Epidemics最新文献

{"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}

{"title":"Effectiveness of interventions to reduce COVID-19 transmission in schools","authors":"Remy Pasco ,&nbsp;Spencer J. Fox ,&nbsp;Michael Lachmann ,&nbsp;Lauren Ancel Meyers","doi":"10.1016/j.epidem.2024.100762","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100762","url":null,"abstract":"<div><p>School reopenings in 2021 and 2022 coincided with the rapid emergence of new SARS-CoV-2 variants in the United States. In-school mitigation efforts varied, depending on local COVID-19 mandates and resources. Using a stochastic age-stratified agent-based model of SARS-CoV-2 transmission, we estimate the impacts of multiple in-school strategies on both infection rates and absenteeism, relative to a baseline scenario in which only symptomatic cases are tested and positive tests trigger a 10-day isolation of the case and 10-day quarantine of their household and classroom. We find that monthly asymptomatic screening coupled with the 10-day isolation and quarantine period is expected to avert 55.4% of infections while increasing absenteeism by 104.3%. Replacing quarantine with test-to-stay would reduce absenteeism by 66.3% (while hardly impacting infection rates), but would require roughly 10-fold more testing resources. Alternatively, vaccination or mask wearing by 50% of the student body is expected to avert 54.1% or 43.1% of infections while decreasing absenteeism by 34.1% or 27.4%, respectively. Separating students into classrooms based on mask usage is expected to reduce infection risks among those who wear masks (by 23.1%), exacerbate risks among those who do not (by 27.8%), but have little impact on overall risk. A combined strategy of monthly screening, household and classroom quarantine, a 50% vaccination rate, and a 50% masking rate (in mixed classrooms) is expected to avert 81.7% of infections while increasing absenteeism by 90.6%. During future public health emergencies, such analyses can inform the rapid design of resource-constrained strategies that mitigate both public health and educational risks.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100762"},"PeriodicalIF":3.8,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000239/pdfft?md5=b0937aa65f753c8e2fa877bb7cbb5376&pid=1-s2.0-S1755436524000239-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140122207","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":"Enhancing seasonal influenza projections: A mechanistic metapopulation model for long-term scenario planning","authors":"James Turtle,&nbsp;Michal Ben-Nun,&nbsp;Pete Riley","doi":"10.1016/j.epidem.2024.100758","DOIUrl":"10.1016/j.epidem.2024.100758","url":null,"abstract":"<div><p>In temperate regions, annual preparation by public health officials for seasonal influenza requires early-season long-term projections. These projections are different from short-term (e.g., 1–4 weeks ahead) forecasts that are typically updated weekly. Whereas short-term forecasts estimate what “will” likely happen in the near term, the goal of scenario projections is to guide long-term decision-making using “what if” scenarios. We developed a mechanistic metapopulation model and used it to provide long-term influenza projections to the Flu Scenario Modeling Hub. The scenarios differed in their assumptions about influenza vaccine effectiveness and prior immunity. The parameters of the model were inferred from early season hospitalization data and then simulated forward in time until June 3, 2023. We submitted two rounds of projections (mid-November and early December), with the second round being a repeat of the first with three more weeks of data (and consequently different model parameters). In this study, we describe the model, its calibration, and projections targets. The scenario projection outcomes for two rounds are compared with each other at state and national level reported daily hospitalizations. We show that although Rounds 2 and 3 were identical in definition, the addition of three weeks of data produced an improvement to model fits. These changes resulted in earlier projections for peak incidence, lower projections for peak magnitude and relatively small changes to cumulative projections. In both rounds, all four scenarios presented conceivable outcomes, with some scenarios agreeing well with observations. We discuss how to interpret this agreement, emphasizing that this does not imply that one scenario or another provides the ground truth. Our model's performance suggests that its underlying assumptions provided plausible bounds for what could happen during an influenza season following two seasons of low circulation. We suggest that such projections would provide actionable estimates for public health officials.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100758"},"PeriodicalIF":3.8,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000197/pdfft?md5=147c58edc14971ccab79e33e0e06c7b0&pid=1-s2.0-S1755436524000197-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092181","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 multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US","authors":"Matteo Chinazzi ,&nbsp;Jessica T. Davis ,&nbsp;Ana Pastore y Piontti ,&nbsp;Kunpeng Mu ,&nbsp;Nicolò Gozzi ,&nbsp;Marco Ajelli ,&nbsp;Nicola Perra ,&nbsp;Alessandro Vespignani","doi":"10.1016/j.epidem.2024.100757","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100757","url":null,"abstract":"<div><p>The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100757"},"PeriodicalIF":3.8,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000185/pdfft?md5=c05aa067f3a8d0bb22048836b65b5c4e&pid=1-s2.0-S1755436524000185-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140137903","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":"Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned","authors":"Kelly Charniga ,&nbsp;Zachary J. Madewell ,&nbsp;Nina B. Masters ,&nbsp;Jason Asher ,&nbsp;Yoshinori Nakazawa ,&nbsp;Ian H. Spicknall","doi":"10.1016/j.epidem.2024.100755","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100755","url":null,"abstract":"<div><p>In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100755"},"PeriodicalIF":3.8,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000161/pdfft?md5=abd65d67dc31cd4cde493adf01b7d575&pid=1-s2.0-S1755436524000161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140042040","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":"flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic","authors":"Joseph C. Lemaitre ,&nbsp;Sara L. Loo ,&nbsp;Joshua Kaminsky ,&nbsp;Elizabeth C. Lee ,&nbsp;Clifton McKee ,&nbsp;Claire Smith ,&nbsp;Sung-mok Jung ,&nbsp;Koji Sato ,&nbsp;Erica Carcelen ,&nbsp;Alison Hill ,&nbsp;Justin Lessler ,&nbsp;Shaun Truelove","doi":"10.1016/j.epidem.2024.100753","DOIUrl":"10.1016/j.epidem.2024.100753","url":null,"abstract":"<div><p>The COVID-19 pandemic led to an unprecedented demand for projections of disease burden and healthcare utilization under scenarios ranging from unmitigated spread to strict social distancing policies. In response, members of the Johns Hopkins Infectious Disease Dynamics Group developed <em>flepiMoP</em> (formerly called the <em>COVID Scenario Modeling Pipeline</em>), a comprehensive open-source software pipeline designed for creating and simulating compartmental models of infectious disease transmission and inferring parameters through these models. The framework has been used extensively to produce short-term forecasts and longer-term scenario projections of COVID-19 at the state and county level in the US, for COVID-19 in other countries at various geographic scales, and more recently for seasonal influenza. In this paper, we highlight how the <em>flepiMoP</em> has evolved throughout the COVID-19 pandemic to address changing epidemiological dynamics, new interventions, and shifts in policy-relevant model outputs. As the framework has reached a mature state, we provide a detailed overview of <em>flepiMoP</em>’s key features and remaining limitations, thereby distributing <em>flepiMoP</em> and its documentation as a flexible and powerful tool for researchers and public health professionals to rapidly build and deploy large-scale complex infectious disease models for any pathogen and demographic setup.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"47 ","pages":"Article 100753"},"PeriodicalIF":3.8,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000148/pdfft?md5=9b58b4f46da0c615358dffb3a8c30622&pid=1-s2.0-S1755436524000148-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140046038","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}