A study on control of novel corona-virus (2019- nCoV) disease process by using PID controller

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Abstract

Background

In this paper, the SEIR dynamic model will be used to model the epidemic of coronvirus (2019-nCoV)disease. The SEIR model has been used to model infectious diseases in Malaysia.Then, the spread and control of the disease is simulated applying a PID controller. The results of this study show that the implementation of strict restrictions such as quarantine, social distancing and closure of gathering centers is effective in controlling the disease. Using the results and analyzing them, it was found that early and strict implementation of strict restrictions such as quarantine, social distance and closure of centers with a high percentage of community is very important to control this disease and prevent irreparable economic losses and depreciation of medical staff.

Objective

Modeling the prevalence and control of corona-virus (2019-nCoV)and the impact of government actions using control engineering methods.

Method

In this study, the SEIR dynamic model was used and the common data on the prevalence of the virus in Wuhan, China and Malaysia were used. As an example, the use of control target schemes is simulated in this paper.

Results

The findings of this study use control methods and forecasting in control engineering to provide a clear picture of macro-decisions for different governments in the field of infectious diseases.

Conclusion

Management and control schemes such as travel restrictions, quarantine, social distance and closure of offices, higher education institutions must be implemented immediately to prevent major economic and social losses. The implementation of these restrictions should not be delayed during the outbreak of corona-virus(2019-nCoV) infectious diseases.

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  1. SciScore for 10.1101/2020.04.19.20071654: (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: 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.