Pub Date : 2025-08-21DOI: 10.1016/j.idm.2025.08.004
Bimandra A. Djaafara , Verry Adrian , Etrina Eriawati , Iqbal R.F. Elyazar , Raph L. Hamers , J. Kevin Baird , Guy E. Thwaites , Hannah E. Clapham
Diphtheria has resurged globally, including in Indonesia, despite widespread vaccination since the 1970s. Knowledge gaps persist in understanding contemporary transmission drivers and effective outbreak control, especially in densely populated areas like Jakarta. We analyzed the 2017 Jakarta outbreak data and developed a compartmental model incorporating estimates of population susceptibility and asymptomatic carriers. Key epidemiological parameters were estimated, and various control measures were simulated. Our study found overall diphtheria susceptibility at 12.9 % (95 % CrI: 8.6 %–19.0 %) and 28.0 % (95 % CrI: 20.5 %–36.0 %) in children under 5 under different modeling scenarios, which were below the 'herd immunity threshold'. We estimated asymptomatic carriers to be highly prevalent, substantially contributing to the reproduction number. The model indicated that contact tracing and treating suspected cases and their contacts were more effective in preventing new cases than catch-up vaccination alone. These findings provide valuable insights for future outbreak management strategies in similar settings.
{"title":"Modeling the transmission dynamics and control strategies during the 2017 diphtheria outbreak in Jakarta, Indonesia","authors":"Bimandra A. Djaafara , Verry Adrian , Etrina Eriawati , Iqbal R.F. Elyazar , Raph L. Hamers , J. Kevin Baird , Guy E. Thwaites , Hannah E. Clapham","doi":"10.1016/j.idm.2025.08.004","DOIUrl":"10.1016/j.idm.2025.08.004","url":null,"abstract":"<div><div>Diphtheria has resurged globally, including in Indonesia, despite widespread vaccination since the 1970s. Knowledge gaps persist in understanding contemporary transmission drivers and effective outbreak control, especially in densely populated areas like Jakarta. We analyzed the 2017 Jakarta outbreak data and developed a compartmental model incorporating estimates of population susceptibility and asymptomatic carriers. Key epidemiological parameters were estimated, and various control measures were simulated. Our study found overall diphtheria susceptibility at 12.9 % (95 % CrI: 8.6 %–19.0 %) and 28.0 % (95 % CrI: 20.5 %–36.0 %) in children under 5 under different modeling scenarios, which were below the 'herd immunity threshold'. We estimated asymptomatic carriers to be highly prevalent, substantially contributing to the reproduction number. The model indicated that contact tracing and treating suspected cases and their contacts were more effective in preventing new cases than catch-up vaccination alone. These findings provide valuable insights for future outbreak management strategies in similar settings.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 1-15"},"PeriodicalIF":2.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019348","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 : 2025-08-12DOI: 10.1016/j.idm.2025.08.003
Juan Campos , Maria C.A. Leite
In this paper, we develop a novel mathematical framework based on the Kermack- McKendrick model to extract epidemiological parameters from real temporal profiles consisting of waves. The approach's key feature is the ability to obtain all model parameters from the geometry of the wave of interest.
We propose three new quantities to measure the negative impact of the epidemic wave on a specific population, called Fraction of endemicity, Severity, and Asymmetry. These three measures, along with a refined definition of the basic reproduction number, provide crucial epidemiological information.
We demonstrate analytically that there is an equivalence among these quantities, and such equivalence gives a way of obtaining all parameters in the model since the Asymmetry of a real epidemic wave is easily computed. This is the heart of the novel methodology we introduce. The framework is suitable for public health decision support, as its implementation does not rely on complex mathematical tools.
We present several case studies to illustrate the simplicity of the framework as well as the distinct aspects of its implementation. In all examples investigated, the numeric solution obtained with the parameterized model shows good agreement with the available data.
{"title":"Novel approach to extract epidemiological information from waves in epidemic's profiles","authors":"Juan Campos , Maria C.A. Leite","doi":"10.1016/j.idm.2025.08.003","DOIUrl":"10.1016/j.idm.2025.08.003","url":null,"abstract":"<div><div>In this paper, we develop a novel mathematical framework based on the Kermack- McKendrick model to extract epidemiological parameters from real temporal profiles consisting of waves. The approach's key feature is the ability to obtain all model parameters from the geometry of the wave of interest.</div><div>We propose three new quantities to measure the negative impact of the epidemic wave on a specific population, called <em>Fraction of endemicity</em>, <em>Severity</em>, and <em>Asymmetry</em>. These three measures, along with a refined definition of the <em>basic reproduction number</em>, provide crucial epidemiological information.</div><div>We demonstrate analytically that there is an equivalence among these quantities, and such equivalence gives a way of obtaining all parameters in the model since the <em>Asymmetry</em> of a real epidemic wave is easily computed. This is the heart of the novel methodology we introduce. The framework is suitable for public health decision support, as its implementation does not rely on complex mathematical tools.</div><div>We present several case studies to illustrate the simplicity of the framework as well as the distinct aspects of its implementation. In all examples investigated, the numeric solution obtained with the parameterized model shows good agreement with the available data.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 87-106"},"PeriodicalIF":2.5,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047954","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 : 2025-08-09DOI: 10.1016/j.idm.2025.08.002
Abdelmalik Moujahid , Fernando Vadillo
Time delays are a fundamental feature in modeling stochastic epidemic systems, as they capture the incubation period and other physiological lags inherent in disease transmission. In this work, we investigate a stochastic SIRC (Susceptible-Infectious-Recovered-Cross-immune) epidemic model where the delay is incorporated into the transmission term to reflect the incubation period. To account for environmental variability, we examine two stochastic formulations: the classical approach, which adds independent white noise to each compartment, and a probabilistic, event-driven model in which stochasticity arises directly from transition probabilities.
A key focus of our study is the comparison between different delay formulations in the transmission term, specifically contrasting the standard approach—where the delay acts only on the infected compartment—with alternative formulations that distribute the delay across both susceptible and infected populations. Through systematic numerical simulations, we find that the choice of delay formulation strongly influences the timing and magnitude of the initial epidemic peak, while the long-term (asymptotic) behavior is more robust but remains sensitive to the underlying stochastic framework. The probabilistic model, in particular, offers a more faithful depiction of correlated fluctuations and extinction phenomena, capturing the biological complexity of epidemic processes more accurately than the classical approach. These results underscore the importance of both the delay representation and the stochastic modeling strategy in shaping the qualitative and quantitative features of epidemic dynamics.
{"title":"Comparing virus incubation time in SIRC models: Deterministic versus stochastic approaches","authors":"Abdelmalik Moujahid , Fernando Vadillo","doi":"10.1016/j.idm.2025.08.002","DOIUrl":"10.1016/j.idm.2025.08.002","url":null,"abstract":"<div><div>Time delays are a fundamental feature in modeling stochastic epidemic systems, as they capture the incubation period and other physiological lags inherent in disease transmission. In this work, we investigate a stochastic SIRC (Susceptible-Infectious-Recovered-Cross-immune) epidemic model where the delay is incorporated into the transmission term to reflect the incubation period. To account for environmental variability, we examine two stochastic formulations: the classical approach, which adds independent white noise to each compartment, and a probabilistic, event-driven model in which stochasticity arises directly from transition probabilities.</div><div>A key focus of our study is the comparison between different delay formulations in the transmission term, specifically contrasting the standard approach—where the delay acts only on the infected compartment—with alternative formulations that distribute the delay across both susceptible and infected populations. Through systematic numerical simulations, we find that the choice of delay formulation strongly influences the timing and magnitude of the initial epidemic peak, while the long-term (asymptotic) behavior is more robust but remains sensitive to the underlying stochastic framework. The probabilistic model, in particular, offers a more faithful depiction of correlated fluctuations and extinction phenomena, capturing the biological complexity of epidemic processes more accurately than the classical approach. These results underscore the importance of both the delay representation and the stochastic modeling strategy in shaping the qualitative and quantitative features of epidemic dynamics.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"11 1","pages":"Pages 16-28"},"PeriodicalIF":2.5,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019349","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 : 2025-08-07DOI: 10.1016/j.idm.2025.08.001
Jing An , Wenhui Hao , Huifen Guo , Juping Zhang
The dynamic system of HPV transmission with age subgroups, sexual and nonsexual transmission is established based on HPV vaccination. Firstly, the transmission threshold R0 of the system is given. Local asymptotically stabilization of disease-free equilibrium when R0 < 1 is proved. It is proved that there is a positive equilibrium and disease persistence in the system when R0 > 1. Secondly, parameters estimation of the system is carried out based on the data from Chinese STD surveillance sites using the least square method. Finally, optimal control theory is applied to the system, the existence of optimal control is proved, and Pontryagin maximum principle is utilized to find optimal control strategy, and the spread of human papillomavirus in different age groups is predicted. The results show that for different age groups, vaccination of 16–45 years old is more beneficial to HPV control than vaccination of 9–15 years old, and that for different control costs, low-cost control is more advantageous.
{"title":"Analysis of human papillomavirus model with vaccination and individual heterogeneity","authors":"Jing An , Wenhui Hao , Huifen Guo , Juping Zhang","doi":"10.1016/j.idm.2025.08.001","DOIUrl":"10.1016/j.idm.2025.08.001","url":null,"abstract":"<div><div>The dynamic system of HPV transmission with age subgroups, sexual and nonsexual transmission is established based on HPV vaccination. Firstly, the transmission threshold <em>R</em><sub>0</sub> of the system is given. Local asymptotically stabilization of disease-free equilibrium when <em>R</em><sub>0</sub> < 1 is proved. It is proved that there is a positive equilibrium and disease persistence in the system when <em>R</em><sub>0</sub> > 1. Secondly, parameters estimation of the system is carried out based on the data from Chinese STD surveillance sites using the least square method. Finally, optimal control theory is applied to the system, the existence of optimal control is proved, and Pontryagin maximum principle is utilized to find optimal control strategy, and the spread of human papillomavirus in different age groups is predicted. The results show that for different age groups, vaccination of 16–45 years old is more beneficial to HPV control than vaccination of 9–15 years old, and that for different control costs, low-cost control is more advantageous.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1533-1574"},"PeriodicalIF":2.5,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894793","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 : 2025-08-05DOI: 10.1016/j.idm.2025.07.013
Shanshan Feng , Wan-Ting Cheng , Xing Li, Xiaofeng Luo
In recent years, the number of HIV/AIDS cases shows an upward trend in China, particularly among the elderly, exerting severe effects on public health and social economy. This paper proposes an HIV/AIDS model incorporating sexual transmission and age structure to study the influence of the elderly on HIV/AIDS spread. Theoretically, the explicit expression for the basic reproduction number is obtained, the globally asymptotically stability of disease-free equilibrium and existence and uniqueness of boundary equilibrium are proved. Numerically, we verify the theoretical results. Based on HIV/AIDS data in Sichuan Province, China, four key parameters of the model are estimated. According to the estimated parameters, we find that homosexual transmission plays an important role in newly HIV/AIDS cases among the elderly in recent years. Sensitivity analysis also shows that homosexual transmission in the elderly has the greatest effect on the basic reproduction number. This study not only contributes to a comprehensive understanding of the dynamical spread process of HIV/AIDS but also provides valuable experience for other sexually transmitted diseases.
{"title":"Contributions of the elderly to the transmission of HIV/AIDS in China","authors":"Shanshan Feng , Wan-Ting Cheng , Xing Li, Xiaofeng Luo","doi":"10.1016/j.idm.2025.07.013","DOIUrl":"10.1016/j.idm.2025.07.013","url":null,"abstract":"<div><div>In recent years, the number of HIV/AIDS cases shows an upward trend in China, particularly among the elderly, exerting severe effects on public health and social economy. This paper proposes an HIV/AIDS model incorporating sexual transmission and age structure to study the influence of the elderly on HIV/AIDS spread. Theoretically, the explicit expression for the basic reproduction number is obtained, the globally asymptotically stability of disease-free equilibrium and existence and uniqueness of boundary equilibrium are proved. Numerically, we verify the theoretical results. Based on HIV/AIDS data in Sichuan Province, China, four key parameters of the model are estimated. According to the estimated parameters, we find that homosexual transmission plays an important role in newly HIV/AIDS cases among the elderly in recent years. Sensitivity analysis also shows that homosexual transmission in the elderly has the greatest effect on the basic reproduction number. This study not only contributes to a comprehensive understanding of the dynamical spread process of HIV/AIDS but also provides valuable experience for other sexually transmitted diseases.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1488-1506"},"PeriodicalIF":2.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828164","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 : 2025-08-05DOI: 10.1016/j.idm.2025.07.014
Laís Picinini Freitas , Danielle Andreza da Cruz Ferreira , Raquel Martins Lana , Daniel Cardoso Portela Câmara , Tatiana P. Portella , Marilia Sá Carvalho , Ayrton Sena Gouveia , Iasmim Ferreira de Almeida , Eduardo Correa Araujo , Luã Bida Vacaro , Fabiana Ganem , Oswaldo Gonçalves Cruz , Flávio Codeço Coelho , Claudia Torres Codeço , Luiz Max Carvalho , Leonardo Soares Bastos
Dengue is a vector-borne disease and a major public health concern in Brazil. Its continuing and rising burden has led the Brazilian Ministry of Health to request for modelling efforts to aid in the preparedness and response to the disease. In this context, we propose a Bayesian forecasting model based on historical data to predict the number of cases 52 weeks ahead for the 118 health districts of Brazil. We leverage the predictions to build probabilistic epidemics bands to be used for dengue monitoring. We define four disjoint probabilistic bands (≤50% (50%, 75%] (75%, 90%], and 90%), based on the percentiles of the predicted cases distribution and interpreted according to the historical number of cases and past occurrence probability (below the median, typical; moderately high, fairly typical; fairly high, atypical; exceptionally high, very atypical). We performed out-of-sample validation for 2022–2023 and 2023–2024 and forecasted 2024–2025. In the 2022–2023 and 2023–2024 seasons, the epidemic bands followed the observed cases’ curve shape, with a sharp increase after January and a decline after the peak around April. In 2022–2023, the observed number of cases (1,436,034) was slightly above the estimated 75% percentile (1,405,191), being classified as “fairly high, atypical”. Most health districts in South Brazil showed exceptionally high numbers of cases during this season. The situation worsened in 2023–2024 and the observed number of cases (6,454,020) was way above the 90% percentile (2,221,557), characterising an “exceptionally high, very atypical” season. For the 2024–2025 season, we estimated a median number of cases of 1,526,523 (maximum value for the “below the median, typical” probabilistic epidemic band. The maximum estimated values for the upper bands were 2,213,282 (moderately high, fairly typical) and 3,803,898 (fairly high, atypical) with the upper limits of the probabilistic epidemic bands of 1,452,359. Probabilistic epidemic bands serve as a valuable monitoring tool by enabling prospective comparisons between observed case curves and historical epidemic patterns, facilitating the assessment of ongoing outbreaks about past occurrences.
{"title":"A statistical model for forecasting probabilistic epidemic bands for dengue cases in Brazil","authors":"Laís Picinini Freitas , Danielle Andreza da Cruz Ferreira , Raquel Martins Lana , Daniel Cardoso Portela Câmara , Tatiana P. Portella , Marilia Sá Carvalho , Ayrton Sena Gouveia , Iasmim Ferreira de Almeida , Eduardo Correa Araujo , Luã Bida Vacaro , Fabiana Ganem , Oswaldo Gonçalves Cruz , Flávio Codeço Coelho , Claudia Torres Codeço , Luiz Max Carvalho , Leonardo Soares Bastos","doi":"10.1016/j.idm.2025.07.014","DOIUrl":"10.1016/j.idm.2025.07.014","url":null,"abstract":"<div><div>Dengue is a vector-borne disease and a major public health concern in Brazil. Its continuing and rising burden has led the Brazilian Ministry of Health to request for modelling efforts to aid in the preparedness and response to the disease. In this context, we propose a Bayesian forecasting model based on historical data to predict the number of cases 52 weeks ahead for the 118 health districts of Brazil. We leverage the predictions to build probabilistic epidemics bands to be used for dengue monitoring. We define four disjoint probabilistic bands (≤50% (50%, 75%] (75%, 90%], and <span><math><mo>></mo></math></span>90%), based on the percentiles of the predicted cases distribution and interpreted according to the historical number of cases and past occurrence probability (below the median, typical; moderately high, fairly typical; fairly high, atypical; exceptionally high, very atypical). We performed out-of-sample validation for 2022–2023 and 2023–2024 and forecasted 2024–2025. In the 2022–2023 and 2023–2024 seasons, the epidemic bands followed the observed cases’ curve shape, with a sharp increase after January and a decline after the peak around April. In 2022–2023, the observed number of cases (1,436,034) was slightly above the estimated 75% percentile (1,405,191), being classified as “fairly high, atypical”. Most health districts in South Brazil showed exceptionally high numbers of cases during this season. The situation worsened in 2023–2024 and the observed number of cases (6,454,020) was way above the 90% percentile (2,221,557), characterising an “exceptionally high, very atypical” season. For the 2024–2025 season, we estimated a median number of cases of 1,526,523 (maximum value for the “below the median, typical” probabilistic epidemic band. The maximum estimated values for the upper bands were 2,213,282 (moderately high, fairly typical) and 3,803,898 (fairly high, atypical) with the upper limits of the probabilistic epidemic bands of 1,452,359. Probabilistic epidemic bands serve as a valuable monitoring tool by enabling prospective comparisons between observed case curves and historical epidemic patterns, facilitating the assessment of ongoing outbreaks about past occurrences.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1479-1487"},"PeriodicalIF":2.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813861","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 : 2025-07-23DOI: 10.1016/j.idm.2025.07.011
Woldegebriel Assefa Woldegerima , Chigozie Louisa J. Ugwu
Mpox remains a signi_cant public health challenge in endemic regions of Africa. Understanding its spatial distribution and identifying key drivers in high-risk countries is critical for guiding e_ective interventions. This study applies a Zero-Inated Poisson (ZIP) model with spatial autocorrelation to estimate the adjusted relative risk (RR) of Mpox incidence across 24 African countries, strati_ed by Human Development Index (HDI) levels. The model accounts for overdispersion and excess zeros by incorporating spatial random e_ects and socio-environmental covariates, and was validated through model diagnostics and sensitivity analysis, demonstrating robustness of results. Spatial analysis revealed substantial heterogeneity in Mpox incidence, with elevated risk in the Democratic Republic of Congo (DRC), Nigeria, and Central African Republic (CAR) persisting after covariate adjustment (p < 0:001). Higher HDI levels were inversely associated with Mpox risk, with HDI quintile Q4 (very high HDI) showing a signi _cant reduction (aRR = 0.431; 95 % CrI: 0.099{0.724). Protective factors in low-risk areas included increased life expectancy at birth (aRR = 0.768; 95 % CrI: 0.688{0.892), higher educational attainment (aRR = 0.774; 95 % CrI: 0.680{0.921), nonlinear increases in gross national income (GNI) per capita, and a greater density of skilled health workers (aRR = 0.788; 95 % CrI: 0.701{0.934). Conversely, higher urban density was associated with increased Mpox risk, underscoring the inuence of population clustering on transmission dynamics. Notably, statistically signi_cant elevated-risk areas persisted in endemic countries of Western and Central Africa after covariate adjustment (p < 0:001). In contrast, previously undetected risk emerged in parts of Southern and Eastern Africa post-adjustment, revealing latent patterns obscured in the crude analysis (p < 0:001). Exceedance probability maps identi_ed countries with P(RR > 1) > 0.9 as priority areas for intensi_ed surveillance and targeted intervention. These patterns were not fully explained by the included covariates, suggesting the inuence of unmeasured factors such as environmental and climate variability, zoonotic reservoirs, or human{animal interactions. Further research is needed to deepen understanding of Mpox epidemiology and support locally tailored interventions.
{"title":"Bayesian hierarchical modeling of Mpox in the African region (2022–2024): Addressing zero-inflation and spatial autocorrelation","authors":"Woldegebriel Assefa Woldegerima , Chigozie Louisa J. Ugwu","doi":"10.1016/j.idm.2025.07.011","DOIUrl":"10.1016/j.idm.2025.07.011","url":null,"abstract":"<div><div>Mpox remains a signi_cant public health challenge in endemic regions of Africa. Understanding its spatial distribution and identifying key drivers in high-risk countries is critical for guiding e_ective interventions. This study applies a Zero-Inated Poisson (ZIP) model with spatial autocorrelation to estimate the adjusted relative risk (RR) of Mpox incidence across 24 African countries, strati_ed by Human Development Index (HDI) levels. The model accounts for overdispersion and excess zeros by incorporating spatial random e_ects and socio-environmental covariates, and was validated through model diagnostics and sensitivity analysis, demonstrating robustness of results. Spatial analysis revealed substantial heterogeneity in Mpox incidence, with elevated risk in the Democratic Republic of Congo (DRC), Nigeria, and Central African Republic (CAR) persisting after covariate adjustment (p < 0:001). Higher HDI levels were inversely associated with Mpox risk, with HDI quintile Q4 (very high HDI) showing a signi _cant reduction (aRR = 0.431; 95 % CrI: 0.099{0.724). Protective factors in low-risk areas included increased life expectancy at birth (aRR = 0.768; 95 % CrI: 0.688{0.892), higher educational attainment (aRR = 0.774; 95 % CrI: 0.680{0.921), nonlinear increases in gross national income (GNI) per capita, and a greater density of skilled health workers (aRR = 0.788; 95 % CrI: 0.701{0.934). Conversely, higher urban density was associated with increased Mpox risk, underscoring the inuence of population clustering on transmission dynamics. Notably, statistically signi_cant elevated-risk areas persisted in endemic countries of Western and Central Africa after covariate adjustment (p < 0:001). In contrast, previously undetected risk emerged in parts of Southern and Eastern Africa post-adjustment, revealing latent patterns obscured in the crude analysis (p < 0:001). Exceedance probability maps identi_ed countries with P(RR > 1) > 0.9 as priority areas for intensi_ed surveillance and targeted intervention. These patterns were not fully explained by the included covariates, suggesting the inuence of unmeasured factors such as environmental and climate variability, zoonotic reservoirs, or human{animal interactions. Further research is needed to deepen understanding of Mpox epidemiology and support locally tailored interventions.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1575-1591"},"PeriodicalIF":2.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044219","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 : 2025-07-20DOI: 10.1016/j.idm.2025.07.008
Guo Jing Yang , Haolong Song , Jue Tao Lim , A. Janhavi , Gregory Gan , Guan Tong , Pei Ma , Nigel Lim Wei Han , Muhammad Hafiz Bin Mohd Aziz , Borame L. Dickens
Background
Yellow Fever (YF) importation remains an active risk to Southeast Asia. This study aims to determine the effectiveness of vector control and ring vaccination as containment strategies.
Methods
We modelled a YF outbreak in Singapore over 1 year using a metapopulation vector-host spatial model to explore the impact of a potential epidemic and intervention effectiveness. 30 different scenarios were examined by varying the vector to human ratio m ([1, 3, 6]), vaccination coverage ([10 %, 50 %, 90 %]) and delay in vaccine rollout ([7, 14, 30 days]), including three non-vaccination scenarios with the vector-to-human ratio m ([1, 3, 6]).
Results
Vector control has a significant protective effect with an 89 % reduction in the cumulative number of exposed cases at Day 365 when lowering m from 6 to 1 in the baseline scenario without ring vaccination. Vaccination coverage levels of 90 %, 50 %, and 10 % reduce the cumulative number of exposed cases by 88 %, 56 %, and 12 %, respectively, compared to baseline, when fixing m = 3 and a 7-day rollout delay. A greater number of severe infections and deaths can be mitigated by decreasing the ratio m compared to ring vaccination strategies. The marginal gains in averting the number of infections and deaths are most significant when m is decreased, followed by increased vaccination coverage and reduced intervention delay as R0 is proportional to . This highlights the central role of vector control. Our findings suggested that ring vaccination is effective under lower mosquito-to-human ratios up to 1-week post-detection, with vaccination coverage of at least 50 %. Under these settings, vaccine doses equal to 25 % of the total population are needed to contain the initial outbreak, allowing time to monitor its progress and restock the supply. After that, further interventions where YF has not yet been declared endemic.
Conclusion
Our findings suggested that ring vaccination is effective under lower mosquito-to-human ratios up to 1-week post-detection, with vaccination coverage of at least 50 %. After that, further interventions are required to bring the effective reproduction number Reff under 1, highlighting the need for rapid response and containment, preparation in the stockpiling of vaccines, and continual suppression of mosquito vector populations when faced with the risk of YF importation and outbreak.
{"title":"Ring vaccination and vector control as control strategies for potential yellow fever outbreak in an Asian city","authors":"Guo Jing Yang , Haolong Song , Jue Tao Lim , A. Janhavi , Gregory Gan , Guan Tong , Pei Ma , Nigel Lim Wei Han , Muhammad Hafiz Bin Mohd Aziz , Borame L. Dickens","doi":"10.1016/j.idm.2025.07.008","DOIUrl":"10.1016/j.idm.2025.07.008","url":null,"abstract":"<div><h3>Background</h3><div>Yellow Fever (YF) importation remains an active risk to Southeast Asia. This study aims to determine the effectiveness of vector control and ring vaccination as containment strategies.</div></div><div><h3>Methods</h3><div>We modelled a YF outbreak in Singapore over 1 year using a metapopulation vector-host spatial model to explore the impact of a potential epidemic and intervention effectiveness. 30 different scenarios were examined by varying the vector to human ratio <em>m</em> ([1, 3, 6]), vaccination coverage ([10 %, 50 %, 90 %]) and delay in vaccine rollout ([7, 14, 30 days]), including three non-vaccination scenarios with the vector-to-human ratio m ([1, 3, 6]).</div></div><div><h3>Results</h3><div>Vector control has a significant protective effect with an 89 % reduction in the cumulative number of exposed cases at Day 365 when lowering <em>m</em> from 6 to 1 in the baseline scenario without ring vaccination. Vaccination coverage levels of 90 %, 50 %, and 10 % reduce the cumulative number of exposed cases by 88 %, 56 %, and 12 %, respectively, compared to baseline, when fixing m = 3 and a 7-day rollout delay. A greater number of severe infections and deaths can be mitigated by decreasing the ratio m compared to ring vaccination strategies. The marginal gains in averting the number of infections and deaths are most significant when m is decreased, followed by increased vaccination coverage and reduced intervention delay as R<sub>0</sub> is proportional to <span><math><mrow><msqrt><mi>m</mi></msqrt></mrow></math></span>. This highlights the central role of vector control. Our findings suggested that ring vaccination is effective under lower mosquito-to-human ratios up to 1-week post-detection, with vaccination coverage of at least 50 %. Under these settings, vaccine doses equal to 25 % of the total population are needed to contain the initial outbreak, allowing time to monitor its progress and restock the supply. After that, further interventions where YF has not yet been declared endemic.</div></div><div><h3>Conclusion</h3><div>Our findings suggested that ring vaccination is effective under lower mosquito-to-human ratios up to 1-week post-detection, with vaccination coverage of at least 50 %. After that, further interventions are required to bring the effective reproduction number <em>R</em><sub><em>eff</em></sub> under 1, highlighting the need for rapid response and containment, preparation in the stockpiling of vaccines, and continual suppression of mosquito vector populations when faced with the risk of YF importation and outbreak.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1398-1417"},"PeriodicalIF":2.5,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739249","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 : 2025-07-17DOI: 10.1016/j.idm.2025.07.010
Xiaomeng Wang , Jianli Hu , Zhiming Wang , Yongli Cai , Daihai He
<div><h3>Background</h3><div>Influenza is a global public health and economic burden. Its seasonality patterns differ considerably between geographic regions, but the factors underlying these differences are not well characterized.</div></div><div><h3>Methods</h3><div>The data on influenza were obtained from 2019 to 2022 in Huaian. A descriptive study was used to describe the epidemiological characteristics.The DLNM(distributed lag nonlinear model) model was established to further analyze the relationship between influenza cases, meteorological factors and pollutants. In addition, the attribution risk analysis and the interaction analysis further explored the interaction between the attributable risk and meteorological factors of influenza in terms of meteorological factors.</div></div><div><h3>Results</h3><div>A total of 9205 cases of influenza were reported in Huaian City from 2019 to 2022, Jiangsu province, of which 4938 cases were males and 4267 cases were females.The DLNM results showed an inverted U-shaped relationship between PM<sub>2.5</sub>(Fine Particulate Matter) and temperature and influenza.The low concentration of PM<sub>2.5</sub> and O<sub>3</sub>(Ozone) showed decreased risks, and the maximum effect values appeared on the 8th day (RR(Relative Ris) = 0.35,95 %CI(Confidence Interval): 0.25–0.49) and the 2nd day (RR = 0.63,95 %CI: 0.52–0.77). At the high concentration, the cumulative RR values of PM<sub>2.5</sub> and O<sub>3</sub> reached their maximum on the 8th day (RR = 1.93,95 %CI: 1.47–2.54) and the 9th day (RR = 2.58,95 %CI: 1.63–4.09). The attribution analysis based on DLNM showed that the AF(attributable fraction) value of influenza attributable to the high concentration of PM<sub>2.5</sub> exposure was 15.90 %, equivalent to 1456 cases. AF of the high concentration of O<sub>3</sub> was 8.12 % (743 cases). The AF of low temperature effect was 30.91 % (2830 cases). The interaction analysis showed that high temperature reduced the influence of PM<sub>2.5</sub> on the onset of influenza, showing an antagonistic effect (RR = 0.31, 95 %CI: 0.15–0.65), IRR(interaction relative risk) and RERI(interaction relative risk) were 0.17 (95 %CI: 0.08–0.37) and −1.62 (95 %CI: 2.65∼-0.68), respectively.</div></div><div><h3>Conclusion</h3><div>The results show that low temperature significantly increases the risk of influenza. At the low concentration of PM<sub>2.5</sub>, the risk of influenza increases with increasing concentration but decreases at the high concentrations. At the high concentration of O<sub>3</sub>, the risk of influenza increases rapidly. 15.90 % of influenza cases may be attributed to the high concentration of PM<sub>2.5</sub>, equivalent to 1456 cases; temperature-induced cases mainly come from the low-temperature effect, with an AF value of 30.91 %, equivalent to 2830 cases. In addition, high temperature can effectively mitigate the impact of PM<sub>2.5</sub> on influenza incidence, and outdoor exposure time should be min
{"title":"Interactive effects of meteorological factors and ambient air pollutants on influenza incidences 2019–2022 in Huaian, China","authors":"Xiaomeng Wang , Jianli Hu , Zhiming Wang , Yongli Cai , Daihai He","doi":"10.1016/j.idm.2025.07.010","DOIUrl":"10.1016/j.idm.2025.07.010","url":null,"abstract":"<div><h3>Background</h3><div>Influenza is a global public health and economic burden. Its seasonality patterns differ considerably between geographic regions, but the factors underlying these differences are not well characterized.</div></div><div><h3>Methods</h3><div>The data on influenza were obtained from 2019 to 2022 in Huaian. A descriptive study was used to describe the epidemiological characteristics.The DLNM(distributed lag nonlinear model) model was established to further analyze the relationship between influenza cases, meteorological factors and pollutants. In addition, the attribution risk analysis and the interaction analysis further explored the interaction between the attributable risk and meteorological factors of influenza in terms of meteorological factors.</div></div><div><h3>Results</h3><div>A total of 9205 cases of influenza were reported in Huaian City from 2019 to 2022, Jiangsu province, of which 4938 cases were males and 4267 cases were females.The DLNM results showed an inverted U-shaped relationship between PM<sub>2.5</sub>(Fine Particulate Matter) and temperature and influenza.The low concentration of PM<sub>2.5</sub> and O<sub>3</sub>(Ozone) showed decreased risks, and the maximum effect values appeared on the 8th day (RR(Relative Ris) = 0.35,95 %CI(Confidence Interval): 0.25–0.49) and the 2nd day (RR = 0.63,95 %CI: 0.52–0.77). At the high concentration, the cumulative RR values of PM<sub>2.5</sub> and O<sub>3</sub> reached their maximum on the 8th day (RR = 1.93,95 %CI: 1.47–2.54) and the 9th day (RR = 2.58,95 %CI: 1.63–4.09). The attribution analysis based on DLNM showed that the AF(attributable fraction) value of influenza attributable to the high concentration of PM<sub>2.5</sub> exposure was 15.90 %, equivalent to 1456 cases. AF of the high concentration of O<sub>3</sub> was 8.12 % (743 cases). The AF of low temperature effect was 30.91 % (2830 cases). The interaction analysis showed that high temperature reduced the influence of PM<sub>2.5</sub> on the onset of influenza, showing an antagonistic effect (RR = 0.31, 95 %CI: 0.15–0.65), IRR(interaction relative risk) and RERI(interaction relative risk) were 0.17 (95 %CI: 0.08–0.37) and −1.62 (95 %CI: 2.65∼-0.68), respectively.</div></div><div><h3>Conclusion</h3><div>The results show that low temperature significantly increases the risk of influenza. At the low concentration of PM<sub>2.5</sub>, the risk of influenza increases with increasing concentration but decreases at the high concentrations. At the high concentration of O<sub>3</sub>, the risk of influenza increases rapidly. 15.90 % of influenza cases may be attributed to the high concentration of PM<sub>2.5</sub>, equivalent to 1456 cases; temperature-induced cases mainly come from the low-temperature effect, with an AF value of 30.91 %, equivalent to 2830 cases. In addition, high temperature can effectively mitigate the impact of PM<sub>2.5</sub> on influenza incidence, and outdoor exposure time should be min","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1384-1397"},"PeriodicalIF":8.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704874","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 : 2025-07-15DOI: 10.1016/j.idm.2025.07.007
Yongwei Zhang , Xiaoling Deng , Yubin Lan
Citrus Huanglongbing (HLB) is an infectious disease transmitted by Asian citrus psyllids (ACP), which leads to serious economic losses in the citrus industry. Therefore, it is crucial to investigate the prevention and control strategy of citrus HLB. In this paper, the dynamics of HLB propagation between citrus trees and ACP is considered. By applying reinforcement learning (RL) technique, an event-driven optimal prevention control (EDOPC) strategy is developed to ensure the HLB propagation model state converges to a disease-free equilibrium point. Initially, in order to address the challenge of obtaining precise models in practice, a radial basis function-based event-driven observer is built by adopting system input-output data to obtain the approximate HLB propagation model. Subsequently, an EDOPC strategy is devised, which updates only at triggering times to reduce management costs. Additionally, a single critic network structure is constructed to obtain the solution of the Hamilton-Jacobi-Bellman equation, thereby deriving an approximate EDOPC strategy. To align with real-world scenarios, the weights of the observer and the critic network are updated only at event occurrence times. Moreover, by employing the Lyapunov stability principle, the critic network weight error is proved to be uniformly ultimately bounded under the novel event-driven weight adjusting law. Finally, simulation experiments confirm the efficacy of the present RL-based EDOPC strategy.
{"title":"Reinforcement learning-based event-driven optimal prevention control strategy for citrus huanglongbing model","authors":"Yongwei Zhang , Xiaoling Deng , Yubin Lan","doi":"10.1016/j.idm.2025.07.007","DOIUrl":"10.1016/j.idm.2025.07.007","url":null,"abstract":"<div><div>Citrus Huanglongbing (HLB) is an infectious disease transmitted by Asian citrus psyllids (ACP), which leads to serious economic losses in the citrus industry. Therefore, it is crucial to investigate the prevention and control strategy of citrus HLB. In this paper, the dynamics of HLB propagation between citrus trees and ACP is considered. By applying reinforcement learning (RL) technique, an event-driven optimal prevention control (EDOPC) strategy is developed to ensure the HLB propagation model state converges to a disease-free equilibrium point. Initially, in order to address the challenge of obtaining precise models in practice, a radial basis function-based event-driven observer is built by adopting system input-output data to obtain the approximate HLB propagation model. Subsequently, an EDOPC strategy is devised, which updates only at triggering times to reduce management costs. Additionally, a single critic network structure is constructed to obtain the solution of the Hamilton-Jacobi-Bellman equation, thereby deriving an approximate EDOPC strategy. To align with real-world scenarios, the weights of the observer and the critic network are updated only at event occurrence times. Moreover, by employing the Lyapunov stability principle, the critic network weight error is proved to be uniformly ultimately bounded under the novel event-driven weight adjusting law. Finally, simulation experiments confirm the efficacy of the present RL-based EDOPC strategy.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 4","pages":"Pages 1334-1354"},"PeriodicalIF":8.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680677","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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