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2000
Volume 6, Issue 2
  • ISSN: 2666-7967
  • E-ISSN: 2666-7975

Abstract

Introduction

To shed light on the potential trajectories in the global mortality rate, the central question posed is the trajectory of global death rates in the years to come. This study was an effort to predict the trend of the global mortality rate following the COVID-19 pandemic until 2032 and, based on it, an attempt to contemplate potential solutions available for decision-making and planning.

Methods

We employed a time series model to predict future mortality rates based on global mortality rate data. Although several forecasting methods exist for time series data, this study utilized the Autoregressive method. This approach facilitated regression and prediction based on past mortality numbers. To predict mortality rates from 2023 to 2032, we applied an autoregressive model on mortality rate data spanning 1980 to 2022.

Results

The predicted global mortality rate in the next 10 years (post-pandemic era) appeared to be higher than the 10 years before COVID-19 (pre-pandemic era). This projection indicates that despite a declining trend in mortality rates since 2023, the mortality rate from 2023 to 2032 exceeds that of the pre-COVID-19 years. We predict that the ongoing COVID-19 outbreak, although transitioning out of a crisis state, will result in an approximate increase in the global mortality rate over the next 10 years.

Conclusion

Our results indicate a noteworthy increase in the global mortality rate following the emergence of COVID-19. Furthermore, our findings suggest that the mortality rates will remain high in the future. Further research is necessary to attain more accurate insights into this matter.

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2025-10-03

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Changes in the Global Mortality Rate Over Time in Association with the COVID-19 Pandemic Until 2032
Coronaviruses 6, e130624231014 (2025); https://doi.org/10.2174/0126667975303261240605043658
/content/journals/covid/10.2174/0126667975303261240605043658
/content/journals/covid/10.2174/0126667975303261240605043658
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  • Article Type:     Research Article
Keyword(s): autoregressive analysis; COVID-19; healthcare sector; malignancies; mortality; time dependent vector

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