COVID-19

Epidemic Forecasting

Started
January 4, 2020
Status
Completed
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Abstract

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. We proposed a general methodology to produce forecasts on a one-week horizon, which is applicable to close to 200 countries, and as many states/regions or provinces. An additional challenge to achieve this goal is that the quality of the reported data varies significantly from country to country. This translates into different fluctuations and irregularities that can be observed in the reported time-series. Many countries do not report on a daily basis or delay their reports to particular days of the week. In particular, seasonal patterns with a weekly cycle are observed for many countries. It is important to note that (a), seasonal patterns are non-stationary and can actually change in time, in particular, if the reporting policies change. Furthermore, delays in reporting, changes in death cause attribution protocols, as well as changes in testing policies lead to abrupt corrections that introduce backlogs on some days, such that a number of daily cases or deaths which are anomalously high or even negative are reported. To take into account these peculiarities, we proposed a forecasting methodology that relies on estimating the underlying trend with a robust seasonal-trend decomposition method and using simple extrapolation techniques to make a forecast over a week.

People

Collaborators

SDSC Team:
Tao Sun
Gavin Lee
Dorina Thanou
Benjamín Béjar Haro
Ekaterina Krymova
Guillaume Obozinski

PI | Partners:

University of Geneva, Institute of Global Health:

  • Prof. Antoine Flahault

More info

description

Motivation

Our goal was to develop a globally applicable method, integrated in a twice-daily updated dashboard (Fig.1) that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as a seven-day forecast and a weekly risk map (Fig. 2).  One of the significant difficulties to manage a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting.

Proposed Approach / Solution

Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple, yet effective extrapolation methods in linear or log scale.

Impact

The dashboard  has been actively used by epidemiologists and global health experts to analyze the evolution of the epidemiological situation and to provide recommendations to several European governments.

Figure 1: Dashboard on 02.05.2022.
Figure 2: Risk map on 25.03.2022.

Gallery

Annexe

Bibliography

Publications

Brockhaus, E. K.; Wolffram, D.; Stadler, T.; Osthege, M.; Mitra, T.; Littek, J. M.; Krymova, E.; Klesen, A. J.; Huisman, J. S.; Heyder, S.; et al. "Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany" PLOS Computational Biology 19 11 e1011653 2023 View publication
Sherratt, K.; Gruson, H.; Grah, R.; Johnson, H.; Niehus, R.; Prasse, B.; Sandmann, F.; Deuschel, J.; Wolffram, D.; Abbott, S.; et al. "Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations" eLife 12 e81916 2023 View publication
Cramer, E. Y.; Huang, Y.; Wang, Y.; Ray, E. L.; Cornell, M.; Bracher, J.; Brennen, A.; Rivadeneira, A. J. C.; Gerding, A.; House, K.; et al. "The United States COVID-19 Forecast Hub dataset" Scientific Data 9 1 462 2022 View publication
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Bhatia, S.; et al. "National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021" Communications Medicine 2 1 136 2022 View publication
Krymova, E.; Béjar, B.; Thanou, D.; Sun, T.; Manetti, E.; Lee, G.; Namigai, K.; Choirat, C.; Flahault, A.; Obozinski, G. "Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide" Proceedings of the National Academy of Sciences 119 32 e2112656119 2022 View publication
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Bhatia, S.; et al. "A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave" Nature Communications 12 1 5173 2021 View publication
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J.; Ullrich, A.; Abbott, S.; Barbarossa, M.; Bertsimas, D.; Bhatia, S.; et al. "Short-term forecasting of COVID-19 in Germany and Poland during the second wave – a preregistered study" Preprint 2020 View publication

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