Epidemics最新文献

{"title":"SIR… or MADAM? The impact of privilege on careers in epidemic modelling","authors":"Anne Cori","doi":"10.1016/j.epidem.2024.100769","DOIUrl":"10.1016/j.epidem.2024.100769","url":null,"abstract":"<div><p>As we emerge from what may be the largest global public health crises of our lives, our community of epidemic modellers is naturally reflecting. What role can modelling play in supporting decision making during epidemics? How could we more effectively interact with policy makers? How should we design future disease surveillance systems? All crucial questions. But who is going to be addressing them in 10 years’ time? With high burnout and poor attrition rates in academia, both magnified in our field by our unprecedented efforts during the pandemic, and with low wages coinciding with inflation at its highest for decades, how do we retain talent? This is a multifaceted challenge, that I argue is underpinned by privilege. In this perspective, I introduce the notion of privilege and highlight how various aspects of privilege (namely gender, ethnicity, sexual orientation, language and caring responsibilities) may affect the ability of individuals to access to and progress within academic modelling careers. I propose actions that members of the epidemic modelling research community may take to mitigate these issues and ensure we have a more diverse and equitable workforce going forward.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100769"},"PeriodicalIF":3.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000306/pdfft?md5=08bbe8f3452b925ab59f859f65f4a312&pid=1-s2.0-S1755436524000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140782200","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":"Dynamic contact networks of residents of an urban jail in the era of SARS-CoV-2","authors":"Samuel M. Jenness ,&nbsp;Karina Wallrafen-Sam ,&nbsp;Isaac Schneider ,&nbsp;Shanika Kennedy ,&nbsp;Matthew J. Akiyama ,&nbsp;Anne C. Spaulding","doi":"10.1016/j.epidem.2024.100772","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100772","url":null,"abstract":"<div><h3>Background</h3><p>In custodial settings such as jails and prisons, infectious disease transmission is heightened by factors such as overcrowding and limited healthcare access. Specific features of social contact networks within these settings have not been sufficiently characterized, especially in the context of a large-scale respiratory infectious disease outbreak. The study aims to quantify contact network dynamics within the Fulton County Jail in Atlanta, Georgia.</p></div><div><h3>Methods</h3><p>Jail roster data were utilized to construct social contact networks. Rosters included resident details, cell locations, and demographic information. This analysis involved 6702 male residents over 140,901 person days. Network statistics, including degree, mixing, and dissolution (movement within and out of the jail) rates, were assessed. We compared outcomes for two distinct periods (January 2022 and April 2022) to understand potential responses in network structures during and after the SARS-CoV-2 Omicron variant peak.</p></div><div><h3>Results</h3><p>We found high cross-sectional network degree at both cell and block levels. While mean degree increased with age, older residents exhibited lower degree during the Omicron peak. Block-level networks demonstrated higher mean degrees than cell-level networks. Cumulative degree distributions increased from January to April, indicating heightened contacts after the outbreak. Assortative age mixing was strong, especially for younger residents. Dynamic network statistics illustrated increased degrees over time, emphasizing the potential for disease spread.</p></div><div><h3>Conclusions</h3><p>Despite some reduction in network characteristics during the Omicron peak, the contact networks within the Fulton County Jail presented ideal conditions for infectious disease transmission. Age-specific mixing patterns suggested unintentional age segregation, potentially limiting disease spread to older residents. This study underscores the necessity for ongoing monitoring of contact networks in carceral settings and provides valuable insights for epidemic modeling and intervention strategies, including quarantine, depopulation, and vaccination, laying a foundation for understanding disease dynamics in such environments.Top of Form</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100772"},"PeriodicalIF":3.8,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000331/pdfft?md5=4ee2cad371be7fa416e147699bcbdde5&pid=1-s2.0-S1755436524000331-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078053","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 simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data","authors":"I. Ogi-Gittins ,&nbsp;W.S. Hart ,&nbsp;J. Song ,&nbsp;R.K. Nash ,&nbsp;J. Polonsky ,&nbsp;A. Cori ,&nbsp;E.M. Hill ,&nbsp;R.N. Thompson","doi":"10.1016/j.epidem.2024.100773","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100773","url":null,"abstract":"<div><p>Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100773"},"PeriodicalIF":3.8,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000343/pdfft?md5=70a2d36ac61daf60a8896d1cec2f447e&pid=1-s2.0-S1755436524000343-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078030","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":"Unveiling ecological/evolutionary insights in HIV viral load dynamics: Allowing random slopes to observe correlational changes to CpG-contents and other molecular and clinical predictors","authors":"Rocío Carrasco-Hernández ,&nbsp;Humberto Valenzuela-Ponce ,&nbsp;Maribel Soto-Nava ,&nbsp;Claudia García-Morales ,&nbsp;Margarita Matías-Florentino ,&nbsp;Joel O. Wertheim ,&nbsp;Davey M. Smith ,&nbsp;Gustavo Reyes-Terán ,&nbsp;Santiago Ávila-Ríos","doi":"10.1016/j.epidem.2024.100770","DOIUrl":"10.1016/j.epidem.2024.100770","url":null,"abstract":"&lt;div&gt;&lt;p&gt;In the context of infectious diseases, the dynamic interplay between ever-changing host populations and viral biology demands a more flexible modeling approach than common fixed correlations. Embracing random-effects regression models allows for a nuanced understanding of the intricate ecological and evolutionary dynamics underlying complex phenomena, offering valuable insights into disease progression and transmission patterns. In this article, we employed a random-effects regression to model an observed decreasing median plasma viral load (pVL) among individuals with HIV in Mexico City during 2019–2021. We identified how these functional slope changes (i.e. random slopes by year) improved predictions of the observed pVL median changes between 2019 and 2021, leading us to hypothesize underlying ecological and evolutionary factors. Our analysis involved a dataset of pVL values from 7325 ART-naïve individuals living with HIV, accompanied by their associated clinical and viral molecular predictors. A conventional fixed-effects linear model revealed significant correlations between pVL and predictors that evolved over time. However, this fixed-effects model could not fully explain the reduction in median pVL; thus, prompting us to adopt random-effects models. After applying a random effects regression model—with random slopes and intercepts by year—, we observed potential \"functional changes\" within the local HIV viral population, highlighting the importance of ecological and evolutionary considerations in HIV dynamics: A notably stronger negative correlation emerged between HIV pVL and the CpG content in the &lt;em&gt;pol&lt;/em&gt; gene, suggesting a changing immune landscape influenced by CpG-induced innate immune responses that could impact viral load dynamics. Our study underscores the significance of random effects models in capturing dynamic correlations and the crucial role of molecular characteristics like CpG content. By enriching our understanding of changing host-virus interactions and HIV progression, our findings contribute to the broader relevance of such models in infectious disease research. They shed light on the changing interplay between host and pathogen, driving us closer to more effective strategies for managing infectious diseases.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Significance of the study&lt;/h3&gt;&lt;p&gt;This study highlights a decreasing trend in median plasma viral loads among ART-naïve individuals living with HIV in Mexico City between 2019 and 2021. It uncovers various predictors significantly correlated with pVL, shedding light on the complex interplay between host-virus interactions and disease progression. By employing a random-slopes model, the researchers move beyond traditional fixed-effects models to better capture dynamic correlations and evolutionary changes in HIV dynamics. The discovery of a stronger negative correlation between pVL and CpG content in HIV-pol sequences suggests potential changes in the immune landscape and innate immune ","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100770"},"PeriodicalIF":3.8,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000318/pdfft?md5=5d45e05c3ae78c8e05108ba6aded0c72&pid=1-s2.0-S1755436524000318-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140960239","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":"When do we need multiple infectious disease models? Agreement between projection rank and magnitude in a multi-model setting","authors":"La Keisha Wade-Malone ,&nbsp;Emily Howerton ,&nbsp;William J.M. Probert ,&nbsp;Michael C. Runge ,&nbsp;Cécile Viboud ,&nbsp;Katriona Shea","doi":"10.1016/j.epidem.2024.100767","DOIUrl":"10.1016/j.epidem.2024.100767","url":null,"abstract":"<div><p>Mathematical models are useful for public health planning and response to infectious disease threats. However, different models can provide differing results, which can hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups to generate ensembles, known to provide more accurate predictions of future outcomes. Yet, these hubs are resource intensive, and how many models are sufficient in a hub is not known. Here, we compare the benefit of predictions from multiple models in different contexts: (1) decision settings that depend on predictions of quantitative outcomes (e.g., hospital capacity planning), where assessments of the benefits of multi-model ensembles have largely focused; and (2) decisions settings that require the ranking of alternative epidemic scenarios (e.g., comparing outcomes under multiple possible interventions and biological uncertainties). We develop a mathematical framework to mimic a multi-model prediction setting, and use this framework to quantify how frequently predictions from different models agree. We further explore multi-model agreement using real-world, empirical data from 14 rounds of U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest that the value of multiple models could be different in different decision contexts, and if only a few models are available, focusing on the rank of alternative epidemic scenarios could be more robust than focusing on quantitative outcomes. Although additional exploration of the sufficient number of models for different contexts is still needed, our results indicate that it may be possible to identify decision contexts where it is robust to rely on fewer models, a finding that can inform the use of modeling resources during future public health crises.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100767"},"PeriodicalIF":3.8,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000288/pdfft?md5=5727c11b68185bbaae0cdf0fbfd46b98&pid=1-s2.0-S1755436524000288-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140768242","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 vaccine efficacy during open-label follow-up of COVID-19 vaccine trials based on population-level surveillance data","authors":"Mia Moore ,&nbsp;Yifan Zhu ,&nbsp;Ian Hirsch ,&nbsp;Tom White ,&nbsp;Robert C. Reiner ,&nbsp;Ryan M. Barber ,&nbsp;David Pigott ,&nbsp;James K. Collins ,&nbsp;Serena Santoni ,&nbsp;Magdalena E. Sobieszczyk ,&nbsp;Holly Janes","doi":"10.1016/j.epidem.2024.100768","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100768","url":null,"abstract":"<div><p>While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a “population model”, to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a “cohort model” was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%–74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%–51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100768"},"PeriodicalIF":3.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175543652400029X/pdfft?md5=39df9c17d4cc575bab188fe562477835&pid=1-s2.0-S175543652400029X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140620930","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":"Characterising information gains and losses when collecting multiple epidemic model outputs","authors":"Katharine Sherratt ,&nbsp;Ajitesh Srivastava ,&nbsp;Kylie Ainslie ,&nbsp;David E. Singh ,&nbsp;Aymar Cublier ,&nbsp;Maria Cristina Marinescu ,&nbsp;Jesus Carretero ,&nbsp;Alberto Cascajo Garcia ,&nbsp;Nicolas Franco ,&nbsp;Lander Willem ,&nbsp;Steven Abrams ,&nbsp;Christel Faes ,&nbsp;Philippe Beutels ,&nbsp;Niel Hens ,&nbsp;Sebastian Müller ,&nbsp;Billy Charlton ,&nbsp;Ricardo Ewert ,&nbsp;Sydney Paltra ,&nbsp;Christian Rakow ,&nbsp;Jakob Rehmann ,&nbsp;Sebastian Funk","doi":"10.1016/j.epidem.2024.100765","DOIUrl":"10.1016/j.epidem.2024.100765","url":null,"abstract":"<div><h3>Background</h3><p>Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results.</p></div><div><h3>Methods</h3><p>We compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data.</p></div><div><h3>Results</h3><p>By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes.</p></div><div><h3>Conclusions</h3><p>We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100765"},"PeriodicalIF":3.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000264/pdfft?md5=a4a8d1d9343a34299a8c537abe1ab40f&pid=1-s2.0-S1755436524000264-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140402831","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 impact of test–trace–isolate–quarantine systems on SARS-CoV-2 transmission in Australia","authors":"Freya M. Shearer ,&nbsp;James M. McCaw ,&nbsp;Gerard E. Ryan ,&nbsp;Tianxiao Hao ,&nbsp;Nicholas J. Tierney ,&nbsp;Michael J. Lydeamore ,&nbsp;Logan Wu ,&nbsp;Kate Ward ,&nbsp;Sally Ellis ,&nbsp;James Wood ,&nbsp;Jodie McVernon ,&nbsp;Nick Golding","doi":"10.1016/j.epidem.2024.100764","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100764","url":null,"abstract":"<div><h3>Background:</h3><p>Australian states and territories used test–trace–isolate–quarantine (TTIQ) systems extensively in their response to the COVID-19 pandemic in 2020-2021. We report on an analysis of Australian case data to estimate the impact of test–trace–isolate–quarantine systems on SARS-CoV-2 transmission.</p></div><div><h3>Methods:</h3><p>Our analysis uses a novel mathematical modelling framework and detailed surveillance data on COVID-19 cases including dates of infection and dates of isolation. First, we directly translate an empirical distribution of times from infection to isolation into reductions in potential for onward transmission during periods of relatively low caseloads (tens to hundreds of reported cases per day). We then apply a simulation approach, validated against case data, to assess the impact of case-initiated contact tracing on transmission during a period of relatively higher caseloads and system stress (up to thousands of cases per day).</p></div><div><h3>Results:</h3><p>We estimate that under relatively low caseloads in the state of New South Wales (tens of cases per day), TTIQ contributed to a 54% reduction in transmission. Under higher caseloads in the state of Victoria (hundreds of cases per day), TTIQ contributed to a 42% reduction in transmission. Our results also suggest that case-initiated contact tracing can support timely quarantine in times of system stress (thousands of cases per day).</p></div><div><h3>Conclusion:</h3><p>Contact tracing systems for COVID-19 in Australia were highly effective and adaptable in supporting the national suppression strategy from 2020–21, prior to the emergence of the Omicron variant in November 2021. TTIQ systems were critical to the maintenance of the strong suppression strategy and were more effective when caseloads were (relatively) low.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100764"},"PeriodicalIF":3.8,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000252/pdfft?md5=a72a8cad2bc5d9ae118139a2c4965901&pid=1-s2.0-S1755436524000252-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308805","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":"Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections","authors":"Przemyslaw Porebski ,&nbsp;Srinivasan Venkatramanan ,&nbsp;Aniruddha Adiga ,&nbsp;Brian Klahn ,&nbsp;Benjamin Hurt ,&nbsp;Mandy L. Wilson ,&nbsp;Jiangzhuo Chen ,&nbsp;Anil Vullikanti ,&nbsp;Madhav Marathe ,&nbsp;Bryan Lewis","doi":"10.1016/j.epidem.2024.100761","DOIUrl":"10.1016/j.epidem.2024.100761","url":null,"abstract":"<div><p>Scenario-based modeling frameworks have been widely used to support policy-making at state and federal levels in the United States during the COVID-19 response. While custom-built models can be used to support one-off studies, sustained updates to projections under changing pandemic conditions requires a <em>robust</em>, <em>integrated</em>, and <em>adaptive</em> framework. In this paper, we describe one such framework, <strong>UVA-adaptive</strong>, that was built to support the CDC-aligned Scenario Modeling Hub (SMH) across multiple rounds, as well as weekly/biweekly projections to Virginia Department of Health (VDH) and US Department of Defense during the COVID-19 response. Building upon an existing metapopulation framework, PatchSim, <strong>UVA-adaptive</strong> uses a calibration mechanism relying on adjustable effective transmissibility as a basis for scenario definition while also incorporating real-time datasets on case incidence, seroprevalence, variant characteristics, and vaccine uptake. Through the pandemic, our framework evolved by incorporating available data sources and was extended to capture complexities of multiple strains and heterogeneous immunity of the population. Here we present the version of the model that was used for the recent projections for SMH and VDH, describe the calibration and projection framework, and demonstrate that the calibrated transmissibility correlates with the evolution of the pathogen as well as associated societal dynamics.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100761"},"PeriodicalIF":3.8,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000227/pdfft?md5=707b068924241297245e664ac1d56b06&pid=1-s2.0-S1755436524000227-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140276351","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 measures to reduce the transmission of SARS-CoV-2 in Australia to guide a ‘National Plan’ to reopening","authors":"Gerard E. Ryan ,&nbsp;Freya M. Shearer ,&nbsp;James M. McCaw ,&nbsp;Jodie McVernon ,&nbsp;Nick Golding","doi":"10.1016/j.epidem.2024.100763","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100763","url":null,"abstract":"<div><p>The availability of COVID-19 vaccines promised a reduction in the severity of disease and relief from the strict public health and social measures (PHSMs) imposed in many countries to limit spread and burden of COVID-19. We were asked to define vaccine coverage thresholds for Australia’s transition to easing restrictions and reopening international borders. Using evidence of vaccine effectiveness against the then-circulating Delta variant, we used a mathematical model to determine coverage targets. The absence of any COVID-19 infections in many sub-national jurisdictions in Australia posed particular methodological challenges. We used a novel metric called Transmission Potential (TP) as a proxy measure of the population-level effective reproduction number. We estimated TP of the Delta variant under a range of PHSMs, test-trace-isolate-quarantine (TTIQ) efficiencies, vaccination coverage thresholds, and age-based vaccine allocation strategies. We found that high coverage across all ages (<span><math><mrow><mo>≥</mo><mn>70</mn><mtext>%</mtext></mrow></math></span>) combined with ongoing TTIQ and minimal PHSMs was sufficient to avoid lockdowns. At lesser coverage (<span><math><mrow><mo>≤</mo><mn>60</mn><mtext>%</mtext></mrow></math></span>) rapid case escalation risked overwhelming of the health sector or the need to reimpose stricter restrictions. Maintaining low case numbers was most beneficial for health and the economy, and at higher coverage levels (<span><math><mrow><mo>≥</mo><mn>80</mn><mtext>%</mtext></mrow></math></span>) further easing of restrictions was deemed possible. These results directly informed easing of COVID-19 restrictions in Australia.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100763"},"PeriodicalIF":3.8,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000240/pdfft?md5=e9e2e1705a2a661f6fd1f189004e98c1&pid=1-s2.0-S1755436524000240-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181338","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}