MODELING THE DEVELOPMENT OF EPIDEMIS BASED ON INFORMATION TECHNOLOGIES OF OPTIMIZATION

Mathematical models of the epidemic have been developed and researched to predict the development of the COVID-19 coronavirus epidemic on thebasis of information technology for optimizing complex dynamic systems. Mathematical models of epidemics SIR, SIRS, SEIR, SIS, MSEIR in theform of nonlinear systems of differential equations are considered and the analysis of use of mathematical models for research of development ofepidemic of coronavirus epidemic COVID-19 is carried out. Based on the statistics of the COVID-19 coronavirus epidemic in the Kharkiv region, theinitial values of the parameters of the models of the last wave of the epidemic were calculated. Using these models, the program of the first-degreesystem method from the module of information technology integration methods for solving nonlinear systems of differential equations simulated thedevelopment of the last wave of the epidemic. Simulation shows that the number of healthy people will decrease and the number of infected peoplewill increase. In 12 months, the number of infected people will reach its maximum and then begin to decline. The information technology ofoptimization of dynamic systems is used to identify the parameters of the COVID-19 epidemic models on the basis of statistical data on diseases in theKharkiv region. Using the obtained models, the development of the last wave of the COVID-19 epidemic in Kharkiv region was predicted. Theprocesses of epidemic development according to the SIR-model with weakening immunity are given, with the values of the model parameters obtainedas a result of identification. Approximately 13 months after the outbreak of the epidemic, the number of infected people will reach its maximum andthen begin to decline. In 10 months, the entire population of Kharkiv region will be infected. These results will allow us to predict possible options forthe development of the epidemic of coronavirus COVID-19 in the Kharkiv region for the timely implementation of adequate anti-epidemic measures.

Dynamic analysis and evaluation of asymptomatic infection in the spread of COVID-19

COVID-19 has spread worldwide for nearly two years. Many countries have experienced repeated epidemics, that is, after the epidemic has been controlled for a period of time, the number of new cases per day is low, and the outbreak will occur again a few months later. In order to study the relationship between this low level of infection and the number of asymptomatic infections, and to evaluate the role of asymptomatic infections in the development of the epidemic, we have established an improved infectious disease dynamics model that can be used to evaluate the spread of the COVID-19 epidemic, and fitted the epidemic data in the three flat periods in England. According to the obtained parameters, according to the calculation of the model, the proportion of asymptomatic infections in these three flat periods are 41%, 53% and 58% respectively. After the first flat period, the number of daily newly confirmed cases predicted by the model began to increase around July 1, 2020. After more than four months of epidemic spread, it reached a peak on November 12, which is consistent with the actual case situation. Unanimous. After the second flat period, the model predicts that the number of new confirmed cases per day will increase from about May 7, 2021, and after about 73 days of epidemic development, it will reach a peak on July 20, showing the overall trend of the epidemic. In the above, the predicted results of the model are consistent with the actual cases. After the third flat period, the number of daily newly diagnosed cases predicted by the model began to increase around December 1, 2021, and reached a peak in December, and the number of cases will drop to a very low level after May 2022. According to our research results, due to the large number of asymptomatic infections, the spread of the epidemic is not easy to stop completely in a short time. However, when the epidemic enters a period of flat time, nucleic acid testing is performed, and asymptomatic infections are isolated at home for 14 days (the recovery period of symptomatic infection is about 10 days) may be an option that can be considered to interrupt the transmission of the case.

Prediction of COVID-19 epidemic situation via fine-tuned IndRNN

The COVID-19 pandemic is the most serious catastrophe since the Second World War. To predict the epidemic more accurately under the influence of policies, a framework based on Independently Recurrent Neural Network (IndRNN) with fine-tuning are proposed for predict the epidemic development trend of confirmed cases and deaths in the United Stated, India, Brazil, France, Russia, China, and the world to late May, 2021. The proposed framework consists of four main steps: data pre-processing, model pre-training and weight saving, the weight fine-tuning, trend predicting and validating. It is concluded that the proposed framework based on IndRNN and fine-tuning with high speed and low complexity, has great fitting and prediction performance. The applied fine-tuning strategy can effectively reduce the error by up to 20.94% and time cost. For most of the countries, the MAPEs of fine-tuned IndRNN model were less than 1.2%, the minimum MAPE and RMSE were 0.05%, and 1.17, respectively, by using Chinese deaths, during the testing phase. According to the prediction and validation results, the MAPEs of the proposed framework were less than 6.2% in most cases, and it generated lowest MAPE and RMSE values of 0.05% and 2.14, respectively, for deaths in China. Moreover, Policies that play an important role in the development of COVID-19 have been summarized. Timely and appropriate measures can greatly reduce the spread of COVID-19; untimely and inappropriate government policies, lax regulations, and insufficient public cooperation are the reasons for the aggravation of the epidemic situations. The code is available at https://github.com/zhhongsh/COVID19-Precdiction. And the prediction by IndRNN model with fine-tuning are now available online (http://47.117.160.245:8088/IndRNNPredict).

Preface

2021 5th International Conference on Electrical, Automation and Mechanical Engineering (EAME2021) was held in virtual form via Tencent Meeting on September 17, 2021 due to current epidemic development and prevention and control policies. This virtual meeting aims to provide an academic sharing and exchanging platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Electrical, Automation and Mechanical Engineering. EAME2021 committee attach high importance on epidemic prevention and control under the general environment of normalized epidemic situation. And we adopt this virtual meeting for all interested attendee can choose to make oral and poster presentations. In order to avoid the uncertainties of online meeting, including software operation and network stability, etc., EAME2021 staff set the test room for attendees before formal opening for the keynote speaker and representatives to test their audio, video and check the stability of network connection. List of EAME 2021 Organization Committees are available in this pdf.

Risk of epidemic development in nurseries from soil inoculum of Phytophthora ramorum

Soilborne inoculum arising from buried, infested leaf debris may contribute to the persistence of Phytophthora ramorum at recurrently positive nurseries. To initiate new epidemics, inoculum must not only survive, but produce sporangia during times conducive to infection at the soil surface. To assess this risk, we performed two year-long experiments in a soil plot at the National Ornamentals Research Site at Dominican University of California. Inoculated rhododendron leaf disks were buried at a depth of 5 or 15 cm in the early summer of 2014 or 2015. Inoculum was baited at the soil surface with non-infested leaf disks (2014 only), then retrieved to assess pathogen viability and sporulation capacity every five weeks. Two 14-week-long trials were conducted in 2016. We were able to consistently culture P. ramorum over all time periods. Soil incubation rapidly reduced the capacity of inoculum to sporulate, especially at 5 cm; however, sporulation capacity increased with the onset of seasonally cooler temperatures. P. ramorum was baited most frequently between November and January, especially from inoculum buried at 5 cm 1-day before the baiting period; in January we also baited P. ramorum from inoculum buried at 15 cm the previous June. We validate prior observations that P. ramorum poses a greater risk after exposure to cooler temperatures and provide evidence that infested leaf debris plays a role in the perpetuation of P. ramorum in nurseries. This work provides novel insights into the survival and epidemic behavior of P. ramorum in nursery soils.

Modeling and Analyzing Transmission of Infectious Diseases Using Generalized Stochastic Petri Nets

Some infectious diseases such as COVID-19 have the characteristics of long incubation period, high infectivity during the incubation period, and carriers with mild or no symptoms which are more likely to cause negligence. Global researchers are working to find out more about the transmission of infectious diseases. Modeling plays a crucial role in understanding the transmission of the new virus and helps show the evolution of the epidemic in stages. In this paper, we propose a new general transmission model of infectious diseases based on the generalized stochastic Petri net (GSPN). First, we qualitatively analyze the transmission mode of each stage of infectious diseases such as COVID-19 and explain the factors that affect the spread of the epidemic. Second, the GSPN model is built to simulate the evolution of the epidemic. Based on this model’s isomorphic Markov chain, the equilibrium state of the system and its changing laws under different influencing factors are analyzed. Our paper demonstrates that the proposed GSPN model is a compelling tool for representing and analyzing the transmission of infectious diseases from system-level understanding, and thus contributes to providing decision support for effective surveillance and response to epidemic development.

Approach for the COVID-19 Epidemic Source Localization in Russia Based on Mathematical Modeling

The paper presents the results of statistical data from open sources on the development of the COVID-19 epidemic processing and a study сarried out to determine the place and time of its beginning in Russia. An overview of the existing models of the processes of the epidemic development and methods for solving direct and inverse problems of its analysis is given. A model for the development of the COVID-19 epidemic via a transport network of nine Russian cities is proposed: Moscow, St. Petersburg, Nizhny Novgorod, Rostov-on-Don, Krasnodar, Yekaterinburg, Novosibirsk, Khabarovsk and Vladivostok. The cities are selected both by geographic location and by the number of population. The model consists of twenty seven differential equations. An algorithm for reverse analysis of the epidemic model has been developed. The initial data for solving the problem were the data on the population, the intensity of process transitions from one state to another, as well as data on the infection rate of the population at given time moments. The paper also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by type of model (basic SEIR model, SIRD model, adaptive behavioral model, modified SEIR models), and by country (in Poland, France, Spain, Greece and others) and an overview of the applications that can be solved using epidemic spread modeling. Additional environmental parameters that affect the modeling of the spread of epidemics and can be taken into account to improve the accuracy of the results are considered. Based on the results of the modeling, the most likely source cities of the epidemic beginning in Russia, as well as the moment of its beginning, have been identified. The reliability of the estimates obtained is largely determined by the reliability of the statistics used on the development of COVID-19 and the available data on transportation network, which are in the public domain.

Assessing the Risk in Urban Public Transport for Epidemiologic Factors

Pandemics have presented new challenges for public transport organisers and operators. New diseases (e.g., influenza H1N1, severe acute respiratory syndrome—SARS, as well as, more recently, SARS-CoV-2) increase the need for new protection measures to prevent epidemic outbreaks in public transport infrastructure. The authors’ goal is to present a set of actions in the area of public transport that are adjusted to different levels of epidemic development. The goal goes back to the following question: how can the highest possible level of passenger safety be ensured and the losses suffered by urban public transport companies kept as low as possible? The sets of pro-active measures for selected epidemic scenarios presented in the article may offer support to local authorities and public transport operators. In the next steps, it is important to develop and implement tools for public transport management to ensure safety and tackle epidemic hazards.

Geospatial Model of COVID-19 Spreading and Vaccination With Event Gillespie Algorithm

We developed a model and a software package for stochastic simulations of transmission of COVID-19 and other similar infectious diseases, that takes into account contact network structures and geographical distribution of population density, detailed up to a level of location of individuals. Our analysis framework includes a surrogate model optimization process for quick fitting of the model’s parameters to the observed epidemic curves for cases, hospitalizations and deaths. This set of instruments (the model, the simulation code, and the optimizer) is a useful tool for policymakers and epidemic response teams who can use it to forecast epidemic development scenarios in local environments (on the scale from towns to large countries) and design optimal response strategies. The simulation code also includes a geospatial visualization subsystem, presenting detailed views of epidemic scenarios directly on population density maps. We used the developed framework to draw predictions for COVID-19 spreading in the canton of Geneva, Switzerland.