance of <em>C. jejuni/coli</em> in Bangladesh and Peru and of <em>Campylobacter</em> spp. in Bangladesh and Pakistan. Fluoroquinolone showed statistically meaningful effects only in Bangladesh but for both <em>Campylobacter</em> groups. Higher prevalence of <em>Campylobacter</em> infection was mainly driven by a high acquisition rate that was close to or surpassing the clearance rate. Acquisition rate usually peaked in 11–17 months of age, indicating the importance of targeting the first year of life for effective interventions to reduce exposures.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100749"},"PeriodicalIF":3.8,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000100/pdfft?md5=9277e1bf96335549d67d5e49f769cca3&pid=1-s2.0-S1755436524000100-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749157","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}

{"title":"Ensemble2: Scenarios ensembling for communication and performance analysis","authors":"Clara Bay ,&nbsp;Guillaume St-Onge ,&nbsp;Jessica T. Davis ,&nbsp;Matteo Chinazzi ,&nbsp;Emily Howerton ,&nbsp;Justin Lessler ,&nbsp;Michael C. Runge ,&nbsp;Katriona Shea ,&nbsp;Shaun Truelove ,&nbsp;Cecile Viboud ,&nbsp;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}

{"title":"Quantifying the impact of interventions against Plasmodium vivax: A model for country-specific use","authors":"C. Champagne ,&nbsp;M. Gerhards ,&nbsp;J.T. Lana ,&nbsp;A. Le Menach ,&nbsp;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}

{"title":"Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study","authors":"Iris Ganser ,&nbsp;David L. Buckeridge ,&nbsp;Jane Heffernan ,&nbsp;Mélanie Prague ,&nbsp;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}

{"title":"Reproducibility of COVID-era infectious disease models","authors":"Alec S. Henderson ,&nbsp;Roslyn I. Hickson ,&nbsp;Morgan Furlong ,&nbsp;Emma S. McBryde ,&nbsp;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}

{"title":"Differences between the true reproduction number and the apparent reproduction number of an epidemic time series","authors":"Oliver Eales ,&nbsp;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}

{"title":"The impact of inaccurate assumptions about antibody test accuracy on the parametrisation and results of infectious disease models of epidemics","authors":"Madhav Chaturvedi ,&nbsp;Denise Köster ,&nbsp;Nicole Rübsamen ,&nbsp;Veronika K Jaeger ,&nbsp;Antonia Zapf ,&nbsp;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}

{"title":"Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation","authors":"John Ellis ,&nbsp;Emma Brown,&nbsp;Claire Colenutt,&nbsp;David Schley ,&nbsp;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}