A Novel Heuristic Global Algorithm to Predict the COVID-19 Pandemic Trend

Asteris, Panagiotis G.; Douvika, Maria; Karamani, Christina; Skentou, Athanasia; Daras, Tryfon; Cavaleri, Liborio; Armaghani, Danial Jahed; Chlichlia, Katerina; Zaoutis, Theoklis E.

Background Mathematical models are useful tools to predict the course of an epidemic. The present manuscript proposes a heuristic global algorithm for predicting the COVID-19 pandemic trend. Methods The proposed method utilizes a Gaussian-function-based algorithm for estimating how the temporal evolution of the pandemic develops by predicting daily COVID-19 deaths, for up to 10 days, from the day the prediction is made. This dataset, the number of daily deaths in each country or region, encapsulates information about (a) the quality of the health system of each country or region, (b) the age profile of the country s or region s population, and (c) environmental and other conditions. Findings The validity of the proposed heuristic global algorithm has been tested in the case of China (at different temporal stages of the pandemic), a country where the disease trend seems to have run its course. It has been applied to ten countries/states/cities, namely California, Germany, China, Greece, Iran, Lombardia/Italy, New York, Sweden, United Kingdom and USA, for each one of which predictions have been obtained. The method has also been applied to the United States as a whole, as well as to the states of New York and California, in order to investigate how the pandemic is developing in different parts of the same country. Interpretation Based on the predicted findings, the proposed algorithm seems to offer a robust and reliable method for revealing the SARS-CoV-2 temporal dynamics and disease trend, as such, can be a useful tool for the relevant authorities.