TY - JOUR

T1 - A simple mathematical tool to forecast COVID-19 cumulative case numbers

AU - Balak, Naci

AU - Inan, Deniz

AU - Ganau, Mario

AU - Zoia, Cesare

AU - Sönmez, Sinan

AU - Kurt, Batuhan

AU - Akgül, Ahmet

AU - Tez, Müjgan

N1 - Funding Information:
We would like to express our gratitude to Prof. Mehmet Oktav for his exceptional support, without which the completion of this article would not have been possible. We also thank Mrs. Ann Hazinedar for her help in proofreading.
Publisher Copyright:
© 2021 The Author(s)

PY - 2021/10/1

Y1 - 2021/10/1

N2 - Objective: Mathematical models are known to help determine potential intervention strategies by providing an approximate idea of the transmission dynamics of infectious diseases. To develop proper responses, not only are more accurate disease spread models needed, but also those that are easy to use. Materials and methods: As of July 1, 2020, we selected the 20 countries with the highest numbers of COVID-19 cases in the world. Using the Verhulst–Pearl logistic function formula, we calculated estimates for the total number of cases for each country. We compared these estimates to the actual figures given by the WHO on the same dates. Finally, the formula was tested for longer-term reliability at t = 18 and t = 40 weeks. Results: The Verhulst–Pearl logistic function formula estimated the actual numbers precisely, with only a 0.5% discrepancy on average for the first month. For all countries in the study and the world at large, the estimates for the 40th week were usually overestimated, although the estimates for some countries were still relatively close to the actual numbers in the forecasting long term. The estimated number for the world in general was about 8 times that actually observed for the long term. Conclusions: The Verhulst–Pearl equation has the advantage of being very straightforward and applicable in clinical use for predicting the demand on hospitals in the short term of 4–6 weeks, which is usually enough time to reschedule elective procedures and free beds for new waves of the pandemic patients.

AB - Objective: Mathematical models are known to help determine potential intervention strategies by providing an approximate idea of the transmission dynamics of infectious diseases. To develop proper responses, not only are more accurate disease spread models needed, but also those that are easy to use. Materials and methods: As of July 1, 2020, we selected the 20 countries with the highest numbers of COVID-19 cases in the world. Using the Verhulst–Pearl logistic function formula, we calculated estimates for the total number of cases for each country. We compared these estimates to the actual figures given by the WHO on the same dates. Finally, the formula was tested for longer-term reliability at t = 18 and t = 40 weeks. Results: The Verhulst–Pearl logistic function formula estimated the actual numbers precisely, with only a 0.5% discrepancy on average for the first month. For all countries in the study and the world at large, the estimates for the 40th week were usually overestimated, although the estimates for some countries were still relatively close to the actual numbers in the forecasting long term. The estimated number for the world in general was about 8 times that actually observed for the long term. Conclusions: The Verhulst–Pearl equation has the advantage of being very straightforward and applicable in clinical use for predicting the demand on hospitals in the short term of 4–6 weeks, which is usually enough time to reschedule elective procedures and free beds for new waves of the pandemic patients.

KW - COVID-19

KW - Epidemic forecasting

KW - Mathematical model

KW - Pandemic

KW - SARS-CoV-2

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U2 - 10.1016/j.cegh.2021.100853

DO - 10.1016/j.cegh.2021.100853

M3 - Article

AN - SCOPUS:85113642043

VL - 12

JO - Clinical Epidemiology and Global Health

JF - Clinical Epidemiology and Global Health

SN - 2213-3984

M1 - 100853

ER -