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

Emily HowertonLucie ContaminLuke C MullanyMichelle QinNicholas G ReichSamantha BentsRebecca K BorcheringSung-Mok JungSara L LooClaire P SmithJohn D LevanderJessica KerrJeremy U EspinoWillem G van PanhuisHarry HochheiserMarta GalantiTeresa K YamanaSen PeiJeffrey L ShamanKaitlin Rainwater-LovettMatt KinseyKate TallaksenShelby WilsonLauren ShinJoseph Chadi LemaitreJoshua KaminskyJuan Dent HulseElizabeth C LeeClifton D McKeeAlison L HillDean KarlenMatteo ChinazziJessica T DavisKunpeng MuXinyue XiongAna Pastore Y PionttiAlessandro VespignaniErik T RosenstromJulie S IvyMaria E MayorgaJulie L SwannGuido EspañaSean CavanySean M MooreT Alex PerkinsThomas HladishAlexander PillaiKok Ben TohIra LonginiShi ChenRajib PaulDaniel JaniesJean-Claude ThillAnass BouchnitaKaiming BiMichael LachmannSpencer J FoxLauren Ancel MeyersAjitesh SrivastavaPrzemyslaw Jerzy PorebskiSrinivasan VenkatramananAniruddha AdigaBryan Leroy LewisBrian KlahnJoseph OuttenBenjamin HurtJiangzhuo ChenHenning MortveitMandy L WilsonMadhav MaratheStefan HoopsParantapa BhattacharyaDustin MachiBetsy L CadwellJessica M HealyRachel B SlaytonMichael A JohanssonMatthew BiggerstaffShaun A TrueloveMichael C RungeKatriona SheaCecile ViboudJustin Lessler
Published in: Nature communications (2023)
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.
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