Four-week forecasts of COVID-19 epidemic trajectories in South Africa, Chile, Peru and Brazil: a model evaluation

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Introduction

From the beginning of the COVID-19 pandemic, epidemiological models have been used in a number of ways to aid governments and organizations in efficient planning of resources and decision making. These models have elucidated important epidemiological transmission parameters, in addition to making short-term projections.

Methods

We constructed a compartmental mathematical model for the transmission, detection and prevention of SARS-CoV-2 infections for regions where Anglo American has mining operations. We fitted the model to publicly available data and used it to make short-term projections. Finally, we evaluated how the model performed by comparing short-term projections to actual confirmed cases, retrospectively.

Findings

The average forecast errors for four-week-ahead projections ranged between 1% and 8% in all the countries and regions considered in this study. All but one region had more than 75% of the true values falling within the range of four-week-ahead projections. The quality of the projections improved with time as expected due to increased historical data.

Conclusion

Our model produced four-week forecasts with a sufficiently high level of accuracy to guide operational and strategic planning for business continuity and COVID-19 responses in Anglo American mining sites.

Article activity feed

  1. SciScore for 10.1101/2021.09.06.21263151: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.


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


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.


    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.