Infectious Disease Modelling最新文献

{"title":"Invariant set theory for predicting potential failure of antibiotic cycling","authors":"Alejandro Anderson ,&nbsp;Matthew W. Kinahan ,&nbsp;Alejandro H. Gonzalez ,&nbsp;Klas Udekwu ,&nbsp;Esteban A. Hernandez-Vargas","doi":"10.1016/j.idm.2025.04.001","DOIUrl":"10.1016/j.idm.2025.04.001","url":null,"abstract":"<div><div>Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies.</div><div>Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic <em>Pseudomonas aeruginosa</em> infections.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 897-908"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839190","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":"Visual preferences for communicating modelling: a global analysis of COVID-19 policy and decision makers","authors":"Liza Hadley ,&nbsp;Caylyn Rich ,&nbsp;Alex Tasker ,&nbsp;Olivier Restif ,&nbsp;Sebastian Funk","doi":"10.1016/j.idm.2025.04.005","DOIUrl":"10.1016/j.idm.2025.04.005","url":null,"abstract":"<div><div>Effective communication of modelling results to policy and decision makers has been a longstanding challenge in times of crises. This communication takes many forms - visualisations, reports, presentations - and requires careful consideration to ensure accurate maintenance of the key scientific messages. Science-to-policy communication is further exacerbated when presenting fundamentally uncertain forms of science such as infectious disease modelling and other types of modelled evidence, something which has been understudied. Here we assess the communication and visualisation of infectious disease modelling results to national COVID-19 policy and decision makers in 13 different countries. We present a synthesis of recommendations on what aspects of visuals, graphs, and plots policymakers found to be most helpful in their COVID-19 response work. This work serves as a first evidence base for developing guidelines on the communication and translation of infectious disease modelling into policy.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 924-934"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888193","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":"The interaction between population age structure and policy interventions on the spread of COVID-19","authors":"Hao Yin ,&nbsp;Zhu Liu ,&nbsp;Daniel M. Kammen","doi":"10.1016/j.idm.2025.03.003","DOIUrl":"10.1016/j.idm.2025.03.003","url":null,"abstract":"<div><div>COVID-19 has triggered an unprecedented public health crisis and a global economic shock. As countries and cities have transitioned away from strict pandemic restrictions, the most effective reopening strategies may vary significantly based on their demographic characteristics and social contact patterns. In this study, we employed an extended age-specific compartment model that incorporates population mobility to investigate the interaction between population age structure and various containment interventions in New York, Los Angeles, Daegu, and Nairobi – four cities with distinct age distributions that served as local epicenters of the epidemic from January 2020 to March 2021. Our results demonstrated that individual social distancing or quarantine strategies alone cannot effectively curb the spread of infection over a one-year period. However, a combined strategy, including school closure, 50 % working from home, 50 % reduction in other mobility, 10 % quarantine rate, and city lockdown interventions, can effectively suppress the infection. Furthermore, our findings revealed that social-distancing policies exhibit strong age-specific effects, and age-targeted interventions can yield significant spillover benefits. Specifically, reducing contact rates among the population under 20 can prevent 14 %, 18 %, 56 %, and 99 % of infections across all age groups in New York, Los Angeles, Daegu, and Nairobi, respectively, surpassing the effectiveness of policies exclusively targeting adults over 60 years old. In particular, to protect the elderly, it is essential to reduce contacts between the younger population and people of all age groups, especially those over 60 years old. While an older population structure may escalate fatality risk, it might also decrease infection risk. Moreover, a higher basic reproduction number amplifies the impact of an older population structure on the fatality risk of the elderly. The considerable variations in susceptibility, severity, and mobility across age groups underscore the need for targeted interventions to effectively control the spread of COVID-19 and mitigate risks in future pandemics.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 758-774"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654521","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 impact of the Modifiable Areal Unit Problem on ecological model inference: A case study of COVID-19 data in Queensland, Australia","authors":"Shovanur Haque ,&nbsp;Aiden Price ,&nbsp;Kerrie Mengersen ,&nbsp;Wenbiao Hu","doi":"10.1016/j.idm.2025.05.003","DOIUrl":"10.1016/j.idm.2025.05.003","url":null,"abstract":"<div><div>Accurate identification of spatial patterns and risk factors of disease occurrence is crucial for public health interventions. However, the Modifiable Areal Unit Problem (MAUP) poses challenges in disease modelling by impacting the reliability of statistical inferences drawn from spatially aggregated data. This study examines the effect of MAUP on ecological model inference using locally and overseas-acquired COVID-19 case data from 2020 to 2023 in Queensland, Australia. Bayesian spatial Besag-York-Mollié (BYM) models were applied across four Statistical Area (SA) levels, as defined by the Australian Statistical Geography Standard, with and without covariates: Socio-Economic Indexes for Areas (SEIFA) and overseas-acquired (OA) COVID-19 cases. OA COVID-19 cases were also considered a response variable in our study. Results indicated that finer spatial scales (SA1 and SA2) captured localized patterns and significant spatial autocorrelation, while coarser levels (SA3 and SA4) smoothed spatial variability, masking potential outbreak clusters. Incorporating SEIFA as a covariate in locally-acquired (LA) cases reduced spatial autocorrelation in residuals, effectively capturing socioeconomic disparities. Conversely, OA cases showed limited effectiveness in reducing autocorrelation at finer scales. For LA cases, higher socioeconomic disadvantage was associated with increased COVID-19 incidence at finer scales, but this association became non-significant at coarser scales. OA cases showed significant positive association with higher SEIFA scores at finer scales. Model parameters displayed narrower credible intervals at finer scales, indicating greater precision, while coarser levels had increased uncertainty. SA2 emerged as an arguably optimal scale, striking a balance between spatial resolution, model stability, and interpretability. To improve inference on COVID-19 incidence, it is recommended to use data from both SA1 and SA2 levels to leverage their respective strengths. The findings emphasize the importance of selecting appropriate spatial scales and covariates or evaluating the inferential impacts of multiple scales, to address MAUP to facilitate more reliable spatial analysis. The study advocates exploring intermediate aggregation levels and multi-scale approaches to better capture nuanced disease dynamics and extend these analyses across Australia and replicating in other countries with low population densities to enhance generalizability.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 1002-1019"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107619","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":"Impact of human mobility and weather conditions on Dengue mosquito abundance during the COVID-19 pandemic in Hong Kong","authors":"Yufan Zheng ,&nbsp;Keqi Yue ,&nbsp;Eric W.M. Wong ,&nbsp;Hsiang-Yu Yuan","doi":"10.1016/j.idm.2025.04.002","DOIUrl":"10.1016/j.idm.2025.04.002","url":null,"abstract":"<div><h3>Background</h3><div>While <em>Aedes</em> mosquitoes, the Dengue vectors, are expected to expand due to climate change, the impact of human mobility on them is largely unclear. Changes in human mobility, such as staying at home during the pandemic, likely affect mosquito abundance.</div></div><div><h3>Objectives</h3><div>We aimed to assess the influence of human mobility on the abundance and extensiveness of <em>Aedes albopictus</em>, taking account of the nonlinear lagged effects of weather, during the COVID-19 pandemic in Hong Kong.</div></div><div><h3>Methods</h3><div>Google human mobility indices (including residential, parks, and workplaces) and weather conditions (total rainfall and mean temperature) along with <em>Aedes albopictus</em> abundance and extensiveness, monitored using Gravidtrap were collected between April 2020 and August 2022. Distributed lag non-linear models with mixed-effects models were used to explore their influence in three areas of Hong Kong.</div></div><div><h3>Results</h3><div>Time spent at home (i.e., residential mobility) was negatively associated with mosquito abundance. The model projected that if residential mobility in 2022 was returned to the pre-pandemic level, the mosquito abundance would increase by an average of 80.49 % compared to actual observation. The relative risk (RR) of mosquito abundance was associated with low rainfall (&lt;50 mm) after 4.5 months, peaking at 1.73, compared with 300 mm. Heavy rainfall (&gt;500 mm) within 3 months was also associated with a peak RR of 1.41. Warm conditions (21–30 °C, compared with 20 °C) were associated with a higher RR of 1.47 after half a month.</div></div><div><h3>Discussion</h3><div>Human mobility is a critical factor along with weather conditions in mosquito prediction, and a stay-at-home policy may be an effective intervention to control <em>Aedes albopictus</em>.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 840-849"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830098","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":"Dynamics of an epidemic controlled by isolation and quarantine: A probability-based deterministic model","authors":"David V. Kalbaugh","doi":"10.1016/j.idm.2025.03.007","DOIUrl":"10.1016/j.idm.2025.03.007","url":null,"abstract":"<div><div>Assuming a homogeneous population, we employ a deterministic model based on first principles of probability to explore dynamics of an epidemic controlled by isolation alone, quarantine alone, and the two together. We develop explicit closed-form equations for key metrics of control performance: cumulative fraction of population infected over the course of the epidemic (final size), maximum fraction infected at any one time, and epidemic duration. We derive an analytical solution for final size of an epidemic controlled by isolation, when final size is small, and develop empirical relations for the other cases. We frame equations in terms of reproduction numbers, measures of intervention effort and initial conditions. We model both strength and speed of interventions, assume second order gamma distributions for intervention waiting times and employ non-time-invariant equations for quarantine. We also account for quarantine of unexposed, susceptible individuals and for imperfect intervention.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 813-839"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820778","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":"Estimation of under-reporting influenza cases in Hong Kong based on bayesian hierarchical framework","authors":"Peiji Li,&nbsp;Mengmeng Dai,&nbsp;Yayi Wang,&nbsp;Yingbo Liu","doi":"10.1016/j.idm.2025.05.002","DOIUrl":"10.1016/j.idm.2025.05.002","url":null,"abstract":"<div><div>Influenza remains a global challenge, imposing a significant burden on society and the economy. Many influenza cases are asymptomatic, leading to greater uncertainty and the under-reporting of cases in influenza transmission and preventing authorities from taking effective control measures. In this study, we propose a Bayesian hierarchical approach to model and correct under-reporting of influenza cases in Hong Kong, incorporating a discrete-time stochastic, Susceptible-Infected-Recovered-Susceptible (DT-SIRS) model that allows transmission rate to vary over time. The incidence of influenza exhibits seasonality. To examine the relationship between meteorological factors and seasonal influenza activity in subtropical areas, five meteorological factors are included in the model. The proposed model explores the effects of meteorological factors on transmission rates and disease detection covariates on under-reporting, and the inclusion of the DT-SIRS model enables more accurate inference regarding true disease counts. The results demonstrate that under-reporting rates of influenza cases vary significantly in different years and epidemic seasons. In conclusion, our method effectively captures the dynamic behavior of the disease, and we can accurately estimate under-reporting and provide new possibilities for early warning of influenza based on meteorological data and routine surveillance data.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 946-959"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935807","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":"A refractory density approach to a multi-scale SEIRS epidemic model","authors":"Anton Chizhov ,&nbsp;Laurent Pujo-Menjouet ,&nbsp;Tilo Schwalger ,&nbsp;Mattia Sensi","doi":"10.1016/j.idm.2025.03.004","DOIUrl":"10.1016/j.idm.2025.03.004","url":null,"abstract":"<div><div>We propose a novel multi-scale modeling framework for infectious disease spreading, borrowing ideas and modeling tools from the so-called Refractory Density (RD) approach. We introduce a microscopic model that describes the probability of infection for a single individual and the evolution of the disease within their body. From the individual-level description, we then present the corresponding population-level model of epidemic spreading on the mesoscopic and macroscopic scale. We conclude with numerical illustrations, taking into account either a white Gaussian noise or an escape noise to showcase the potential of our approach in producing both transient and asymptotic complex dynamics as well as finite-size fluctuations consistently across multiple scales. A comparison with the epidemiology of coronaviruses is also given to corroborate the qualitative relevance of our new approach.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 787-801"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686191","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":"2025-09-01","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":"Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting","authors":"I. Ogi-Gittins ,&nbsp;J. Polonsky ,&nbsp;M. Keita ,&nbsp;S. Ahuka-Mundeke ,&nbsp;W.S. Hart ,&nbsp;M.J. Plank ,&nbsp;B. Lambert ,&nbsp;E.M. Hill ,&nbsp;R.N. Thompson","doi":"10.1016/j.idm.2025.03.009","DOIUrl":"10.1016/j.idm.2025.03.009","url":null,"abstract":"<div><div>Professor Pierre Magal made important contributions to the field of mathematical biology before his death on February 20, 2024, including research in which epidemiological models were used to study the ends of infectious disease outbreaks. In related work, there has been interest in inferring (in real-time) when outbreaks have ended and control interventions can be relaxed. Here, we analyse data from the 2018 Ebola outbreak in Équateur Province, Democratic Republic of the Congo, during which an Ebola Response Team (ERT) was deployed to implement public health measures. We use a renewal equation transmission model to perform a <em>quasi</em> real-time investigation into when the ERT could be withdrawn safely at the tail end of the outbreak. Specifically, each week following the arrival of the ERT, we calculate the probability of future cases if the ERT is withdrawn. First, we show that similar estimates of the probability of future cases can be obtained from either daily or weekly case reports. This demonstrates that high temporal resolution case reporting may not always be necessary to determine when interventions can be relaxed. Second, we demonstrate how case under-reporting can be accounted for rigorously when estimating the probability of future cases. We find that, the lower the level of case reporting, the longer it is necessary to wait after the apparent final case before interventions can be removed safely (with only a small probability of additional cases). Finally, we show how uncertainty in the extent of case reporting can be included in estimates of the probability of future cases. Our research highlights the importance of accounting for under-reporting in deciding when to remove interventions at the tail ends of infectious disease outbreaks.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 935-945"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911693","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}