Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection

  1. Thibaut Jombart
  2. Stéphane Ghozzi
  3. Dirk Schumacher
  4. Timothy J. Taylor
  5. Quentin J. Leclerc
  6. Mark Jit
  7. Stefan Flasche
  8. Felix Greaves
  9. Tom Ward
  10. Rosalind M. Eggo
  11. Emily Nightingale
  12. Sophie Meakin
  13. Oliver J. Brady
  14. Centre for Mathematical Modelling of Infectious Diseases COVID-19 Working Group
  15. Graham F. Medley
  16. Michael Höhle
  17. W. John Edmunds

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker .

This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Article activity feed

  1. Version published to 10.1098/rstb.2020.0266
  2. ScreenIT

    SciScore for 10.1101/2020.09.02.20186502: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The proposed method has a number of limitations. The main one relates to reporting delays, which are an intrinsic feature of most epidemiological data. ASMODEE takes incidence data on face value, i.e. without accounting for the potential effect of reporting delays, which typically cause incidence time series to artificially decrease over the last few days of data. While this limitation is not specific to ASMODEE, it will clearly hinder the method’s capacity to detect recent increases in case counts. A possible improvement of the method would be to characterise reporting delays, and then use augmented data / nowcasting to simulate the true underlying incidence, on which ASMODEE would be run. This approach would undoubtedly increase computational time, but would be easy to parallelise and most likely still fast enough to be used in daily surveillance of hundreds of geographic locations. A second limitation of our approach is that ASMODEE does not consider spatial spread of epidemics. While multiple locations can be analysed separately as illustrated in our analysis of NHS 111/999 data, the approach does not account for transmission across different locations. The general framework used in ASMODEE could in theory be extended to multivariate time series models incorporating spatial dependency, but the current implementation would need additional work to support such features. In practice, we expect this may only be a substantial limitation when very good data on patient locations...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.09.02.20186502v1 on medRxiv