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The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the quality of the health system in each area, the age distribution of population, geographical and environmental factors as well as other conditions. Based on derived predicted epidemic curves, a new 3D-epidemic surface is proposed to assess the epidemic phenomenon at any time of its evolution. This research highlights the potential of the proposed model as a tool which can assist in the risk assessment of the COVID-19. Mapping its development through 3D-epidemic surface can assist in revealing its dynamic nature as well as differences and similarities among different districts.

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 All rights reserved. No reuse allowed without permission. : medRxiv preprint 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/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, 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.