Logistic Approximations for Characterizing Novel Outbreak Patterns in the 2020 COVID-19 Pandemic

Apiano F. Morais

Abstract: This study employs the logistic growth model to analyze COVID-19 pandemic mortality data across multiple countries. Daily death statistics from China, Iran, Italy, South Korea, Spain, and the United States were fitted to logistic functions to characterize outbreak dynamics. Based on current pandemic growth trajectories, our model predicts final death tolls of 3,277-3,327 in China, 2,035-2,107 in Iran, 120-134 in South Korea, 11,227-12,793 in Italy, and 6,217-7,405 in Spain. The analysis reveals distinct secondary outbreaks within individual countries, particularly Iran, China, and the United States. Among the studied countries, South Korea exhibited the lowest mortality growth rate (0.14701?0.00923), followed by China (0.16667?0.00284). Italy (0.22594?0.00599) and Spain (0.31213?0.02337) demonstrated the highest growth rates, with Iran's second wave reaching 0.37893?0.02712. This work was submitted to arXiv with data available through April 2020, and subsequent validation with modern data confirms the continued applicability of this technique for detecting multiple outbreak patterns throughout the extended pandemic period.

Keywords: logistic growth model, COVID-19 mortality, secondary outbreaks, epidemiological modeling, pandemic prediction

How to Cite?: Apiano F. Morais, "Logistic Approximations for Characterizing Novel Outbreak Patterns in the 2020 COVID-19 Pandemic", Volume 14 Issue 8, August 2025, International Journal of Science and Research (IJSR), Pages: 817-822, https://www.ijsr.net/getabstract.php?paperid=MR25815071442, DOI: https://dx.doi.org/10.21275/MR25815071442