Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
Research output: Contribution to conference › Paper › Research › peer-review
Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty? Without this correction, raw data will often mislead by suggesting an improving situation. We present a flexible approach using a latent Gaussian process that is capable of describing the changing auto-correlation structure present in the reporting time-delay surface. This approach also yields robust estimates of uncertainty for the estimated nowcasted numbers of deaths. We test assumptions in model specification such as the choice of kernel or hyper priors, and evaluate model performance on a challenging real dataset from Brazil. Our experiments show that Gaussian process nowcasting performs favourably against both comparable methods, and against a small sample of expert human predictions. Our approach has substantial practical utility in disease modelling - by applying our approach to COVID-19 mortality data from Brazil, where reporting delays are large, we can make informative predictions on important epidemiological quantities such as the current effective reproduction number.
| Original language | English |
|---|---|
| Publication date | 2021 |
| Number of pages | 11 |
| Publication status | Published - 2021 |
| Event | 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 - Virtual, Online Duration: 27 Jul 2021 → 30 Jul 2021 |
Conference
| Conference | 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 |
|---|---|
| City | Virtual, Online |
| Period | 27/07/2021 → 30/07/2021 |
| Sponsor | Google, Microsoft, Morgan Stanley, Swiss National Science Foundation (FNSNF) |
Bibliographical note
Funding Information:
Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. This research was also partly funded by the Imperial College COVID-19 Research Fund. SB acknowledges The UK Research and Innovation (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), The MRC (MR/R015600/1), The BMGF (OPP1197730), Imperial College Healthcare NHS Trust-BRC Funding (RDA02), The Novo Nordisk Young Investigator Award (NNF20OC0059309) and The NIHR Health Protection Research Unit in Modelling Methodology. IH was funded by a MRC PhD studentship (MR/S502388/1). CW acknowledges a Medical Research Council Doctoral Training Partnership PhD studentship.
Funding Information:
The authors acknowledge funding from the MRC
Funding Information:
The authors thank the CADDE group for insight into COVID-19 reporting in Brazil, and are grateful to Bruce Nelson for consistent public reporting of COVID-19 in Amazonas. T.A.M is grateful to Daniel A. M. Villela for helpful discussion of nowcasting. The authors would also like to thank the group of anonymous epidemiology experts from Imperial College London Department of Infectious Disease Epidemiology, who participated in testing the nowcasting model against the human predictions. The authors acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. This research was also partly funded by the Imperial College COVID-19 Research Fund. SB acknowledges The UK Research and Innovation (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), The MRC (MR/R015600/1), The BMGF (OPP1197730), Imperial College Healthcare NHS Trust- BRC Funding (RDA02), The Novo Nordisk Young Investigator Award (NNF20OC0059309) and The NIHR Health Protection Research Unit in Modelling Methodology. IH was funded by a MRC PhD studentship (MR/S502388/1). CW acknowledges a Medical Research Council Doctoral Training Partnership PhD studentship.
Publisher Copyright:
© 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021. All Rights Reserved.
ID: 385516825