An inaugural forum on epidemiological modeling for public health stakeholders in Arizona DOI Creative Commons
Joseph R. Mihaljevic, Carmenlita Chief, Mehreen Malik

et al.

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: May 31, 2024

Epidemiological models—which help us understand and forecast the spread of infectious disease—can be valuable tools for public health. However, barriers exist that can make it difficult to employ epidemiological models routinely within repertoire health planning. These include technical challenges associated with constructing models, in obtaining appropriate data model parameterization, problems clear communication modeling outputs uncertainty. To learn about unique opportunities state Arizona, we gathered a diverse set 48 stakeholders day-and-a-half forum. Our research group was motivated specifically by our work building software health-relevant earnest desire collaborate closely ensure are practical useful face evolving needs. Here outline planning structure forum, highlight as case study some lessons learned from breakout discussions. While implementing health, there is also keen interest doing so across sectors State Local government, although issues equal fair access knowledge technologies remain key future development. We found this forum relationships informing development, plan continue such meetings annually create continual feedback loop between academic molders practitioners.

Language: Английский

Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations DOI Creative Commons
Sarabeth M. Mathis, Alexander E. Webber, Tomás M. León

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 26, 2024

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 seasons, 26 forecasting teams provided national jurisdiction-specific probabilistic predictions of weekly confirmed hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using Weighted Interval Score (WIS), relative WIS, coverage. Six out 23 models outperform baseline model across forecast locations in 12 18 2022-23. Averaging all targets, FluSight ensemble 2

Language: Английский

Citations

16
Advancing epidemic modeling: The role of LLMs and generative agent-based models Comment on LLMs and generative agent-based models for complex systems research by Lu et al. DOI
Gui-Quan Sun, Li Li,

Y Pei

et al.

Physics of Life Reviews, Journal Year: 2025, Volume and Issue: 52, P. 175 - 177

Published: Jan. 5, 2025

Language: Английский

Citations

1
A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US DOI Creative Commons
Matteo Chinazzi, Jessica T. Davis, Ana Pastore y Piontti

et al.

Epidemics, Journal Year: 2024, Volume and Issue: 47, P. 100757 - 100757

Published: March 5, 2024

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 SMH is generated multiscale model that combines global metapopulation modeling approach (GLEAM) with local and mobility US (LEAM-US), first introduced here. LEAM-US consists 3142 subpopulations each representing single county across 50 states District Columbia, enabling us project state national trajectories COVID-19 cases, hospitalizations, deaths under different scenarios. age-structured, multi-strain. It integrates data on vaccine administration, human mobility, non-pharmaceutical interventions. contributed all 17 rounds SMH, allows for mechanistic characterization spatio-temporal heterogeneities observed during pandemic. Here we describe mathematical computational structure underpinning our model, present as case study results concerning emergence SARS-CoV-2 Alpha variant (lineage designation B.1.1.7). findings reveal considerable spatial temporal heterogeneity introduction diffusion variant, both at level individual combined statistical areas, it competes against ancestral lineage. We discuss key factors driving time required rise dominance within population, quantify significant impact had effective reproduction number level. Overall, show able capture complexity pandemic response US.

Language: Английский

Citations

7
Projecting Omicron scenarios in the US while tracking population-level immunity DOI Creative Commons
Anass Bouchnita, Kaiming Bi, Spencer J. Fox

et al.

Epidemics, Journal Year: 2024, Volume and Issue: 46, P. 100746 - 100746

Published: Feb. 10, 2024

Throughout the COVID-19 pandemic, changes in policy, shifts behavior, and emergence of new SARS-CoV-2 variants spurred multiple waves transmission. Accurate assessments changing risks were vital for ensuring adequate healthcare capacity, designing mitigation strategies, communicating effectively with public. Here, we introduce a model transmission vaccination that provided rapid reliable projections as BA.1, BA.4 BA.5 emerged spread across US. For example, our three-week ahead national projection early 2021 peak hospitalizations was only one day later 11.6-13.3% higher than actual peak, while projected mortality two days earlier 0.22-4.7% reported. We track population-level immunity from prior infections terms percent reduction overall susceptibility relative to completely naive population. As October 1, 2022, estimate US population had 36.52% BA.4/BA.5 variants, 61.8%, 15.06%, 23.54% attributable infections, primary series vaccination, booster respectively. retrospectively potential impact expanding coverage starting on July 15, found five-fold increase weekly boosting rates would have resulted 70% people over 65 vaccinated by Oct 10, 2022 averted 25,000 (95% CI: 14,400-35,700) deaths during surge. Our provides coherent variables tracking increasingly complex landscape vaccines enables robust simulations plausible scenarios novel COVID variants.

Language: Английский

Citations

6
Inference of epidemic dynamics in the COVID-19 era and beyond DOI Creative Commons
Anne Cori, Adam J. Kucharski

Epidemics, Journal Year: 2024, Volume and Issue: 48, P. 100784 - 100784

Published: July 31, 2024

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats supporting decision making real-time. Motivated by unprecedented volume breadth of data generated during pandemic, we review modern opportunities for analysis to address questions emerge a major epidemic. Following broad chronology insights required - from understanding initial dynamics retrospective evaluation interventions, describe theoretical foundations each approach underlying intuition. Through series case studies, illustrate real life applications, discuss implications future work.

Language: Английский

Citations

6
Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report DOI Creative Commons
Marta C. Nunes, Edward W. Thommes, Holger Fröhlich

et al.

Infectious Disease Modelling, Journal Year: 2024, Volume and Issue: 9(2), P. 501 - 518

Published: Feb. 23, 2024

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and lessons learnt from Covid-19 pandemic. This report summarizes rich discussions that occurred during workshop. The participants discussed multisource data integration highlighted benefits combining traditional surveillance with more novel sources like mobility data, social media, wastewater monitoring. Significant advancements were noted development predictive models, examples various countries showcasing use machine learning artificial intelligence detecting monitoring disease trends. role open collaboration between stakeholders was stressed, advocating for continuation such partnerships beyond A major gap identified absence common international framework sharing, which is crucial global pandemic preparedness. Overall, underscored need robust, adaptable frameworks different across sectors, as key elements enhancing future response

Language: Английский

Citations

5
The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy DOI Creative Commons
Sara L. Loo, Emily Howerton, Lucie Contamin

et al.

Epidemics, Journal Year: 2023, Volume and Issue: 46, P. 100738 - 100738

Published: Dec. 29, 2023

Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of burden in US to guide pandemic planning decision-making context high uncertainty. This effort was born out an attempt coordinate, synthesize effectively use unprecedented amount predictive modeling that emerged throughout pandemic. Here we describe history this massive collective research effort, process convening maintaining open hub active over multiple years, provide a blueprint for future efforts. We detail generating 17 rounds scenarios at different stages pandemic, disseminating results public health community lay public. also highlight how SMH expanded generate influenza during 2022-23 season. identify key impacts on draw lessons improve collaborative efforts, scenario projections, interface between models policy.

Language: Английский

Citations

13
Ensemble2: Scenarios ensembling for communication and performance analysis DOI Creative Commons
Clara Bay, Guillaume St-Onge, Jessica T. Davis

et al.

Epidemics, Journal Year: 2024, Volume and Issue: 46, P. 100748 - 100748

Published: Feb. 8, 2024

Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping decision-making process of public health policies. Unlike forecasts, projections rely on specific assumptions about future that consider different plausible states-of-the-world may or not realize and depend policy interventions, unpredictable changes epidemic outlook, etc. As consequence, long-term require evaluation criteria than ones used for traditional short-term forecasts. Here, we propose novel ensemble procedure assessing pandemic using results Scenario Modeling Hub (SMH) US. By defining "scenario ensemble" each model models, termed "Ensemble2", provide synthesis potential outcomes, which use to assess projections' performance, bypassing identification most scenario. We find overall Ensemble2 models are well-calibrated better performance individual models. The accounts full range outcomes highlights importance design effective communication. ensembling approach can be extended any strategy, with refinements including weighting scenarios allowing evolve over time.

Language: Английский

Citations

4
When do we need multiple infectious disease models? Agreement between projection rank and magnitude in a multi-model setting DOI Creative Commons

La Keisha Wade-Malone,

Emily Howerton, William J. M. Probert

et al.

Epidemics, Journal Year: 2024, Volume and Issue: 47, P. 100767 - 100767

Published: April 17, 2024

Mathematical models are useful for public health planning and response to infectious disease threats. However, different can provide differing results, which hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups generate ensembles, known more accurate predictions of future outcomes. Yet, these resource intensive, how many sufficient in a hub is known. Here, we compare the benefit from multiple contexts: (1) settings that depend on quantitative outcomes (e.g., hospital capacity planning), where assessments benefits ensembles have largely focused; (2) decisions require ranking alternative epidemic scenarios comparing under possible interventions biological uncertainties). We develop mathematical framework mimic prediction setting, use quantify frequently agree. further explore agreement using real-world, empirical data 14 rounds U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest value could be contexts, only few available, focusing rank robust than Although additional exploration number contexts still needed, our indicate it may identify rely fewer models, finding inform resources during crises.

Language: Английский

Citations

4
Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub DOI Creative Commons
Sung-mok Jung, Sara L. Loo, Emily Howerton

et al.

PLoS Medicine, Journal Year: 2024, Volume and Issue: 21(4), P. e1004387 - e1004387

Published: April 17, 2024

Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden impact of annually reformulated vaccines remain unclear. Here, we present projections COVID-19 States for next 2 years under plausible assumptions about immune escape (20% per year 50% year) 3 possible CDC recommendations use (no recommendation, vaccination those aged 65 over, all eligible age groups based on FDA approval).

Language: Английский

Citations

4