Infectious Disease Modelling最新文献

{"title":"Ebola virus disease model with a nonlinear incidence rate and density-dependent treatment","authors":"Jacques Ndé Kengne ,&nbsp;Calvin Tadmon","doi":"10.1016/j.idm.2024.03.007","DOIUrl":"https://doi.org/10.1016/j.idm.2024.03.007","url":null,"abstract":"<div><p>This paper studies an Ebola epidemic model with an exponential nonlinear incidence function that considers the efficacy and the behaviour change. The current model also incorporates a new density-dependent treatment that catches the impact of the disease transmission on the treatment. Firstly, we provide a theoretical study of the nonlinear differential equations model obtained. More precisely, we derive the effective reproduction number and, under suitable conditions, prove the stability of equilibria. Afterwards, we show that the model exhibits the phenomenon of backward-bifurcation whenever the bifurcation parameter and the reproduction number are less than one. We find that the bi-stability and backward-bifurcation are not automatically connected in epidemic models. In fact, when a backward-bifurcation occurs, the disease-free equilibrium may be globally stable. Numerically, we use well-known standard tools to fit the model to the data reported for the 2018–2020 Kivu Ebola outbreak, and perform the sensitivity analysis. To control Ebola epidemics, our findings recommend a combination of a rapid behaviour change and the implementation of a proper treatment strategy with a high level of efficacy. Secondly, we propose and analyze a fractional-order Ebola epidemic model, which is an extension of the first model studied. We use the Caputo operator and construct the Grünwald-Letnikov nonstandard finite difference scheme, and show its advantages.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 775-804"},"PeriodicalIF":8.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S246804272400054X/pdfft?md5=b2c4b150c4d0d197e920d6133f09d833&pid=1-s2.0-S246804272400054X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140650510","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":"Estimating the effective reproduction number of COVID-19 from population-wide wastewater data: An application in Kagawa, Japan","authors":"Yuta Okada,&nbsp;Hiroshi Nishiura","doi":"10.1016/j.idm.2024.03.006","DOIUrl":"https://doi.org/10.1016/j.idm.2024.03.006","url":null,"abstract":"<div><p>Although epidemiological surveillance of COVID-19 has been gradually downgraded globally, the transmission of COVID-19 continues. It is critical to quantify the transmission dynamics of COVID-19 using multiple datasets including wastewater virus concentration data. Herein, we propose a comprehensive method for estimating the effective reproduction number using wastewater data. The wastewater virus concentration data, which were collected twice a week, were analyzed using daily COVID-19 incidence data obtained from Takamatsu, Japan between January 2022 and September 2022. We estimated the shedding load distribution (SLD) as a function of time since the date of infection, using a model employing the delay distribution, which is assumed to follow a gamma distribution, multiplied by a scaling factor. We also examined models that accounted for the temporal smoothness of viral load measurement data. The model that smoothed temporal patterns of viral load was the best fit model (WAIC = 2795.8), which yielded a mean estimated distribution of SLD of 3.46 days (95% CrI: 3.01–3.95 days). Using this SLD, we reconstructed the daily incidence, which enabled computation of the effective reproduction number. Using the best fit posterior draws of parameters directly, or as a prior distribution for subsequent analyses, we first used a model that assumed temporal smoothness of viral load concentrations in wastewater, as well as infection counts by date of infection. In the subsequent approach, we examined models that also incorporated weekly reported case counts as a proxy for weekly incidence reporting. Both approaches enabled estimations of the epidemic curve as well as the effective reproduction number from twice-weekly wastewater viral load data. Adding weekly case count data reduced the uncertainty of the effective reproduction number. We conclude that wastewater data are still a valuable source of information for inferring the transmission dynamics of COVID-19, and that inferential performance is enhanced when those data are combined with weekly incidence data.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 645-656"},"PeriodicalIF":8.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000459/pdfft?md5=04a02f108d128864253a352fffd3e820&pid=1-s2.0-S2468042724000459-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140539387","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":"Epidemiological feature analysis of SVEIR model with control strategy and variant evolution","authors":"Kaijing Chen ,&nbsp;Fengying Wei ,&nbsp;Xinyan Zhang ,&nbsp;Hao Jin ,&nbsp;Zuwen Wang ,&nbsp;Yue Zuo ,&nbsp;Kai Fan","doi":"10.1016/j.idm.2024.03.005","DOIUrl":"10.1016/j.idm.2024.03.005","url":null,"abstract":"<div><p>The complex interactions were performed among non-pharmaceutical interventions, vaccinations, and hosts for all epidemics in mainland China during the spread of COVID-19. Specially, the small-scale epidemic in the city described by SVEIR model was less found in the current studies. The SVEIR model with control was established to analyze the dynamical and epidemiological features of two epidemics in Jinzhou City led by Omicron variants before and after Twenty Measures. In this study, the total population (<em>N</em>) of Jinzhou City was divided into five compartments: the susceptible (<em>S</em>), the vaccinated (<em>V</em>), the exposed (<em>E</em>), the infected (<em>I</em>), and the recovered (<em>R</em>). By surveillance data and the SVEIR model, three methods (maximum likelihood method, exponential growth rate method, next generation matrix method) were governed to estimate basic reproduction number, and the results showed that an increasing tendency of basic reproduction number from Omicron BA.5.2 to Omicron BA.2.12.1. Meanwhile, the effective reproduction number for two epidemics were investigated by surveillance data, and the results showed that Jinzhou wave 1 reached the peak on November 1 and was controlled 7 days later, and that Jinzhou wave 2 reached the peak on November 28 and was controlled 5 days later. Moreover, the impacts of non-pharmaceutical interventions (awareness delay, peak delay, control intensity) were discussed extensively, the variations of infection scales for Omicron variant and EG.5 variant were also discussed. Furthermore, the investigations on peaks and infection scales for two epidemics in dynamic zero-COVID policy were operated by the SVEIR model with control. The investigations on public medical requirements of Jinzhou City and Liaoning Province were analyzed by using SVEIR model without control, which provided a possible perspective on variant evolution in the future.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 689-700"},"PeriodicalIF":8.8,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000381/pdfft?md5=18eb26a6e31d96f380b7658fb6cf672c&pid=1-s2.0-S2468042724000381-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406732","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":"Influence of waning immunity on vaccination decision-making: A multi-strain epidemic model with an evolutionary approach analyzing cost and efficacy","authors":"Md. Mamun-Ur-Rashid Khan ,&nbsp;Jun Tanimoto","doi":"10.1016/j.idm.2024.03.004","DOIUrl":"10.1016/j.idm.2024.03.004","url":null,"abstract":"<div><p>In this research, we introduce a comprehensive epidemiological model that accounts for multiple strains of an infectious disease and two distinct vaccination options. Vaccination stands out as the most effective means to prevent and manage infectious diseases. However, when there are various vaccines available, each with its costs and effectiveness, the decision-making process for individuals becomes paramount. Furthermore, the factor of waning immunity following vaccination also plays a significant role in influencing these choices. To understand how individuals make decisions in the context of multiple strains and waning immunity, we employ a behavioral model, allowing an epidemiological model to be coupled with the dynamics of a decision-making process. Individuals base their choice of vaccination on factors such as the total number of infected individuals and the cost-effectiveness of the vaccine. Our findings indicate that as waning immunity increases, people tend to prioritize vaccines with higher costs and greater efficacy. Moreover, when more contagious strains are present, the equilibrium in vaccine adoption is reached more rapidly. Finally, we delve into the social dilemma inherent in our model by quantifying the social efficiency deficit (SED) under various parameter combinations.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 657-672"},"PeriodicalIF":8.8,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S246804272400037X/pdfft?md5=ee5c500e2684c4e22c108380d62e3295&pid=1-s2.0-S246804272400037X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404194","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":"Origins of the problematic E in SEIR epidemic models","authors":"Donald S. Burke","doi":"10.1016/j.idm.2024.03.003","DOIUrl":"10.1016/j.idm.2024.03.003","url":null,"abstract":"<div><p>During the COVID-19 pandemic, over one thousand papers were published on “Susceptible-Exposed-Infectious-Removed” (SEIR) epidemic computational models. The English word “exposed” in its vernacular and public health usage means a state of having been in contact with an infectious individual, but not necessarily infected. In contrast, the term “exposed” in SEIR modeling usage typically stands for a state of already being infected but not yet being infectious to others, a state more properly termed “latently infected.” In public health language, “exposed” means <em>possibly infected</em>, yet in SEIR modeling language, “exposed” means <em>already infected.</em> This paper retraces the conceptual and mathematical origins of this terminological disconnect and concludes that epidemic modelers should consider using the “SLIR” notational short-hand (L for Latent) instead of SEIR.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 3","pages":"Pages 673-679"},"PeriodicalIF":8.8,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000368/pdfft?md5=be3910a3ea0460876990a6b8e5d5391c&pid=1-s2.0-S2468042724000368-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406230","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 decision-making in relieving global high temperature-related disease burden by data-driven simulation","authors":"Xin-Chen Li ,&nbsp;Hao-Ran Qian ,&nbsp;Yan-Yan Zhang ,&nbsp;Qi-Yu Zhang ,&nbsp;Jing-Shu Liu ,&nbsp;Hong-Yu Lai ,&nbsp;Wei-Guo Zheng ,&nbsp;Jian Sun ,&nbsp;Bo Fu ,&nbsp;Xiao-Nong Zhou ,&nbsp;Xiao-Xi Zhang","doi":"10.1016/j.idm.2024.03.001","DOIUrl":"10.1016/j.idm.2024.03.001","url":null,"abstract":"<div><p>The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010–2019. The burdens of five categories of disease causes – cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases – were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure &amp; Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 2","pages":"Pages 618-633"},"PeriodicalIF":8.8,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000344/pdfft?md5=25093cab9963d86b8f9e79bcb445acc7&pid=1-s2.0-S2468042724000344-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140271219","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":"Impact of infectious density-induced additional screening and treatment saturation on COVID-19: Modeling and cost-effective optimal control","authors":"Sonu Lamba ,&nbsp;Tanuja Das ,&nbsp;Prashant K. Srivastava","doi":"10.1016/j.idm.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.idm.2024.03.002","url":null,"abstract":"<div><p>This study introduces a novel <em>SI</em>2<em>HR</em> model, where “<em>I</em>2” denotes two infectious classes representing asymptomatic and symptomatic infections, aiming to investigate and analyze the cost-effective optimal control measures for managing COVID-19. The model incorporates a novel concept of infectious density-induced additional screening (IDIAS) and accounts for treatment saturation. Furthermore, the model considers the possibility of reinfection and the loss of immunity in individuals who have previously recovered. To validate and calibrate the proposed model, real data from November–December 2022 in Hong Kong are utilized. The estimated parameters obtained from this calibration process are valuable for prediction purposes and facilitate further numerical simulations. An analysis of the model reveals that delays in screening, treatment, and quarantine contribute to an increase in the basic reproduction number <em>R</em>₀, indicating a tendency towards endemicity. In particular, from the elasticity of <em>R</em>₀, we deduce that normalized sensitivity indices of baseline screening rate (<em>θ</em>), quarantine rates (<em>γ</em>, <em>α</em>_<em>s</em>), and treatment rate (<em>α</em>) are negative, which shows that delaying any of these may cause huge surge in <em>R</em>₀, ultimately increases the disease burden. Further, by the contour plots, we note the two-parameter behavior of the infectives (both symptomatic and asymptomatic). Expanding upon the model analysis, an optimal control problem (OCP) is formulated, incorporating three control measures: precautionary interventions, boosted IDIAS, and boosted treatment. The Pontryagin's maximum principle and the forward-backward sweep method are employed to solve the OCP. The numerical simulations highlight that enhanced screening and treatment, coupled with preventive interventions, can effectively contribute to sustainable disease control. However, the cost-effectiveness analysis (CEA) conducted in this study suggests that boosting IDIAS alone is the most economically efficient and cost-effective approach compared to other strategies. The CEA results provide valuable insights into identifying specific strategies based on their cost-efficacy ranking, which can be implemented to maximize impact while minimizing costs. Overall, this research offers significant insights for policymakers and healthcare professionals, providing a framework to optimize control efforts for COVID-19 or similar epidemics in the future.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 2","pages":"Pages 569-600"},"PeriodicalIF":8.8,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000356/pdfft?md5=8677b3126138074c8db5a84cbe5675f2&pid=1-s2.0-S2468042724000356-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188278","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":"Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach","authors":"Farhad Waseel ,&nbsp;George Streftaris ,&nbsp;Bhuvendhraa Rudrusamy ,&nbsp;Sarat C. Dass","doi":"10.1016/j.idm.2024.02.010","DOIUrl":"https://doi.org/10.1016/j.idm.2024.02.010","url":null,"abstract":"<div><p>The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57–66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 2","pages":"Pages 527-556"},"PeriodicalIF":8.8,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000307/pdfft?md5=5a36e36b4ff9dc349d8cb4dff9b72255&pid=1-s2.0-S2468042724000307-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140138818","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":"Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects: a modeling analysis","authors":"Salihu S. Musa ,&nbsp;Shi Zhao ,&nbsp;Ismail Abdulrashid ,&nbsp;Sania Qureshi ,&nbsp;Andrés Colubri ,&nbsp;Daihai He","doi":"10.1016/j.idm.2024.02.011","DOIUrl":"https://doi.org/10.1016/j.idm.2024.02.011","url":null,"abstract":"<div><p>Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation<strong><em>,</em></strong> from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 2","pages":"Pages 601-617"},"PeriodicalIF":8.8,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000319/pdfft?md5=e05a839000fbe3ce4257556152096a1f&pid=1-s2.0-S2468042724000319-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188229","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 the unexpected dynamics of COVID-19 in Manaus, Brazil","authors":"Daihai He ,&nbsp;Yael Artzy-Randrup ,&nbsp;Salihu S. Musa ,&nbsp;Tiago Gräf ,&nbsp;Felipe Naveca ,&nbsp;Lewi Stone","doi":"10.1016/j.idm.2024.02.012","DOIUrl":"https://doi.org/10.1016/j.idm.2024.02.012","url":null,"abstract":"<div><p>In late March 2020, SARS-CoV-2 arrived in Manaus, Brazil, and rapidly developed into a large-scale epidemic that collapsed the local health system and resulted in extreme death rates. Several key studies reported that ∼76% of residents of Manaus were infected (attack rate AR≃76%) by October 2020, suggesting protective herd immunity had been reached. Despite this, an unexpected second wave of COVID-19 struck again in November and proved to be larger than the first, creating a catastrophe for the unprepared population. It has been suggested that this could be possible if the second wave was driven by reinfections. However, it is widely reported that reinfections were at a low rate (before the emergence of Omicron), and reinfections tend to be mild. Here, we use novel methods to model the epidemic from mortality data without considering reinfection-caused deaths and evaluate the impact of interventions to explain why the second wave appeared. The method fits a “flexible” reproductive number <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> that changes over the epidemic, and it is demonstrated that the method can successfully reconstruct <span><math><mrow><msub><mi>R</mi><mn>0</mn></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> from simulated data. For Manaus, the method finds AR≃34% by October 2020 for the first wave, which is far less than required for herd immunity yet in-line with seroprevalence estimates. The work is complemented by a two-strain model. Using genomic data, the model estimates transmissibility of the new P.1 virus lineage as 1.9 times higher than that of the non-P.1. Moreover, an age class model variant that considers the high mortality rates of older adults show very similar results. These models thus provide a reasonable explanation for the two-wave dynamics in Manaus without the need to rely on large reinfection rates, which until now have only been found in negligible to moderate numbers in recent surveillance efforts.</p></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"9 2","pages":"Pages 557-568"},"PeriodicalIF":8.8,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468042724000320/pdfft?md5=39ceee34ad7cf572da2f7fe3f975291c&pid=1-s2.0-S2468042724000320-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140160977","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}