Epidemics最新文献

{"title":"Assessing population-level target product profiles of universal human influenza A vaccines","authors":"Qiqi Yang ,&nbsp;Sang Woo Park ,&nbsp;Chadi M. Saad-Roy ,&nbsp;Isa Ahmad ,&nbsp;Cécile Viboud ,&nbsp;Nimalan Arinaminpathy ,&nbsp;Bryan T. Grenfell","doi":"10.1016/j.epidem.2024.100776","DOIUrl":"10.1016/j.epidem.2024.100776","url":null,"abstract":"<div><p>Influenza A has two hemagglutinin groups, with stronger cross-immunity to reinfection within than between groups. Here, we explore the implications of this heterogeneity for proposed cross-protective influenza vaccines that may offer broad, but not universal, protection. While the development goal for the breadth of human influenza A vaccine is to provide cross-group protection, vaccines in current development stages may provide better protection against target groups than non-target groups. To evaluate vaccine formulation and strategies, we propose a novel perspective: a vaccine population-level target product profile (PTPP). Under this perspective, we use dynamical models to quantify the epidemiological impacts of future influenza A vaccines as a function of their properties. Our results show that the interplay of natural and vaccine-induced immunity could strongly affect seasonal subtype dynamics. A broadly protective bivalent vaccine could lower the incidence of both groups and achieve elimination with sufficient vaccination coverage. However, a univalent vaccine at low vaccination rates could permit a resurgence of the non-target group when the vaccine provides weaker immunity than natural infection. Moreover, as a proxy for pandemic simulation, we analyze the invasion of a variant that evades natural immunity. We find that a future vaccine providing sufficiently broad and long-lived cross-group protection at a sufficiently high vaccination rate, could prevent pandemic emergence and lower the pandemic burden. This study highlights that as well as effectiveness, breadth and duration should be considered in epidemiologically informed TPPs for future human influenza A vaccines.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100776"},"PeriodicalIF":3.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000379/pdfft?md5=c1efd5b827a91b927963652ca683cdd9&pid=1-s2.0-S1755436524000379-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141471928","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":"Modeling the transmission of antibiotic-resistant Enterobacterales in the community: A systematic review","authors":"Eve Rahbé ,&nbsp;Philippe Glaser ,&nbsp;Lulla Opatowski","doi":"10.1016/j.epidem.2024.100783","DOIUrl":"10.1016/j.epidem.2024.100783","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;p&gt;Antibiotic-resistant Enterobacterales (ARE) are a public health threat worldwide. Dissemination of these opportunistic pathogens has been largely studied in hospitals. Despite high prevalence of asymptomatic colonization in the community in some regions of the world, less is known about ARE acquisition and spread in this setting. As explaining the community ARE dynamics has not been straightforward, mathematical models can be key to explore underlying phenomena and further evaluate the impact of interventions to curb ARE circulation outside of hospitals.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;p&gt;We conducted a systematic review of mathematical modeling studies focusing on the transmission of AR-E in the community, excluding models only specific to hospitals. We extracted model features (population, setting), formalism (compartmental, individual-based), biological hypotheses (transmission, infection, antibiotic impact, resistant strain specificities) and main findings. We discussed additional mechanisms to be considered, open scientific questions, and most pressing data needs.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;p&gt;We identified 18 modeling studies focusing on the human transmission of ARE in the community (n=11) or in both community and hospital (n=7). Models aimed at (i) understanding mechanisms driving resistance dynamics; (ii) identifying and quantifying transmission routes; or (iii) evaluating public health interventions to reduce resistance. To overcome the difficulty of reproducing observed ARE dynamics in the community using the classical two-strains competition model, studies proposed to include mechanisms such as within-host strain competition or a strong host population structure. Studies inferring model parameters from longitudinal carriage data were mostly based on models considering the ARE strain only. They showed differences in ARE carriage duration depending on the acquisition mode: returning travelers have a significantly shorter carriage duration than discharged hospitalized patient or healthy individuals. Interestingly, predictions across models regarding the success of public health interventions to reduce ARE rates depended on pathogens, settings, and antibiotic resistance mechanisms. For &lt;em&gt;E. coli&lt;/em&gt;, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For &lt;em&gt;Klebsiella pneumoniae&lt;/em&gt;, reducing antibiotic use in hospitals was more efficient than reducing community use.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;p&gt;This study raises the limited number of modeling studies specifically addressing the transmission of ARE in the community. It highlights the need for model development and community-based data collection especially in low- and middle-income countries to better understand acquisition routes and their relative contribution to observed ARE levels. Such modeling will be critical to correctly design and evaluate public health interventions","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100783"},"PeriodicalIF":3.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000446/pdfft?md5=6fcf3dc9c59e75dcc65b20f9e031f69d&pid=1-s2.0-S1755436524000446-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141471929","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":"Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling","authors":"Shi Chen ,&nbsp;Daniel Janies ,&nbsp;Rajib Paul ,&nbsp;Jean-Claude Thill","doi":"10.1016/j.epidem.2024.100782","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100782","url":null,"abstract":"<div><p>Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today’s complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100782"},"PeriodicalIF":3.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000434/pdfft?md5=66b4b845bc293b9536b2c77f14da946e&pid=1-s2.0-S1755436524000434-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542803","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":"Corrigendum to “The impact of inaccurate assumptions about antibody test accuracy on the parametrisation and results of infectious disease models of epidemics” [Epidemics 46 (2024) 100741]","authors":"Madhav Chaturvedi ,&nbsp;Denise Köster ,&nbsp;Nicole Rübsamen ,&nbsp;Veronika K. Jaeger ,&nbsp;Antonia Zapf ,&nbsp;André Karch","doi":"10.1016/j.epidem.2024.100766","DOIUrl":"10.1016/j.epidem.2024.100766","url":null,"abstract":"","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100766"},"PeriodicalIF":3.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000276/pdfft?md5=0caf3f5823ad4ce1b17911fb9b7d7566&pid=1-s2.0-S1755436524000276-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140795164","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":"Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design","authors":"Michael C. Runge ,&nbsp;Katriona Shea ,&nbsp;Emily Howerton ,&nbsp;Katie Yan ,&nbsp;Harry Hochheiser ,&nbsp;Erik Rosenstrom ,&nbsp;William J.M. Probert ,&nbsp;Rebecca Borchering ,&nbsp;Madhav V. Marathe ,&nbsp;Bryan Lewis ,&nbsp;Srinivasan Venkatramanan ,&nbsp;Shaun Truelove ,&nbsp;Justin Lessler ,&nbsp;Cécile Viboud","doi":"10.1016/j.epidem.2024.100775","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100775","url":null,"abstract":"<div><p>Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100775"},"PeriodicalIF":3.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000367/pdfft?md5=ac9c31920d8475666ae1347f172fafeb&pid=1-s2.0-S1755436524000367-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141249475","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":"Social contacts in Switzerland during the COVID-19 pandemic: Insights from the CoMix study","authors":"Martina L. Reichmuth ,&nbsp;Leonie Heron ,&nbsp;Philippe Beutels ,&nbsp;Niel Hens ,&nbsp;Nicola Low ,&nbsp;Christian L. Althaus","doi":"10.1016/j.epidem.2024.100771","DOIUrl":"10.1016/j.epidem.2024.100771","url":null,"abstract":"<div><p>To mitigate the spread of SARS-CoV-2, the Swiss government enacted restrictions on social contacts from 2020 to 2022. In addition, individuals changed their social contact behavior to limit the risk of COVID-19. In this study, we aimed to investigate the changes in social contact patterns of the Swiss population. As part of the CoMix study, we conducted a survey consisting of 24 survey waves from January 2021 to May 2022. We collected data on social contacts and constructed contact matrices for the age groups 0–4, 5–14, 15–29, 30–64, and 65 years and older. We estimated the change in contact numbers during the COVID-19 pandemic to a synthetic pre-pandemic contact matrix. We also investigated the association of the largest eigenvalue of the social contact and transmission matrices with the stringency of pandemic measures, the effective reproduction number (<em>R</em>_<em>e</em>), and vaccination uptake. During the pandemic period, 7084 responders reported an average number of 4.5 contacts (95% confidence interval, CI: 4.5–4.6) per day overall, which varied by age and survey wave. Children aged 5–14 years had the highest number of contacts with 8.5 (95% CI: 8.1–8.9) contacts on average per day and participants that were 65 years and older reported the fewest (3.4, 95% CI: 3.2–3.5) per day. Compared with the pre-pandemic baseline, we found that the 15–29 and 30–64 year olds had the largest reduction in contacts. We did not find statistically significant associations between the largest eigenvalue of the social contact and transmission matrices and the stringency of measures, <em>R</em>_<em>e</em>, or vaccination uptake. The number of social contacts in Switzerland fell during the COVID-19 pandemic and remained below pre-pandemic levels after contact restrictions were lifted. The collected social contact data will be critical in informing modeling studies on the transmission of respiratory infections in Switzerland and to guide pandemic preparedness efforts.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100771"},"PeriodicalIF":3.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175543652400032X/pdfft?md5=ce6aefa0618830042ac6febda36108fd&pid=1-s2.0-S175543652400032X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141040960","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":"Agent-based modeling of the COVID-19 pandemic in Florida","authors":"Alexander N. Pillai ,&nbsp;Kok Ben Toh ,&nbsp;Dianela Perdomo ,&nbsp;Sanjana Bhargava ,&nbsp;Arlin Stoltzfus ,&nbsp;Ira M. Longini Jr ,&nbsp;Carl A.B. Pearson ,&nbsp;Thomas J. Hladish","doi":"10.1016/j.epidem.2024.100774","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100774","url":null,"abstract":"<div><p>The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like “Should we lockdown?” During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100774"},"PeriodicalIF":3.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000355/pdfft?md5=89214eb1f01ec0dcc17d1ad1c5da8ac3&pid=1-s2.0-S1755436524000355-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141294434","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":"SIR… or MADAM? The impact of privilege on careers in epidemic modelling","authors":"Anne Cori","doi":"10.1016/j.epidem.2024.100769","DOIUrl":"10.1016/j.epidem.2024.100769","url":null,"abstract":"<div><p>As we emerge from what may be the largest global public health crises of our lives, our community of epidemic modellers is naturally reflecting. What role can modelling play in supporting decision making during epidemics? How could we more effectively interact with policy makers? How should we design future disease surveillance systems? All crucial questions. But who is going to be addressing them in 10 years’ time? With high burnout and poor attrition rates in academia, both magnified in our field by our unprecedented efforts during the pandemic, and with low wages coinciding with inflation at its highest for decades, how do we retain talent? This is a multifaceted challenge, that I argue is underpinned by privilege. In this perspective, I introduce the notion of privilege and highlight how various aspects of privilege (namely gender, ethnicity, sexual orientation, language and caring responsibilities) may affect the ability of individuals to access to and progress within academic modelling careers. I propose actions that members of the epidemic modelling research community may take to mitigate these issues and ensure we have a more diverse and equitable workforce going forward.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100769"},"PeriodicalIF":3.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000306/pdfft?md5=08bbe8f3452b925ab59f859f65f4a312&pid=1-s2.0-S1755436524000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140782200","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":"Dynamic contact networks of residents of an urban jail in the era of SARS-CoV-2","authors":"Samuel M. Jenness ,&nbsp;Karina Wallrafen-Sam ,&nbsp;Isaac Schneider ,&nbsp;Shanika Kennedy ,&nbsp;Matthew J. Akiyama ,&nbsp;Anne C. Spaulding","doi":"10.1016/j.epidem.2024.100772","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100772","url":null,"abstract":"<div><h3>Background</h3><p>In custodial settings such as jails and prisons, infectious disease transmission is heightened by factors such as overcrowding and limited healthcare access. Specific features of social contact networks within these settings have not been sufficiently characterized, especially in the context of a large-scale respiratory infectious disease outbreak. The study aims to quantify contact network dynamics within the Fulton County Jail in Atlanta, Georgia.</p></div><div><h3>Methods</h3><p>Jail roster data were utilized to construct social contact networks. Rosters included resident details, cell locations, and demographic information. This analysis involved 6702 male residents over 140,901 person days. Network statistics, including degree, mixing, and dissolution (movement within and out of the jail) rates, were assessed. We compared outcomes for two distinct periods (January 2022 and April 2022) to understand potential responses in network structures during and after the SARS-CoV-2 Omicron variant peak.</p></div><div><h3>Results</h3><p>We found high cross-sectional network degree at both cell and block levels. While mean degree increased with age, older residents exhibited lower degree during the Omicron peak. Block-level networks demonstrated higher mean degrees than cell-level networks. Cumulative degree distributions increased from January to April, indicating heightened contacts after the outbreak. Assortative age mixing was strong, especially for younger residents. Dynamic network statistics illustrated increased degrees over time, emphasizing the potential for disease spread.</p></div><div><h3>Conclusions</h3><p>Despite some reduction in network characteristics during the Omicron peak, the contact networks within the Fulton County Jail presented ideal conditions for infectious disease transmission. Age-specific mixing patterns suggested unintentional age segregation, potentially limiting disease spread to older residents. This study underscores the necessity for ongoing monitoring of contact networks in carceral settings and provides valuable insights for epidemic modeling and intervention strategies, including quarantine, depopulation, and vaccination, laying a foundation for understanding disease dynamics in such environments.Top of Form</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100772"},"PeriodicalIF":3.8,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000331/pdfft?md5=4ee2cad371be7fa416e147699bcbdde5&pid=1-s2.0-S1755436524000331-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078053","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":"A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data","authors":"I. Ogi-Gittins ,&nbsp;W.S. Hart ,&nbsp;J. Song ,&nbsp;R.K. Nash ,&nbsp;J. Polonsky ,&nbsp;A. Cori ,&nbsp;E.M. Hill ,&nbsp;R.N. Thompson","doi":"10.1016/j.epidem.2024.100773","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100773","url":null,"abstract":"<div><p>Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100773"},"PeriodicalIF":3.8,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000343/pdfft?md5=70a2d36ac61daf60a8896d1cec2f447e&pid=1-s2.0-S1755436524000343-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078030","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}