Infectious Disease Modelling最新文献

{"title":"A deterministic model for homologous antibody dependant enhancement on influenza infection","authors":"","doi":"10.1016/j.idm.2024.07.003","DOIUrl":"10.1016/j.idm.2024.07.003","url":null,"abstract":"<div><p>Antibody dependant enhancement refers that viral infectivity was unexpectedly enhanced at low antibody concentration compared to when antibodies were absent, such as Dengue, Zika and influenza virus. To mathematically describe switch from enhancement to neutralisation with increase of antibody concentration, one hyperbolic tangent variant is used as switching function in existed models. However, switching function with hyperbolic tangent contains four parameters, and does not always increase with antibody concentration. To address this problem, we proposed a monotonically increasing Logistical function variant as switching function, which only contains position parameter and magnitude parameter. Analysing influenza viral titre estimated from 21 focus reduction assay (FRA) datasets from neutralisation group (viral titre lower than negative control on all serial dilutions) and 20 FRA dataset from enhancement group (viral titre higher than negative control on high serial dilution), switching function with Logistic function performs better than existed model independent of both groups and exhibited different behaviour/character; specifically, magnitude parameter estimated from enhancement group is lower, but position parameter estimated from enhancement group is higher. A lower magnitude parameter refers that enhancement group more rapidly switches from enhancement to neutralisation with increase of antibody concentration, and a higher position parameter indicates that enhancement group provides a larger antibody concentration interval corresponding to enhancement. Integrating estimated neutralisation kinetics with viral replication, we demonstrated that antibody-induced bistable influenza kinetics exist independent of both groups. However, comparing with neutralisation group, enhancement group provides higher threshold value of antibody concentration corresponding to influenza infectivity. This explains the observed phenomenon that antibody dependent enhancement enhances susceptibility, severity, and mortality to influenza infection. On population level, antibody dependant enhancement can promote H1N1 and H3N2 influenza virus cooperate to sustain long-term circulation on human populations according to antigenic seniority theory.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000927/pdfft?md5=47745bc0061a3e6a5b3ca1ae3eef2adf&pid=1-s2.0-S2468042724000927-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845447","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":"Corrigendum to “Counterfactual analysis of the 2023 Omicron XBB wave in China” [Infect. Dis. Model. 9 (2024) 195–203]","authors":"","doi":"10.1016/j.idm.2024.07.001","DOIUrl":"10.1016/j.idm.2024.07.001","url":null,"abstract":"","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000903/pdfft?md5=773de07e781f30a9d2acb764391d246b&pid=1-s2.0-S2468042724000903-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636875","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":"Optimal vaccination strategies on networks and in metropolitan areas","authors":"","doi":"10.1016/j.idm.2024.06.007","DOIUrl":"10.1016/j.idm.2024.06.007","url":null,"abstract":"<div><p>This study presents a mathematical model for optimal vaccination strategies in interconnected metropolitan areas, considering commuting patterns. It is a compartmental model with a vaccination rate for each city, acting as a control function. The commuting patterns are incorporated through a weighted adjacency matrix and a parameter that selects day and night periods. The optimal control problem is formulated to minimize a functional cost that balances the number of hospitalizations and vaccines, including restrictions of a weekly availability cap and an application capacity of vaccines per unit of time. The key findings of this work are bounds for the basic reproduction number, particularly in the case of a metropolitan area, and the study of the optimal control problem. Theoretical analysis and numerical simulations provide insights into disease dynamics and the effectiveness of control measures. The research highlights the importance of prioritizing vaccination in the capital to better control the disease spread, as we depicted in our numerical simulations. This model serves as a tool to improve resource allocation in epidemic control across metropolitan regions.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000897/pdfft?md5=4d4c0f55be464442ade651f5576a7505&pid=1-s2.0-S2468042724000897-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622529","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 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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}