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Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
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Forecasting COVID-19 in Pakistan.

Muhammad Ali1, Dost Muhammad Khan1, Muhammad Aamir1

  • 1Department of Statistics, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan.

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|November 30, 2020
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Summary
This summary is machine-generated.

Autoregressive Integrated Moving Average (ARIMA) models accurately forecast COVID-19 trends in Pakistan. These models provide reliable predictions for confirmed cases, recoveries, and deaths, aiding public health strategies.

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Area of Science:

  • Epidemiology
  • Time Series Analysis
  • Public Health

Background:

  • Accurate epidemic forecasting is vital for public health preparedness and response.
  • COVID-19's rapid spread necessitates reliable predictive models for resource allocation and policy-making.

Purpose of the Study:

  • To forecast the cumulative number of confirmed, recovered, and deceased COVID-19 cases in Pakistan.
  • To evaluate the accuracy of Autoregressive Integrated Moving Average (ARIMA) models for epidemic forecasting.

Main Methods:

  • Utilized daily COVID-19 data for Pakistan from February 27, 2020, to June 24, 2020.
  • Applied two ARIMA models to generate a 10-day ahead forecast (June 25 to July 4, 2020).
  • Employed RStudio with "forecast", "ggplot2", "tseries", and "seasonal" packages for data analysis.

Main Results:

  • Forecasted cumulative confirmed cases: 231,239 (95% CI: 219,648-242,832).
  • Forecasted cumulative recovered cases: 111,616 (95% CI: 101,063-122,168).
  • Forecasted cumulative deaths: 5,043 (95% CI: 4,791-5,295).
  • ARIMA and seasonal ARIMA models demonstrated superior forecasting accuracy compared to other time series models, validated by RMSE and MAE.

Conclusions:

  • ARIMA models are effective tools for forecasting epidemic spread, recoveries, and deaths.
  • The study provides valuable insights for short-term public health strategy development.
  • Accurate forecasting aids decision-makers in managing disease outbreaks effectively.