Infectious Disease Modelling最新文献

{"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}

{"title":"Treatment failure and the threshold of disease extinction","authors":"Pichaya Voottipruex ,&nbsp;Nichaphat Patanarapeelert ,&nbsp;Klot Patanarapeelert","doi":"10.1016/j.idm.2024.12.007","DOIUrl":"10.1016/j.idm.2024.12.007","url":null,"abstract":"<div><div>Antibiotic treatment failure related to carriers poses a serious problem to physicians and epidemiologists. Due to the sparsity of data, assessing the role in infection dynamics is difficult. In this study, we examined the possibility that a particular therapeutic effectiveness will be regarded as the disease extinction threshold through the mathematical modelling approach. Including the treatment state in the generic epidemic model with carrier allows us to describe the role of carriers in the treatment failure. The parameterized extinction thresholds were derived via the basic reproduction number for deterministic model, and via the Jury stability criterion for the stochastic model. Existence conditions for the stochastic threshold were derived without the exact formula of the spectral radius of the expectation matrix. The results show that the transmissibility of carrier is necessary for the extinction threshold via treatment failure. The expected extinction threshold may occur subject to the certain range of the transmission potential of the symptomatic infection. This existence conditions are independent of the rate at which the carriers undergo treatment and can be used to support a control strategy.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 453-465"},"PeriodicalIF":8.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017068","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":"Quantifying West Nile virus circulation in the avian host population in Northern Italy","authors":"Alex De Nardi ,&nbsp;Giovanni Marini ,&nbsp;Ilaria Dorigatti ,&nbsp;Roberto Rosà ,&nbsp;Marco Tamba ,&nbsp;Luca Gelmini ,&nbsp;Alice Prosperi ,&nbsp;Francesco Menegale ,&nbsp;Piero Poletti ,&nbsp;Mattia Calzolari ,&nbsp;Andrea Pugliese","doi":"10.1016/j.idm.2024.12.009","DOIUrl":"10.1016/j.idm.2024.12.009","url":null,"abstract":"<div><div>West Nile virus (WNV) is one of the most threatening mosquito-borne pathogens in Italy where hundreds of human cases were recorded during the last decade. Here, we estimated the WNV incidence in the avian population in the Emilia-Romagna region through a modelling framework which enabled us to eventually assess the fraction of birds that present anti-WNV antibodies at the end of each epidemiological season.</div><div>We fitted an SIR model to ornithological data, consisting of 18,989 specimens belonging to Corvidae species collected between 2013 and 2022: every year from May to November birds are captured or shot and tested for WNV genome presence. We found that the incidence peaks between mid-July and late August, infected corvids seem on average 17% more likely to be captured with respect to susceptible ones and seroprevalence was estimated to be larger than other years at the end of 2018, consistent with the anomalous number of recorded human infections.</div><div>Thanks to our modelling study we quantified WNV infection dynamics in the corvid community, which is still poorly investigated despite its importance for the virus circulation. To the best of our knowledge, this is among the first studies providing quantitative information on infection and immunity in the bird population, yielding new important insights on WNV transmission dynamics.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 375-386"},"PeriodicalIF":8.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017063","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":"Can the prevalence of one STI serve as a predictor for another? A mathematical modeling analysis","authors":"Ryosuke Omori ,&nbsp;Hiam Chemaitelly ,&nbsp;Laith J. Abu-Raddad","doi":"10.1016/j.idm.2024.12.008","DOIUrl":"10.1016/j.idm.2024.12.008","url":null,"abstract":"<div><div>We aimed to understand to what extent knowledge of the prevalence of one sexually transmitted infection (STI) can predict the prevalence of another STI, with application for men who have sex with men (MSM). An individual-based simulation model was used to study the concurrent transmission of HIV, HSV-2, chlamydia, gonorrhea, and syphilis in MSM sexual networks. Using the model outputs, 15 multiple linear regression models were conducted for each STI prevalence, treating the prevalence of each as the dependent variable and the prevalences of up to four other STIs as independent variables in various combinations. For HIV, HSV-2, chlamydia, gonorrhea, and syphilis, the proportion of variation in prevalence explained by the 15 models ranged from 34.2% to 88.3%, 19.5%–70.5%, 43.7%–82.9%, 48.7%–86.3%, and 19.5%–67.2%, respectively. Including multiple STI prevalences as independent variables enhanced the models' predictive power. Gonorrhea prevalence was a strong predictor of HIV prevalence, while HSV-2 and syphilis prevalences were weak predictors of each other. Propagation of STIs in sexual networks reveals intricate dynamics, displaying varied epidemiological profiles while also demonstrating how the shared mode of transmission creates ecological associations that facilitate predictive relationships between STI prevalences.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 423-428"},"PeriodicalIF":8.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017141","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":"State-space modelling for infectious disease surveillance data: Dynamic regression and covariance analysis","authors":"Christopher D. Prashad","doi":"10.1016/j.idm.2024.12.005","DOIUrl":"10.1016/j.idm.2024.12.005","url":null,"abstract":"<div><div>We analyze COVID-19 surveillance data from Ontario, Canada, using state-space modelling techniques to address key challenges in understanding disease transmission dynamics. The study applies component linear Gaussian state-space models to capture periodicity, trends, and random fluctuations in case counts. We explore the relationships between COVID-19 cases, hospitalizations, workdays, and wastewater viral loads through dynamic regression models, offering insights into how these factors influence public health outcomes. Our analysis extends to multivariate covariance estimation, utilizing a novel methodology to provide time-varying correlation estimates that account for non-stationary data. Results demonstrate the significance of incorporating environmental covariates, such as wastewater data, in improving model robustness and uncovering the complex interplay between epidemiological factors. This work highlights the limitations of simpler models and emphasizes the advantages of state-space approaches for analyzing dynamic infectious disease data. By illustrating the application of advanced modelling techniques, this study contributes to a deeper understanding of disease transmission and informs public health interventions.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 591-627"},"PeriodicalIF":8.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139808","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 and their impact on the transmission of respiratory pathogens in rural China","authors":"Yuxia Liang ,&nbsp;Qian You ,&nbsp;Qianli Wang ,&nbsp;Xiaohong Yang ,&nbsp;Guangjie Zhong ,&nbsp;Kaige Dong ,&nbsp;Zeyao Zhao ,&nbsp;Nuolan Liu ,&nbsp;Xuemei Yan ,&nbsp;Wanying Lu ,&nbsp;Cheng Peng ,&nbsp;Jiaxin Zhou ,&nbsp;Jiqun Lin ,&nbsp;Maria Litvinova ,&nbsp;Mark Jit ,&nbsp;Marco Ajelli ,&nbsp;Hongjie Yu ,&nbsp;Juanjuan Zhang","doi":"10.1016/j.idm.2024.12.006","DOIUrl":"10.1016/j.idm.2024.12.006","url":null,"abstract":"<div><h3>Introduction</h3><div>Social contact patterns significantly influence the transmission dynamics of respiratory pathogens. Previous surveys have quantified human social contact patterns, yielding heterogeneous results across different locations. However, significant gaps remain in understanding social contact patterns in rural areas of China.</div></div><div><h3>Methods</h3><div>We conducted a pioneering study to quantify social contact patterns in Anhua County, Hunan Province, China, from June to October 2021, when there were minimal coronavirus disease-related restrictions in the area. Additionally, we simulated the epidemics under different assumptions regarding the relative transmission risks of various contact types (e.g., indoor versus outdoor, and physical versus non-physical).</div></div><div><h3>Results</h3><div>Participants reported an average of 12.0 contacts per day (95% confidence interval: 11.3–12.6), with a significantly higher number of indoor contacts compared to outdoor contacts. The number of contacts was associated with various socio-demographic characteristics, including age, education level, income, household size, and travel patterns. Contact patterns were assortative by age and varied based on the type of contact (e.g., physical versus non-physical). The reproduction number, daily incidence, and infection attack rate of simulated epidemics were remarkably stable.</div></div><div><h3>Discussion</h3><div>We found many intergenerational households and contacts that pose challenges in preventing and controlling infections among the elderly in rural China. Our study also underscores the importance of integrating various types of contact pattern data into epidemiological models and provides guidance to public health authorities and other major stakeholders in preparing and responding to infectious disease threats in rural China.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 439-452"},"PeriodicalIF":8.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017066","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":"Dynamics and asymptotic profiles of a local-nonlocal dispersal SIR epidemic model with spatial heterogeneity","authors":"Salih Djilali ,&nbsp;Ghilmana Sarmad ,&nbsp;Abdessamad Tridane","doi":"10.1016/j.idm.2024.12.003","DOIUrl":"10.1016/j.idm.2024.12.003","url":null,"abstract":"<div><div>This research investigates a novel approach to modeling an SIR epidemic in a heterogeneous environment by imposing certain restrictions on population mobility. Our study reveals the influence of partially restricting the mobility of the infected population, who are allowed to diffuse locally and can be modeled using random dispersion. In contrast, the non-infective population, which includes susceptible and recovered individuals, has more freedom in their movements. This greater mobility can be modeled using nonlocal dispersion. Our approach is valid for a class of nonlocal dispersion kernels. For the analysis, we first establish the well-posedness of the solution, ensuring the existence, uniqueness, and positivity of this solution. Additionally, we identify the basic reproduction number R₀ with its threshold role. Specifically, when R₀ &lt; 1, we prove the global asymptotic stability of the disease-free steady state. Conversely, when R₀ &gt; 1, we demonstrate the corresponding semiflow of the model is uniformly persistent and establish behavior at endemic steady state. Lastly, we examine the asymptotic profiles of the positive steady state as the rate at which susceptible or infected individuals disperse tends to zero or infinity. Our findings reveal that when the movement of infected individuals is restricted, the infection concentrates in specific locations that may be described as the infected preferred spots.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 387-409"},"PeriodicalIF":8.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017143","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":"Lockdown policy in pandemics: Enforcement, adherence, and effectiveness in the case of COVID-19","authors":"Yu Shi ,&nbsp;Canyao Liu ,&nbsp;Linjia Wu ,&nbsp;Han Wu ,&nbsp;Kevin Han ,&nbsp;Daming Li ,&nbsp;Sheridan B. Green ,&nbsp;Kunal Sangani","doi":"10.1016/j.idm.2024.11.002","DOIUrl":"10.1016/j.idm.2024.11.002","url":null,"abstract":"","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 493-504"},"PeriodicalIF":8.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029445","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}