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On Estimating the Number of Deaths Related to Covid-19

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  • Hoang Pham

    (Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA)

Abstract

In this paper, we discuss an explicit model function that can estimate the total number of deaths in the population, and particularly, estimate the cumulative number of deaths in the United States due to the current Covid-19 virus. We compare the modeling results to two related existing models based on a new criteria and several existing criteria for model selection. The results show the proposed model fits significantly better than the other two related models based on the U.S. Covid-19 death data. We observe that the errors of the fitted data and the predicted data points on the total number of deaths in the U.S. on the last available data point and the next coming day are less than 0.5% and 2.0%, respectively. The results show very encouraging predictability for the model. The new model predicts that the maximum total number of deaths will be approximately 62,100 across the United States due to the Covid-19 virus, and with a 95% confidence that the expected total death toll will be between 60,951 and 63,249 deaths based on the data until 22 April, 2020. If there is a significant change in the coming days due to various testing strategies, social-distancing policies, the reopening of community strategies, or a stay-home policy, the predicted death tolls will definitely change. Future work can be explored further to apply the proposed model to global Covid-19 death data and to other applications, including human population mortality, the spread of disease, and different topics such as movie reviews in recommender systems.

Suggested Citation

  • Hoang Pham, 2020. "On Estimating the Number of Deaths Related to Covid-19," Mathematics, MDPI, vol. 8(5), pages 1-9, April.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:655-:d:350511
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    References listed on IDEAS

    as
    1. Triet Pham & Hoang Pham, 2019. "A generalized software reliability model with stochastic fault-detection rate," Annals of Operations Research, Springer, vol. 277(1), pages 83-93, June.
    2. Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, June.
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    Cited by:

    1. Gabriel Sepulveda & Abraham J. Arenas & Gilberto González-Parra, 2023. "Mathematical Modeling of COVID-19 Dynamics under Two Vaccination Doses and Delay Effects," Mathematics, MDPI, vol. 11(2), pages 1-30, January.

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