Infectious Disease Modelling最新文献

{"title":"A parsimonious Bayesian predictive model for forecasting new reported cases of West Nile disease","authors":"Saman Hosseini ,&nbsp;Lee W. Cohnstaedt ,&nbsp;John M. Humphreys ,&nbsp;Caterina Scoglio","doi":"10.1016/j.idm.2024.06.004","DOIUrl":"https://doi.org/10.1016/j.idm.2024.06.004","url":null,"abstract":"<div><p>Upon researching predictive models related to West Nile virus disease, it is discovered that there are numerous parameters and extensive information in most models, thus contributing to unnecessary complexity. Another challenge frequently encountered is the lead time, which refers to the period for which predictions are made and often is too short. This paper addresses these issues by introducing a parsimonious method based on ICC curves, offering a logistic distribution model derived from the vector-borne SEIR model. Unlike existing models relying on diverse environmental data, our approach exclusively utilizes historical and present infected human cases (number of new cases). With a year-long lead time, the predictions extend throughout the 12 months, gaining precision as new data emerge. Theoretical conditions are derived to minimize Bayesian loss, enhancing predictive precision. We construct a Bayesian forecasting probability density function using carefully selected prior distributions. Applying these functions, we predict month-specific infections nationwide, rigorously evaluating accuracy with probabilistic metrics. Additionally, HPD credible intervals at 90%, 95%, and 99% levels is performed. Precision assessment is conducted for HPD intervals, measuring the proportion of intervals that does not include actual reported cases for 2020–2022.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1175-1197"},"PeriodicalIF":8.8,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000861/pdfft?md5=b19c26f3fcba86e011139eb88c2b809c&pid=1-s2.0-S2468042724000861-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141607966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confounding amplifies the effect of environmental factors on COVID-19","authors":"Zihan Hao ,&nbsp;Shujuan Hu ,&nbsp;Jianping Huang ,&nbsp;Jiaxuan Hu ,&nbsp;Zhen Zhang ,&nbsp;Han Li ,&nbsp;Wei Yan","doi":"10.1016/j.idm.2024.06.005","DOIUrl":"https://doi.org/10.1016/j.idm.2024.06.005","url":null,"abstract":"<div><p>The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1163-1174"},"PeriodicalIF":8.8,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000873/pdfft?md5=a2f4d7e0a0678b74384184556e30b138&pid=1-s2.0-S2468042724000873-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting plant disease epidemics using boosted regression trees","authors":"Chun Peng ,&nbsp;Xingyue Zhang ,&nbsp;Weiming Wang","doi":"10.1016/j.idm.2024.06.006","DOIUrl":"https://doi.org/10.1016/j.idm.2024.06.006","url":null,"abstract":"<div><p>Plant epidemics are often associated with weather-related variables. It is difficult to identify weather-related predictors for models predicting plant epidemics. In the article by Shah et al., to predict Fusarium head blight (FHB) epidemics of wheat, they explored a functional approach using scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) with respect to weather time series spanning 140 days relative to anthesis. The scalar-on-function models fit the data better than previously described logistic regression models. In this work, given the same dataset and models, we attempt to reproduce the article by Shah et al. using a different approach, boosted regression trees. After fitting, the classification accuracy and model statistics are surprisingly good.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1138-1146"},"PeriodicalIF":8.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000885/pdfft?md5=4620ee579c9bb340da0e3c630db0691d&pid=1-s2.0-S2468042724000885-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and investigating malaria P. Falciparum and P. Vivax infections: Application to Djibouti data","authors":"Yahyeh Souleiman ,&nbsp;Liban Ismail ,&nbsp;Raluca Eftimie","doi":"10.1016/j.idm.2024.06.003","DOIUrl":"https://doi.org/10.1016/j.idm.2024.06.003","url":null,"abstract":"<div><p>Malaria is an infectious and communicable disease, caused by one or more species of Plasmodium parasites. There are five species of parasites responsible for malaria in humans, of which two, Plasmodium Falciparum and Plasmodium Vivax, are the most dangerous. In Djibouti, the two species of Plasmodium are present in different proportions in the infected population: 77% of P. Falciparum and 33% of P. Vivax. In this study we present a new mathematical model describing the temporal dynamics of Plasmodium Falciparum and Plasmodium Vivax co-infection. We focus briefly on the well posedness of this model and on the calculation of the basic reproductive numbers for the infections with each Plasmodium species that help us understand the long-term dynamics of this model (i.e., existence and stability of various eqiuilibria). Then we use computational approaches to: (a) identify model parameters using real data on malaria infections in Djibouti; (b) illustrate the influence of different estimated parameters on the basic reproduction numbers; (c) perform global sensitivity and uncertainty analysis for the impact of various model parameters on the transient dynamics of infectious mosquitoes and infected humans, for infections with each of the Plasmodium species. The originality of this research stems from employing the FAST method and the LHS method to identify the key factors influencing the progression of the disease within the population of Djibouti. In addition, sensitivity analysis identified the most influential parameter for Falciparium and Vivax reproduction rates. Finally, the uncertainty analysis enabled us to understand the variability of certain parameters on the infected compartments.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1095-1116"},"PeriodicalIF":8.8,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S246804272400085X/pdfft?md5=ba9013b2a5d9bfb4b9cda6fa48f68944&pid=1-s2.0-S246804272400085X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simple modification to the classical SIR model to estimate the proportion of under-reported infections using case studies in flu and COVID-19","authors":"Leonid Kalachev ,&nbsp;Jon Graham ,&nbsp;Erin L. Landguth","doi":"10.1016/j.idm.2024.06.002","DOIUrl":"https://doi.org/10.1016/j.idm.2024.06.002","url":null,"abstract":"<div><h3>Background</h3><p>Under-reporting and, thus, uncertainty around the true incidence of health events is common in all public health reporting systems. While the problem of under-reporting is acknowledged in epidemiology, the guidance and methods available for assessing and correcting the resulting bias are obscure.</p></div><div><h3>Objective</h3><p>We aim to design a simple modification to the Susceptible – Infected – Removed (SIR) model for estimating the fraction or proportion of reported infection cases.</p></div><div><h3>Methods</h3><p>The suggested modification involves rescaling of the classical SIR model producing its mathematically equivalent version with explicit dependence on the reporting parameter (true proportion of cases reported). We justify the rescaling using the phase plane analysis of the SIR model system and show how this rescaling parameter can be estimated from the data along with the other model parameters.</p></div><div><h3>Results</h3><p>We demonstrate how the proposed method is cross-validated using simulated data with known disease cases and then apply it to two empirical reported data sets to estimate the fraction of reported cases in Missoula County, Montana, USA, using: (1) flu data for 2016–2017 and (2) COVID-19 data for fall of 2020.</p></div><div><h3>Conclusions</h3><p>We establish with the simulated and COVID-19 data that when most of the disease cases are presumed reported, the value of the additional reporting parameter in the modified SIR model is close or equal to one, so that the original SIR model is appropriate for data analysis. Conversely, the flu example shows that when the reporting parameter is close to zero, the original SIR model is not accurately estimating the usual rate parameters, and the re-scaled SIR model should be used. This research demonstrates the role of under-reporting of disease data and the importance of accounting for under-reporting when modeling simulated, endemic, and pandemic disease data. Correctly reporting the “true” number of disease cases will have downstream impacts on predictions of disease dynamics. A simple parameter adjustment to the SIR modeling framework can help alleviate bias and uncertainty around crucial epidemiological metrics (e.g.: basic disease reproduction number) and public health decision making.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1147-1162"},"PeriodicalIF":8.8,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000848/pdfft?md5=4a2260ac63652639f45a355fcd3800e1&pid=1-s2.0-S2468042724000848-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning","authors":"Hong-Mei Li ,&nbsp;Jin-Xin Zheng ,&nbsp;Nicholas Midzi ,&nbsp;Masceline Jenipher Mutsaka- Makuvaza ,&nbsp;Shan Lv ,&nbsp;Shang Xia ,&nbsp;Ying-jun Qian ,&nbsp;Ning Xiao ,&nbsp;Robert Berguist ,&nbsp;Xiao-Nong Zhou","doi":"10.1016/j.idm.2024.06.001","DOIUrl":"https://doi.org/10.1016/j.idm.2024.06.001","url":null,"abstract":"<div><p>Zimbabwe, located in Southern Africa, faces a significant public health challenge due to schistosomiasis. We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population. To this end, we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public sources. We studied the population requiring praziquantel with regard to whether or not mass drug administration (MDA) had been regularly applied. Three machine-learning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error (MAE), the root mean squared error (RMSE) and the coefficient of determination (R²). The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between <em>Schistosoma haematobium</em> and <em>S. mansoni</em> infections. We found that the top-five correlation factors, such as the past (rather than current) time, unsettled MDA implementation, constrained economy, high rainfall during the warmest season, and high annual precipitation were closely associated with higher <em>S. haematobium</em> prevalence, while lower elevation, high rainfall during the warmest season, steeper slope, past (rather than current) time, and higher minimum temperature in the coldest month were rather related to higher <em>S. mansoni</em> prevalence. The random forest (RF) algorithm was considered as the formal best model construction method, with MAE = 0.108; RMSE = 0.143; and R² = 0.517 for <em>S. haematobium</em>, and with the corresponding figures for <em>S. mansoni</em> being 0.053; 0.082; and 0.458. Based on this optimal model, the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%, with that of <em>S. haematobium</em> at 13.8% and that of <em>S. mansoni</em> at 7.1%, requiring annual MDA based on a population of 3,003,928. Without MDA, the current total schistosomiasis prevalence would be 23.2%, that of <em>S. haematobium</em> 17.1% and that of <em>S. mansoni</em> prevalence at 7.4%, requiring annual MDA based on a population of 3,521,466. The study reveals that MDA alone is insufficient for schistosomiasis elimination, especially that due to <em>S. mansoni</em>. This study predicts a moderate prevalence of schistosomiasis in Zimbabwe, with its elimination requiring comprehensive control measures beyond the currently used strategies, including health education, snail control, population surveillance and environmental management.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1081-1094"},"PeriodicalIF":8.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000836/pdfft?md5=0ca0fe15be3a0ff21ec6513e3e69e6d6&pid=1-s2.0-S2468042724000836-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141323345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simple modification to the classical SIR model to estimate the proportion of under-reported infections using case studies in flu and COVID-19","authors":"Leonid Kalachev, Jon Graham, E. Landguth","doi":"10.2139/ssrn.4774196","DOIUrl":"https://doi.org/10.2139/ssrn.4774196","url":null,"abstract":"","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"50 8","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141391453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gonorrhea cluster detection in Manitoba, Canada: Spatial, temporal, and spatio-temporal analysis","authors":"Amin Abed ,&nbsp;Mahmoud Torabi ,&nbsp;Zeinab Mashreghi","doi":"10.1016/j.idm.2024.05.009","DOIUrl":"https://doi.org/10.1016/j.idm.2024.05.009","url":null,"abstract":"<div><p>In Canada, Gonorrhea infection ranks as the second most prevalent sexually transmitted infection. In 2018, Manitoba reported an incidence rate three times greater than the national average. This study aims to investigate the spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba, using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016. Age and sex patterns indicate that females are affected by infections at younger ages compared to males. Moreover, there is an increase in repeated infections in 2016, accounting for 16% of the total infections. Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation, demonstrating a clustered distribution of the infection. Northern districts of Manitoba and central Winnipeg were identified as significant clusters. Temporal analysis shows seasonal patterns, with higher infections in late summer and fall. Additionally, spatio-temporal analysis reveals clusters during high-risk periods, with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014, and a secondary cluster in central Winnipeg from June 2004 to November 2012. This study identifies that Gonorrhea infection transmission in Manitoba has temporal, spatial, and spatio-temporal variations. The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention, control measures, and resource allocation.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1045-1056"},"PeriodicalIF":8.8,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000757/pdfft?md5=f70e3a636e1a5271be627115b9d44f59&pid=1-s2.0-S2468042724000757-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141314038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysing vaccine efficacy evaluated in phase 3 clinical trials carried out during outbreaks","authors":"Francisco Antonio Bezerra Coutinho ,&nbsp;Marcos Amaku ,&nbsp;Fernanda Castro Boulos ,&nbsp;José Alfredo de Sousa Moreira ,&nbsp;João Italo Dias Franca ,&nbsp;Julio Antonio do Amaral ,&nbsp;Eliana Nogueira Castro de Barros ,&nbsp;Claudio José Struchiner ,&nbsp;Esper Jorge Kallas ,&nbsp;Eduardo Massad","doi":"10.1016/j.idm.2024.05.007","DOIUrl":"10.1016/j.idm.2024.05.007","url":null,"abstract":"<div><p>In this paper we examine several definitions of vaccine efficacy (VE) that we found in the literature, for diseases that express themselves in outbreaks, that is, when the force of infection grows in time, reaches a maximum and then vanishes. The fact that the disease occurs in outbreaks results in several problems that we analyse. We propose a mathematical model that allows the calculation of VE for several scenarios. Vaccine trials usually needs a large number of volunteers that must be enrolled. Ideally, all volunteers should be enrolled in approximately the same time, but this is generally impossible for logistic reasons and they are enrolled in a fashion that can be replaced by a continuous density function (for example, a Gaussian function). The outbreak can also be replaced by a continuous density function, and the use of these density functions simplifies the calculations. Assuming, for example Gaussian functions, one of the problems one can immediately notice is that the peak of the two curves do not occur at the same time. The model allows us to conclude: First, the calculated vaccine efficacy decreases when the force of infection increases; Second, the calculated vaccine efficacy decreases when the gap between the peak in the force of infection and the peak in the enrollment rate increases; Third, different trial protocols can be simulated with this model; different vaccine efficacy definitions can be calculated and in our simulations, all result are approximately the same. The final, and perhaps most important conclusion of our model, is that vaccine efficacy calculated during outbreaks must be carefully examined and the best way we can suggest to overcome this problem is to stratify the enrolled volunteer's in a cohort-by-cohort basis and do the survival analysis for each cohort, or apply the Cox proportional hazards model for each cohort.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1027-1044"},"PeriodicalIF":8.8,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000733/pdfft?md5=bf673cb5728d92d0037c548d36ece3d4&pid=1-s2.0-S2468042724000733-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141131586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the impact of HIV on mpox transmission in the MSM population: A mathematical modeling study","authors":"Andrew Omame ,&nbsp;Qing Han ,&nbsp;Sarafa A. Iyaniwura ,&nbsp;Adeniyi Ebenezer ,&nbsp;Nicola L. Bragazzi ,&nbsp;Xiaoying Wang ,&nbsp;Jude D. Kong ,&nbsp;Woldegebriel A. Woldegerima","doi":"10.1016/j.idm.2024.05.008","DOIUrl":"https://doi.org/10.1016/j.idm.2024.05.008","url":null,"abstract":"<div><p>The recent mpox outbreak (in 2022–2023) has different clinical and epidemiological features compared with previous outbreaks of the disease. During this outbreak, sexual contact was believed to be the primary transmission route of the disease. In addition, the community of men having sex with men (MSM) was disproportionately affected by the outbreak. This population is also disproportionately affected by HIV infection. Given that both diseases can be transmitted sexually, the endemicity of HIV, and the high sexual behavior associated with the MSM community, it is essential to understand the effect of the two diseases spreading simultaneously in an MSM population. Particularly, we aim to understand the potential effects of HIV on an mpox outbreak in the MSM population. We develop a mechanistic mathematical model of HIV and mpox co-infection. Our model incorporates the dynamics of both diseases and considers HIV treatment with anti-retroviral therapy (ART). In addition, we consider a potential scenario where HIV infection increases susceptibility to mpox, and investigate the potential impact of this mechanism on mpox dynamics. Our analysis shows that HIV can facilitate the spread of mpox in an MSM population, and that HIV treatment with ART may not be sufficient to control the spread of mpox in the population. However, we showed that a moderate use of condoms or reduction in sexual contact in the population combined with ART is beneficial in controlling mpox transmission. Based on our analysis, it is evident that effective control of HIV, specifically through substantial ART use, moderate condom compliance, and reduction in sexual contact, is imperative for curtailing the transmission of mpox in an MSM population and mitigating the compounding impact of these intertwined epidemics.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1117-1137"},"PeriodicalIF":8.8,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000745/pdfft?md5=e2f1e85ef09d13881efa2fb4d8a9e202&pid=1-s2.0-S2468042724000745-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}