A compartmental Mathematical model of COVID-19 intervention scenarios for Mumbai

  1. Avaneesh Singh
  2. Manish Kumar Bajpai

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Abstract

A new mathematical method with an outstanding potential to predict the incidence of COVID-19 diseases has been proposed. The model proposed is an improvement to the SEIR model. In order to improve the basic understanding of disease spread and outcomes, four compartments included presymptomatic, asymptomatic, quarantine hospitalized and hospitalized. We have studied COVID-19 cases in the city of Mumbai. We first gather clinical details and fit it on death cases using the Lavenberg-Marquardt model to approximate the various parameters. The model uses logistic regression to calculate the basic reproduction number over time and the case fatality rate based on the age-category scenario of the city of Mumbai. Two types of case fatality rate are calculated by the model: one is CFR daily, and the other is total CFR. The total case fatality rate is 4.2, which is almost the same as the actual scenario. The proposed model predicts the approximate time when the disease is at its worst and the approximate time when death cases barely arise and determines how many hospital beds in the peak days of infection would be expected. The proposed model outperforms the classic ARX, SARIMAX and the ARIMA model. And It also outperforms the deep learning models LSTM and Seq2Seq model. To validate results, RMSE, MAPE and R squared matrices are used and are represented using Taylor diagrams graphically.

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    SciScore for 10.1101/2022.02.28.22271624: (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

    Experimental Models: Organisms/Strains
    SentencesResources
    We are consider that N = S + E + A + P + I + Q + H + R + D is constant, where N is the size of the population modeled.
    E + A + P + I + Q + H + R
    suggested: None

    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:
    Limitation: Modeling is one of the most powerful tools that give intuitive effects when multiple factors act together. No model is perfect. Models are always simplifications of the real world. No models are perfect; there are some shortcomings in it. Proposed model also has some limitations, which are as follows: Our system of differential equations is very sensitive to initial parameters. We have to very careful while given the initial parameters. Small changes in parameters can cause a massive difference in results. Our present manuscript has especially focused on severe cases and death cases. We have consented the severe cases that do not get treatment in the critical compartment. We have considered recovered cases not infected again in the future. R0 value cannot be increased; it either decreases or remains constant. We have assumed that the cases that have been recovered will be immunized, meaning that they will not be infected again.

    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.

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  2. Version published to 10.1101/2022.02.28.22271624 on medRxiv