Infectious Disease Modelling最新文献

{"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}

{"title":"Evaluating the effectiveness of vaccination campaigns: Insights from unvaccinated mortality data","authors":"Lixin Lin ,&nbsp;Haydar Demirhan ,&nbsp;Lewi Stone","doi":"10.1016/j.idm.2024.12.004","DOIUrl":"10.1016/j.idm.2024.12.004","url":null,"abstract":"<div><div>This paper examines a recently developed statistical approach for evaluating the effectiveness of vaccination campaigns in terms of deaths averted. The statistical approach makes predictions by comparing death rates in the vaccinated and unvaccinated populations. The statistical approach is preferred for its simplicity and straightforwardness, especially when compared to the difficulties involved when fitting the many parameters of a dynamic SIRD-type model, which may even be an impossible task.</div><div>We compared the estimated number of deaths averted by the statistical approach to the “ground truth” number of deaths averted in a relatively simple scheme (e.g., constant vaccination, constant <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span>, pure SIR dynamics, no age stratification) through mathematical analysis, and quantified the difference and degree of underestimation. The results indicate that the statistical approach consistently produces conservative estimates and will always underestimate the number of deaths averted by the direct effect of vaccination, and thus obviously the combined total effect (direct and indirect effect).</div><div>For high <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> values (e.g. <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub><mo>≥</mo></mrow></math></span> 8), the underestimation is relatively small as long as the vaccination level (<span><math><mrow><mi>v</mi></mrow></math></span>) remains below the herd immunity vaccination threshold. However, for low <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> values (e.g. <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub><mo>≤</mo></mrow></math></span> 1.5), the statistical approach significantly underestimates the number of deaths averted by vaccination, with the underestimation greater than 20%. Applying an approximate correction to the statistical approach, however, can improve the accuracy of estimates for low <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> and low <span><math><mrow><mi>v</mi></mrow></math></span>.</div><div>In conclusion, the statistical approach can provide reasonable estimates in scenarios involving high <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> values and low <span><math><mrow><mi>v</mi></mrow></math></span>, such as during the Omicron variant epidemic in Australia. For low <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub></mrow></math></span> values and low <span><math><mrow><mi>v</mi></mrow></math></span>, applying an approximate correction to the statistical approach can lead to more accurate estimates, although there are caveats even for this. These results suggest that the statistical method needs to be used with caution.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 365-373"},"PeriodicalIF":8.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11719296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973573","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":"Deep learning model meets community-based surveillance of acute flaccid paralysis","authors":"Gelan Ayana ,&nbsp;Kokeb Dese ,&nbsp;Hundessa Daba Nemomssa ,&nbsp;Hamdia Murad ,&nbsp;Efrem Wakjira ,&nbsp;Gashaw Demlew ,&nbsp;Dessalew Yohannes ,&nbsp;Ketema Lemma Abdi ,&nbsp;Elbetel Taye ,&nbsp;Filimona Bisrat ,&nbsp;Tenager Tadesse ,&nbsp;Legesse Kidanne ,&nbsp;Se-woon Choe ,&nbsp;Netsanet Workneh Gidi ,&nbsp;Bontu Habtamu ,&nbsp;Jude Kong","doi":"10.1016/j.idm.2024.12.002","DOIUrl":"10.1016/j.idm.2024.12.002","url":null,"abstract":"<div><div>Acute flaccid paralysis (AFP) case surveillance is pivotal for the early detection of potential poliovirus, particularly in endemic countries such as Ethiopia. The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance. However, challenges like delayed detection and disorganized communication persist. This work proposes a simple deep learning model for AFP surveillance, leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones. The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset. The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch, achieving superior accuracy, F1-score, precision, recall, and area under the receiver operating characteristic curve (AUC). It emerged as the optimal model, demonstrating the highest average AUC of 0.870 ± 0.01. Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches (<em>P</em> &lt; 0.001). By bridging community reporting with health system response, this study offers a scalable solution for enhancing AFP surveillance in low-resource settings. The study is limited in terms of the quality of image data collected, necessitating future work on improving data quality. The establishment of a dedicated platform that facilitates data storage, analysis, and future learning can strengthen data quality. Nonetheless, this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images, with substantial implications for addressing global health challenges and disease eradication strategies.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 353-364"},"PeriodicalIF":8.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886535","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":"Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic","authors":"Wei-Hua Hu ,&nbsp;Hui-Min Sun ,&nbsp;Yong-Yue Wei ,&nbsp;Yuan-Tao Hao","doi":"10.1016/j.idm.2024.12.001","DOIUrl":"10.1016/j.idm.2024.12.001","url":null,"abstract":"<div><div>An early warning model for infectious diseases is a crucial tool for timely monitoring, prevention, and control of disease outbreaks. The integration of diverse multi-source data using big data and artificial intelligence techniques has emerged as a key approach in advancing these early warning models. This paper presents a comprehensive review of widely utilized early warning models for infectious diseases around the globe. Unlike previous review studies, this review encompasses newly developed approaches such as the combined model and Hawkes model after the COVID-19 pandemic, providing a thorough evaluation of their current application status and development prospects for the first time. These models not only rely on conventional surveillance data but also incorporate information from various sources. We aim to provide valuable insights for enhancing global infectious disease surveillance and early warning systems, as well as informing future research in this field, by summarizing the underlying modeling concepts, algorithms, and application scenarios of each model.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 2","pages":"Pages 410-422"},"PeriodicalIF":8.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11731462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017062","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}