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How Misuse of Statistics Can Spread Misinformation: A Study of Misrepresentation of COVID-19 Data

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  • Shailesh Bharati
  • Rahul Batra

Abstract

This paper investigates various ways in which a pandemic such as the novel coronavirus, could be predicted using different mathematical models. It also studies the various ways in which these models could be depicted using various visualization techniques. This paper aims to present various statistical techniques suggested by the Centres for Disease Control and Prevention in order to represent the epidemiological data. The main focus of this paper is to analyse how epidemiological data or contagious diseases are theorized using any available information and later may be presented wrongly by not following the guidelines, leading to inaccurate representation and interpretations of the current scenario of the pandemic; with a special reference to the Indian Subcontinent.

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  • Shailesh Bharati & Rahul Batra, 2021. "How Misuse of Statistics Can Spread Misinformation: A Study of Misrepresentation of COVID-19 Data," Papers 2102.07198, arXiv.org.
    • Handle: RePEc:arx:papers:2102.07198
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    1. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    2. Sheryl L. Chang & Nathan Harding & Cameron Zachreson & Oliver M. Cliff & Mikhail Prokopenko, 2020. "Modelling transmission and control of the COVID-19 pandemic in Australia," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
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