COVID-19

Epidemic Forecasting

Started
January 4, 2020
Status
Completed
Share this project

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
Benjamin 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

Publications

  • Krymova, E., Béjar, B., Thanou, D., Sun, T., Manetti, E., Lee, G., Namigai, K., Choirat, C., Flahault, A. and Obozinski, G.(2022). Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide. Proceedings of the National Academy of Sciences, 119(32), e2112656119.
  • Sherratt, K., Gruson, H., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., ... & Funk, S. (2023). Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. Elife, 12, e81916.
  • Bracher, J., Wolffram, D., Deuschel, J., Görgen, K., Ketterer, J. L., Ullrich, A., ... & USC-SIkJalpha Srivastava Ajitesh 30 Prasanna Viktor K. 30 Xu Frost Tianjian 30. (2021). A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave. Nature communications, 12(1), 5173.
  • Sherratt, K., Gruson, H., Johnson, H., Niehus, R., Prasse, B., Sandman, F., ... & Funk, S. (2022). European covid-19 forecast hub.
  • Cramer, E. Y., Huang, Y., Wang, Y., Ray, E. L., Cornell, M., Bracher, J., ... & Reich, N. G. (2022). The United States COVID-19 forecast hub dataset. Scientific data, 9(1), 462.

Bibliography

Publications

More projects

ML-L3DNDT

Completed
Robust and scalable Machine Learning algorithms for Laue 3-Dimensional Neutron Diffraction Tomography
Big Science Data

BioDetect

Completed
Deep Learning for Biodiversity Detection and Classification
Energy, Climate & Environment

IRMA

In Progress
Interpretable and Robust Machine Learning for Mobility Analysis
No items found.

FLBI

In Progress
Feature Learning for Bayesian Inference
No items found.

News

Latest news

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data
May 1, 2024

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

We’ve developed a smart solution for wind tunnel testing that learns as it works, providing accurate results faster. It provides an accurate mean flow field and turbulence field reconstruction while shortening the sampling time.
The Promise of AI in Pharmaceutical Manufacturing
April 22, 2024

The Promise of AI in Pharmaceutical Manufacturing

The Promise of AI in Pharmaceutical Manufacturing

Innovation in pharmaceutical manufacturing raises key questions: How will AI change our operations? What does this mean for the skills of our workforce? How will it reshape our collaborative efforts? And crucially, how can we fully leverage these changes?
Efficient and scalable graph generation through iterative local expansion
March 20, 2024

Efficient and scalable graph generation through iterative local expansion

Efficient and scalable graph generation through iterative local expansion

Have you ever considered the complexity of generating large-scale, intricate graphs akin to those that represent the vast relational structures of our world? Our research introduces a pioneering approach to graph generation that tackles the scalability and complexity of creating such expansive, real-world graphs.

Contact us

Let’s talk Data Science

Do you need our services or expertise?
Contact us for your next Data Science project!