Infectious Disease Modelling最新文献

{"title":"Rapid aging of influenza epidemics in China from 2005/06 to 2016/17: A population-based study","authors":"Weibo Tang ,&nbsp;Hao Lei ,&nbsp;Nan Zhang ,&nbsp;Yaojing Wang ,&nbsp;Shimeng Cai ,&nbsp;Shuyi Ji ,&nbsp;Lei Yang ,&nbsp;Mengya Yang ,&nbsp;Can Chen ,&nbsp;Shigui Yang ,&nbsp;Dayan Wang ,&nbsp;Yuelong Shu ,&nbsp;RIDPHE Group","doi":"10.1016/j.idm.2025.02.003","DOIUrl":"10.1016/j.idm.2025.02.003","url":null,"abstract":"<div><h3>Background</h3><div>China is an aging society, and the older population is at a higher risk of influenza infection and influenza-related mortality. However, there is limited knowledge regarding the aging of influenza epidemics, which is crucial for estimating the disease burden.</div></div><div><h3>Methods</h3><div>We collected weekly influenza surveillance data from 2005/06 to 2016/17, and quantified the aging of influenza-like illness (ILI) and influenza virus-positive cases in China via the mean age of the influenza cases and the proportion of individuals aged 65 and above among the influenza cases.</div></div><div><h3>Results</h3><div>On average, the mean age of ILI cases and influenza-positive cases increased by 0.52 years and 0.60 years per year, respectively, which is approximately three times the annual increase in the mean age of the population. Additionally, the proportion of individuals aged 65 and above among influenza-positive cases increased from 0.5% to 4.0%. The aging of patients infected with influenza B/Yamagata was the most rapid, with a mean age increase of 0.73 years per year, followed by those infected with influenza A (H1N1) and influenza A (H3N2). Conversely, there was no significant increase in the mean age of patients infected with influenza B/Victoria. The aging rate of influenza epidemics in southern China was significantly higher than in northern China.</div></div><div><h3>Conclusions</h3><div>Based on estimates of excess mortality due to influenza from 2010/11 to 2014/15, by 2050, the annual number of respiratory disease-related deaths associated with influenza is projected to increase 2.5-fold. This finding highlights the importance of influenza vaccination among older individuals in China.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 639-648"},"PeriodicalIF":8.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378130","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 mechanistic modeling approach to assessing the sensitivity of outcomes of water, sanitation, and hygiene interventions to local contexts and intervention factors","authors":"Andrew F. Brouwer ,&nbsp;Alicia N.M. Kraay ,&nbsp;Mondal H. Zahid ,&nbsp;Marisa C. Eisenberg ,&nbsp;Matthew C. Freeman ,&nbsp;Joseph N.S. Eisenberg","doi":"10.1016/j.idm.2025.02.002","DOIUrl":"10.1016/j.idm.2025.02.002","url":null,"abstract":"<div><div>Diarrheal disease is a leading cause of morbidity and mortality in young children. Water, sanitation, and hygiene (WASH) improvements have historically been responsible for major public health gains, but many individual interventions have failed to consistently reduce diarrheal disease burden. Analytical tools that can estimate the potential impacts of individual WASH improvements in specific contexts would support program managers and policymakers to set targets that would yield health gains. We developed a disease transmission model to simulate an intervention trial with a single intervention. We accounted for contextual factors, including preexisting WASH conditions and baseline disease prevalence, as well as intervention WASH factors, including community coverage, compliance, efficacy, and the intervenable fraction of transmission. We illustrated the sensitivity of intervention effectiveness to the contextual and intervention factors in each of two plausible disease transmission scenarios with the same disease transmission potential and intervention effectiveness but differing baseline disease burden and contextual/intervention factors. Whether disease elimination could be achieved through a single factor depended on the values of the other factors, so that changes that could achieve disease elimination in one scenario could be ineffective in the other scenario. Community coverage interacted strongly with both the contextual and the intervention factors. For example, the positive impact of increasing intervention community coverage increased non-linearly with increasing intervention compliance. With lower baseline disease prevalence in Scenario 1 (among other differences), our models predicted substantial reductions could be achieved with relatively low coverage. In contrast, in Scenario 2, where baseline disease prevalence was higher, high coverage and compliance were necessary to achieve strong intervention effectiveness. When developing interventions, it is important to account for both contextual conditions and the intervention parameters. Our mechanistic modeling approach can provide guidance for developing locally specific policy recommendations.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 649-659"},"PeriodicalIF":8.8,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387547","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":"Hybrid metapopulation agent-based epidemiological models for efficient insight on the individual scale: A contribution to green computing","authors":"Julia Bicker ,&nbsp;René Schmieding ,&nbsp;Michael Meyer-Hermann ,&nbsp;Martin J. Kühn","doi":"10.1016/j.idm.2024.12.015","DOIUrl":"10.1016/j.idm.2024.12.015","url":null,"abstract":"<div><div>Emerging infectious diseases and climate change are two of the major challenges in 21st century. Although over the past decades, highly-resolved mathematical models have contributed in understanding dynamics of infectious diseases and are of great aid when it comes to finding suitable intervention measures, they may need substantial computational effort and produce significant CO₂ emissions. Two popular modeling approaches for mitigating infectious disease dynamics are agent-based and population-based models. Agent-based models (ABMs) offer a microscopic view and are thus able to capture heterogeneous human contact behavior and mobility patterns. However, insights on individual-level dynamics come with high computational effort that scales with the number of agents. On the other hand, population-based models (PBMs) using e.g. ordinary differential equations (ODEs) are computationally efficient even for large populations due to their complexity being independent of the population size. Yet, population-based models are restricted in their granularity as they assume a (to some extent) homogeneous and well-mixed population. To manage the trade-off between computational complexity and level of detail, we propose spatial- and temporal-hybrid models that use ABMs only in an area or time frame of interest. To account for relevant influences to disease dynamics, e.g., from outside, due to commuting activities, we use population-based models, only adding moderate computational costs. Our hybridization approach demonstrates significant reduction in computational effort by up to 98% – without losing the required depth in information in the focus frame. The hybrid models used in our numerical simulations are based on two recently proposed models, however, any suitable combination of ABM and PBM could be used, too. Concluding, hybrid epidemiological models can provide insights on the individual scale where necessary, using aggregated models where possible, thereby making a contribution to green computing.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 571-590"},"PeriodicalIF":8.8,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139809","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 limited medical resources in an epidemic model with media report and general birth rate","authors":"Yicheng Hao,&nbsp;Yantao Luo,&nbsp;Zhidong Teng","doi":"10.1016/j.idm.2025.01.001","DOIUrl":"10.1016/j.idm.2025.01.001","url":null,"abstract":"<div><div>This paper formulates an SEIRSHM epidemic model with general birth rate, media report and limited medical resources. Firstly, the well-posedness of the solutions and the extinction of the disease are discussed. Then, the existence of the endemic equilibrium is discussed and we find when <em>R</em>∗ &gt; 1 and <em>R</em>₀ = 1, there exhibits a backward bifurcation, if <em>R</em>∗ &lt; 1 and <em>R</em>₀ = 1, there exhibits a forward bifurcation. Finally, numerical simulations are carried out to illustrate the main results and show that media report and limited medical resources have a great impact on disease transmission.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 522-535"},"PeriodicalIF":8.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11772943/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061598","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":"HCV transmission model with protection awareness in an SEACTR community","authors":"Liangwei Wang ,&nbsp;Fengying Wei ,&nbsp;Zhen Jin ,&nbsp;Xuerong Mao ,&nbsp;Shaojian Cai ,&nbsp;Guangmin Chen ,&nbsp;Kuicheng Zheng ,&nbsp;Jianfeng Xie","doi":"10.1016/j.idm.2024.12.014","DOIUrl":"10.1016/j.idm.2024.12.014","url":null,"abstract":"<div><h3>Background</h3><div>Hepatitis C virus (HCV) is a bloodborne virus that causes both acute and chronic hepatitis with the severity from a mild illness to liver cirrhosis and cancer. As one of the major infectious diseases in China, the monthly surveillance data from the Fujian Provincial Center for Disease Control and Prevention shows the increasing tendency from 2004 to 2011, the stable tendency from 2012 to 2016, and the declining tendency from 2017 to 2022. The 2004–2022 HCV infection tendency of Fujian Province is affected by nation-wide main control measures of Chinese government, because no control measures for HCV are modified from 2020 to 2022 during the prevalence of COVID-19 in Fujian Province.</div></div><div><h3>Methods</h3><div>The SEACTR (the susceptible, the exposed, the acutely infected, the chronically infected, the treated, the recovered) models with protection awareness are proposed. The next generation matrix method is used to compute basic reproduction number of toy model and dynamic analysis method is used to produce stochastic reproduction number of modified model. The least squares method and toy model are used to perform the optimal fitting against the monthly surveillance data. The positive preserving truncated Euler-Maruyama method is applied in modified model for the positivity of numerical simulations.</div></div><div><h3>Results</h3><div>The optimal fitting is performed using the monthly surveillance data provided by the Fujian Provincial Center for Disease Control and Prevention from 2004 to 2022. The sensitivities of protection efficiency and conversion rate to basic reproduction number and stochastic reproduction number are analyzed. The reproduction numbers and HCV infection scale with measures (single-measure, double-measure, triple-measure, and none-measure) are compared using toy model and modified model. The impacts of protection efficiency and conversion rate on exposed population, acutely infected population, chronically infected population, and treated population are analyzed. The tendency predictions for infected population and treated population in Fujian Province from 2023 to 2035 are conducted.</div></div><div><h3>Conclusions</h3><div>The HCV infection scale mainly depends on both protection efficiency and conversion rate, in which protection efficiency is the most important contributor. The reproduction numbers show the declining tendencies by phases, which indicate that the prevention and control of HCV in Fujian Province has achieved a remarkable achievement. The 2023–2035 tendency predictions of HCV infection scale in Fujian Province grow slowly due to approximately 19–109 monthly infections. The overall HCV growth tendency of Fujian Province is consistent with the nation-wide elimination objective.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 559-570"},"PeriodicalIF":8.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140356","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":"Exploring Zika's dynamics: A scoping review journey from epidemic to equations through mathematical modelling","authors":"Jhoana P. Romero-Leiton ,&nbsp;Elda K.E. Laison ,&nbsp;Rowin Alfaro ,&nbsp;E. Jane Parmley ,&nbsp;Julien Arino ,&nbsp;Kamal R. Acharya ,&nbsp;Bouchra Nasri","doi":"10.1016/j.idm.2024.12.016","DOIUrl":"10.1016/j.idm.2024.12.016","url":null,"abstract":"<div><div>Zika virus (ZIKV) infection, along with the concurrent circulation of other arboviruses, presents a great public health challenge, reminding the utilization of mathematical modelling as a crucial tool for explaining its intricate dynamics and interactions with co-circulating pathogens. Through a scoping review, we aimed to discern current mathematical models investigating ZIKV dynamics, focusing on its interplay with other pathogens, and to identify underlying assumptions and deficiencies supporting attention, particularly regarding the epidemiological attributes characterizing Zika outbreaks. Following the PRISMA-Sc guidelines, a systematic search across PubMed, Web of Science, and MathSciNet provided 137 pertinent studies from an initial pool of 2446 papers, showing a diversity of modelling approaches, predominantly centered on vector-host compartmental models, with a notable concentration on the epidemiological landscapes of Colombia and Brazil during the 2015–2016 Zika epidemic. While modelling studies have been important in explaining Zika transmission dynamics and their intersections with diseases such as Dengue, Chikungunya, and COVID-19 so far, future Zika models should prioritize robust data integration and rigorous validation against diverse datasets to improve the accuracy and reliability of epidemic prediction. In addition, models could benefit from adaptable frameworks incorporating human behavior, environmental factors, and stochastic parameters, with an emphasis on open-access tools to foster transparency and research collaboration.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 536-558"},"PeriodicalIF":8.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082204","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 modelling approach to characterise the interaction between behavioral response and epidemics: A study based on COVID-19","authors":"Xinyu Chen,&nbsp;Suxia Zhang,&nbsp;Jinhu Xu","doi":"10.1016/j.idm.2024.12.013","DOIUrl":"10.1016/j.idm.2024.12.013","url":null,"abstract":"<div><div>During epidemic outbreaks, human behavior is highly influential on the disease transmission and hence affects the course, duration and outcome of the epidemics. In order to examine the feedback effect between the dynamics of the behavioral response and disease outbreak, a simple SIR-<em>β</em> type model is established by introducing the independent variable <em>β</em> of effective contact rate, characterizing how human behavior interacts with disease transmission dynamics and allowing for the feedback changing over time along the progress of epidemic and population's perception of risk. By a particle swarm optimization algorithm in the solution procedures and time series of COVID-19 data with different shapes of infection peaks, we show that the proposed model, together with such behavioral change mechanism, is capable of capturing the trend of the selected data and can give rise to oscillatory prevalence of different magnitude over time, revealing how different levels of behavioral response affect the waves of infection as well as the evolution of the disease.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 477-492"},"PeriodicalIF":8.8,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025805","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 dynamic modeling of immune variability and multistage vaccination strategies: A case study in Malaysia","authors":"Emmanuel A. Nwaibeh ,&nbsp;Majid K.M. Ali","doi":"10.1016/j.idm.2024.12.011","DOIUrl":"10.1016/j.idm.2024.12.011","url":null,"abstract":"<div><div>Hybrid-immune and immunodeficient individuals have been identified by the World Health Organization as two vulnerable groups in the context of COVID-19, but their distinct characteristics remain underexplored. To address this gap, we developed an extended <em>SIVS</em> compartmental model that simulates the spread of COVID-19 and the impact of administering three doses of the vaccine (first, second, and booster). This study aims to provide insights into how these vulnerable populations respond to vaccination and the dynamics of waning immunity. Using real-time data from the Ministry of Health of Malaysia (May 2023–April 2024), we estimated key parameters through numerical methods and fitted the model to the data using MATLAB's lsqcurvefit package. We carried out stability and equilibrium analyses, computed the basic reproduction number (<em>R</em>₀), and identified conditions for Hopf bifurcation. Sensitivity analysis highlights the parameters with the greatest impact on infection dynamics. The calculated basic reproduction number and stability results suggest that with current vaccination rates, COVID-19 will persist in the population over an extended period. Our findings provide valuable information for public health agencies, offering recommendations for vaccination strategies targeting hybrid-immune and immunodeficient groups. These insights can inform decisions about vaccine booster schedules and resource allocation to better manage the pandemic.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 505-521"},"PeriodicalIF":8.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11758414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048776","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":"Bayesian spatio-temporal modeling of severe acute respiratory syndrome in Brazil: A comparative analysis across pre-, during, and post-COVID-19 eras","authors":"Rodrigo de Souza Bulhões ,&nbsp;Jonatha Sousa Pimentel ,&nbsp;Paulo Canas Rodrigues","doi":"10.1016/j.idm.2024.12.010","DOIUrl":"10.1016/j.idm.2024.12.010","url":null,"abstract":"<div><div>This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome (SARS) across the diverse health regions of Brazil from 2016 to 2024. Leveraging extensive datasets that include SARS cases, climate data, hospitalization records, and COVID-19 vaccination information, our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset. The analysis reveals significant variations in the incidence of SARS cases over time, particularly during and between the distinct eras of pre-COVID-19, during, and post-COVID-19. Our modeling approach accommodates explanatory variables such as humidity, temperature, and COVID-19 vaccine doses, providing a comprehensive understanding of the factors influencing SARS dynamics. Our modeling revealed unique temporal trends in SARS cases for each region, resembling neighborhood patterns. Low temperature and high humidity were linked to decreased cases, while in the COVID-19 era, temperature and vaccination coverage played significant roles. The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil, offering a foundation for targeted public health interventions and preparedness strategies.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 466-476"},"PeriodicalIF":8.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11743096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017139","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 cumulative infection rate of COVID-19 after adjusting the dynamic zero-COVID policy in China","authors":"Sijia Zhou ,&nbsp;Miao Lai ,&nbsp;Shuhan Tang ,&nbsp;Wen Liu ,&nbsp;Mingwang Shen ,&nbsp;Zhihang Peng","doi":"10.1016/j.idm.2024.12.012","DOIUrl":"10.1016/j.idm.2024.12.012","url":null,"abstract":"<div><h3>Background</h3><div>At the end of 2022, China adjusted its coronavirus disease 2019 (COVID-19) prevention and control strategy. How this adjustment affected the cumulative infection rate is debated, and how second booster dose vaccination affected the pandemic remains unclear.</div></div><div><h3>Methods</h3><div>We collected COVID-19 case data for China's mainland from December 7, 2022, to January 7, 2023, reported by the World Health Organization. We also collected cumulative infection rate data from five large-scale population-based surveys. Next, we developed a dynamic transmission compartment model to characterize the COVID-19 pandemic and to estimate the cumulative infection rate. In addition, we estimated the impact of second booster vaccination on the pandemic by examining nine scenarios with different vaccination coverages (0%, 20%, and 40%) and vaccine effectiveness (30%, 50%, and 70%).</div></div><div><h3>Results</h3><div>By January 7, 2023, when COVID-19 was classified as a Class B infectious disease, the cumulative infection rate of the Omicron variant nationwide had reached 84.11% (95% confidence interval [CI]: 78.13%–90.08%). We estimated that the cumulative infection rates reached 50.50% (95% CI: 39.58%–61.43%), 56.15% (95% CI: 49.05%–67.22%), 73.82% (95% CI: 64.63%–83.02%), 75.76% (95% CI: 67.02%–84.50%), and 84.99% (95% CI: 79.45%–90.53%) on December 19, 20, 25, and 26, 2022, and on January 15, 2023, respectively. These results are similar to those of the population survey conducted on the corresponding dates, that is 46.93%, 61%, 63.52%, 74%, and 84.7%, respectively. In addition, we estimated that by January 7, 2023, the cumulative infection rate decreased to 29.55% (64.25%) if vaccination coverage and the effectiveness of second booster vaccination were 40% (20%) and 70% (30%), respectively.</div></div><div><h3>Conclusion</h3><div>We estimate that, in late 2022, the cumulative infection rate was approximately 84% and that second booster vaccination before the policy adjustment was effective in reducing this rate.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 429-438"},"PeriodicalIF":8.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017147","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}