Predict the next moves of COVID-19: reveal the temperate and tropical countries scenario

This article has been Reviewed by the following groups

Read the full article

Abstract

The spread of COVID-19 engulfs almost all the countries and territories of the planet, and infections and fatality are increasing rapidly. The first epi-center of its’ massive spread was in Wuhan, Hubei province, China having a temperate weather, but the spread has got an unprecedented momentum in European temperate countries mainly in Italy and Spain (as of March 30, 2020). However, Malaysia and Singapore and the neighboring tropical countries of China got relatively low spread and fatality that created a research interest on whether there are potential impacts of weather condition on COVID-19 spread. Adopting the SIR (Susceptible Infected Removed) deviated model to predict potential cases and death in the coming days from COVID-19 was done using the secondary and official sources of data. This study shows that COVID-19 spread and fatality tend to be high across the world but compared to tropical countries, it is going to be incredibly high in the temperate countries having lower temperature (7-16°C) and humidity (80-90%) in last March. However, some literature predicted that this might not to be true, rather irrespective of weather conditions there might be a continuous spread and death. Moreover, a large number of asymptotic COVID-19 carrier in both temperate and tropical countries may re-outbreak in the coming winter. Therefore, a comprehensive global program with the leadership of WHO for testing of entire population of the world is required, which will be very useful for the individual states to take proper political action, social movement and medical services.

Article activity feed

  1. SciScore for 10.1101/2020.04.04.20052928: (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.