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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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This study analyzes the COVID-19 pandemic

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Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • COVID-19 (Corona Virus) 2019 became a global pandemic, causing significant mortality and economic disruption.
  • Precautionary measures like social distancing and mask-wearing were implemented to curb the airborne spread.
  • Tracking the virus's spread rate is crucial for implementing effective control strategies.

Purpose of the Study:

  • To analyze the growth and death rates of the COVID-19 pandemic in India.
  • To predict future trends of the pandemic using a statistical model.
  • To inform preventive measures for controlling the spread of COVID-19.

Main Methods:

  • Utilized the Auto-Regressive Integrated Moving Average (ARIMA) model for time series analysis.
  • Analyzed COVID-19 growth and death rate data from India.
  • Performed future predictions based on the ARIMA model.

Main Results:

  • The ARIMA model provided insights into the growth and death rate dynamics of COVID-19 in India.
  • Future predictions were generated to forecast pandemic trends.
  • The analysis highlighted the importance of data-driven approaches in managing public health crises.

Conclusions:

  • The study demonstrates the utility of the ARIMA model in understanding and predicting COVID-19 spread.
  • Findings can guide public health officials in developing targeted interventions.
  • Proactive measures based on predictive modeling are essential for mitigating the impact of pandemics.