Pub Date : 2025-05-21DOI: 10.1016/j.idm.2025.05.005
Christopher D. Prashad
We present an exploration of advanced stochastic simulation techniques for state-space models, with a specific focus on their applications in infectious disease modelling. Utilizing COVID-19 surveillance data from the province of Ontario, Canada, we employ Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods to detect structural changes and pre-dict future trends in case counts. Our approach begins with the application of a Kalman smoothing technique, integrated with MCMC for state sampling within local level and seasonal models, alongside Bayesian inference for non-linear dynamic regression models. We then assess the effectiveness of various priors, including normal, Student's t, Laplace, and horseshoe distributions, in capturing abrupt changes within the data using a Rao-Blackwellized par-ticle filter. Our findings highlight the superior performance of the horseshoe prior in identifying change points and adapting to complex data structures, offering valuable insights for real-time monitoring and forecasting in public health. This study emphasizes the efficacy of state-space models, particu-larly when enhanced with sophisticated prior distributions, in providing a nuanced understanding of infectious disease transmission.
{"title":"State-space modelling for infectious disease surveillance data: Stochastic simulation techniques and structural change detection","authors":"Christopher D. Prashad","doi":"10.1016/j.idm.2025.05.005","DOIUrl":"10.1016/j.idm.2025.05.005","url":null,"abstract":"<div><div>We present an exploration of advanced stochastic simulation techniques for state-space models, with a specific focus on their applications in infectious disease modelling. Utilizing COVID-19 surveillance data from the province of Ontario, Canada, we employ Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods to detect structural changes and pre-dict future trends in case counts. Our approach begins with the application of a Kalman smoothing technique, integrated with MCMC for state sampling within local level and seasonal models, alongside Bayesian inference for non-linear dynamic regression models. We then assess the effectiveness of various priors, including normal, Student's t, Laplace, and horseshoe distributions, in capturing abrupt changes within the data using a Rao-Blackwellized par-ticle filter. Our findings highlight the superior performance of the horseshoe prior in identifying change points and adapting to complex data structures, offering valuable insights for real-time monitoring and forecasting in public health. This study emphasizes the efficacy of state-space models, particu-larly when enhanced with sophisticated prior distributions, in providing a nuanced understanding of infectious disease transmission.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1507-1532"},"PeriodicalIF":2.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879023","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}
Pub Date : 2025-05-20DOI: 10.1016/j.idm.2025.05.008
Sarah Rollason , Eleanor Riley , Joanne Lello
Malaria and schistosomiasis are two of the most clinically important human parasitic diseases in terms of morbidity and mortality, collectively causing approximately 800,000 deaths annually. Coinfection with their causative parasites, Plasmodium spp. and Schistosoma spp., is common, particularly in sub-Saharan Africa. These parasites may interact with each other via their effects on the host immune system, but studies to date report conflicting consequences of such interactions, some suggesting that schistosomes are associated with reduced parasitaemia in malaria infection while others report increased parasitaemia. Schistosomes stimulate different immune components in early versus late infection. Using agent-based modelling we explore whether stage of infection could be a factor explaining the conflicting coinfection outcomes. Effects of schistosomes on blood stage malaria were modelled by adjusting the immune components within the model according to the response provoked by each schistosome stage. We find the dynamics of malaria infections are greatly influenced by the stage of schistosomes, with acute and chronic schistosome infections having opposite effects on both peak infected erythrocyte counts and duration. Our findings offer a possible explanation for the apparent contradictions between studies and highlight the importance of considering the stage of schistosome infection when exploring the relationship between these two parasites.
{"title":"Stage specific immune responses to schistosomes may explain conflicting results in malaria-schistosome coinfection studies","authors":"Sarah Rollason , Eleanor Riley , Joanne Lello","doi":"10.1016/j.idm.2025.05.008","DOIUrl":"10.1016/j.idm.2025.05.008","url":null,"abstract":"<div><div>Malaria and schistosomiasis are two of the most clinically important human parasitic diseases in terms of morbidity and mortality, collectively causing approximately 800,000 deaths annually. Coinfection with their causative parasites, <em>Plasmodium</em> spp. and <em>Schistosoma</em> spp., is common, particularly in sub-Saharan Africa. These parasites may interact with each other via their effects on the host immune system, but studies to date report conflicting consequences of such interactions, some suggesting that schistosomes are associated with reduced parasitaemia in malaria infection while others report increased parasitaemia. Schistosomes stimulate different immune components in early versus late infection. Using agent-based modelling we explore whether stage of infection could be a factor explaining the conflicting coinfection outcomes. Effects of schistosomes on blood stage malaria were modelled by adjusting the immune components within the model according to the response provoked by each schistosome stage. We find the dynamics of malaria infections are greatly influenced by the stage of schistosomes, with acute and chronic schistosome infections having opposite effects on both peak infected erythrocyte counts and duration. Our findings offer a possible explanation for the apparent contradictions between studies and highlight the importance of considering the stage of schistosome infection when exploring the relationship between these two parasites.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1003-1018"},"PeriodicalIF":8.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137979","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}
Pub Date : 2025-05-16DOI: 10.1016/j.idm.2025.05.007
Joanna X.R. Tan , Lalitha Kurupatham , Zubaidah Said , Jeremy Chan , Kelvin Bryan Tan , Marc Ho , Vernon Lee , Alex R. Cook
Introduction
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.
Methods
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.
Results
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
{"title":"Comparison of contact tracing methods: A modelling study","authors":"Joanna X.R. Tan , Lalitha Kurupatham , Zubaidah Said , Jeremy Chan , Kelvin Bryan Tan , Marc Ho , Vernon Lee , Alex R. Cook","doi":"10.1016/j.idm.2025.05.007","DOIUrl":"10.1016/j.idm.2025.05.007","url":null,"abstract":"<div><h3>Introduction</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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":"2025-05-16","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}
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.
准确识别疾病发生的空间格局和风险因素对公共卫生干预至关重要。然而，可修改面积单位问题（MAUP）通过影响从空间聚合数据中得出的统计推断的可靠性，对疾病建模提出了挑战。本研究利用澳大利亚昆士兰州2020年至2023年本地和海外获得的COVID-19病例数据，检验了MAUP对生态模型推断的影响。贝叶斯空间besag - york - molli (BYM)模型应用于澳大利亚统计地理标准定义的四个统计区域（SA）水平，包括和不包括协变量：地区社会经济指数（SEIFA）和海外获得性（OA） COVID-19病例。在我们的研究中，OA COVID-19病例也被认为是一个反应变量。结果表明，较细的空间尺度（SA1和SA2）捕获了局部模式和显著的空间自相关性，而较粗的空间尺度（SA3和SA4）平滑了空间变异性，掩盖了潜在的爆发集群。将SEIFA作为协变量纳入本地获得（LA）病例中，降低了残差的空间自相关性，有效地捕获了社会经济差异。相反，OA病例在更细尺度上降低自相关性的效果有限。对于洛杉矶病例，在较细的尺度上，较高的社会经济劣势与COVID-19发病率增加有关，但在较粗的尺度上，这种关联变得不显著。OA病例在更精细的尺度上显示更高的SEIFA评分显著正相关。模型参数在更细的尺度上显示出更窄的可信区间，表明精度更高，而更粗的水平则增加了不确定性。SA2可以说是一个最佳尺度，在空间分辨率、模式稳定性和可解释性之间取得了平衡。为提高对COVID-19发病率的推断，建议同时使用SA1和SA2级别的数据，以发挥各自的优势。研究结果强调了选择合适的空间尺度和协变量或评估多尺度的推断影响的重要性，以解决MAUP问题，以促进更可靠的空间分析。该研究提倡探索中间聚集水平和多尺度方法，以更好地捕捉细微的疾病动态，并将这些分析扩展到整个澳大利亚，并在其他人口密度低的国家复制，以提高普遍性。
{"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 , Aiden Price , Kerrie Mengersen , 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":"2025-05-10","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}
Pub Date : 2025-05-09DOI: 10.1016/j.idm.2025.05.004
Ying Xin , Yuming Wang , Qiang Li , Xianghong Zhang , Kaifa Wang , Guangyu Huang
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.
{"title":"Dynamic predicting hepatitis B surface antigen decline rate during treatment for patients with chronic hepatitis B","authors":"Ying Xin , Yuming Wang , Qiang Li , Xianghong Zhang , Kaifa Wang , 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":"2025-05-09","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}
Pub Date : 2025-05-06DOI: 10.1016/j.idm.2025.05.002
Peiji Li, Mengmeng Dai, Yayi Wang, Yingbo Liu
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.
{"title":"Estimation of under-reporting influenza cases in Hong Kong based on bayesian hierarchical framework","authors":"Peiji Li, Mengmeng Dai, Yayi Wang, 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":"2025-05-06","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}
Pub Date : 2025-04-30DOI: 10.1016/j.idm.2025.04.006
Banghua Chen , Jie Pan , Ying Peng , Yuanyuan Zhang , Yunan Wan , Hongjie Wei , Kangguo Li , Wentao Song , Yunkang Zhao , Kang Fang , Huiming Ye , Jiali Cao , Jia Rui , Zeyu Zhao , Tianmu Chen
Objectives
Mycoplasma pneumoniae (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.
Methods and results
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-Chlamydia pneumoniae (C. pneumoniae) / 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 C. pneumoniae, Pseudomonas aeruginosa (P. aeruginosa), Haemophilus influenzae (H. influenzae), and Streptococcus pneumoniae (S. pneumoniae), increased hospitalization rates. The most severe outcomes and deaths occurred in patients coinfected with C. pneumoniae-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.
Conclusions
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.
{"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 , Jie Pan , Ying Peng , Yuanyuan Zhang , Yunan Wan , Hongjie Wei , Kangguo Li , Wentao Song , Yunkang Zhao , Kang Fang , Huiming Ye , Jiali Cao , Jia Rui , Zeyu Zhao , 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":"2025-04-30","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}
Pub Date : 2025-04-23DOI: 10.1016/j.idm.2025.04.005
Liza Hadley , Caylyn Rich , Alex Tasker , Olivier Restif , Sebastian Funk
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.
{"title":"Visual preferences for communicating modelling: a global analysis of COVID-19 policy and decision makers","authors":"Liza Hadley , Caylyn Rich , Alex Tasker , Olivier Restif , 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":"2025-04-23","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}
Pub Date : 2025-04-16DOI: 10.1016/j.idm.2025.04.004
Congjie Shi , Silvio C. Ferreira , Hugo P. Maia , Seyed M. Moghadas
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.
{"title":"Impact of information dissemination and behavioural responses on epidemic dynamics: A multi-layer network analysis","authors":"Congjie Shi , Silvio C. Ferreira , Hugo P. Maia , 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":"2025-04-16","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}
Pub Date : 2025-04-15DOI: 10.1016/j.idm.2025.04.002
Yufan Zheng , Keqi Yue , Eric W.M. Wong , Hsiang-Yu Yuan
Background
While Aedes 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.
Objectives
We aimed to assess the influence of human mobility on the abundance and extensiveness of Aedes albopictus, taking account of the nonlinear lagged effects of weather, during the COVID-19 pandemic in Hong Kong.
Methods
Google human mobility indices (including residential, parks, and workplaces) and weather conditions (total rainfall and mean temperature) along with Aedes albopictus 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.
Results
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 (<50 mm) after 4.5 months, peaking at 1.73, compared with 300 mm. Heavy rainfall (>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.
Discussion
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 Aedes albopictus.
{"title":"Impact of human mobility and weather conditions on Dengue mosquito abundance during the COVID-19 pandemic in Hong Kong","authors":"Yufan Zheng , Keqi Yue , Eric W.M. Wong , 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 (<50 mm) after 4.5 months, peaking at 1.73, compared with 300 mm. Heavy rainfall (>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":"2025-04-15","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}
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