A new accessible adaptable COVID-19 model

  1. Michael G Baker
  2. Maximilian de Courten
  3. David A Sidloff
  4. Alexander D L Baker
  5. Walid S El Sayes
  6. Faisal A Alaklobi
  7. Abdulrahman S Alqahtani
  8. Edward Fraser
  9. Charles Cohen
  10. Isam SM Osman

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Objectives

Sophisticated epidemic models have been created to help governments and large healthcare organisations plan the necessary resources to manage the COVID-19 pandemic. Whilst helpful, current modelling systems are not widely accessible or easily adapted to different populations and circumstances. Our objective was to develop a widely applicable, easily accessible, adaptable model for projecting new COVID-19 infections and deaths that requires minimal expertise or resources to use. The model should be adaptable to different populations and able to accommodate social and pharmaceutical interventions as well as changes in the disease.

Design

A Susceptible, Infected and Removed (SIR) infectious disease model was created using widely available Microsoft Excel© software. The model is deterministic, generating projections based on the available data and assumptions made. It uses a process of Monitored Forecasting through Visual Matching of predicated vs observed curves to improve accuracy and facilitate adaptability. A review of the COVID-19 literature was performed in order to produce an initial set of adjustable parameters on which to base the output of the model.

Setting

This model can be adapted to different regions or countries for which the requisite input data (population size and number of deaths due to the disease) are available. This model has been successfully used with data from England, Sudan and Saudi Arabia. Data from NHS England were used for producing the illustrative results presented here. The model is a generic infectious disease forecast model which may be adapted to other epidemics.

Intervention

Governments, public health organisations, pharmaceutical companies and other public institutions may introduce interventions that affect disease transmission or severity. Other unknown factors such as new variants of the infective agent may do the same. The effects of changes in disease transmission are identified by the model when predicted and observed curves deviate. By aligning the curves an evaluation of the effect of the changes can be made.

Outcome Measures

The model graphically demonstrates projections for daily deaths, cumulative deaths, case mix (asymptomatic, symptomatic and severe infections requiring admission), hospital admissions and bed occupancy (ICU, general medical and total).

Results

The model successfully produced projections for the outcome measures using NHS England data. Users can adapt and continuously update the model correcting its projections as further local data becomes available. The Microsoft Excel platform allows the model to be used without expensive health information systems or computing infrastructure.

Conclusion

We present an SIR epidemic model that projects COVID-19 disease progression, is widely accessible, adaptable to different populations and environments as the disease progresses and is likely to be of benefit for identifying changing population healthcare needs.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.02.28.21252633: (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

    Software and Algorithms
    SentencesResources
    PubMed databases (Medline, PubMed Central) were searched on 12 May 2020 for full text articles using the terms “COVID-19” OR “SARS-CoV-2” OR “Coronavirus” AND “outcomes” OR “mortality” OR “death”.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)

    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: We detected the following sentences addressing limitations in the study:
    There are some limitations of an SIR based approach. One assumption of the SIR approach is that the population is fixed, however, populations are not entirely fixed even with the introduction of travel bans and movement restrictions. Another assumption is that after infection, a person is immune from the virus and therefore cannot be infected again (or pass the virus on). Although the ability to factor in temporary immunity exists in our model, we recognise this may not be the case and future research is required in order to fully understand the duration of immunity after infection with COVID-19 (or vaccination). None the less, SIR type models are a well-accepted methodology for projecting surge capacity requirements in viral epidemics. All models rely on the quality of data available. The data reported in this study utilises deaths in the hospital setting, however, England is a good example of how excluding data (e.g. out of hospital deaths or social care settings) can underestimate the true number of deaths. This is accentuated in the case of COVID-19 as it disproportionately affects the elderly who are more likely to die out of hospital. Model projections are affected by the assumptions regarding the disease process on which the model is based. The generic assumptions applied to this model from our literature review are likely to be weak when applied to regions with divergent socioeconomics, demographics and healthcare access, as more often than not the published literatur...

    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 10.1101/2021.02.28.21252633v2 on medRxiv
  3. Version published to 10.1101/2021.02.28.21252633v1 on medRxiv