“…The simulation results show that the processes describing the COVID-19 infection and the subsequent results are accompanied by the function I ( t ) which allows the conclusion regarding the increase in I ( t ) to correspond to the increase of the components of Y ( t ) and vice versa. This result is confirmed by the behavioral effects of the instability indicator for the atmosphere/ocean system ( Varotsos et al, 2019 ). Fig.…”
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.
“…The simulation results show that the processes describing the COVID-19 infection and the subsequent results are accompanied by the function I ( t ) which allows the conclusion regarding the increase in I ( t ) to correspond to the increase of the components of Y ( t ) and vice versa. This result is confirmed by the behavioral effects of the instability indicator for the atmosphere/ocean system ( Varotsos et al, 2019 ). Fig.…”
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.
“…Krapivin et al employed sequential analysis and seepage theory tools to analyze the process of the ocean–atmosphere coupling system; additionally, they adopted the SVM to detect the state features of this system; such a method helped to monitor the changes and directions of the ocean transition process and could predict significant changes in the state of the ocean–atmosphere system [ 13 ]. Varotsos et al proposed an information modeling tracker for tropical cyclones based on the clustering algorithm to assess the instability of the atmosphere–ocean system; the synthesized functional prediction structure could be a reliable global ocean monitoring system, which could effectively reduce the risk of tropical cyclones [ 14 ]. Zhu et al (2019) established a short-term heavy rain recognition model based on physical parameters and deep learning; this model could automatically predict the probability of heavy rain occurrence based on data from various monitoring stations [ 15 ].…”
Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional–Long Short-Term Memory Network (GC–LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010–2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that: the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.35%, and the prediction accuracy of typhoons and super typhoons reaches 95.12%. The model can accurately identify typhoon eye and spiral cloud belt, and the prediction results are always kept in the minimum range compared with the actual results, which proves that the GC–LSTM model has stronger stability. The model can accurately identify the levels of different typhoons according to the satellite cloud pictures. In summary, the results can provide a theoretical basis for the related research of typhoon level classification.
“…However, in these models the study of nature-society system is limited by simple considerations of the main integrated properties of the system dynamics without digital space analysis. Varotsos, Krapivin, and Soldatov (2019) have introduced a new type of global modelling technology, based on an adaptive combination of models, algorithms and experiments. Different environmental and anthropogenic processes are well configured using various approaches to synthesize a new global model that describes all aspects of human interactions with environmental bodies and their physical, biological and chemical systems.…”
Section: Remote Sensing Letters Contribution To the Success Of The Sumentioning
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