A Multi-Team Multi-Model Collaborative Covid-19 Forecasting Hub for India.
Winter Simulation Conference（2023）
摘要
During the COVID-19 pandemic, India has seen some of the highest number of cases and deaths. Quality of data, continuously changing policy, and public health response made forecasting extremely difficult. Given the challenges in real-time forecasting, several countries had started a multi-team collaborative effort. Inspired by these works, academic partners from India and the United States setup a repository for aggregating India-specific forecasts from multiple teams. In this paper, we describe the effort and the challenges in setting up the repository. We discuss the development of simulations of compartmental models to model specific waves of the pandemic and show that the simulation model designed specifically for the Omicron wave was able to predict the onset and peak sizes accurately. We employed a median-based ensemble model to aggregate the individual forecasts. We observed that median-based ensemble was relatively stable compared to the constituent models and was one of better performing models.
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关键词
COVID-19 Forecast,Data Quality,Simulation Model,Collaborative Efforts,Ensemble Model,Compartmental Model,Multiple Teams,Real-time Forecasting,Best Fit,Model Performance,Classification Model,Deep Learning Models,Incident Cases,Individual Models,Ordinary Differential Equations,Case Counts,Omicron Variant,Linear Activation,Phase Of The Pandemic,Delta Activity,Dengue Fever Outbreaks,Probabilistic Forecasts,Autoregressive Integrated Moving Average Model,Short-term Forecasting,SIR Model,SEIR Model,Ensemble Forecasts,Performance Of Individual Models,Specific Humidity,Set Of Models
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