Are the SIR and SEIR models suitable to estimate the basic reproduction number for the CoViD-19 epidemic?

  1. Hyun Mo Yang
  2. Luis Pedro Lombardi Junior
  3. Ariana Campos Yang

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

The transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) becomes pandemic but presents different incidences in the world. Mathematical models were formulated to describe the coronavirus disease 2019 (CoViD-19) epidemic in each country or region. At the beginning of the pandemic, many authors used the SIR (susceptible, infectious, and recovered compartments) and SEIR (including exposed compartment) models to estimate the basic reproduction number R 0 for the CoViD-19 epidemic. These simple deterministic models assumed that the only available collection of the severe CoViD-19 cases transmitted the SARS-CoV-2 and estimated lower values for R 0 , ranging from 1.5 to 3.0. However, the major flaw in the estimation of R 0 provided by the SIR and SEIR models was that the severe CoViD-19 patients were hospitalized, and, consequently, not transmitting. Hence, we proposed a more elaborate model considering the natural history of CoViD-19: the inclusion of asymptomatic, pre-symptomatic, mild and severe CoViD-19 compartments. The model also encompassed the fatality rate depending on age. This SEAPMDR model estimated R 0 using the severe CoViD-19 data from São Paulo State (Brazil) and Spain, yielding higher values for R 0 , that is, 6.54 and 5.88, respectively. It is worth stressing that this model assumed that severe CoViD-19 cases were not participating in the SARS-CoV-2 transmission chain. Therefore, the SIR and SEIR models are not suitable to estimate R 0 at the beginning of the epidemic by considering the isolated severe CoViD-19 data as transmitters.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.11.20210831: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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.

    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.

  2. Version published to 10.1101/2020.10.11.20210831 on medRxiv