A new model for the spread of COVID-19 and the improvement of safety

Abstract

COVID-19 has been spreading rapidly around the world since December 2019. The main goal of this study is to develop a more effective method for diagnosing and predicting the COVID-19 spread and to evaluate the effectiveness of control measures to reduce and stop the virus spread. To this end, the COVID-19 Decision-Making System (CDMS) was developed to study disease transmission. CDMS divides the population into groups as susceptible, infected, cured and dead. The trends of the people’s number in these groups have deterministic and stochastic components. The deterministic components are described by a differential equations system with parameters determined by the data reported. The stochastic components are represented as an indicator of instability that characterizes the tendency of COVID-19 spread. The simulation experiments have shown a good agreement between the CDMS estimates and the data reported in Russia and Greece. The analysis performed showed that the newly-introduced instability indicator may be the precursor to the pandemic dynamics. In this context, our results showed three potential candidates for a second wave of COVID-19 disease: USA, Russia and Brazil. Although the proportion of infected individuals in countries with high temperatures is lower than in European countries and Russia, temperature and humidity are slowly affecting the effects of the pandemic. Finally, the results presented may contribute to the urgent need to reduce the risks associated with the second wave of the COVID-19, to improve public health intervention and safety measures to be taken by various countries.

Developing a Risk Model for Assessment and Control of the Spread of COVID-19

“…The number of cases may not be affected by the number of sunny days and air pollution. In other research, it was concluded that the proportion of infected individuals in areas with high temperatures is lower than countries of low temperatures, whereas temperature and humidity are gradually affecting the pandemic spread (Varotsos and Krapivin 2020). Another risk group was studied and concerned by the importance of the people's responses to and impressions about the media reports, and the vital role of specialists and governments in endorsing the public under self-quarantine (Yan et al 2020).…”

The Signature of the Coronavirus Lockdown in Air Pollution in Greece

“…In addition, air pollution in China is responsible for 4000 preventable deaths every day or 1.6 million deaths in 2016 (Wang et al 2012;Rohde and Muller 2015). Considering the above-mentioned figures for the first 2 months of lockdown in China a reduction in air pollution could indicate a 6 % reduction in mortality (i.e., about 100,000 people) (Dutheil et al 2020;Varotsos and Krapivin 2020;Varotsos et al 2021).…”

Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset

“…However, our focus is on the healthcare field and medical data. Given the consequences of COVID-19, many research studies have been published based on different kinds of modelling with a goal of predicting the prevalence and spread of the disease [34][35][36][37][38]. Our plan in future work is to use our model framework to classify patients with COVID-19 according to levels of severity as a basis for determining the kind of facility at which they should be treated in order to optimize the use of scarce medical resources overall.…”