Infectious Disease Modelling最新文献

{"title":"Should public health policy exempt cases with low viral load from isolation during an epidemic?: a modelling study","authors":"Jiahao Diao ,&nbsp;Rebecca H. Chisholm ,&nbsp;Nicholas Geard ,&nbsp;James M. McCaw","doi":"10.1016/j.idm.2025.07.003","DOIUrl":"10.1016/j.idm.2025.07.003","url":null,"abstract":"<div><div>As demonstrated during the COVID-19 pandemic, non-pharmaceutical interventions, such as case isolation, are an important element of pandemic response. The overall impact of case isolation on epidemic dynamics depends on a number of factors, including the timing of isolation relative to the onset of contagiousness for each individual instructed to isolate by public health authorities. While there is an extensive literature examining the importance of minimising the delay from exposure to direction to isolate in determining the impact of case isolation policy, less is known about how underlying epidemic dynamics may also contribute to that impact. Empirical observation and modelling studies have shown that, as an epidemic progresses, the distribution of viral loads among cases changes systematically. In principle, this may allow for more targeted and efficient isolation strategies to be implemented. Here, we describe a multi-scale agent-based model developed to investigate how isolation strategies that account for cases viral loads could be incorporated into policy. We compare the impact and efficiency of isolation strategies in which all cases, regardless of their viral load, are required to isolate to strategies in which some cases may be exempt from isolation. Our findings show that, following the epidemic peak, the vast majority of cases identified with a low viral load are in the declining phase of their infection and so contribute less to overall contagiousness. This observation prompts the question about the potential public health value of discontinuing isolation for such individuals. Our numerical investigation of this ‘adaptive’ strategy shows that exempting individuals with low viral loads from isolation following the epidemic peak leads to a modest increase in new infections. Surprisingly, it also leads to a <em>drop</em> in efficiency, as measured by the average number of infections averted per isolated case. Our findings therefore suggest caution in adopting such flexible or adaptive isolation policies. Our multi-scale modelling framework is sufficiently flexible to enable extensive numerical evaluation of more complex isolation strategies that incorporate more disease-specific biological and epidemiological features, supporting the development and evaluation of future public health pandemic response plans.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1307-1321"},"PeriodicalIF":8.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634014","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":"Transmission of respiratory infectious diseases based on real close contact behavior in an emergency room","authors":"Bing Cao,&nbsp;Haochen Zhang,&nbsp;Nan Zhang","doi":"10.1016/j.idm.2025.07.004","DOIUrl":"10.1016/j.idm.2025.07.004","url":null,"abstract":"<div><h3>Background</h3><div>The risk of transmission of respiratory infectious diseases in emergency rooms is high, posing a severe threat to the health of healthcare workers (HCWs).</div></div><div><h3>Methods</h3><div>The study was conducted in an emergency room of a medical school at a university in Hong Kong during a clinical skills competition. A total of 19,246 s of video surveillance data were collected, recording the treatment of three types of patients (P1: infusion patient, P2: critically ill patient, P3: agitated patient). Taking coronavirus disease 2019 (COVID-19) as an example, a multi-route transmission model was established to assess the infection risk for HCWs and the effectiveness of various interventions.</div></div><div><h3>Results</h3><div>The average distances between HCWs and patients during the treatment of P1, P2, and P3 were 0.8 (25–75 percentile: 0.6, 1.1) m, 1.0 (0.8, 1.2) m, and 0.5 (0.4, 0.7) m, respectively. When treating P2, due to intubation procedures, the hourly risk of infection was highest at 43.4 % if no HCWs wore masks, which was 5.1 and 3.1 times higher than it during treatment of P1 (8.5 %) and P3 (13.9 %), respectively. During the treatment, without mask protection, the average hourly infection risk for nurses was 11.0 % (P1), 41.2 % (P2), and 16.8 % (P3), which was 1.8 times (P1), 0.9 times (P2), and 1.5 times (P3) that of doctors. If HCWs wear N95 respirators and surgical masks throughout, the total infection risk can be reduced by 94.7 % and 53.9 %, respectively. Increasing the ventilation rate from 1 ACH to 6 ACH reduced the infection risk through long-range airborne transmission by 43.8 % (P1), 36.1 % (P2), and 31.6 % (P3), with a total infection risk reduction of 2.4 % (P1), 5.6 % (P2), and 1.6 % (P3), respectively.</div></div><div><h3>Conclusions</h3><div>The findings of the study provide a scientific support for the precise prevention and control of respiratory infectious diseases under different treatments in emergency rooms.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1238-1251"},"PeriodicalIF":8.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605187","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":"Analysis of the SEIR mean-field model in dynamic networks under intervention","authors":"Jiangmin Li ,&nbsp;Zhen Jin ,&nbsp;Ming Tang","doi":"10.1016/j.idm.2025.03.002","DOIUrl":"10.1016/j.idm.2025.03.002","url":null,"abstract":"<div><div>For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 850-874"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839216","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":"Characteristics and risk factors for outcomes in patients with Mycoplasma pneumoniae mono- and coinfections: A multicenter surveillance study in Wuhan, China, 2023","authors":"Banghua Chen ,&nbsp;Jie Pan ,&nbsp;Ying Peng ,&nbsp;Yuanyuan Zhang ,&nbsp;Yunan Wan ,&nbsp;Hongjie Wei ,&nbsp;Kangguo Li ,&nbsp;Wentao Song ,&nbsp;Yunkang Zhao ,&nbsp;Kang Fang ,&nbsp;Huiming Ye ,&nbsp;Jiali Cao ,&nbsp;Jia Rui ,&nbsp;Zeyu Zhao ,&nbsp;Tianmu Chen","doi":"10.1016/j.idm.2025.04.006","DOIUrl":"10.1016/j.idm.2025.04.006","url":null,"abstract":"<div><h3>Objectives</h3><div><em>Mycoplasma pneumoniae</em> (MP) is a key cause of community-acquired pneumonia, and coinfections lead to varied patient outcomes. A comprehensive understanding of the outcome characteristics and associated etiologies of coinfections in MP patients is lacking.</div></div><div><h3>Methods and results</h3><div>We analyzed 121,357 MP cases from 522,292,680 visits in Wuhan, China, in 2023 (the final year of the COVID-19 pandemic). Children aged 1–10 years had the highest incidence, whereas those over 60 years had elevated hospitalization, severe infection, and fatality rates. Coinfection patterns differed by age, with bacterial-viral-<em>Chlamydia pneumoniae</em> (<em>C. pneumoniae</em>) / other pathogens prevalent in infants, bacterial-viral pathogens prevalent in preschoolers, and viral-viral pathogens prevalent in school-aged children. Bacterial coinfections were most common in MP-infected patients, especially those who were hospitalized. Coinfection, especially with <em>C. pneumoniae</em>, <em>Pseudomonas aeruginosa</em> (<em>P. aeruginosa</em>)<em>, Haemophilus influenzae</em> (<em>H. influenzae</em>), and <em>Streptococcus pneumoniae</em> (<em>S. pneumoniae</em>), increased hospitalization rates. The most severe outcomes and deaths occurred in patients coinfected with <em>C. pneumoniae</em>-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A-parainfluenza virus (PIV) or adenovirus-PIV. Logistic regression analysis demonstrated that male sex and adult age (particularly ≥40 years) were significantly associated with adverse outcomes in MP monoinfection. For coinfections, significantly higher hospitalization rates were reported among very young children (0–5 years) and adults aged ≥40 years, whereas adults presented an increased risk of severe disease. Coinfection outcomes were significantly associated with seasons of the year (winter, spring, and summer), specific age groups (3–5 years, 18–39 years, 40–50 years, and 60 years and over), gender (male), and longer onset-to-diagnosis periods. Middle-aged and elderly patients, coinfection, spring and summer, gender (male), and longer onset-to-diagnosis periods were significantly associated with increased hospitalization and serious illness risk. Coinfection, winter, older (adult) age, and gender (male) were significantly associated with an increased risk of death.</div></div><div><h3>Conclusions</h3><div>Compared with adults, children with MP have a greater morbidity risk, whereas middle-aged and older adults face greater risks of hospitalization, serious illness, and death. Coinfection with other pathogens heightens hospitalization and death risks. These insights are crucial for etiological screening, diagnosing multiple pathogens, and preventing and treating infections.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 989-1001"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089445","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":"Evolution into chaos – Implications of the trade-off between transmissibility and immune evasion","authors":"Golsa Sayyar ,&nbsp;Ábel Garab ,&nbsp;Gergely Röst","doi":"10.1016/j.idm.2025.04.003","DOIUrl":"10.1016/j.idm.2025.04.003","url":null,"abstract":"<div><div>Predicting viral evolution presents a significant challenge and is a critical public health priority. In response to this challenge, we develop a novel model for viral evolution that considers a trade-off between immunity evasion and transmissibility. The model selects for a new strain with the highest invasion fitness, taking into account this trade-off. When the dominant strain of the pathogen is highly transmissible, evolution tends to favor immune evasion, whereas for less contagious strains the direction of evolution leads toward increasing transmissibility. Assuming a linear functional form of this trade-off, we can express the long-term evolutionary patterns following the emergence of subsequent strains by a non-linear difference equation. We provide sufficient criteria for when evolution converges, and successive strains exhibit similar transmissibility. We also identify scenarios characterized by a two-periodic pattern in upcoming strains, indicating a situation where a highly transmissible but not immune-evasive strain is replaced by a less transmissible but highly immune-evasive strain, and vice versa, creating a cyclic pattern. Finally, we show that under certain conditions, viral evolution becomes chaotic and thus future transmissibilites become unpredictable in the long run. Visualization via bifurcation diagrams elucidates our analytical findings, revealing complex dynamic behaviors that include the presence of multiple periodic solutions and extend to chaotic regimes. Our analysis provides valuable insights into the complexities of viral evolution in the light of the trade-off between immune evasion and transmissibility.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 909-923"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851474","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":"Early prediction of the outbreak risk of dengue fever in Ba Ria-Vung Tau province, Vietnam: An analysis based on Google trends and statistical models","authors":"Dang Anh Tuan ,&nbsp;Pham Vu Nhat Uyen","doi":"10.1016/j.idm.2025.03.001","DOIUrl":"10.1016/j.idm.2025.03.001","url":null,"abstract":"<div><div>Dengue fever (DF), caused by the Dengue virus through the Aedes mosquito vector, is a dangerous infectious disease with the potential to become a global epidemic. Vietnam, particularly Ba Ria-Vung Tau (BRVT) province, is facing a high risk of DF. This study aims to determine the relationship between the search volume for DF on Google Trends and DF cases in BRVT province, thereby constructing a model to predict the early outbreak risk of DF locally. Using Poisson regression (adjusted by quasi-Poisson), considering the lagged effect of Google Trends Index (GTI) search volume on DF cases, and removing the autocorrelation (AC) of DF cases by using appropriate transformations, seven forecast models were surveyed based on the dataset of DF cases and GTI search volume weekly with the phrase \"sốt xuất huyết\" (dengue fever) in BRVT province from January 2019 to August 2023 (243 weeks). The model selected is the one with the lowest dispersion index. The results show that the correlation coefficient (95% confidence interval) and dispersion index of the 7 models including Basis TSR; Basis TSR + AC: Lag(Residuals,1); Basis TSR + AC: Lag(SXH,1); Basis TSR + AC: Lag(log(SXH+1),1); TSR Lag(GTI,2) + AC: Lag(log(SXH+1),2); TSR Lag(GTI,3) + AC: Lag(log(SXH+1),3); TSR Lag(GTI,0) + AC: Lag(log(SXH+1),1) are 0.71 (0.63–0.76) and 74.2; 0.79 (0.73–0.83) and 48.6; 0.89 (0.87–0.92) and 37.3; 0.98 (0.97–0.99) and 7.2; 0.96 (0.95–0.97) and 14.3; 0.93 (0.91–0.94) and 25.7; 0.98 (0.97–0.99) and 6.8, respectively. Therefore, the final model is the most suitable one selected. Testing the accuracy of the selected model using the ROC curve with the Youden criterion, the AUC (threshold 75%) is 0.982, and the AUC (threshold 95%) is 0.984, indicating the very good predictive ability of the model. In summary, the research results show the potential for applying this model in Vietnam, especially in BRVT, to enhance the effectiveness of epidemic prevention measures and protect public health.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 743-757"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631905","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":"Comparison of contact tracing methods: A modelling study","authors":"Joanna X.R. Tan ,&nbsp;Lalitha Kurupatham ,&nbsp;Zubaidah Said ,&nbsp;Jeremy Chan ,&nbsp;Kelvin Bryan Tan ,&nbsp;Marc Ho ,&nbsp;Vernon Lee ,&nbsp;Alex R. Cook","doi":"10.1016/j.idm.2025.05.007","DOIUrl":"10.1016/j.idm.2025.05.007","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Introduction&lt;/h3&gt;&lt;div&gt;Contact tracing has been a key tool to contain the spread of diseases and was widely used by countries during the COVID-19 pandemic. However, evaluating the effectiveness of contact tracing has been challenging. Approaches to contact tracing were diverse and country-dependent, with operations utilizing different tracing methods under varied environments. To provide guidance on contact tracing for future preparedness, we assessed the effectiveness of contact tracing methods under varied environments using Singapore's population structure and COVID-19 as the disease model.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We developed a transmission network model using Singapore's contact tracing data and the characteristics of COVID-19 disease. We explored three different tracing methods that could be employed by contact tracing operations: forward tracing, extended tracing and cluster tracing. The forward tracing method covered the period starting two days before case isolation, the extended tracing method covered the period starting 16 days before case isolation, and the cluster tracing method combined forward tracing with cluster identification. Contact tracing operations traced detected cases from surveillance and issued interventions for identified contacts, and we constructed combinations of varied scenarios to replicate variability during pandemic, namely low case-ascertainment or high case-ascertainment and either testing of contacts or quarantine of contacts. We examined the impact of varied contact tracing operations on disease transmission and provider costs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Model simulations showed that the effectiveness of contact tracing methods varied under the four different scenarios. Firstly, under low case-ascertainment with testing of contacts, contact tracing reduced transmission by 12 %–22 %, with provider costs ranging between US$2943.56 to US$5226.82 per infection prevented. The most effective tracing method to control infection was cluster tracing, followed by extended tracing and forward tracing. Secondly, under low case-ascertainment with quarantine of contacts, transmission was reduced by 46 %–62 %, with provider costs below US$4000 per infection prevented. The cluster method reduced transmission by 62 %, enough to bring the reproduction number to close to unity and was the least costly. Extended tracing reduced transmission by 50 % but costed the most, while forward tracing reduced transmission by 46 %. Thirdly, under high case-ascertainment with testing of contacts, the average transmission was reduced by 20 %–26 %, with provider costs to prevent an infection ranging between US$1872.72 to US$3165.09. There was less variability between tracing methods, with cluster tracing reducing transmission the most, followed by extended tracing and forward tracing. Lastly, under high case-ascertainment and quarantine of contacts, contact tracing was the most effective, with provider costs bel","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 1020-1032"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115887","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":"Dynamic predicting hepatitis B surface antigen decline rate during treatment for patients with chronic hepatitis B","authors":"Ying Xin ,&nbsp;Yuming Wang ,&nbsp;Qiang Li ,&nbsp;Xianghong Zhang ,&nbsp;Kaifa Wang ,&nbsp;Guangyu Huang","doi":"10.1016/j.idm.2025.05.004","DOIUrl":"10.1016/j.idm.2025.05.004","url":null,"abstract":"<div><div>Prediction of hepatitis B surface antigen (HBsAg) decline rates during treatment is crucial for achieving a higher proportion of functional cure outcomes in patients with chronic hepatitis B (CHB), and so is the identification of favorable patients. A total of 371 patients who received pegylated interferon alpha monotherapy or sequential/combined nucleos(t)ide analogues therapy between May 2018 and July 2024 were included for follow-up analysis. The patients were divided into a training set, a validation set and a test set via time series partitioning and random partitioning methods. The primary outcome was the prediction of HBsAg decline rate at each medical visit via linear mixed effects model. Patient stratification was secondary outcomes assessed using group-based trajectory model. The cumulative number of functional cures among 371 patients was 76 (20%, 95% CI: 16%–25%). Three groups, namely rapid high-clearance, delayed high-clearance, and slow low-clearance, were identified by the group trajectory model. The overall accuracy of the time-plus-group dual-effect prediction model was 84% (95% CI: 81%–87%), which was approximately 10% higher than that of the time-effect prediction model after 24 weeks of treatment. When the computational cost was combined, a pragmatic prediction strategy with robust individual prediction performance was obtained. The constructed group trajectory model and prediction strategy may have the potential to dynamically identify favorable patients and dynamically predict the HBsAg decline rate, thereby improving the functional cure rate in clinical practice.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 979-988"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948952","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":"Stochastic SIRS models on networks: mean and variance of infection","authors":"Tingting Chen ,&nbsp;Guirong Liu ,&nbsp;Zhen Jin","doi":"10.1016/j.idm.2025.03.008","DOIUrl":"10.1016/j.idm.2025.03.008","url":null,"abstract":"<div><div>Due to the heterogeneity of contact structure, it is more reasonable to model on networks for epidemics. Because of the stochastic nature of events and the discrete number of individuals, the spread of epidemics is more appropriately viewed as a Markov chain. Therefore, we establish stochastic SIRS models with vaccination on networks to study the mean and variance of the number of susceptible and infected individuals for large-scale populations. Using van Kampen's system-size expansion, we derive a high-dimensional deterministic system which describes the mean behaviour and a Fokker-Planck equation which characterizes the variance around deterministic trajectories. Utilizing the qualitative analysis technique and Lyapunov function, we demonstrate that the disease-free equilibrium of the deterministic system is globally asymptotically stable if the basic reproduction number <em>R</em>₀ &lt; 1; and the endemic equilibrium is globally asymptotically stable if <em>R</em>₀ &gt; 1. Through the analysis of the Fokker-Planck equation, we obtain the asymptotic expression for the variance of the number of susceptible and infected individuals around the endemic equilibrium, which can be approximated by the elements of principal diagonal of the solution of the corresponding Lyapunov equation. Here, the solution of Lyapunov equation is expressed by vectorization operator of matrices and Kronecker product. Finally, numerical simulations illustrate that vaccination can reduce infections and increase fluctuations of the number of infected individuals and show that individuals with greater degree are more easily infected.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 875-896"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844306","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 information dissemination and behavioural responses on epidemic dynamics: A multi-layer network analysis","authors":"Congjie Shi ,&nbsp;Silvio C. Ferreira ,&nbsp;Hugo P. Maia ,&nbsp;Seyed M. Moghadas","doi":"10.1016/j.idm.2025.04.004","DOIUrl":"10.1016/j.idm.2025.04.004","url":null,"abstract":"<div><div>Network models adeptly capture heterogeneities in individual interactions, making them well-suited for describing a wide range of real-world and virtual connections, including information diffusion, behavioural tendencies, and disease dynamic fluctuations. However, there is a notable methodological gap in existing studies examining the interplay between physical and virtual interactions and the impact of information dissemination and behavioural responses on disease propagation. We constructed a three-layer (information, cognition, and epidemic) network model to investigate the adoption of protective behaviours, such as wearing masks or practising social distancing, influenced by the diffusion and correction of misinformation. We examined five key events influencing the rate of information spread: (i) rumour transmission, (ii) information suppression, (iii) renewed interest in spreading misinformation, (iv) correction of misinformation, and (v) relapse to a stifler state after correction. We found that adopting information-based protection behaviours is more effective in mitigating disease spread than protection adoption induced by neighbourhood interactions. Specifically, our results show that warning and educating individuals to counter misinformation within the information network is a more effective strategy for curbing disease spread than suspending gossip spreaders from the network. Our study has practical implications for developing strategies to mitigate the impact of misinformation and enhance protective behavioural responses during disease outbreaks.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 960-978"},"PeriodicalIF":8.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948951","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}