A Multi-dimensional Reliability Method for Predicting the Future of COVID-19 in the World
College of Engineering, Shanghai Ocean University, Shanghai 200000, China
| Abstract: | [Objectives] Novel coronavirus disease 2019 (COVID-19) is a contagious disease with high transmissibility to spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of epidemic waves, it is challenging to model such a phenomenon. Few mathematical models can be used because epidemic data are generally not normally distributed. [Methods] This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of the highly pathogenic virus outbreak probability. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. For this study, new COVID-19 daily numbers of recorded patients in all 195 world countries were chosen. [Results] In the mathematical example, typical influenza epidemic thresholds are about 20% of the local population, COVID-19 infection rates predicted for any world country in given day for the next 100 years were found less than 1.41%. [Conclusions] This work aims to benchmark state of the art method, which makes it possible to extract the necessary information from dynamically observed new daily patient numbers, while taking into account relevant territorial mapping. The method proposed in this paper opens up the possibility of accurately predicting epidemic outbreak probability for multi-regional biological systems. |
| Keywords: | COVID-19; Epidemic Outbreak; Reliability; Probability Forecast; Dynamic System; Public Health; Mathematical Biology |
| DOI: | 10.57237/j.wjms.2022.01.001 |
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Received: 8 September 2022
Accepted: 13 October 2022
Published Online: 1 December 2022
References
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