Epidemics最新文献

{"title":"Infectious diseases: Household modeling with missing data.","authors":"Oron Madmon, Yair Goldberg","doi":"10.1016/j.epidem.2024.100811","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100811","url":null,"abstract":"<p><p>Over three years since the first identified SARS-CoV-2 case was discovered, the role of adolescents and children in spreading the virus remains unclear. Specifically, estimating the relative susceptibility of a child with respect to an adult is still an open question. In our work, we generalize a well-known household model for modeling infectious diseases, to include missing tests. Due to missingness, the likelihood of the generalized model cannot be maximized directly. Thus, we propose an estimation methodology, using a novel EM algorithm, for estimating the MLE in the presence of missing data. We implement the proposed mechanism using R software. Using a simulation study, we illustrate the performance of the proposed estimation methodology compared with the estimation procedure in the complete case. Finally, using the proposed estimation methodology we analyzed a dataset containing SARS-CoV-2 testing results, collected from the city of Bnei Brak, Israel, during the beginning of the pandemic. Using this dataset, we show that adolescents are less susceptible than adults, and children are less susceptible than adolescents.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"100811"},"PeriodicalIF":3.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transmission models of respiratory infections in carceral settings: A systematic review.","authors":"Sara N Levintow, Molly Remch, Emily P Jones, Justin Lessler, Jessie K Edwards, Lauren Brinkley-Rubinstein, Dana K Rice, David L Rosen, Kimberly A Powers","doi":"10.1016/j.epidem.2024.100809","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100809","url":null,"abstract":"<p><strong>Background: </strong>The prevention and control of infectious disease outbreaks in carceral settings face unique challenges. Transmission modeling is a powerful tool for understanding and addressing these challenges, but reviews of modeling work in this context pre-date the proliferation of outbreaks in jails and prisons during the SARS-CoV-2 pandemic. We conducted a systematic review of studies using transmission models of respiratory infections in carceral settings before and during the pandemic.</p><p><strong>Methods: </strong>We searched PubMed, Embase, Scopus, CINAHL, and PsycInfo to identify studies published between 1970 and 2024 that modeled transmission of respiratory infectious diseases in carceral settings. We extracted information on the diseases, populations, and settings modeled; approaches used for parameterizing models and simulating transmission; outcomes of interest and techniques for model calibration, validation, and sensitivity analyses; and types, impacts, and ethical aspects of modeled interventions.</p><p><strong>Results: </strong>Forty-six studies met eligibility criteria, with transmission dynamics of tuberculosis modeled in 24 (52 %), SARS-CoV-2 in 20 (43 %), influenza in one (2 %), and varicella-zoster virus in one (2 %). Carceral facilities in the United States were the most common focus (15, 33 %), followed by Brazil (8, 17 %). Most studies (36, 80 %) used compartmental models (vs. individual- or agent-based). Tuberculosis studies typically modeled transmission within a single facility, while most SARS-CoV-2 studies simulated transmission in multiple places, including between carceral and community settings. Half of studies fit models to epidemiological data; three validated model predictions. Models were used to estimate past or potential future intervention impacts in 32 (70 %) studies, forecast the status quo (without changing conditions) in six (13 %), and examine only theoretical aspects of transmission in eight (17 %). Interventions commonly involved testing and treatment, quarantine and isolation, and/or facility ventilation. Modeled interventions substantially reduced transmission, but some were not well-defined or did not consider ethical issues.</p><p><strong>Conclusion: </strong>The pandemic prompted urgent attention to transmission dynamics in jails and prisons, but there has been little modeling of respiratory infections other than SARS-CoV-2 and tuberculosis. Increased attention to calibration, validation, and the practical and ethical aspects of intervention implementation could improve translation of model estimates into tangible benefits for the highly vulnerable populations in carceral settings.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"100809"},"PeriodicalIF":3.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating effective reproduction numbers using wastewater data from multiple sewersheds for SARS-CoV-2 in California counties.","authors":"Sindhu Ravuri, Elisabeth Burnor, Isobel Routledge, Natalie M Linton, Mugdha Thakur, Alexandria Boehm, Marlene Wolfe, Heather N Bischel, Colleen C Naughton, Alexander T Yu, Lauren A White, Tomás M León","doi":"10.1016/j.epidem.2024.100803","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100803","url":null,"abstract":"<p><p>The effective reproduction number serves as a metric of population-wide, time-varying disease spread. During the early years of the COVID-19 pandemic, this metric was primarily derived from case data, which has varied in quality and representativeness due to changes in testing volume, test-seeking behavior, and resource constraints. Deriving nowcasting estimates from alternative data sources such as wastewater provides complementary information that could inform future public health responses. We estimated county-aggregated, sewershed-restricted wastewater-based SARS-CoV-2 effective reproduction numbers from May 1, 2022 to April 30, 2023 for five counties in California with heterogeneous population sizes, clinical testing rates, demographics, wastewater coverage, and sampling frequencies. We used two methods to produce sewershed-restricted effective reproduction numbers, both based on smoothed and deconvolved wastewater concentrations. We then population-weighted and aggregated these sewershed-level estimates to arrive at county-level effective reproduction numbers. Using mean absolute error (MAE), Spearman's rank correlation (ρ), confusion matrix classification, and cross-correlation analyses, we compared the timing and trajectory of our two wastewater-based models to: (1) a publicly available, county-level ensemble of case-based estimates, and (2) county-aggregated, sewershed-restricted case-based estimates. Both wastewater models demonstrated high concordance with the traditional case-based estimates, as indicated by low mean absolute errors (MAE ≤ 0.09), significant positive Spearman correlation (ρ ≥ 0.66), and high confusion matrix classification accuracy (≥ 0.81). The relative timings of wastewater- and case-based estimates were less clear, with cross-correlation analyses suggesting strong associations for a wide range of temporal lags that varied by county and wastewater model type. This methodology provides a generalizable, robust, and operationalizable framework for estimating county-level wastewater-based effective reproduction numbers. Our retrospective evaluation supports the potential usage of real-time wastewater-based nowcasting as a complementary epidemiological tool for surveillance by public health agencies at the state and local levels. Based on this research, we produced publicly available wastewater-based nowcasts for the California Communicable diseases Assessment Tool (calcat.cdph.ca.gov).</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"100803"},"PeriodicalIF":3.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the impact of non-pharmaceutical interventions against COVID-19 on Mycoplasma pneumoniae in the United States.","authors":"Sang Woo Park, Brooklyn Noble, Emily Howerton, Bjarke F Nielsen, Sarah Lentz, Lilliam Ambroggio, Samuel Dominguez, Kevin Messacar, Bryan T Grenfell","doi":"10.1016/j.epidem.2024.100808","DOIUrl":"10.1016/j.epidem.2024.100808","url":null,"abstract":"<p><p>The introduction of non-pharmaceutical interventions (NPIs) against COVID-19 disrupted circulation of many respiratory pathogens and eventually caused large, delayed outbreaks, owing to the build up of the susceptible pool during the intervention period. In contrast to other common respiratory pathogens that re-emerged soon after the NPIs were lifted, longer delays (> 3 years) in the outbreaks of Mycoplasma pneumoniae (Mp), a bacterium commonly responsible for respiratory infections and pneumonia, have been reported in Europe and Asia. As Mp cases are continuing to increase in the US, predicting the size of an imminent outbreak is timely for public health agencies and decision makers. Here, we use simple mathematical models to provide robust predictions about a large Mp outbreak ongoing in the US. Our model further illustrates that NPIs and waning immunity are important factors in driving long delays in epidemic resurgence.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"100808"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Serodynamics: A primer and synthetic review of methods for epidemiological inference using serological data.","authors":"James A Hay, Isobel Routledge, Saki Takahashi","doi":"10.1016/j.epidem.2024.100806","DOIUrl":"10.1016/j.epidem.2024.100806","url":null,"abstract":"<p><p>We present a review and primer of methods to understand epidemiological dynamics and identify past exposures from serological data, referred to as serodynamics. We discuss processing and interpreting serological data prior to fitting serodynamical models, and review approaches for estimating epidemiological trends and past exposures, ranging from serocatalytic models applied to binary serostatus data, to more complex models incorporating quantitative antibody measurements and immunological understanding. Although these methods are seemingly disparate, we demonstrate how they are derived within a common mathematical framework. Finally, we discuss key areas for methodological development to improve scientific discovery and public health insights in seroepidemiology.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"100806"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11649536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796246","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":"Real-time estimates of the emergence and dynamics of SARS-CoV-2 variants of concern: A modeling approach.","authors":"Nicolò Gozzi, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore Y Piontti, Marco Ajelli, Alessandro Vespignani, Nicola Perra","doi":"10.1016/j.epidem.2024.100805","DOIUrl":"10.1016/j.epidem.2024.100805","url":null,"abstract":"<p><p>The emergence of SARS-CoV-2 variants of concern (VOCs) punctuated the dynamics of the COVID-19 pandemic in multiple occasions. The stages subsequent to their identification have been particularly challenging due to the hurdles associated with a prompt assessment of transmissibility and immune evasion characteristics of the newly emerged VOC. Here, we retrospectively analyze the performance of a modeling strategy developed to evaluate, in real-time, the risks posed by the Alpha and Omicron VOC soon after their emergence. Our approach utilized multi-strain, stochastic, compartmental models enriched with demographic information, age-specific contact patterns, the influence of non-pharmaceutical interventions, and the trajectory of vaccine distribution. The models' preliminary assessment about Omicron's transmissibility and immune evasion closely match later findings. Additionally, analyses based on data collected since our initial assessments demonstrate the retrospective accuracy of our real-time projections in capturing the emergence and subsequent dominance of the Alpha VOC in seven European countries and the Omicron VOC in South Africa. This study shows the value of relatively simple epidemic models in assessing the impact of emerging VOCs in real time, the importance of timely and accurate data, and the need for regular evaluation of these methodologies as we prepare for future global health crises.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"100805"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building in-house capabilities in health agencies and outsourcing to academia or industry: Considerations for effective infectious disease modelling.","authors":"Rachael Pung, Adam J Kucharski","doi":"10.1016/j.epidem.2024.100802","DOIUrl":"10.1016/j.epidem.2024.100802","url":null,"abstract":"<p><p>Infectious disease models provide a systematic way to estimate crucial features of epidemic dynamics and explore different transmission and control scenarios. Given the importance of model-based analysis in managing public health crises, there has been an increase in post-pandemic creation of both academia-driven modelling centres, hubs and consortiums and government-driven public health agencies with in-house modelling units or teams. However, in the past, the delineation of roles and responsibilities between government- and academia-led modelling groups has often been unclear. Who should perform which tasks and when? This ambiguity can increase the risk of duplicated work or unaddressed gaps in analysis. It also raises questions about the sustainability of modelling capacity for addressing routine operational analytical needs while also developing new approaches that can be tailored for emergencies. In the sections below, we discuss factors that could inform decisions about where to locate infectious disease modelling activity. Rather than giving a fixed set of rules, we outlined key considerations and trade-offs that could be taken into account to enable academic and government modelling activities to complement each other effectively, which can in turn be refined as new public health crises emerge in future.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":" ","pages":"100802"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hospital population density and risk of respiratory infection: Is close contact density dependent?","authors":"George Shirreff, Anne C M Thiébaut, Bich-Tram Huynh, Guillaume Chelius, Antoine Fraboulet, Didier Guillemot, Lulla Opatowski, Laura Temime","doi":"10.1016/j.epidem.2024.100807","DOIUrl":"10.1016/j.epidem.2024.100807","url":null,"abstract":"<p><p>Respiratory infections acquired in hospital depend on close contact, which may be affected by hospital population density. Models of infectious disease transmission typically assume that contact rates are independent of density (frequency dependence) or proportional to it (linear density dependence), without justification. We evaluate these assumptions by measuring contact rates in hospitals under different population densities. We analysed data from a study in 15 wards in which staff, patients and visitors carried wearable sensors which detected close contacts. We proposed a general model, non-linear density dependence, and fit this to data on several types of interactions. Finally, we projected the fitted models to predict the effect of increasing population density on epidemic risk. We identified considerable heterogeneity in density dependence between wards, even those with the same medical specialty. Interactions between all persons present usually depended little on the population density. However, increasing patient density was associated with higher rates of patient contact for staff and for other patients. Simulations suggested that a 10 % increase in patient population density would carry a markedly increased risk in many wards. This study highlights the variance in density dependent dynamics and the complexity of predicting contact rates.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"100807"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting SARS-CoV-2 epidemic dynamic in Poland with the pDyn agent-based model","authors":"Karol Niedzielewski ,&nbsp;Rafał P. Bartczuk ,&nbsp;Natalia Bielczyk ,&nbsp;Dominik Bogucki ,&nbsp;Filip Dreger ,&nbsp;Grzegorz Dudziuk ,&nbsp;Łukasz Górski ,&nbsp;Magdalena Gruziel-Słomka ,&nbsp;Jędrzej Haman ,&nbsp;Artur Kaczorek ,&nbsp;Jan Kisielewski ,&nbsp;Bartosz Krupa ,&nbsp;Antoni Moszyński ,&nbsp;Jędrzej M. Nowosielski ,&nbsp;Maciej Radwan ,&nbsp;Marcin Semeniuk ,&nbsp;Urszula Tymoszuk ,&nbsp;Jakub Zieliński ,&nbsp;Franciszek Rakowski","doi":"10.1016/j.epidem.2024.100801","DOIUrl":"10.1016/j.epidem.2024.100801","url":null,"abstract":"<div><div>We employ pDyn (derived from “pandemics dynamics”), an agent-based epidemiological model, to forecast the fourth wave of the SARS-CoV-2 epidemic, primarily driven by the Delta variant, in Polish society. The model captures spatiotemporal dynamics of the epidemic spread, predicting disease-related states based on pathogen properties and behavioral factors. We assess pDyn’s validity, encompassing pathogen variant succession, immunization level, and the proportion of vaccinated among confirmed cases. We evaluate its predictive capacity for pandemic dynamics, including wave peak timing, magnitude, and duration for confirmed cases, hospitalizations, ICU admissions, and deaths, nationally and regionally in Poland. Validation involves comparing pDyn’s estimates with real-world data (excluding data used for calibration) to evaluate whether pDyn accurately reproduced the epidemic dynamics up to the simulation time. To assess the accuracy of pDyn’s predictions, we compared simulation results with real-world data acquired after the simulation date. The findings affirm pDyn’s accuracy in forecasting and enhancing our understanding of epidemic mechanisms.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100801"},"PeriodicalIF":3.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649434","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":"Optimizing spatial distribution of wastewater-based epidemiology to advance health equity","authors":"Maria L. Daza-Torres ,&nbsp;J. Cricelio Montesinos-López ,&nbsp;César Herrera ,&nbsp;Yury E. García ,&nbsp;Colleen C. Naughton ,&nbsp;Heather N. Bischel ,&nbsp;Miriam Nuño","doi":"10.1016/j.epidem.2024.100804","DOIUrl":"10.1016/j.epidem.2024.100804","url":null,"abstract":"<div><div>In 2022, the US Centers for Disease Control and Prevention commissioned the National Academies of Sciences, Engineering, and Medicine to assess the role of community-level wastewater-based epidemiology (WBE) beyond COVID-19. WBE is recognized as a promising mechanism for promptly identifying infectious diseases, including COVID-19 and other novel pathogens. An important conclusion from this initiative is the critical importance of maintaining equity and expanding access to fully realize the benefits of wastewater surveillance for marginalized communities. To address this need, we propose an optimization framework that strategically allocates wastewater monitoring resources at the wastewater treatment plant (WWTP) level, ensuring more effective and equitable distribution of surveillance efforts to serve underserved populations.</div><div>The purpose of the framework is to obtain a balanced spatial distribution, inclusive population coverage, and efficient representation of disadvantaged groups in the allocation of resources for WBE. Furthermore, the framework concentrates on areas with high population density and gives priority to vulnerable regions, as well as identifying signals that display significant variations from other monitored sources. The optimization objective is to maximize a weighted combination of these critical factors. This problem is formulated as an integer optimization problem and solved using simulated annealing. We evaluate various scenarios, considering different weighting factors, to optimize the allocation of WWTPs with monitoring systems. This optimization framework provides an opportunity to enhance WBE by providing customized monitoring strategies created to address specific priorities and situations, thus enhancing the decision-making processes in public health responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100804"},"PeriodicalIF":3.0,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644710","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}