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Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

ABSTRACT: Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

SUBMITTER: Howerton E

PROVIDER: S-EPMC10661184 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

Howerton Emily E Contamin Lucie L Mullany Luke C LC Qin Michelle M Reich Nicholas G NG Bents Samantha S Borchering Rebecca K RK Jung Sung-Mok SM Loo Sara L SL Smith Claire P CP Levander John J Kerr Jessica J Espino J J van Panhuis Willem G WG Hochheiser Harry H Galanti Marta M Yamana Teresa T Pei Sen S Shaman Jeffrey J Rainwater-Lovett Kaitlin K Kinsey Matt M Tallaksen Kate K Wilson Shelby S Shin Lauren L Lemaitre Joseph C JC Kaminsky Joshua J Hulse Juan Dent JD Lee Elizabeth C EC McKee Clifton D CD Hill Alison A Karlen Dean D Chinazzi Matteo M Davis Jessica T JT Mu Kunpeng K Xiong Xinyue X Pastore Y Piontti Ana A Vespignani Alessandro A Rosenstrom Erik T ET Ivy Julie S JS Mayorga Maria E ME Swann Julie L JL España Guido G Cavany Sean S Moore Sean S Perkins Alex A Hladish Thomas T Pillai Alexander A Ben Toh Kok K Longini Ira I Chen Shi S Paul Rajib R Janies Daniel D Thill Jean-Claude JC Bouchnita Anass A Bi Kaiming K Lachmann Michael M Fox Spencer J SJ Meyers Lauren Ancel LA Srivastava Ajitesh A Porebski Przemyslaw P Venkatramanan Srini S Adiga Aniruddha A Lewis Bryan B Klahn Brian B Outten Joseph J Hurt Benjamin B Chen Jiangzhuo J Mortveit Henning H Wilson Amanda A Marathe Madhav M Hoops Stefan S Bhattacharya Parantapa P Machi Dustin D Cadwell Betsy L BL Healy Jessica M JM Slayton Rachel B RB Johansson Michael A MA Biggerstaff Matthew M Truelove Shaun S Runge Michael C MC Shea Katriona K Viboud Cécile C Lessler Justin J

Nature communications 20231120 1

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we fin ...[more]

PMID: 37985664

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Project description:ImportanceCOVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear.ObjectiveTo project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups).DesignThe COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario.SettingThe entire United States.ParticipantsNone.ExposureAnnually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster.Main outcomes and measuresEnsemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period.ResultsFrom April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths.Conclusion and relevanceCOVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.

Project description:IntroductionComparing multiple, diverse outcomes with cost-effectiveness analysis (CEA) is important, yet challenging in areas like palliative care where domains are unamenable to integration with survival. Generic multi-attribute utility values exclude important domains and non-health outcomes, while partial analyses-where outcomes are considered separately, with their joint relationship under uncertainty ignored-lead to incorrect inference regarding preferred strategies.ObjectiveThe objective of this paper is to consider whether such decision making can be better informed with alternative presentation and summary measures, extending methods previously shown to have advantages in multiple strategy comparison.MethodsMultiple outcomes CEA of a home-based palliative care model (PEACH) relative to usual care is undertaken in cost disutility (CDU) space and compared with analysis on the cost-effectiveness plane. Summary measures developed for comparing strategies across potential threshold values for multiple outcomes include: expected net loss (ENL) planes quantifying differences in expected net benefit; the ENL contour identifying preferred strategies minimising ENL and their expected value of perfect information; and cost-effectiveness acceptability planes showing probability of strategies minimising ENL.ResultsConventional analysis suggests PEACH is cost-effective when the threshold value per additional day at home (𝕜1) exceeds $1,068 or dominated by usual care when only the proportion of home deaths is considered. In contrast, neither alternative dominate in CDU space where cost and outcomes are jointly considered, with the optimal strategy depending on threshold values. For example, PEACH minimises ENL when 𝕜1=$2,000 and 𝕜2=$2,000 (threshold value for dying at home), with a 51.6% chance of PEACH being cost-effective.ConclusionComparison in CDU space and associated summary measures have distinct advantages to multiple domain comparisons, aiding transparent and robust joint comparison of costs and multiple effects under uncertainty across potential threshold values for effect, better informing net benefit assessment and related reimbursement and research decisions.

Dataset Information

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

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Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

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