Epidemics最新文献

{"title":"Dynamics of Neisseria gonorrhoeae transmission among female sex workers and clients: A mathematical modeling study","authors":"Houssein H. Ayoub ,&nbsp;Milan Tomy ,&nbsp;Hiam Chemaitelly ,&nbsp;Ryosuke Omori ,&nbsp;Kent Buse ,&nbsp;Nicola Low ,&nbsp;Sarah Hawkes ,&nbsp;Laith J. Abu-Raddad","doi":"10.1016/j.epidem.2024.100785","DOIUrl":"10.1016/j.epidem.2024.100785","url":null,"abstract":"<div><h3>Background</h3><p>This study aimed to examine the transmission dynamics of <em>Neisseria gonorrhoeae</em> (NG) in heterosexual sex work networks (HSWNs) and the impact of variation in sexual behavior and interventions on NG epidemiology.</p></div><div><h3>Methods</h3><p>The study employed an individual-based mathematical model to simulate NG transmission dynamics in sexual networks involving female sex workers (FSWs) and their clients, primarily focusing on the Middle East and North Africa region. A deterministic model was also used to describe NG transmission from clients to their spouses.</p></div><div><h3>Results</h3><p>NG epidemiology in HSWNs displays two distinct patterns. In the common low-partner-number HSWNs, a significant proportion of NG incidence occurs among FSWs, with NG prevalence 13 times higher among FSWs than clients, and three times higher among clients than their spouses. Interventions substantially reduce incidence. Increasing condom use from 10 % to 50 % lowers NG prevalence among FSWs, clients, and their spouses from 12.2 % to 6.4 %, 1.2 % to 0.5 %, and 0.4 % to 0.2 %, respectively. Increasing symptomatic treatment coverage among FSWs from 0 % to 100 % decreases prevalence from 10.6 % to 4.5 %, 0.8 % to 0.4 %, and 0.3 % to 0.1 %, respectively. Increasing asymptomatic treatment coverage among FSWs from 0 % to 50 % decreases prevalence from 8.2 % to 0.4 %, 0.6 % to 0.1 %, and 0.2 % to 0.0 %, respectively, with very low prevalence when coverage exceeds 50 %. In high-partner-number HSWNs, prevalence among FSWs saturates at a high level, and the vast majority of incidence occurs among clients and their spouses, with a limited impact of incremental increases in interventions.</p></div><div><h3>Conclusion</h3><p>NG epidemiology in HSWNs is typically a \"fragile epidemiology\" that is responsive to a range of interventions even if the interventions are incremental, partially efficacious, and only applied to FSWs.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100785"},"PeriodicalIF":3.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175543652400046X/pdfft?md5=0cbc2180fa22a9a122ea0088659deed4&pid=1-s2.0-S175543652400046X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898705","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":"Inference of epidemic dynamics in the COVID-19 era and beyond","authors":"Anne Cori ,&nbsp;Adam Kucharski","doi":"10.1016/j.epidem.2024.100784","DOIUrl":"10.1016/j.epidem.2024.100784","url":null,"abstract":"<div><p>The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required — from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100784"},"PeriodicalIF":3.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000458/pdfft?md5=12b720ca69c05460bd44c8300c5b79f1&pid=1-s2.0-S1755436524000458-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012248","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":"COVID-19 in Scottish care homes: A metapopulation model of spread among residents and staff","authors":"Matthew Baister ,&nbsp;Ewan McTaggart ,&nbsp;Paul McMenemy ,&nbsp;Itamar Megiddo ,&nbsp;Adam Kleczkowski","doi":"10.1016/j.epidem.2024.100781","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100781","url":null,"abstract":"<div><p>The movement of populations between locations and activities can result in complex transmission dynamics, posing significant challenges in controlling infectious diseases like COVID-19. Notably, networks of care homes create an ecosystem where staff and visitor movement acts as a vector for disease transmission, contributing to the heightened risk for their vulnerable communities. Care homes in the UK were disproportionately affected by the first wave of the COVID-19 pandemic, accounting for almost half of COVID-19 deaths during the period of 6th March – 15th June 2020 and so there is a pressing need to explore modelling approaches suitable for such systems. We develop a generic compartmental Susceptible - Exposed - Infectious - Recovered - Dead (<strong>SEIRD</strong>) metapopulation model, with care home residents, care home workers, and the general population modelled as subpopulations, interacting on a network describing their mixing habits. We illustrate the model application by analysing the spread of COVID-19 over the first wave of the COVID-19 pandemic in the NHS Lothian health board, Scotland. We explicitly model the outbreak’s reproduction rate and care home visitation level over time for each subpopulation and execute a data fit and sensitivity analysis, focusing on parameters responsible for inter-subpopulation mixing: staff-sharing, staff shift patterns and visitation. The results from our sensitivity analysis show that restricting staff sharing between homes and staff interaction with the general public would significantly mitigate the disease burden. Our findings indicate that protecting care home staff from disease, coupled with reductions in staff-sharing across care homes and expedient cancellations of visitations, can significantly reduce the size of outbreaks in care home settings.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100781"},"PeriodicalIF":3.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000422/pdfft?md5=8ce8872d61aa25c3648559eaa80cd993&pid=1-s2.0-S1755436524000422-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141583076","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 contact patterns following the COVID-19 pandemic: a snapshot of post-pandemic behaviour from the CoMix study","authors":"Christopher I. Jarvis ,&nbsp;Pietro Coletti ,&nbsp;Jantien A. Backer ,&nbsp;James D. Munday ,&nbsp;Christel Faes ,&nbsp;Philippe Beutels ,&nbsp;Christian L. Althaus ,&nbsp;Nicola Low ,&nbsp;Jacco Wallinga ,&nbsp;Niel Hens ,&nbsp;W.John Edmunds","doi":"10.1016/j.epidem.2024.100778","DOIUrl":"10.1016/j.epidem.2024.100778","url":null,"abstract":"<div><p>The COVID-19 pandemic led to unprecedented changes in behaviour. To estimate if these persisted, a final round of the CoMix social contact survey was conducted in four countries at a time when all societal restrictions had been lifted for several months. We conducted a survey on a nationally representative sample in the UK, Netherlands (NL), Belgium (BE), and Switzerland (CH). Participants were asked about their contacts and behaviours on the previous day. We calculated contact matrices and compared the contact levels to a pre-pandemic baseline to estimate R₀. Data collection occurred from 17 November to 7 December 2022. 7477 participants were recruited. Some were asked to undertake the survey on behalf of their children. Only 14.4 % of all participants reported wearing a facemask on the previous day. Self-reported vaccination rates in adults were similar for each country at around 86 %. Trimmed mean recorded contacts were highest in NL with 9.9 (95 % confidence interval [CI] 9.0–10.8) contacts per person per day and lowest in CH at 6.0 (95 % CI 5.4–6.6). Contacts at work were lowest in the UK (1.4 contacts per person per day) and highest in NL at 2.8 contacts per person per day. Other contacts were also lower in the UK at 1.6 per person per day (95 % CI 1.4–1.9) and highest in NL at 3.4 recorded per person per day (95 % CI 43.0–4.0). The next-generation approach suggests that R₀ for a close-contact disease would be roughly half pre-pandemic levels in the UK, 80 % in NL and intermediate in the other two countries. The pandemic appears to have resulted in lasting changes in contact patterns expected to have an impact on the epidemiology of many different pathogens. Further post-pandemic surveys are necessary to confirm this finding.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100778"},"PeriodicalIF":3.0,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000392/pdfft?md5=32ff2f43acbbf685449b15583ca8488d&pid=1-s2.0-S1755436524000392-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535740","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":"Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub","authors":"Jiangzhuo Chen ,&nbsp;Parantapa Bhattacharya ,&nbsp;Stefan Hoops ,&nbsp;Dustin Machi ,&nbsp;Abhijin Adiga ,&nbsp;Henning Mortveit ,&nbsp;Srinivasan Venkatramanan ,&nbsp;Bryan Lewis ,&nbsp;Madhav Marathe","doi":"10.1016/j.epidem.2024.100779","DOIUrl":"10.1016/j.epidem.2024.100779","url":null,"abstract":"<div><p>UVA-EpiHiper is a national scale agent-based model to support the US COVID-19 Scenario Modeling Hub (SMH). UVA-EpiHiper uses a detailed representation of the underlying social contact network along with data measured during the course of the pandemic to initialize and calibrate the model. In this paper, we study the role of heterogeneity on model complexity and resulting epidemic dynamics using UVA-EpiHiper. We discuss various sources of heterogeneity that we encounter in the use of UVA-EpiHiper to support modeling and analysis of epidemic dynamics under various scenarios. We also discuss how this affects model complexity and computational complexity of the corresponding simulations. Using round 13 of the SMH as an example, we discuss how UVA-EpiHiper was initialized and calibrated. We then discuss how the detailed output produced by UVA-EpiHiper can be analyzed to obtain interesting insights. We find that despite the complexity in the model, the software, and the computation incurred to an agent-based model in scenario modeling, it is capable of capturing various heterogeneities of real-world systems, especially those in networks and behaviors, and enables analyzing heterogeneities in epidemiological outcomes between different demographic, geographic, and social cohorts. In applying UVA-EpiHiper to round 13 scenario modeling, we find that disease outcomes are different between and within states, and between demographic groups, which can be attributed to heterogeneities in population demographics, network structures, and initial immunity.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100779"},"PeriodicalIF":3.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000409/pdfft?md5=b8fa0d127b7ee1ad81ea18ce3693a4cb&pid=1-s2.0-S1755436524000409-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638765","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":"Assessing the impact of autologous virus neutralizing antibodies on viral rebound time in postnatally SHIV-infected ART-treated infant rhesus macaques","authors":"Ellie Mainou ,&nbsp;Stella J. Berendam ,&nbsp;Veronica Obregon-Perko ,&nbsp;Emilie A. Uffman ,&nbsp;Caroline T. Phan ,&nbsp;George M. Shaw ,&nbsp;Katharine J. Bar ,&nbsp;Mithra R. Kumar ,&nbsp;Emily J. Fray ,&nbsp;Janet M. Siliciano ,&nbsp;Robert F. Siliciano ,&nbsp;Guido Silvestri ,&nbsp;Sallie R. Permar ,&nbsp;Genevieve G. Fouda ,&nbsp;Janice McCarthy ,&nbsp;Ann Chahroudi ,&nbsp;Jessica M. Conway ,&nbsp;Cliburn Chan","doi":"10.1016/j.epidem.2024.100780","DOIUrl":"10.1016/j.epidem.2024.100780","url":null,"abstract":"<div><p>While the benefits of early antiretroviral therapy (ART) initiation in perinatally infected infants are well documented, early initiation is not always possible in postnatal pediatric HIV infections. The timing of ART initiation is likely to affect the size of the latent viral reservoir established, as well as the development of adaptive immune responses, such as the generation of neutralizing antibody responses against the virus. How these parameters impact the ability of infants to control viremia and the time to viral rebound after ART interruption is unclear and has never been modeled in infants. To investigate this question we used an infant nonhuman primate Simian/Human Immunodeficiency Virus (SHIV) infection model. Infant Rhesus macaques (RMs) were orally challenged with SHIV.C.CH505 375H dCT and either given ART at 4-7 days post-infection (early ART condition), at 2 weeks post-infection (intermediate ART condition), or at 8 weeks post-infection (late ART condition). These infants were then monitored for up to 60 months post-infection with serial viral load and immune measurements. To gain insight into early after analytic treatment interruption (ATI), we constructed mathematical models to investigate the effect of time of ART initiation in delaying viral rebound when treatment is interrupted, focusing on the relative contributions of latent reservoir size and autologous virus neutralizing antibody responses. We developed a stochastic mathematical model to investigate the joint effect of latent reservoir size, the autologous neutralizing antibody potency, and CD4+ T cell levels on the time to viral rebound for RMs rebounding up to 60 days post-ATI. We find that the latent reservoir size is an important determinant in explaining time to viral rebound in infant macaques by affecting the growth rate of the virus. The presence of neutralizing antibodies can also delay rebound, but we find this effect for high potency antibody responses only. Finally, we discuss the therapeutic implications of our findings.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"48 ","pages":"Article 100780"},"PeriodicalIF":3.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000410/pdfft?md5=8d613cadb218eb75cd648d89f8bd3941&pid=1-s2.0-S1755436524000410-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535739","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":"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":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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 <em>E. coli</em>, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For <em>Klebsiella pneumoniae</em>, reducing antibiotic use in hospitals was more efficient than reducing community use.</p></div><div><h3>Conclusions</h3><p>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}