Infectious Disease Modelling最新文献

{"title":"Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables","authors":"Xinyi Lu ,&nbsp;Su Yean Teh ,&nbsp;Chai Jian Tay ,&nbsp;Nur Faeza Abu Kassim ,&nbsp;Pei Shan Fam ,&nbsp;Edy Soewono","doi":"10.1016/j.idm.2024.10.007","DOIUrl":"10.1016/j.idm.2024.10.007","url":null,"abstract":"<div><div>Despite the implementation of various initiatives, dengue remains a significant public health concern in Malaysia. Given that dengue has no specific treatment, dengue prediction remains a useful early warning mechanism for timely and effective deployment of public health preventative measures. This study aims to develop a comprehensive approach for forecasting dengue cases in Selangor, Malaysia by incorporating climate variables. An ensemble of Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM), and Susceptible-Infected mosquito vectors, Susceptible-Infected-Recovered human hosts (SI-SIR) model were used to establish a relation between climate variables (temperature, humidity, precipitation) and mosquito biting rate. Dengue incidence subject to climate variability can then be projected by SI-SIR model using the forecasted mosquito biting rate. The proposed approach outperformed three alternative approaches and expanded the temporal horizon of dengue prediction for Selangor with the ability to forecast approximately 60 weeks ahead with a Mean Absolute Percentage Error (MAPE) of 13.97 for the chosen prediction window before the implementation of the Movement Control Order (MCO) in Malaysia. Extended validation across subsequent periods also indicates relatively satisfactory forecasting performance (with MAPE ranging from 13.12 to 17.09). This research contributed to the field by introducing a novel framework for the prediction of dengue cases over an extended temporal range.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 240-256"},"PeriodicalIF":8.8,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579046","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":"Conditional logistic individual-level models of spatial infectious disease dynamics","authors":"Tahmina Akter ,&nbsp;Rob Deardon","doi":"10.1016/j.idm.2024.10.008","DOIUrl":"10.1016/j.idm.2024.10.008","url":null,"abstract":"<div><div>Here, we introduce a novel framework for modelling the spatiotemporal dynamics of disease spread known as conditional logistic individual-level models (CL-ILM's). This framework alleviates much of the computational burden associated with traditional spatiotemporal individual-level models for epidemics, and facilitates the use of standard software for fitting logistic models when analysing spatiotemporal disease patterns. The models can be fitted in either a frequentist or Bayesian framework. Here, we apply the new spatial CL-ILM to simulated data, semi-real data from the UK 2001 foot-and-mouth disease epidemic, and real data from a greenhouse experiment on the spread of tomato spotted wilt virus.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 268-286"},"PeriodicalIF":8.8,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656483","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":"Information-guided adaptive learning approach for active surveillance of infectious diseases","authors":"Qi Tan ,&nbsp;Chenyang Zhang ,&nbsp;Jiwen Xia ,&nbsp;Ruiqi Wang ,&nbsp;Lian Zhou ,&nbsp;Zhanwei Du ,&nbsp;Benyun Shi","doi":"10.1016/j.idm.2024.10.005","DOIUrl":"10.1016/j.idm.2024.10.005","url":null,"abstract":"<div><div>The infectious disease surveillance system is a key support tool for public health decision making. Current research concentrates on optimizing static sentinel deployment to address the problem of incomplete data due to the lack of sufficient surveillance resources. In this study, we introduce an information-guided adaptive learning strategy for the dynamic surveillance of infectious diseases. The goal is to improve monitoring effectiveness in situations where it is possible to adjust the focus of surveillance, such as serial surveys and allocation of testing tools. Specifically, we develop a probabilistic neural network model to learn spatio-temporal correlations among the numbers of infections. Based on a probabilistic model, we evaluate the information gain of monitoring a spatio-temporal target and design a greedy selection algorithm for monitoring targets selection. Moreover, we integrate two major surveillance objectives, i.e., informativeness and coverage, in the monitoring target selection. The experimental results on the synthetic dataset and two real-world datasets demonstrate the effectiveness of our approach, showcasing the promise of further exploration and application of dynamic adaptive active surveillance.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 257-267"},"PeriodicalIF":8.8,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656482","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":"Network-based virus dynamic simulation: Evaluating the fomite disinfection effectiveness on SARS-CoV-2 transmission in indoor environment","authors":"Syun-suke Kadoya ,&nbsp;Sewwandi Bandara ,&nbsp;Masayuki Ogata ,&nbsp;Takayuki Miura ,&nbsp;Michiko Bando ,&nbsp;Daisuke Sano","doi":"10.1016/j.idm.2024.10.004","DOIUrl":"10.1016/j.idm.2024.10.004","url":null,"abstract":"<div><div>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is involved in aerosol particles and droplets excreted from a coronavirus disease 2019 (COVID-19) patient. Such aerosol particles or droplets including infectious virions can be attached on fomite, so fomite is not a negligible route for SARS-CoV-2 transmission within a community, especially in indoor environment. This necessarily evokes a need of fomite disinfection to remove virions, but the extent to which fomite disinfection breaks off virus transmission chain in indoor environment is still elusive. In this study, we evaluated the fomite disinfection effectiveness on COVID-19 case number using network analysis that reproduced the reported indoor outbreaks. In the established network, virus can move around not only human but also air and fomite while growing in human and decaying in air and on fomite, and infection success was determined based on the exposed virus amount and the equation of probability of infection. The simulation results have demonstrated that infectious virions on fomite should be kept less than a hundred to sufficiently reduce COVID-19 case, and every-hour disinfection was required to avoid stochastic increase in the infection case. This study gives us a practical disinfection manner for fomite to control SARS-CoV-2 transmission in indoor environment.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 229-239"},"PeriodicalIF":8.8,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539544","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":"Stronger binding affinities of gp120/CD4 in Catarrhini provide insights into HIV/host interactions","authors":"Vladimir Li ,&nbsp;Chul Lee ,&nbsp;TaeHyun Park ,&nbsp;Erich D. Jarvis ,&nbsp;Heebal Kim","doi":"10.1016/j.idm.2024.10.003","DOIUrl":"10.1016/j.idm.2024.10.003","url":null,"abstract":"<div><div>Human immunodeficiency virus-1 (HIV-1) exploits the viral <em>gp120</em> protein and host <em>CD4</em>/<em>CCR5</em> receptors for the pandemic infection to humans. The host co-receptors of not only humans but also several primates and HIV-model mice can interact with the HIV receptor. However, the molecular mechanisms of these interactions remain unclear. Using Shaik et al. (2019)'s <em>gp120/CD4/CCR5</em> structure of HIV-1B and human, here, we investigate the molecular dynamics between HIV sub-lineages (B, C, N, and O) and potential hosts in <em>Euarchontoglires</em> (primates and rodents). Although both host genes show similar protein structures conserved in all animals, <em>CD4</em> gene demonstrates significantly stronger binding affinities in <em>Catarrhini</em> (apes and Old-World monkeys). Its known candidate residues interacted with gp120 fail to explain these affinity variations. Therefore, we identified novel candidate sites under positive selection on the <em>Catarrhini</em> lineage. Among four positively selected sites, residue R58 in humans is located within an antigen-antibody binding domain, exhibiting apomorphic amino acid substitutions as Arginine (R) in <em>Catarrhini</em>, which are mutually exclusive to the other animals where Lysine (K) is prevalent. Applying for artificial mutation test, we validated that K to R substitutions can lead stronger binding affinities of <em>Catarrhini</em>. Ecologically, these dynamics may relate to shared equatorial habitats in Africa and Asia. Our findings suggest a new candidate site R58 driven by the lineage-specific evolution as a molecular foundation on HIV infection.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 287-301"},"PeriodicalIF":8.8,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656417","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":"Dynamics of an SVEIR transmission model with protection awareness and two strains","authors":"Kaijing Chen ,&nbsp;Fengying Wei ,&nbsp;Xinyan Zhang ,&nbsp;Hao Jin ,&nbsp;Ruiyang Zhou ,&nbsp;Yue Zuo ,&nbsp;Kai Fan","doi":"10.1016/j.idm.2024.10.001","DOIUrl":"10.1016/j.idm.2024.10.001","url":null,"abstract":"<div><div>As of May 2024, the main strains of COVID-19 caused hundreds of millions of infection cases and millions of deaths worldwide. In this study, we consider the COVID-19 epidemics with the main strains in the Chinese mainland. We study complex interactions among hosts, non-pharmaceutical interventions, and vaccinations for the main strains by a differential equation model called SVEIR. The disease transmission model incorporates two strains and protection awareness of the susceptible population. Results of this study show that the protection awareness plays a crucial role against infection of the population, and that the vaccines are effective against the circulation of the earlier strains, but ineffective for emerging strains. By using the next generation matrix method, the basic reproduction number of the SVEIR model is firstly obtained. Our analysis by Hurwitz criterion and LaSalle's invariance principle shows that the disease free-equilibrium point is locally and globally asymptotically stable when the threshold value is below one. The existences of endemic equilibrium points are also established, and the global asymptotic stabilities are analyzed using the Lyapunov function method. Further, the SVEIR model is confirmed to satisfy the principle of competitive exclusion, of which the strain with the larger value of the basic reproduction number is dominant. Numerically, the surveillance data with the Omicron strain and the XBB strain are split by the cubic spline interpolation method. The fitting curves against the surveillance data are plotted using the least-squares method from MATLAB. The results indicate that the XBB strain dominates in this study. Moreover, a global sensitivity analysis of the key parameters is performed by using of PRCC. The numerical simulations imply that combination control strategy positively impacts on the infection scale than what separate control strategy does, and that the earlier time producing protection awareness for the public creates less infection scale, further that the increment of protection awareness also reduces the infection scale. Therefore, the policymakers of the local government are suggested to concern the changes of protection awareness of the public.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 207-228"},"PeriodicalIF":8.8,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445086","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 tentative exploration for the association between influenza virus infection and SARS-CoV-2 infection in Shihezi, China: A test-negative study","authors":"Songsong Xie ,&nbsp;Yinxia Su ,&nbsp;Yanji Zhao ,&nbsp;Yaling Du ,&nbsp;Zihao Guo ,&nbsp;Xiu Gu ,&nbsp;Jie Sun ,&nbsp;Mohammad Javanbakht ,&nbsp;Daihai He ,&nbsp;Jiazhen Zhang ,&nbsp;Yan Zhang ,&nbsp;Kai Wang ,&nbsp;Shi Zhao","doi":"10.1016/j.idm.2024.10.002","DOIUrl":"10.1016/j.idm.2024.10.002","url":null,"abstract":"<div><div>The outbreak of respiratory diseases, such as COVID-19 and influenza, has drawn global attention. However, it remains unclear whether the risk of influenza A infection may be affected by the history of SARS-CoV-2 infection. In this study, we conducted a test-negative case-control study, and utilized a logistic regression model to analyze the relationship between SARS-CoV-2 and influenza A infections. Among 258 eligible patient samples with influenza-like illness (ILI), we did not detect a statistically significant association between the history of SARS-CoV-2 infection and the risk of influenza A infection. These findings might indicate that antibodies against COVID-19 acquired through vaccination or natural immunity have not protected against influenza.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 201-206"},"PeriodicalIF":8.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428652","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":"Modelling and investigating memory immune responses in infectious disease. Application to influenza a virus and sars-cov-2 reinfections","authors":"Mathilde Massard ,&nbsp;Bruno Saussereau ,&nbsp;Catherine Chirouze ,&nbsp;Quentin Lepiller ,&nbsp;Raluca Eftimie ,&nbsp;Antoine Perasso","doi":"10.1016/j.idm.2024.09.009","DOIUrl":"10.1016/j.idm.2024.09.009","url":null,"abstract":"<div><div>Understanding effector and memory immune responses against influenza A virus (IAV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and re-infections is extremely important, given that they are now endemic in the community. The goal of this study is to investigate the role of memory cells and antibodies in the immune responses against IAV and SARS-CoV-2 re-infections. To this end, we adapt a previously-published within-host mathematical model (Sadria &amp; Layton, 2021) for the primary immune response against SARS-CoV-2 infections, by including two types of memory immune cells, i.e., memory CD8⁺ T-cells and memory B-cells, and by parametrising the new model with values specific to the two viruses. We first investigate the long-term dynamics of the model by identifying the virus-free steady states and studying the conditions that ensure the stability of these states. Then, we investigate the transient dynamics of this in-host model by simulating different viral reinfection times: 20 days, 60 days and 400 days after the first encounter with the pathogen. This allows us to highlight which memory immune components have the greatest impact on the viral elimination depending on the time of reinfection. Our results suggest that memory immune responses have a greater impact in the case of IAV infections compared to SARS-CoV-2 infections. Moreover, we observe that the immune response after a secondary infection is more efficient when the reinfection occurs at a shorter time.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 163-188"},"PeriodicalIF":8.8,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428650","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":"Gradient boosting: A computationally efficient alternative to Markov chain Monte Carlo sampling for fitting large Bayesian spatio-temporal binomial regression models","authors":"Rongjie Huang ,&nbsp;Christopher McMahan ,&nbsp;Brian Herrin ,&nbsp;Alexander McLain ,&nbsp;Bo Cai ,&nbsp;Stella Self","doi":"10.1016/j.idm.2024.09.008","DOIUrl":"10.1016/j.idm.2024.09.008","url":null,"abstract":"<div><div>Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time. Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo (MCMC) methods are commonly used for such data. When the spatio-temporal support of the model is large, implementing an MCMC algorithm becomes a significant computational burden. This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatio-temporal mixed effects binomial regression model. We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach. Both methods are used to produce monthly forecasts for Lyme disease, anaplasmosis, ehrlichiosis, and heartworm disease in domestic dogs for the contiguous United States. The data have a spatial support of 3108 counties and a temporal support of 108–138 months with 71–135 million test results. The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm, with a similar mean absolute prediction error.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 189-200"},"PeriodicalIF":8.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428651","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":"Nonlinear mixed models and related approaches in infectious disease modeling: A systematic and critical review","authors":"Olaiya Mathilde Adéoti ,&nbsp;Schadrac Agbla ,&nbsp;Aliou Diop ,&nbsp;Romain Glèlè Kakaï","doi":"10.1016/j.idm.2024.09.001","DOIUrl":"10.1016/j.idm.2024.09.001","url":null,"abstract":"<div><div>The level of surveillance and preparedness against epidemics varies across countries, resulting in different responses to outbreaks. When conducting an in-depth analysis of microinfection dynamics, one must account for the substantial heterogeneity across countries. However, many commonly used statistical model specifications lack the flexibility needed for sound and accurate analysis and prediction in such contexts. Nonlinear mixed effects models (NLMMs) constitute a specific statistical tool that can overcome these significant challenges. While compartmental models are well-established in infectious disease modeling and have seen significant advancements, Nonlinear Mixed Models (NLMMs) offer a flexible approach for handling heterogeneous and unbalanced repeated measures data, often with less computational effort than some individual-level compartmental modeling techniques. This study provides an overview of their current use and offers a solid foundation for developing guidelines that may help improve their implementation in real-world situations. Relevant scientific databases in the <em>Research4life</em> Access initiative programs were used to search for papers dealing with key aspects of NLMMs in infectious disease modeling (IDM). From an initial list of 3641 papers, 124 were finally included and used for this systematic and critical review spanning the last two decades, following the PRISMA guidelines. NLMMs have evolved rapidly in the last decade, especially in IDM, with most publications dating from 2017 to 2021 (83.33%). The routine use of normality assumption appeared inappropriate for IDM, leading to a wealth of literature on NLMMs with non-normal errors and random effects under various estimation methods. We noticed that NLMMs have attracted much attention for the latest known epidemics worldwide (COVID-19, Ebola, Dengue and Lassa) with the robustness and reliability of relaxed propositions of the normality assumption. A case study of the application of COVID-19 data helped to highlight NLMMs’ performance in modeling infectious diseases. Out of this study, estimation methods, assumptions, and random terms specification in NLMMs are key aspects requiring particular attention for their application in IDM.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 1","pages":"Pages 110-128"},"PeriodicalIF":8.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724001003/pdfft?md5=a1cfb322095780bbffb2c061082d891e&pid=1-s2.0-S2468042724001003-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312262","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}