Infectious Disease Modelling最新文献

{"title":"A graph-theoretic framework for integrating mobility data into mathematical epidemic models","authors":"Razvan G. Romanescu","doi":"10.1016/j.idm.2025.02.008","DOIUrl":"10.1016/j.idm.2025.02.008","url":null,"abstract":"<div><div>Advances in modeling the spread of infectious diseases have allowed modellers to relax the homogeneous mixing assumption of traditional compartmental models. The recently introduced synthetic network model, which is an SIRS type model based on a non-linear transmission rate, effectively decouples the underlying population network structure from the epidemiological parameters of disease, and has been shown to produce superior fits to multi-wave epidemics. However, inference from case counts alone is generally problematic due to the partial unidentifiability between probability of person to person transmission and the average number of contacts per individual. An alternate source of data that can inform the network alone has the potential to improve overall modeling results. Aggregate cell phone mobility data, which record daily numbers of visits to points of interest, provide a proxy for the number of contacts that people establish during their visits. In this paper, we link the contact rate from an epidemic model to the total number of contacts formed in the population. Inferring the latter from Google Community Mobility Reports data, we develop an integrated epidemic model whose transmission adapts to population mobility. This model is illustrated on the first four waves of the COVID-19 pandemic.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 716-730"},"PeriodicalIF":8.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488588","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 hepatitis B-related deaths in China to achieve the WHO's impact target","authors":"Wenjun Liu ,&nbsp;Renjie Liu ,&nbsp;Peng Li ,&nbsp;Ruyi Xia ,&nbsp;Zhuoru Zou ,&nbsp;Lei Zhang ,&nbsp;Mingwang Shen ,&nbsp;Guihua Zhuang","doi":"10.1016/j.idm.2025.02.010","DOIUrl":"10.1016/j.idm.2025.02.010","url":null,"abstract":"<div><h3>Background</h3><div>The World Health Organization (WHO) targets a 65% reduction in hepatitis B-related deaths by 2030 compared to 2015 to eliminate viral hepatitis as a major public health threat. It is unknown whether and how China can achieve this target despite significant intervention achievements. We aimed to predict the hepatitis B-related deaths in China and identify key developments needed to achieve the target.</div></div><div><h3>Methods</h3><div>An age- and time-dependent dynamic hepatitis B virus (HBV) transmission compartmental model was developed to predict the trend of hepatitis B-related deaths under base-case and subsequent scenarios from 2015 to 2040. In base-case scenario, we assumed the diagnosis and treatment (D&amp;T) rate would reach 72% in 2030, as proposed by WHO. Subsequent scenarios were set based on the results of base-case and one-way sensitivity analysis.</div></div><div><h3>Results</h3><div>Compared with 2015, hepatitis B-related deaths would be reduced by 23.89% in 2030 and 51.79% in 2040, respectively, and the WHO's impact target of 65% reduction would not be achieved until 2038 at the earliest under base-case scenario. HBV clearance rate and current treatment effectiveness were the most sensitive parameters that significantly influenced the decline of hepatitis B-related deaths from 2015 to 2040. In the subsequent scenario, when D&amp;T rate improving to 90% by 2030, with the current treatment effectiveness and HBV clearance rate being optimized from 2016, the WHO's impact target would be achieved in 2038. Increasing the clearance rate further from 2% to 2.8% during 2016–2030 linearly, the impact target would be achieved on time.</div></div><div><h3>Conclusions</h3><div>It is difficult for China to achieve the WHO's impact target of 65% reduction in hepatitis B-related deaths by 2030 even we assumed the D&amp;T rate would reach 72% in 2030 and beyond. A comprehensive scale-up of available strategies, especially innovative drugs and technologies will ensure that China achieves the target on schedule.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 731-742"},"PeriodicalIF":8.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526587","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":"Prediction of monthly occurrence number of scrub typhus in Ganzhou City, China, based on SARIMA and BPNN models","authors":"Renfa Huang ,&nbsp;Kailun Pan ,&nbsp;Qingfeng Cai ,&nbsp;Fen Lin ,&nbsp;Hua Xue ,&nbsp;Mingpeng Li ,&nbsp;Yong Liao","doi":"10.1016/j.idm.2025.02.009","DOIUrl":"10.1016/j.idm.2025.02.009","url":null,"abstract":"<div><div>Scrub typhus poses a serious public health risk globally. Forecasting the occurrence of the disease is essential for policymakers to develop prevention and control strategies. This study investigated the application of modelling techniques to predict the occurrence of scrub typhus and establishes an early warning system aimed at providing a foundational reference for its effective prevention and control. In this study, the monthly occurrence of scrub typhus in Ganzhou City from January 2008 to December 2022 was utilized as the training set for the first part of the analysis, while the data from January 2008 to December 2019 served as the training set for the second part. Based¹ on these data, the SARIMA model, the BPNN model, and the combined SARIMA-BPNN model were developed and validated using data from January to December 2023. The most effective model was then selected to predict the number of occurrences of scrub typhus for the years 2024 and 2025, respectively. The root mean square error (RMSE) and mean absolute error (MAE) of the BPNN (3-9-1) model, developed using data from January 2008 to December 2022, were 8.472 and 6.4, respectively. In contrast, the RMSE and MAE of the combined SARIMA-BPNN (1-9-1) model, constructed using data from January 2008 to December 2019, were 19.361 and 16.178, respectively. In addition, the BPNN (3-9-1) model predicted 284 cases of scrub typhus in Ganzhou City for 2024, and 163 cases for 2025. The BPNN (3-9-1) model demonstrated strong applicability in predicting the monthly occurrence of scrub typhus. Furthermore, incorporating three years of data on the occurrence of new crown outbreaks when developing a predictive model for infectious diseases can substantially enhance prediction accuracy.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 691-701"},"PeriodicalIF":8.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the effect of contact tracing during the early stage of an epidemic","authors":"Manting Wang,&nbsp;Junling Ma,&nbsp;P. van den Driessche,&nbsp;Laura L.E. Cowen","doi":"10.1016/j.idm.2025.02.005","DOIUrl":"10.1016/j.idm.2025.02.005","url":null,"abstract":"<div><div>Contact tracing is an important public health measure to control disease transmission. However, it is difficult to assess contact tracing during the exponential stage of an epidemic with multiple control measures, because the exponential growth rate is influenced by all measures. We present new SEIR and SEAIR contact tracing models that track contacts in randomly mixed populations, and calibrate them to simulated epidemic curves to determine the data that allow us to assess the effect of contact tracing. We find that new-case counts, counts of cases identified by contact tracing (or voluntary tests), and counts of symptomatic onset are necessary to identify model parameters and evaluate the effect of contact tracing. We fit our contact tracing models to COVID-19 pandemic data in Ontario, Canada, during March 16–May 1, 2020. Our results show that approximately 29% of cases were identified via contact tracing of close contacts. Contact tracing moderately reduces the control reproduction number by about 25%, but significantly reduces the prevalence by more than half. Ignoring asymptomatic transmissions gives similar estimates for the effect of contact tracing, but significantly underestimates the prevalence.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 1033-1052"},"PeriodicalIF":8.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controlling endemic foot-and-mouth disease: Vaccination is more important than movement bans. A simulation study in the Republic of Turkey","authors":"Glen Guyver-Fletcher ,&nbsp;Erin E. Gorsich ,&nbsp;Chris Jewell ,&nbsp;Michael J. Tildesley","doi":"10.1016/j.idm.2025.02.006","DOIUrl":"10.1016/j.idm.2025.02.006","url":null,"abstract":"","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 702-715"},"PeriodicalIF":8.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473986","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":"Diphtheria transmission dynamics – Unveiling generation time and reproduction numbers from the 2022–2023 outbreak in Kano state, Nigeria","authors":"Raoul Kamadjeu ,&nbsp;Oyeladun Okunromade ,&nbsp;Bola Biliaminu Lawal ,&nbsp;Muzammil Gadanya ,&nbsp;Salma Ali Suwaid ,&nbsp;Eduardo Celades Blanco ,&nbsp;Ifedayo Adetifa ,&nbsp;Elizabeth A. Kelvin","doi":"10.1016/j.idm.2025.02.007","DOIUrl":"10.1016/j.idm.2025.02.007","url":null,"abstract":"<div><h3>Background</h3><div>Diphtheria, caused by Corynebacterium diphtheriae, remains a serious public health threat in areas with low vaccination coverage, despite global declines due to widespread immunization and improved clinical management. A major outbreak in Nigeria from 2022 to 2023 underscored the persistent risk in regions with inadequate vaccination. This study aims to assess the transmission dynamics of diphtheria in Kano State, the epicenter of the outbreak, by estimating key epidemiological parameters, including the generation time (GT), approximated in our study by serial interval, and effective reproduction number (Rₜ).</div></div><div><h3>Methods</h3><div>We analyzed diphtheria case-based data from Kano State, Nigeria, collected between August 18, 2022, and November 29, 2023. Generation time was approximated using serial intervals in confirmed cases within the same geographical areas. The effective reproduction number (Rₜ) was calculated using four methods: Maximum Likelihood Estimation (MLE), Exponential Growth (EG), Sequential Bayesian (SB), and Time-Dependent (TD), focusing on the period of maximum exponential growth. A sensitivity analysis was conducted to quantify the impact of uncertainties in the GT derived from our data on the estimation of Rₜ.</div></div><div><h3>Results</h3><div>Over the 469-day outbreak period, 13,899 diphtheria cases were reported, with complete data available for 9406 cases. The estimated mean generation time was 2.8 days (SD = 3.48 days), with 97% of cases having a GT of less than 21 days. The Rₜ estimates varied across methods, with the TD method producing the highest reproduction number of 2.21 during the peak growth period. Sensitivity analysis showed that Rₜ estimates increased with longer generation times. The models, except for the SB method, demonstrated a generally strong fit with the outbreak exponential growth period.</div></div><div><h3>Conclusion</h3><div>The ongoing diphtheria outbreak in Nigeria highlights the critical threat posed by declining vaccination coverage. This study provides valuable insights into the transmission dynamics of diphtheria during a prolonged and widespread outbreak, enhancing our understanding of disease spread in this context. While certain limitations may influence the interpretation of our estimates, the findings offer valuable information for future diphtheria outbreak preparedness and response in the African context.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 680-690"},"PeriodicalIF":8.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421046","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":"Data fitting and optimal control strategies for HBV acute patient cases in the United States","authors":"Xuebing Chen ,&nbsp;Yong Li ,&nbsp;Nurbek Azimaqin ,&nbsp;Yan Wu ,&nbsp;Changlei Tan ,&nbsp;Xuyue Duan ,&nbsp;Yiyi Yuan","doi":"10.1016/j.idm.2025.02.004","DOIUrl":"10.1016/j.idm.2025.02.004","url":null,"abstract":"<div><div>Infection with Hepatitis B Virus (HBV) has been a serious public health issue worldwide. It caused more than one million fatalities per year. The mathematical modelling of the disease allows better understanding of the transmission of the disease and help the government policy makers to choose the best control strategies. With this inspiration, we proposed a novel dynamic model by incorporating infection-age structure to imitate the transmission of HBV, especially the age heterogeneity in horizontal and vertical (mother-to-child) transmission modes. We also discussed its impact on control measures and analyzed the dynamics of waning immunity and reinfection. We conducted sensitivity analysis to evaluate the effectiveness of each control measure. Our research concentrates on HBV acute patient cases in the United States data from Centre for Disease Control and Prevention (CDC). Our findings show that a mixed approach by including vaccination, medication and periodic health assessments can effectively control HBV transmission. Among these measures, we found that early vaccination with a single-dose vaccine of US$50 is the most cost-effective control strategy.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 660-679"},"PeriodicalIF":8.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421045","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":"Interventions for SARS-CoV-2 prevention among Jailed adults: A network-based modeling analysis","authors":"Isaac Schneider ,&nbsp;Karina Wallrafen-Sam ,&nbsp;Shanika Kennedy ,&nbsp;Matthew J. Akiyama ,&nbsp;Anne C. Spaulding ,&nbsp;Samuel M. Jenness","doi":"10.1016/j.idm.2025.02.001","DOIUrl":"10.1016/j.idm.2025.02.001","url":null,"abstract":"<div><h3>Background</h3><div>Airborne pathogens present challenges in settings like jails or prisons with a high density of contacts. The state of Georgia has the highest percentage of its citizens under correctional supervision in the United States. Yet, it had slow COVID vaccine uptake among jail residents, requiring prevention also using non-pharmaceutical interventions. Using a network-based SARS-CoV-2 transmission model parameterized with data from the Fulton County Jail, this study investigates the impact of three SARS-CoV-2 prevention strategies: vaccination, contact tracing and quarantining, and jail release to reduce jail population density.</div></div><div><h3>Methods</h3><div>Social contact networks were simulated at two different overlapping network layers: cell and block. Cell-level contacts represented shared confined sleeping space, whereas block-level contacts represented shared socialization space. Contact tracing and quarantining were simulated at the cell-level or both cell- and block-levels, hereafter referred to as all-level. A reference scenario and nine intervention scenarios were simulated three hundred times to estimate the median and interquartile range (IQR) of the outcome measures. Each scenario simulated a 185-day period to measure the prolonged effects of the interventions amid a potential COVID outbreak in the jail. The cumulative incidence, number of infections averted (NIA), and percentage of infections averted (PIA) were calculated comparing interventions against a base scenario without them. For the seven scenarios involving contact tracing and quarantining, total quarantines over the simulation and the number of quarantines per day were calculated to determine the quarantine requirements. Sensitivity analyses compared the impact of jointly varying vaccination rates and contact tracing rates.</div></div><div><h3>Results</h3><div>Cell-level contact tracing alone was an ineffective intervention (3.2% PIA), but its impact increased in combination with other interventions (i.e., vaccination or increased jail release rate). The other intervention strategies each produced a PIA over 10%, with the jail release scenario producing a PIA of nearly 20% despite only resulting in a 13% reduction in the jail population. The all-level contact tracing only scenario was effective at both 50% and 100% of contacts traced, but feasibility would be limited without a reduction in the jail population.</div></div><div><h3>Conclusions</h3><div>Implementing a combination intervention approach could substantially reduce the morbidity from COVID-19 and future respiratory viruses in this jail setting while providing secondary protection to the community.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 628-638"},"PeriodicalIF":8.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378129","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":"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}