Epidemics最新文献

{"title":"Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned","authors":"Kelly Charniga ,&nbsp;Zachary J. Madewell ,&nbsp;Nina B. Masters ,&nbsp;Jason Asher ,&nbsp;Yoshinori Nakazawa ,&nbsp;Ian H. Spicknall","doi":"10.1016/j.epidem.2024.100755","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100755","url":null,"abstract":"<div><p>In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100755"},"PeriodicalIF":3.8,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000161/pdfft?md5=abd65d67dc31cd4cde493adf01b7d575&pid=1-s2.0-S1755436524000161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140042040","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":"flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic","authors":"Joseph C. Lemaitre ,&nbsp;Sara L. Loo ,&nbsp;Joshua Kaminsky ,&nbsp;Elizabeth C. Lee ,&nbsp;Clifton McKee ,&nbsp;Claire Smith ,&nbsp;Sung-mok Jung ,&nbsp;Koji Sato ,&nbsp;Erica Carcelen ,&nbsp;Alison Hill ,&nbsp;Justin Lessler ,&nbsp;Shaun Truelove","doi":"10.1016/j.epidem.2024.100753","DOIUrl":"10.1016/j.epidem.2024.100753","url":null,"abstract":"<div><p>The COVID-19 pandemic led to an unprecedented demand for projections of disease burden and healthcare utilization under scenarios ranging from unmitigated spread to strict social distancing policies. In response, members of the Johns Hopkins Infectious Disease Dynamics Group developed <em>flepiMoP</em> (formerly called the <em>COVID Scenario Modeling Pipeline</em>), a comprehensive open-source software pipeline designed for creating and simulating compartmental models of infectious disease transmission and inferring parameters through these models. The framework has been used extensively to produce short-term forecasts and longer-term scenario projections of COVID-19 at the state and county level in the US, for COVID-19 in other countries at various geographic scales, and more recently for seasonal influenza. In this paper, we highlight how the <em>flepiMoP</em> has evolved throughout the COVID-19 pandemic to address changing epidemiological dynamics, new interventions, and shifts in policy-relevant model outputs. As the framework has reached a mature state, we provide a detailed overview of <em>flepiMoP</em>’s key features and remaining limitations, thereby distributing <em>flepiMoP</em> and its documentation as a flexible and powerful tool for researchers and public health professionals to rapidly build and deploy large-scale complex infectious disease models for any pathogen and demographic setup.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100753"},"PeriodicalIF":3.8,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000148/pdfft?md5=9b58b4f46da0c615358dffb3a8c30622&pid=1-s2.0-S1755436524000148-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140046038","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 the future impact of emerging SARS-CoV-2 variants under uncertainty: Modeling the initial Omicron outbreak","authors":"Sean Moore,&nbsp;Sean Cavany,&nbsp;T. Alex Perkins,&nbsp;Guido Felipe Camargo España","doi":"10.1016/j.epidem.2024.100759","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100759","url":null,"abstract":"<div><p>Over the past several years, the emergence of novel SARS-CoV-2 variants has led to multiple waves of increased COVID-19 incidence. When the Omicron variant emerged, there was considerable concern about its potential impact in the winter of 2021–2022 due to its increased fitness. However, there was also considerable uncertainty regarding its likely impact due to questions about its relative transmissibility, severity, and degree of immune escape. We sought to evaluate the ability of an agent-based model to forecast incidence in the context of this emerging pathogen variant. To project COVID-19 cases and deaths in Indiana, we calibrated our model to COVID-19 hospitalizations, deaths, and test-positivity rates through November 2021, and then projected COVID-19 incidence through April 2022 under four different scenarios that covered the plausible ranges of Omicron’s severity, transmissibility, and degree of immune escape. Our initial projections from December 2021 through March 2022 indicated that under a pessimistic scenario with high disease severity, the peak in weekly COVID-19 deaths in Indiana would be larger than the previous peak in December 2020. However, retrospective analyses indicate that Omicron’s severity was closer to the optimistic scenario, and even though cases and hospitalizations reached a new peak, fewer deaths occurred than during the previous peak. According to our results, Omicron’s rapid spread was consistent with a combination of higher transmissibility and immune escape relative to earlier variants. Our updated projections starting in January 2022 accurately predicted that cases would peak in mid-January and decline rapidly over the next several months. The performance of our projections shows that following the emergence of a new pathogen variant, models can help quantify the potential range of outbreak magnitudes and trajectories. Agent-based models are particularly useful in these scenarios because they can efficiently track individual vaccination and infection histories with multiple variants with varying degrees of cross-protection.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100759"},"PeriodicalIF":3.8,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000203/pdfft?md5=bac51b4ea1de71c020e9eb665ad3ffab&pid=1-s2.0-S1755436524000203-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140041569","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":"Age-time-specific transmission of hand-foot-and-mouth disease enterovirus serotypes in Vietnam: A catalytic model with maternal immunity","authors":"Yining Chen ,&nbsp;Lam Anh Nguyet ,&nbsp;Le Nguyen Thanh Nhan ,&nbsp;Phan Tu Qui ,&nbsp;Le Nguyen Truc Nhu ,&nbsp;Nguyen Thi Thu Hong ,&nbsp;Nguyen Thi Han Ny ,&nbsp;Nguyen To Anh ,&nbsp;Le Kim Thanh ,&nbsp;Huynh Thi Phuong ,&nbsp;Nguyen Ha Thao Vy ,&nbsp;Nguyen Thi Le Thanh ,&nbsp;Truong Huu Khanh ,&nbsp;Nguyen Thanh Hung ,&nbsp;Do Chau Viet ,&nbsp;Nguyen Tran Nam ,&nbsp;Nguyen Van Vinh Chau ,&nbsp;H. Rogier van Doorn ,&nbsp;Le Van Tan ,&nbsp;Hannah Clapham","doi":"10.1016/j.epidem.2024.100754","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100754","url":null,"abstract":"<div><p>Hand, foot and mouth disease (HFMD) is highly prevalent in the Asia Pacific region, particularly in Vietnam. To develop effective interventions and efficient vaccination programs, we inferred the age-time-specific transmission patterns of HFMD serotypes enterovirus A71 (EV-A71), coxsackievirus A6 (CV-A6), coxsackievirus A10 (CV-A10), coxsackievirus A16 (CV-A16) in Ho Chi Minh City, Vietnam from a case data collected during 2013–2018 and a serological survey data collected in 2015 and 2017. We proposed a catalytic model framework with good adaptability to incorporate maternal immunity using various mathematical functions. Our results indicate the high-level transmission of CV-A6 and CV-A10 which is not obvious in the case data, due to the variation of disease severity across serotypes. Our results provide statistical evidence supporting the strong association between severe illness and CV-A6 and EV-A71 infections. The HFMD dynamic pattern presents a cyclical pattern with large outbreaks followed by a decline in subsequent years. Additionally, we identify the age group with highest risk of infection as 1-2 years and emphasise the risk of future outbreaks as over 50% of children aged 6-7 years were estimated to be susceptible to CV-A16 and EV-A71. Our study highlights the importance of multivalent vaccines and active surveillance for different serotypes, supports early vaccination prior to 1 year old, and points out the potential utility for vaccinating children older than 5 years old in Vietnam.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100754"},"PeriodicalIF":3.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175543652400015X/pdfft?md5=260cd6192c02fdf92d2daa0a455e5513&pid=1-s2.0-S175543652400015X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139998956","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":"Estimation of the infection attack rate of mumps in an outbreak among college students using paired serology","authors":"Michiel van Boven ,&nbsp;Jantien A. Backer ,&nbsp;Irene Veldhuijzen ,&nbsp;Justin Gomme ,&nbsp;Rob van Binnendijk ,&nbsp;Patricia Kaaijk","doi":"10.1016/j.epidem.2024.100751","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100751","url":null,"abstract":"<div><p>Mumps virus is a highly transmissible pathogen that is effectively controlled in countries with high vaccination coverage. Nevertheless, outbreaks have occurred worldwide over the past decades in vaccinated populations. Here we analyse an outbreak of mumps virus genotype G among college students in the Netherlands over the period 2009–2012 using paired serological data. To identify infections in the presence of preexisting antibodies we compared mumps specific serum IgG concentrations in two consecutive samples (<span><math><mrow><mi>n</mi><mo>=</mo><mn>746</mn></mrow></math></span>), whereby the first sample was taken when students started their study prior to the outbreaks, and the second sample was taken 2–5 years later. We fit a binary mixture model to the data. The two mixing distributions represent uninfected and infected classes. Throughout we assume that the infection probability increases with the ratio of antibody concentrations of the second to first sample. The estimated infection attack rate in this study is higher than reported earlier (0.095 versus 0.042). The analyses yield probabilistic classifications of participants, which are mostly quite precise owing to the high intraclass correlation of samples in uninfected participants (0.85, 95%CrI: <span><math><mrow><mn>0</mn><mo>.</mo><mn>82</mn><mo>−</mo><mn>0</mn><mo>.</mo><mn>87</mn></mrow></math></span>). The estimated probability of infection increases with decreasing antibody concentration in the pre-outbreak sample, such that the probability of infection is 0.12 (95%CrI: <span><math><mrow><mn>0</mn><mo>.</mo><mn>10</mn><mo>−</mo><mn>0</mn><mo>.</mo><mn>13</mn></mrow></math></span>) for the lowest quartile of the pre-outbreak samples and 0.056 (95%CrI: <span><math><mrow><mn>0</mn><mo>.</mo><mn>044</mn><mo>−</mo><mn>0</mn><mo>.</mo><mn>068</mn></mrow></math></span>) for the highest quartile. We discuss the implications of these insights for the design of booster vaccination strategies.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100751"},"PeriodicalIF":3.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000124/pdfft?md5=9e9fa7f4d61dcd3a812b558f6563d1b8&pid=1-s2.0-S1755436524000124-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140030729","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":"Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data","authors":"Thomas McAndrew ,&nbsp;Graham C. Gibson ,&nbsp;David Braun ,&nbsp;Abhishek Srivastava ,&nbsp;Kate Brown","doi":"10.1016/j.epidem.2024.100756","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100756","url":null,"abstract":"<div><p>Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100756"},"PeriodicalIF":3.8,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000173/pdfft?md5=398548cb8f5fa1400b832d7e3238f8f8&pid=1-s2.0-S1755436524000173-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140042041","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":"COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina","authors":"Erik T. Rosenstrom ,&nbsp;Julie S. Ivy ,&nbsp;Maria E. Mayorga ,&nbsp;Julie L. Swann","doi":"10.1016/j.epidem.2024.100752","DOIUrl":"10.1016/j.epidem.2024.100752","url":null,"abstract":"<div><p>We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"46 ","pages":"Article 100752"},"PeriodicalIF":3.8,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000136/pdfft?md5=e515f80b764ddbf79160b28c3b03a731&pid=1-s2.0-S1755436524000136-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956172","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 COVID-19 vaccination campaign in Switzerland and its impact on disease spread","authors":"M. Bekker-Nielsen Dunbar,&nbsp;L. Held","doi":"10.1016/j.epidem.2024.100745","DOIUrl":"10.1016/j.epidem.2024.100745","url":null,"abstract":"<div><p>We analyse infectious disease case surveillance data to estimate COVID-19 spread and gain an understanding of the impact of introducing vaccines to counter the disease in Switzerland. The data used in this work is extensive and detailed and includes information on weekly number of cases and vaccination rates by age and region. Our approach takes into account waning immunity. The statistical analysis allows us to determine the effects of choosing alternative vaccination strategies. Our results indicate greater uptake of vaccine would have led to fewer cases with a particularly large effect on undervaccinated regions. An alternative distribution scheme not targeting specific age groups also leads to fewer cases overall but could lead to more cases among the elderly (a potentially vulnerable population) during the early stage of prophylaxis rollout.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100745"},"PeriodicalIF":3.8,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000069/pdfft?md5=202306139627110167824bb34c906108&pid=1-s2.0-S1755436524000069-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956249","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":"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}