COVID19

Epidemic Forecasting

Started
January 4, 2020
Status
In Progress
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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:
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

description

Description

  • 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.

Gallery

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

Annexe

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