On the reliability of model-based predictions in the context of the current COVID epidemic event: impact of outbreak peak phase and data paucity
Daunizeau, J.; Moran, R. J.; Mattout, J.; Friston, K.
Show abstract
The pandemic spread of the COVID-19 virus has, as of 20th of April 2020, reached most countries of the world. In an effort to design informed public health policies, many modelling studies have been performed to predict crucial outcomes of interest, including ICU solicitation, cumulated death counts, etc... The corresponding data analyses however, mostly rely on restricted (openly available) data sources, which typically include daily death rates and confirmed COVID cases time series. In addition, many of these predictions are derived before the peak of the outbreak has been observed yet (as is still currently the case for many countries). In this work, we show that peak phase and data paucity have a substantial impact on the reliability of model predictions. Although we focus on a recent model of the COVID pandemics, our conclusions most likely apply to most existing models, which are variants of the so-called "Susceptible-Infected-Removed" or SIR framework. Our results highlight the need for performing systematic reliability evaluations for all models that currently inform public health policies. They also motivate a plea for gathering and opening richer and more reliable data time series (e.g., ICU occupancy, negative test rates, social distancing commitment reports, etc).
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling 96%
- Simple discrete-time self-exciting models can describe complex dynamic processes: a case study of COVID-19 96%
- On the use of growth models for forecasting epidemic outbreaks with application to COVID-19 data 96%
Similar papers in this journal
- Covid-19 Belgium: Extended SEIR-QD model with nursing homes and long-term scenarios-based forecasts 96%
- Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories 96%
- Foundation time series models for forecasting and policy evaluation in infectious disease epidemics 96%
Similar papers in this journal
Similar papers in this journal
- An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City 96%
- A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic 96%
- Gaussian Process Emulation for Modeling Dengue Outbreak Dynamics 95%
Similar papers in this journal
- Estimate of the rate of unreported COVID-19 cases during the first outbreak in Rio de Janeiro 96%
- Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy 96%
- Modelling the Test, Trace and Quarantine Strategy to Control the COVID-19 Epidemic in the State of São Paulo, Brazil 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.