Epidemic Forecasting

January 4, 2020
In Progress
Share this project


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.



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

PI | Partners:

University of Geneva:

  • Prof. Antoine Flahault
  • Elisa Manetti
  • Kristen Namigai
  • Adeline Dugerdil

More info



  • Our goal was to develop a globally applicable method, integrated in a twice-daily updated dashboard 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.
  • 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.
  • 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.
  • The dashboard has been actively used by epidemiologists and global health experts to analyse the evolution of the epidemiological situation and to provide recommendations to several European governments.


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




Related Pages

More projects


In Progress
Machine Learning for the Future Circular Collider Design
Big Science Data


In Progress
Real-time cleansing of snow and weather data for operational avalanche forecasting
Energy, Climate & Environment


AI-augmented architectural design
Energy, Climate & Environment


In Progress
Extracting activity from large 4D whole-brain image datasets
Biomedical Data Science


Latest news

PassGPT | Using language models to enhance password security
February 6, 2024

PassGPT | Using language models to enhance password security

PassGPT | Using language models to enhance password security

PassGPT is a Large Language Model for password generation trained on leaked passwords, which can outperform existing methods based on generative adversarial networks by guessing twice as many unseen passwords.
ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning
January 24, 2024

ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning

ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning

Applying machine learning to ecotoxicology could help reduce the number of animal tests, costs, and animals sacrificed while preserving the accuracy of the in vivo tests.
License Flowers | Art and AI at SDSC
February 21, 2024

License Flowers | Art and AI at SDSC

License Flowers | Art and AI at SDSC

An adventure to create art using AI to raise awareness on code licenses

Contact us

Let’s talk Data Science

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