Infectious Disease Modelling最新文献

{"title":"Bayesian estimation of the time-varying reproduction number for pulmonary tuberculosis in Iran: A registry-based study from 2018 to 2022 using new smear-positive cases","authors":"Maryam Rastegar ,&nbsp;Eisa Nazar ,&nbsp;Mahshid Nasehi ,&nbsp;Saeed Sharafi ,&nbsp;Vahid Fakoor ,&nbsp;Mohammad Taghi Shakeri","doi":"10.1016/j.idm.2024.05.003","DOIUrl":"10.1016/j.idm.2024.05.003","url":null,"abstract":"<div><h3>Introduction</h3><p>Tuberculosis (TB) is one of the most prevalent infectious diseases in the world, causing major public health problems in developing countries. The rate of TB incidence in Iran was estimated to be 13 per 100,000 in 2021. This study aimed to estimate the reproduction number and serial interval for pulmonary tuberculosis in Iran.</p></div><div><h3>Material and methods</h3><p>The present national historical cohort study was conducted from March 2018 to March 2022 based on data from the National Tuberculosis and Leprosy Registration Center of Iran's Ministry of Health and Medical Education (MOHME). The study included 30,762 tuberculosis cases and 16,165 new smear-positive pulmonary tuberculosis patients in Iran. We estimated the reproduction number of pulmonary tuberculosis in a Bayesian framework, which can incorporate uncertainty in estimating it. Statistical analyses were accomplished in R software.</p></div><div><h3>Results</h3><p>The mean age at diagnosis of patients was 52.3 ± 21.2 years, and most patients were in the 35–63 age group (37.1%). Among the data, 9121 (56.4%) cases were males, and 7044 (43.6%) were females. Among patients, 7459 (46.1%) had a delayed diagnosis between 1 and 3 months. Additionally, 3039 (18.8%) cases were non-Iranians, and 2978 (98%) were Afghans. The time-varying reproduction number for pulmonary tuberculosis disease was calculated at an average of 1.06 ± 0.05 (95% Crl 0.96–1.15).</p></div><div><h3>Conclusions</h3><p>In this study, the incidence and the time-varying reproduction number of pulmonary tuberculosis showed the same pattern. The mean of the time-varying reproduction number indicated that each infected person is causing at least one new infection over time, and the chain of transmission is not being disrupted.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 963-974"},"PeriodicalIF":8.8,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000691/pdfft?md5=4503a59ae57697c09ae32c478e41d0a4&pid=1-s2.0-S2468042724000691-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141030261","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":"Chronic disease patients have fewer social contacts: A pilot survey with implications for transmission dynamics","authors":"J. Vanderlocht ,&nbsp;S. Møgelmose ,&nbsp;K. Van Kerckhove ,&nbsp;P. Beutels ,&nbsp;N. Hens","doi":"10.1016/j.idm.2024.05.002","DOIUrl":"10.1016/j.idm.2024.05.002","url":null,"abstract":"<div><p>Non-communicable diseases (NCD) are the most important cause of death in the world. The socio-economic costs associated with NCDs makes it imperative to prevent and control them in the 21st century. The severe toll that the COVID-19 pandemic has taken worldwide is an unfortunate illustration of our limited insight into the infectious risk for the global population. Co-incidence between NCD and infection offers an underexplored opportunity to design preventive policies. In a pilot survey, we observed that the NCD population displays a substantial reduction in their social contacting behavior as compared to the general population. This indicates that existing mathematical models based on contact surveys in the general population are not applicable to the NCD population and that the risk of acquiring an infection following a contact is probably underestimated. Our demonstration of reduced social mixing in several chronic conditions, raises the question to what extent the social mixing is influenced by the burden of disease. We advocate the design of disease-specific contact surveys to address how the burden of disease associates with social contact behavior and the risk of infection. The SARS-CoV-2 pandemic offers an unprecedented opportunity to gain insight into the importance of infection in the NCD population and to find ways to improve healthcare procedures.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 926-930"},"PeriodicalIF":8.8,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S246804272400068X/pdfft?md5=ae26178b51873ef97cea52d0a8d7165e&pid=1-s2.0-S246804272400068X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141028066","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 measles transmission in adults and children: Implications to vaccination for eradication","authors":"Anjana Pokharel ,&nbsp;Khagendra Adhikari ,&nbsp;Ramesh Gautam ,&nbsp;Kedar Nath Uprety ,&nbsp;Naveen K. Vaidya","doi":"10.1016/j.idm.2024.04.012","DOIUrl":"10.1016/j.idm.2024.04.012","url":null,"abstract":"<div><p>Despite the availability of successful vaccines, measles outbreaks have occurred frequently in recent years, presumably due to the lack of proper vaccination implementation. Moreover, measles cases in adult groups, albeit small in number, indicate that the previously neglected adult group may need to be brought into vaccine coverage to achieve WHO's goal of measles eradication from the globe. In this study, we develop a novel transmission dynamics model to describe measles cases in adults and children to evaluate the role of adult infection in persistent measles cases and vaccination programs for eradication. Analysis of our model, validated by measles cases from outbreaks in Nepal, provides the vaccination reproduction number (conditions for measles eradication or persistence) and the role of contact network size. Our results highlight that while children are primary targets for measles outbreaks, a small number of infections in adults may act as a reservoir for measles, causing obstacles to eradication. Furthermore, our model analysis shows that while impactful controls can be achieved by children-focused vaccines, a combined adult-child vaccination program may help assert eradication of the disease.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1007-1026"},"PeriodicalIF":8.8,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000666/pdfft?md5=bc38b31f69db1a59b6417803b47525e8&pid=1-s2.0-S2468042724000666-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141038562","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 Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters","authors":"Brandon Robinson ,&nbsp;Philippe Bisaillon ,&nbsp;Jodi D. Edwards ,&nbsp;Tetyana Kendzerska ,&nbsp;Mohammad Khalil ,&nbsp;Dominique Poirel ,&nbsp;Abhijit Sarkar","doi":"10.1016/j.idm.2024.04.002","DOIUrl":"10.1016/j.idm.2024.04.002","url":null,"abstract":"<div><p>We consider state and parameter estimation for compartmental models having both time-varying and time-invariant parameters. In this manuscript, we first detail a general Bayesian computational framework as a continuation of our previous work. Subsequently, this framework is specifically tailored to the susceptible-infectious-removed (SIR) model which describes a basic mechanism for the spread of infectious diseases through a system of coupled nonlinear differential equations. The SIR model consists of three states, namely, the susceptible, infectious, and removed compartments. The coupling among these states is controlled by two parameters, the infection rate and the recovery rate. The simplicity of the SIR model and similar compartmental models make them applicable to many classes of infectious diseases. However, the combined assumption of a deterministic model and time-invariance among the model parameters are two significant impediments which critically limit their use for long-term predictions. The tendency of certain model parameters to vary in time due to seasonal trends, non-pharmaceutical interventions, and other random effects necessitates a model that structurally permits the incorporation of such time-varying effects. Complementary to this, is the need for a robust mechanism for the estimation of the parameters of the resulting model from data. To this end, we consider an augmented state vector, which appends the time-varying parameters to the original system states whereby the time evolution of the time-varying parameters are driven by an artificial noise process in a standard manner. Distinguishing between time-varying and time-invariant parameters in this fashion limits the introduction of artificial dynamics into the system, and provides a robust, fully Bayesian approach for estimating the time-invariant system parameters as well as the elements of the process noise covariance matrix. This computational framework is implemented by leveraging the robustness of the Markov chain Monte Carlo algorithm permits the estimation of time-invariant parameters while nested nonlinear filters concurrently perform the joint estimation of the system states and time-varying parameters. We demonstrate performance of the framework by first considering a series of examples using synthetic data, followed by an exposition on public health data collected in the province of Ontario.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 4","pages":"Pages 1224-1249"},"PeriodicalIF":8.8,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000563/pdfft?md5=b478716014d99f5201f5866b74321147&pid=1-s2.0-S2468042724000563-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637131","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":"Prediction and control of cholera outbreak: Study case of Cameroon","authors":"C. Hameni Nkwayep ,&nbsp;R. Glèlè Kakaï ,&nbsp;S. Bowong","doi":"10.1016/j.idm.2024.04.009","DOIUrl":"https://doi.org/10.1016/j.idm.2024.04.009","url":null,"abstract":"<div><p>This paper deals with the problem of the prediction and control of cholera outbreak using real data of Cameroon. We first develop and analyze a deterministic model with seasonality for the cholera, the novelty of which lies in the incorporation of undetected cases. We present the basic properties of the model and compute two explicit threshold parameters <span><math><msub><mrow><mover><mrow><mi>R</mi></mrow><mo>¯</mo></mover></mrow><mrow><mn>0</mn></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>R</mi></mrow><mo>̲</mo></munder></mrow><mrow><mn>0</mn></mrow></msub></math></span> that bound the effective reproduction number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, from below and above, that is <span><math><msub><mrow><munder><mrow><mi>R</mi></mrow><mo>̲</mo></munder></mrow><mrow><mn>0</mn></mrow></msub><mo>≤</mo><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>≤</mo><msub><mrow><mover><mrow><mi>R</mi></mrow><mo>¯</mo></mover></mrow><mrow><mn>0</mn></mrow></msub></math></span>. We prove that cholera tends to disappear when <span><math><msub><mrow><mover><mrow><mi>R</mi></mrow><mo>¯</mo></mover></mrow><mrow><mn>0</mn></mrow></msub><mo>≤</mo><mn>1</mn></math></span>, while when <span><math><msub><mrow><munder><mrow><mi>R</mi></mrow><mo>̲</mo></munder></mrow><mrow><mn>0</mn></mrow></msub><mo>&gt;</mo><mn>1</mn></math></span>, cholera persists uniformly within the population. After, assuming that the cholera transmission rates and the proportions of newly symptomatic are unknown, we develop the <em>EnKf</em> approach to estimate unmeasurable state variables and these unknown parameters using real data of cholera from 2014 to 2022 in Cameroon. We use this result to estimate the upper and lower bound of the effective reproduction number and reconstructed active asymptomatic and symptomatic cholera cases in Cameroon, and give a short-term forecasts of cholera in Cameroon until 2024. Numerical simulations show that (i) the transmission rate from free <em>Vibrio cholerae</em> in the environment is more important than the human transmission and begin to be high few week after May and in October, (ii) 90% of newly cholera infected cases that present the symptoms of cholera are not diagnosed and (iii) 60.36% of asymptomatic are detected at 14% and 86% of them recover naturally. The future trends reveals that an outbreak appeared from July to November 2023 with the number of cases reported monthly peaked in October 2023. An impulsive control strategy is incorporated in the model with the aim to avoid or prevent the cholera outbreak. In the first year of monitoring, we observed a reduction of more than 75% of incidences and the disappearance of the peaks when no control are available in Cameroon. A second monitoring of control led to a further reduction of around 60% of incidences the following year, showing how impulse control could be an effective means of eradicating ","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 892-925"},"PeriodicalIF":8.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000630/pdfft?md5=1602be592e27aab84fa7cf3491854d45&pid=1-s2.0-S2468042724000630-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140893971","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 influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1","authors":"Xingxing Zhang ,&nbsp;Liuyang Yang ,&nbsp;Teng Chen ,&nbsp;Qing Wang ,&nbsp;Jin Yang ,&nbsp;Ting Zhang ,&nbsp;Jiao Yang ,&nbsp;Hongqing Zhao ,&nbsp;Shengjie Lai ,&nbsp;Luzhao Feng ,&nbsp;Weizhong Yang","doi":"10.1016/j.idm.2024.04.010","DOIUrl":"https://doi.org/10.1016/j.idm.2024.04.010","url":null,"abstract":"<div><h3>Background</h3><p>Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.</p></div><div><h3>Methods</h3><p>The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA—GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models.</p></div><div><h3>Results</h3><p>Considering the MAPE, RMSE, and R squared values, the ARMA—GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models’ predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.</p></div><div><h3>Conclusions</h3><p>Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 816-827"},"PeriodicalIF":8.8,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000642/pdfft?md5=7be190d3baa9e1f1f010c2c653af27b2&pid=1-s2.0-S2468042724000642-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822616","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":"Epidemicity indices and reproduction numbers from infectious disease data in connected human populations","authors":"Cristiano Trevisin ,&nbsp;Lorenzo Mari ,&nbsp;Marino Gatto ,&nbsp;Andrea Rinaldo","doi":"10.1016/j.idm.2024.04.011","DOIUrl":"https://doi.org/10.1016/j.idm.2024.04.011","url":null,"abstract":"<div><p>We focus on distinctive data-driven measures of the fate of ongoing epidemics. The relevance of our pursuit is suggested by recent results proving that the short-term temporal evolution of infection spread is described by an epidemicity index related to the maximum instantaneous growth rate of new infections, echoing concepts and tools developed to study the reactivity of ecosystems. Suitable epidemicity indices can showcase the dynamics of infections, together with commonly employed effective reproduction numbers, especially when the latter assume values less than 1. In particular, epidemicity evaluates the short-term reactivity to perturbations of a disease-free equilibrium. Here, we show that sufficient epidemicity thresholds to prevent transient epidemic outbreaks in a spatially connected setting can be estimated by generalizing existing analogues derived when spatial effects are neglected. We specifically account for the discrete nature, in both space and time, of surveillance data of the type typically employed to estimate effective reproduction numbers that formed the bulk of the communication of the state of the COVID-19 pandemic and its controls. After analyzing the effects of spatial heterogeneity on the considered prognostic indicators, we perform a short- and long-term analysis on the COVID-19 pandemic in Italy, showing that endemic conditions were maintained throughout the duration of our simulation despite stringent control measures. Our method provides a portfolio of prognostic indices that are essential to pinpoint the ongoing pandemic in both a qualitative and quantitative manner, as our results demonstrate. We base our conclusions on extended investigations of the effects of spatial fragmentation of communities of different sizes owing to connectivity by human mobility and contact scenarios, within real geographic contexts and synthetic setups designed to test our framework.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 875-891"},"PeriodicalIF":8.8,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000654/pdfft?md5=ea03a8ce903db3d142061a5c51167bfb&pid=1-s2.0-S2468042724000654-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140880598","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":"Mathematical assessment of the roles of age heterogeneity and vaccination on the dynamics and control of SARS-CoV-2","authors":"Binod Pant ,&nbsp;Abba B. Gumel","doi":"10.1016/j.idm.2024.04.007","DOIUrl":"https://doi.org/10.1016/j.idm.2024.04.007","url":null,"abstract":"<div><p>The COVID-19 pandemic, caused by SARS-CoV-2, disproportionately affected certain segments of society, particularly the elderly population (which suffered the brunt of the burden of the pandemic in terms of severity of the disease, hospitalization, and death). This study presents a generalized multigroup model, with <em>m</em> heterogeneous sub-populations, to assess the population-level impact of age heterogeneity and vaccination on the transmission dynamics and control of the SARS-CoV-2 pandemic in the United States. Rigorous analysis of the model for the homogeneous case (i.e., the model with <em>m</em> = 1) reveal that its disease-free equilibrium is globally-asymptotically stable for two special cases (with perfect vaccine efficacy or negligible disease-induced mortality) whenever the associated reproduction number is less than one. The model has a unique and globally-asymptotically stable endemic equilibrium, for special a case, when the associated reproduction threshold exceeds one. The homogeneous model was fitted using the observed cumulative mortality data for the United States during three distinct waves (Waves A (October 17, 2020 to April 5, 2021), B (July 9, 2021 to November 7, 2021) and C (January 1, 2022 to May 7, 2022)) chosen to align with time periods when the Alpha, Delta and Omicron were, respectively, the predominant variants in the United States. The calibrated model was used to derive a theoretical expression for achieving vaccine-derived herd immunity (needed to eliminate the disease in the United States). It was shown that, using the one-group homogeneous model, vaccine-derived herd immunity is not attainable during Wave C of the pandemic in the United States, regardless of the coverage level of the fully-vaccinated individuals. Global sensitivity analysis was carried out to determine the parameters of the model that have the most influence on the disease dynamics and burden. These analyses reveal that control and mitigation strategies that may be very effective during one wave may not be so very effective during the other wave or waves. However, strategies that target asymptomatic and pre-symptomatic infectious individuals are shown to be consistently effective across all waves. To study the impact of the disproportionate effect of COVID-19 on the elderly population, we considered the heterogeneous model for the case where the total population is subdivided into the sub-populations of individuals under 65 years of age and those that are 65 and older. The resulting two-group heterogeneous model, which was also fitted using the cumulative mortality data for wave C, was also rigorously analysed. Unlike for the case of the one-group model, it was shown, for the two-group model, that vaccine-derived herd immunity can indeed be achieved during Wave C of the pandemic if at least 61% of the populace is fully vaccinated. Thus, this study shows that adding age heterogeneity into a SARS-CoV-2 vaccination model with homogeneo","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 828-874"},"PeriodicalIF":8.8,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000617/pdfft?md5=4173f89027009f57f890dc1c2d8b19df&pid=1-s2.0-S2468042724000617-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822617","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":"Mathematical modeling for estimating influenza vaccine efficacy: A case study of the Valencian Community, Spain.","authors":"Carlos Andreu-Vilarroig ,&nbsp;Rafael J. Villanueva ,&nbsp;Gilberto González-Parra","doi":"10.1016/j.idm.2024.04.006","DOIUrl":"https://doi.org/10.1016/j.idm.2024.04.006","url":null,"abstract":"<div><p>Vaccine efficacy and its quantification is a crucial concept for the proper design of public health vaccination policies. In this work we proposed a mathematical model to estimate the efficacy of the influenza vaccine in a real-word scenario. In particular, our model is a SEIR-type epidemiological model, which distinguishes vaccinated and unvaccinated populations. Mathematically, its dynamics is governed by a nonlinear system of ordinary differential equations, where the non-linearity arises from the effective contacts between susceptible and infected individuals. Two key aspects of this study is that we use a vaccine distribution over time that is based on real data specific to the elderly people in the Valencian Community and the calibration process takes into account that over one influenza season a specific proportion of the population becomes infected with influenza. To consider the effectiveness of the vaccine, the model incorporates a parameter, the vaccine attenuation factor, which is related with the vaccine efficacy against the influenza virus. With this framework, in order to calibrate the model parameters and to obtain an influenza vaccine efficacy estimation, we considered the 2016–2017 influenza season in the Valencian Community, Spain, using the influenza reported cases of vaccinated and unvaccinated. In order to ensure the model identifiability, we choose to deterministically calibrate the parameters for different scenarios and we find the one with the minimum error in order to determine the vaccine efficacy. The calibration results suggest that the influenza vaccine developed for 2016–2017 influenza season has an efficacy of approximately 76.7%, and that the risk of becoming infected is five times higher for an unvaccinated individual in comparison with a vaccinated one. This estimation partially agrees with some previous studies related to the influenza vaccine. This study presents a new integrated mathematical approach to study the influenza vaccine efficacy and gives further insight into this important public health topic.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 744-762"},"PeriodicalIF":8.8,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000605/pdfft?md5=8fc1d6caf8b368db5d3314fc89bd250c&pid=1-s2.0-S2468042724000605-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140639152","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":"An SEIHR model with age group and social contact for analysis of Fuzhou COVID-19 large wave","authors":"Xiaomin Lan ,&nbsp;Guangmin Chen ,&nbsp;Ruiyang Zhou ,&nbsp;Kuicheng Zheng ,&nbsp;Shaojian Cai ,&nbsp;Fengying Wei ,&nbsp;Zhen Jin ,&nbsp;Xuerong Mao","doi":"10.1016/j.idm.2024.04.003","DOIUrl":"https://doi.org/10.1016/j.idm.2024.04.003","url":null,"abstract":"<div><h3>Background</h3><p>The structure of age groups and social contacts of the total population influenced infection scales and hospital-bed requirements, especially influenced severe infections and deaths during the global prevalence of COVID-19. Before the end of the year 2022, Chinese government implemented the national vaccination and had built the herd immunity cross the country, and announced Twenty Measures (November 11) and Ten New Measures (December 7) for further modifications of dynamic zero-COVID polity on the Chinese mainland. With the nation-wide vaccination and modified measures background, Fuzhou COVID-19 large wave (November 19, 2022–February 9, 2023) led by Omicron BA.5.2 variant was recorded and prevailed for three months in Fujian Province.</p></div><div><h3>Methods</h3><p>A multi-age groups susceptible-exposed-infected-hospitalized-recovered (SEIHR) COVID-19 model with social contacts was proposed in this study. The main object was to evaluate the impacts of age groups and social contacts of the total population. The idea of Least Squares method was governed to perform the data fittings of four age groups against the surveillance data from Fujian Provincial Center for Disease Control and Prevention (Fujian CDC). The next generation matrix method was used to compute basic reproduction number for the total population and for the specific age group. The tendencies of effective reproduction number of four age groups were plotted by using the Epiestim R package and the SEIHR model for in-depth discussions. The sensitivity analysis by using sensitivity index and partial rank correlation coefficients values (PRCC values) were operated to reveal the differences of age groups against the main parameters.</p></div><div><h3>Results</h3><p>The main epidemiological features such as basic reproduction number, effective reproduction number and sensitivity analysis were extensively discussed for multi-age groups SEIHR model in this study. Firstly, by using of the next generation matrix method, basic reproduction number <em>R</em>₀ of the total population was estimated as 1.57 using parameter values of four age groups of Fuzhou COVID-19 large wave. Given age group <em>k</em>, the values of <em>R</em>_0<em>k</em> (age group <em>k</em> to age group <em>k</em>), the values of <span><math><msubsup><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow><mrow><mi>k</mi></mrow></msubsup></math></span> (an infected of age group <em>k</em> to the total population) and the values of <span><math><msubsup><mrow><mover><mrow><mi>R</mi></mrow><mo>^</mo></mover></mrow><mrow><mn>0</mn></mrow><mrow><mi>k</mi></mrow></msubsup></math></span> (an infected of the total population to age group <em>k</em>) were also estimated, in which the explorations of the impacts of age groups revealed that the relationship <span><math><msubsup><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow><mrow><mi>k</mi></mrow></msubsup><mo>&gt;</mo><msub><mrow><mi>R</mi></mrow","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 728-743"},"PeriodicalIF":8.8,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000575/pdfft?md5=67edae2dc28cb9d838a6fe0d0a2ad7a2&pid=1-s2.0-S2468042724000575-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140632680","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}