Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

  1. Akshay Kumar
  2. Farhan Mohammad Khan
  3. Rajiv Gupta
  4. Harish Puppala

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Abstract

The outbreak of COVID-19 is first identified in China, which later spread to various parts of the globe and was pronounced pandemic by the World Health Organization (WHO). The disease of transmissible person-to-person pneumonia caused by the extreme acute respiratory coronavirus 2 syndrome (SARS-COV-2, also known as COVID-19), has sparked a global warning. Thermal screening, quarantining, and later lockdown were methods employed by various nations to contain the spread of the virus. Though exercising various possible plans to contain the spread help in mitigating the effect of COVID-19, projecting the rise and preparing to face the crisis would help in minimizing the effect. In the scenario, this study attempts to use Machine Learning tools to forecast the possible rise in the number of cases by considering the data of daily new cases. To capture the uncertainty, three different techniques: (i) Decision Tree algorithm, (ii) Support Vector Machine algorithm, and (iii) Gaussian process regression are used to project the data and capture the possible deviation. Based on the projection of new cases, recovered cases, deceased cases, medical facilities, population density, number of tests conducted, and facilities of services, are considered to define the criticality index (CI). CI is used to classify all the districts of the country in the regions of high risk, low risk, and moderate risk. An online dashpot is created, which updates the data on daily bases for the next four weeks. The prospective suggestions of this study would aid in planning the strategies to apply the lockdown/ any other plan for any country, which can take other parameters to define the CI.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.26.20080655: (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: We detected the following sentences addressing limitations in the study:
    For a better understanding of this fact, the statistics of a few of the states are shown in Table 1, and the identified limitations are put as remarks. Therefore, it can conclusively be stated that decisions just based on the number of cases may lead to inadmissible strategies. From the attribute mentioned in Table 1, it is apparent that apart from active COVID cases, other factors also play an important role, and that is why deceased cases are different. The difference can be attributed to medical infrastructure, average age/ gender of citizens in the district, literacy rate, socio-culture issues, and administration From the mapped risk in terms of the criticality index, it is observed that for a few of the districts, the trend is going to come down, whereas, for a few of the districts, it is anticipated that the cases would rise. It may have to be noted since, for the majority of the period, there is a significant rise in trend of the positive cases in most of the region, it is quite natural that the historical characteristics of the curve will be reflected in the predictions made. Owing to this nature, there are possible chances of reality deviate with the prediction of the long term. In this regard, it is recommended to update the repository and revise the forecast maps on a daily/weekly basis. This exercise would certainly help to combat the COVID-19. In this study machine, learning-based algorithms are proposed to observe the transmission pattern of Covid-19. Performanc...

    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.

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  2. Version published to 10.1101/2020.04.26.20080655v1 on medRxiv