The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
ANALYSIS OF COVID-19 IN THE UNITED STATES USING MACHINE LEARNINGmlaij
The unprecedented outbreak of COVID-19 also known as the coronavirus has caused a pandemic like none
ever seen before this century. Its impact has been massive on a global level. The deadly virus has
commanded nations around the world to increase their efforts to fight against the spread of the virus after
the stress it has put on resources. With the number of new cases increasing day by day around the world,
the objective of this paper is to contribute towards the analysis of the virus by leveraging machine learning
models to understand its behavior and predict future patterns in the United States (US) based on data
obtained from the COVID-19 Tracking Project.
Analysis of Covid-19 in the United States using Machine Learningmlaij
The unprecedented outbreak of COVID-19 also known as the coronavirus has caused a pandemic like none ever seen before this century. Its impact has been massive on a global level. The deadly virus has commanded nations around the world to increase their efforts to fight against the spread of the virus after the stress it has put on resources. With the number of new cases increasing day by day around the world, the objective of this paper is to contribute towards the analysis of the virus by leveraging machine learning models to understand its behavior and predict future patterns in the United States (US) based on data obtained from the COVID-19 Tracking Project.
PANDEMIC INFORMATION DISSEMINATION WEB APPLICATION: A MANUAL DESIGN FOR EVERYONEijcsitcejournal
The aim of this research is to generate a web application from an inedited methodology with a series of
instructions indicating the coding in a flow diagram. The primary purpose of this methodology is to aid
non-profits in disseminating information regarding the COVID-19 pandemic, so that users can share vital
and up-to-date information. This is a functional design, and a series of screenshots demonstrating its
behaviour is presented below. This unique design arose from the necessity to create a web application for
an information dissemination platform; it also addresses an audience that does not have programming
knowledge. This document uses the scientific method in its writing. The authors understand that there is a
similar design in the bibliography; therefore, the differences between the designs are described herein; it
is very important to point out that this proposal can be taken as an alternative to the design of any web
application.
impact of metropolitanization on covid 19 cases in india using entropy weight...ijtsrd
The global pandemic COVID 19 which was started early this year spreading rapidly in developed countries as all well as developing countries of the world. The noticeable fact is that most of the metropolitan cities of the world are severely affected by Corona pandemic and toped in their respective country among the COVID cases. The impact of the metropolitan city on COVID 19 cases, example can be cited around every corner of the world, From Wuhan to New York, Mumbai to Sao Paulo and Moscow to Madrid the Metropolitan cities of the world’s come out as deep rooted hotspots of novel coronavirus pandemic. Mumbai, Delhi, Chennai, and Kolkata are the leading metropolis in India also leading in COVID 19 cases it can be explained by their connectivity to the rest of the world by the people and products. Therefore this article aims to summerise the impact of six metropolitan cities on the total COVID 19 cases of their states and try to find out which city have best suited in the concept of the Metropolitanization of COVID 19 cases. Entropy based TOPSIS methods are applied to compare the dataset of six metropolitan cities of India, and try to find out which city best fitted in the concept of metropolitanization of COVID 19 cases. Seven factors are chosen to analyze the impact of metropolitan cities on COVID 19 cases such as city population, percentage of slum population, number of COVID cases, airport traffic movement, relative humidity, and temperature. Entropy methods applied to weights the criterion for finding which criteria have maximum influence on COVID 19 cases. After that on the basis of Entropy weights, the TOPSIS method has been used to evaluate the dataset of six cities to track down the relative position of cities on the concentration of COVID 19 cases. After comparing the alternatives in TOPSIS method i.e those six cities , Delhi came in the first position, followed by Mumbai 2nd , Chennai 3rd , Kolkata 4th , Ahmedabad 5th , Hyderabad 6th based on the concentration of COVID 19 cases in the metropolitan cities. Sanu Dolui | Sayani Chakraborty "Impact of Metropolitanization on Covid-19 Cases in India using Entropy Weights Based Topsis Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32929.pdf Paper Url :https://www.ijtsrd.com/other-scientific-research-area/enviormental-science/32929/impact-of-metropolitanization-on-covid19-cases-in-india-using-entropy-weights-based-topsis-approach/sanu-dolui
Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Explorato...Konstantinos Demertzis
Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and accuracy in the modeling of temporal spread can prove to be effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management
of the available health resources.
An agent-based model to assess coronavirus disease 19 spread and health syst...IJECEIAES
The present pandemic has tremendously raised the health systems’ burden around the globe. It is important to understand the transmission dynamics of the infection and impose localized strategies across different geographies to curtail the spread of the infection. The present study was designed to assess the transmission dynamics and the health systems’ burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based modeling (ABM) approach. The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana, India. The effects of imposing and lifting lockdowns, nonpharmaceutical interventions, and the role of immunity were analyzed. The distribution of people in different health states was measured separately for each district of Telangana. The spread dramatically increased and reached a peak soon after the lockdowns were relaxed. It was evident that is the protection offered is higher when a higher proportion of the population is exposed to the interventions. ABMs help to analyze grassroots details compared to compartmental models. Risk estimates provide insights on the proportion of the population protected by the adoption of one or more of the control measures, which is of practical significance for policymaking.
ANALYZING THE EFFECTS OF DIFFERENT POLICIES AND STRICTNESS LEVELS ON MONTHLY ...IJDKP
The corona virus is one of the most unprecedented events of recent decades. Countries struggled to identify
appropriate COVID-19 policies to prevent virus spread effectively. Although much research has been
done, little focused on policy effectiveness and their enforcement levels. As corona virus cases and death
numbers fluctuated among countries, questions of which policies are most effective in preventing corona
virus spread and how strict they should be implemented have yet to be answered. Countries are prone to
making policy and implementation errors that could cost lives. This research identified the most effective
policies and their most effective enforcement levels through data analysis of 12 common coronavirus
policies. A monthly case increase rate prediction model was developed to enable decision makers to
evaluate the effectiveness of COVID-19 policies and their enforcement levels so that they can implement
policies efficiently to save lives, time, and money.
ANALYSIS OF COVID-19 IN THE UNITED STATES USING MACHINE LEARNINGmlaij
The unprecedented outbreak of COVID-19 also known as the coronavirus has caused a pandemic like none
ever seen before this century. Its impact has been massive on a global level. The deadly virus has
commanded nations around the world to increase their efforts to fight against the spread of the virus after
the stress it has put on resources. With the number of new cases increasing day by day around the world,
the objective of this paper is to contribute towards the analysis of the virus by leveraging machine learning
models to understand its behavior and predict future patterns in the United States (US) based on data
obtained from the COVID-19 Tracking Project.
Analysis of Covid-19 in the United States using Machine Learningmlaij
The unprecedented outbreak of COVID-19 also known as the coronavirus has caused a pandemic like none ever seen before this century. Its impact has been massive on a global level. The deadly virus has commanded nations around the world to increase their efforts to fight against the spread of the virus after the stress it has put on resources. With the number of new cases increasing day by day around the world, the objective of this paper is to contribute towards the analysis of the virus by leveraging machine learning models to understand its behavior and predict future patterns in the United States (US) based on data obtained from the COVID-19 Tracking Project.
PANDEMIC INFORMATION DISSEMINATION WEB APPLICATION: A MANUAL DESIGN FOR EVERYONEijcsitcejournal
The aim of this research is to generate a web application from an inedited methodology with a series of
instructions indicating the coding in a flow diagram. The primary purpose of this methodology is to aid
non-profits in disseminating information regarding the COVID-19 pandemic, so that users can share vital
and up-to-date information. This is a functional design, and a series of screenshots demonstrating its
behaviour is presented below. This unique design arose from the necessity to create a web application for
an information dissemination platform; it also addresses an audience that does not have programming
knowledge. This document uses the scientific method in its writing. The authors understand that there is a
similar design in the bibliography; therefore, the differences between the designs are described herein; it
is very important to point out that this proposal can be taken as an alternative to the design of any web
application.
impact of metropolitanization on covid 19 cases in india using entropy weight...ijtsrd
The global pandemic COVID 19 which was started early this year spreading rapidly in developed countries as all well as developing countries of the world. The noticeable fact is that most of the metropolitan cities of the world are severely affected by Corona pandemic and toped in their respective country among the COVID cases. The impact of the metropolitan city on COVID 19 cases, example can be cited around every corner of the world, From Wuhan to New York, Mumbai to Sao Paulo and Moscow to Madrid the Metropolitan cities of the world’s come out as deep rooted hotspots of novel coronavirus pandemic. Mumbai, Delhi, Chennai, and Kolkata are the leading metropolis in India also leading in COVID 19 cases it can be explained by their connectivity to the rest of the world by the people and products. Therefore this article aims to summerise the impact of six metropolitan cities on the total COVID 19 cases of their states and try to find out which city have best suited in the concept of the Metropolitanization of COVID 19 cases. Entropy based TOPSIS methods are applied to compare the dataset of six metropolitan cities of India, and try to find out which city best fitted in the concept of metropolitanization of COVID 19 cases. Seven factors are chosen to analyze the impact of metropolitan cities on COVID 19 cases such as city population, percentage of slum population, number of COVID cases, airport traffic movement, relative humidity, and temperature. Entropy methods applied to weights the criterion for finding which criteria have maximum influence on COVID 19 cases. After that on the basis of Entropy weights, the TOPSIS method has been used to evaluate the dataset of six cities to track down the relative position of cities on the concentration of COVID 19 cases. After comparing the alternatives in TOPSIS method i.e those six cities , Delhi came in the first position, followed by Mumbai 2nd , Chennai 3rd , Kolkata 4th , Ahmedabad 5th , Hyderabad 6th based on the concentration of COVID 19 cases in the metropolitan cities. Sanu Dolui | Sayani Chakraborty "Impact of Metropolitanization on Covid-19 Cases in India using Entropy Weights Based Topsis Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32929.pdf Paper Url :https://www.ijtsrd.com/other-scientific-research-area/enviormental-science/32929/impact-of-metropolitanization-on-covid19-cases-in-india-using-entropy-weights-based-topsis-approach/sanu-dolui
Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Explorato...Konstantinos Demertzis
Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and accuracy in the modeling of temporal spread can prove to be effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management
of the available health resources.
An agent-based model to assess coronavirus disease 19 spread and health syst...IJECEIAES
The present pandemic has tremendously raised the health systems’ burden around the globe. It is important to understand the transmission dynamics of the infection and impose localized strategies across different geographies to curtail the spread of the infection. The present study was designed to assess the transmission dynamics and the health systems’ burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based modeling (ABM) approach. The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana, India. The effects of imposing and lifting lockdowns, nonpharmaceutical interventions, and the role of immunity were analyzed. The distribution of people in different health states was measured separately for each district of Telangana. The spread dramatically increased and reached a peak soon after the lockdowns were relaxed. It was evident that is the protection offered is higher when a higher proportion of the population is exposed to the interventions. ABMs help to analyze grassroots details compared to compartmental models. Risk estimates provide insights on the proportion of the population protected by the adoption of one or more of the control measures, which is of practical significance for policymaking.
ANALYZING THE EFFECTS OF DIFFERENT POLICIES AND STRICTNESS LEVELS ON MONTHLY ...IJDKP
The corona virus is one of the most unprecedented events of recent decades. Countries struggled to identify
appropriate COVID-19 policies to prevent virus spread effectively. Although much research has been
done, little focused on policy effectiveness and their enforcement levels. As corona virus cases and death
numbers fluctuated among countries, questions of which policies are most effective in preventing corona
virus spread and how strict they should be implemented have yet to be answered. Countries are prone to
making policy and implementation errors that could cost lives. This research identified the most effective
policies and their most effective enforcement levels through data analysis of 12 common coronavirus
policies. A monthly case increase rate prediction model was developed to enable decision makers to
evaluate the effectiveness of COVID-19 policies and their enforcement levels so that they can implement
policies efficiently to save lives, time, and money.
Coronavirus disease situation analysis and prediction using machine learning...IJECEIAES
During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
Root cause analysis of COVID-19 cases by enhanced text mining processIJECEIAES
The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.
Cloud Based Spatial Analysis for the Health Sector: a Case Study of EgyptEswar Publications
As it is one of the most important and sensitive sectors, decisions related to the health sector are always critical.
So it must be built on accurate information in order to weight the positives and negatives of each option and consider all the alternatives to determine which option is the best for that particular situation. The health sector ultimately faces basic challenges of operations, logistics, resource allocation, customers, and management. As it’s true that information can help overcome the hurdles, this paper raises the relation between the spatial analyses based on Cloud Computing and the health sector improvement. It presents the vital role of the spatial analysis and the health geography in improving the health sector services. In addition, it presents the significance of the Cloud based GIS. Finally, it presents the usage of the Cloud based GIS for the hospitals distribution in Egypt as a case study.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
A general stochastic information diffusion model in social networks based on ...IJCNCJournal
Social networks are an important infrastructure for information, viruses and innovations propagation. Since users’
behavior has influenced by other users’ activity, some groups of people would be made regard to similarity of users’
interests. On the other hand, dealing with many events in real worlds, can be justified in social networks; spreading
disease is one instance of them. People’s manner and infection severity are more important parameters in
dissemination of diseases. Both of these reasons derive, whether the diffusion leads to an epidemic or not. SIRS is a
hybrid model of SIR and SIS disease models to spread contamination. A person in this model can be returned to
susceptible state after it removed. According to communities which are established on the social network, we use the
compartmental type of SIRS model. During this paper, a general compartmental information diffusion model would
be proposed and extracted some of the beneficial parameters to analyze our model. To adapt our model to realistic
behaviors, we use Markovian model, which would be helpful to create a stochastic manner of the proposed model.
In the case of random model, we can calculate probabilities of transaction between states and predicting value of
each state. The comparison between two mode of the model shows that, the prediction of population would be
verified in each state.
Socio economic attributes of covid-19 pandemic in Maharashtra State IndiaSoumik Chakraborty
Discussion about factors affecting Covid-19 spread and situation of virus spread. Relationship and generation of Covid susceptibility map in the region
Clustering analysis on news from health OSINT data regarding CORONAVIRUS-COVI...ALexandruDaia1
Our primarly goal was to detect clusters via gensim libraries in news data consisting ofinformation regarding health and threats. We identified clusters for the periodscorresponding: i) Jannuary 2006 until the end of 2019, as December 2019 is considered thefirst month in which information about CORONVIRUS COVID-19 was made public; ii)between the 1st of Jannuary 2019 and 31st December 2019; and iii) between the 31st ofDecember 2019 and the 14th of April 2020. We conducted experiments using naturallanguage on open source intelligence data offered generously by brica.de, a providerspecialized in Business Risk Intelligence & Cyberthreat Awareness.
Digital technology and COVID-19
The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.
Daniel Shu Wei Ting, Lawrence Carin, Victor Dzau and Tien Y. Wong, publicado en Nature Medicine.
An open toolbox for generating map of actively confirmed SARS-CoV-2 or COVID-...journalBEEI
The recent outbreak of novel coronavirus, SARS-CoV-2 or COVID-19, discovered in late 2019, being continued to spread across regions worldwide, has resulted in 1,914,916 “confirmed” cases with up to 123,010 deaths, as in situation report –85 by World Health Organization (WHO). Most of the developed disease monitoring and tracking tools currently available only present the reported cases up to country-level and not detail down to provincial- or state-, city- level within the countries. This is insignificant for supporting activities in quickly reducing and preventing the spread of the disease within a certain country because further detail potential infectious locations are not provided for people to avoid traveling or passing by there. Thus, this work presents an open toolbox for generating map of actively “Confirmed” cases in a country, i.e., Vietnam, given a dataset containing their statuses and current locations, detail down to provincial-or state-, city-level. The newly released algorithm reduced approximately 24.41% of processing time of the preceding one. In addition, the algorithm can be easily extended for supporting other countries given suitable datasets.
Using Geographic Information Systems (GIS) to Model the Clustering Effect of ...IJEACS
The purpose of the study was to model the clustering of COVID-19 in Bulawayo, Zimbabwe. A cross-sectional study design was used to provide a snapshot of the occurrence of COVID-19 in Bulawayo at a particular time. About 246 COVID-19 cases were randomly selected from the list of cases that occurred in Bulawayo as of 1 August 2020. The data was analyzed in ArcGIS using spatial autocorrelation and hotspot analysis. From the observed pattern, the results demonstrated a significant overall spatial autocorrelation and clustering of COVID-19 cases in Bulawayo. The hotspot analysis showed hotspot localities around the Western Suburbs such as Nkulumane, Cowdry Park, and Luveve. These are high-density suburbs, endorsing that pattern of COVID-19 infections is related to the population density pattern in Bulawayo. In conclusion, hotspot areas detected in this study can help identify future infectious disease surveillance.
Highlighted notes while preparing for project on Computational Epidemics:
Computational Epidemiology (Review)
By Madhav Marathe, Anil Kumar S. Vullikanti
Communications of the ACM, July 2013, Vol. 56 No. 7, Pages 88-96
10.1145/2483852.2483871
An epidemic is said to arise in a community or region when cases of an illness or other health-related events occur in excess of normal expectancy. Epidemics are considered to have influenced significant historical events, including the plagues in Roman times and Middle Ages, the fall of the Han empire in the 3rd century in China, and the defeat of the Aztecs in the 1500s, due to a smallpox outbreak. The 1918 flu pandemic in the U.S. was responsible for more deaths than those due to World War I. The last 50 years have seen epidemics caused by HIV/AIDS, SARS, and influenza-like illnesses. Despite significant medical advances, according to the World Health organization (WHO), infectious diseases account for more than 13 million deaths a year.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
2 nd International Conference on Soft Computing, Data mining and Data Scienc...rinzindorjej
2
nd International Conference on Soft Computing, Data mining and Data Science (SCDD
2024) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Soft Computing, Data mining, and Data Science.
The Conference looks for significant contributions to all major fields of the Soft Computing,
Data mining, and Data Science in theoretical and practical aspects. The aim of the
Conference is to provide a platform to the researchers and practitioners from both academia
as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the Conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the following areas, but are not limited to
More Related Content
Similar to The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE)
Coronavirus disease situation analysis and prediction using machine learning...IJECEIAES
During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
Root cause analysis of COVID-19 cases by enhanced text mining processIJECEIAES
The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.
Cloud Based Spatial Analysis for the Health Sector: a Case Study of EgyptEswar Publications
As it is one of the most important and sensitive sectors, decisions related to the health sector are always critical.
So it must be built on accurate information in order to weight the positives and negatives of each option and consider all the alternatives to determine which option is the best for that particular situation. The health sector ultimately faces basic challenges of operations, logistics, resource allocation, customers, and management. As it’s true that information can help overcome the hurdles, this paper raises the relation between the spatial analyses based on Cloud Computing and the health sector improvement. It presents the vital role of the spatial analysis and the health geography in improving the health sector services. In addition, it presents the significance of the Cloud based GIS. Finally, it presents the usage of the Cloud based GIS for the hospitals distribution in Egypt as a case study.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
A general stochastic information diffusion model in social networks based on ...IJCNCJournal
Social networks are an important infrastructure for information, viruses and innovations propagation. Since users’
behavior has influenced by other users’ activity, some groups of people would be made regard to similarity of users’
interests. On the other hand, dealing with many events in real worlds, can be justified in social networks; spreading
disease is one instance of them. People’s manner and infection severity are more important parameters in
dissemination of diseases. Both of these reasons derive, whether the diffusion leads to an epidemic or not. SIRS is a
hybrid model of SIR and SIS disease models to spread contamination. A person in this model can be returned to
susceptible state after it removed. According to communities which are established on the social network, we use the
compartmental type of SIRS model. During this paper, a general compartmental information diffusion model would
be proposed and extracted some of the beneficial parameters to analyze our model. To adapt our model to realistic
behaviors, we use Markovian model, which would be helpful to create a stochastic manner of the proposed model.
In the case of random model, we can calculate probabilities of transaction between states and predicting value of
each state. The comparison between two mode of the model shows that, the prediction of population would be
verified in each state.
Socio economic attributes of covid-19 pandemic in Maharashtra State IndiaSoumik Chakraborty
Discussion about factors affecting Covid-19 spread and situation of virus spread. Relationship and generation of Covid susceptibility map in the region
Clustering analysis on news from health OSINT data regarding CORONAVIRUS-COVI...ALexandruDaia1
Our primarly goal was to detect clusters via gensim libraries in news data consisting ofinformation regarding health and threats. We identified clusters for the periodscorresponding: i) Jannuary 2006 until the end of 2019, as December 2019 is considered thefirst month in which information about CORONVIRUS COVID-19 was made public; ii)between the 1st of Jannuary 2019 and 31st December 2019; and iii) between the 31st ofDecember 2019 and the 14th of April 2020. We conducted experiments using naturallanguage on open source intelligence data offered generously by brica.de, a providerspecialized in Business Risk Intelligence & Cyberthreat Awareness.
Digital technology and COVID-19
The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.
Daniel Shu Wei Ting, Lawrence Carin, Victor Dzau and Tien Y. Wong, publicado en Nature Medicine.
An open toolbox for generating map of actively confirmed SARS-CoV-2 or COVID-...journalBEEI
The recent outbreak of novel coronavirus, SARS-CoV-2 or COVID-19, discovered in late 2019, being continued to spread across regions worldwide, has resulted in 1,914,916 “confirmed” cases with up to 123,010 deaths, as in situation report –85 by World Health Organization (WHO). Most of the developed disease monitoring and tracking tools currently available only present the reported cases up to country-level and not detail down to provincial- or state-, city- level within the countries. This is insignificant for supporting activities in quickly reducing and preventing the spread of the disease within a certain country because further detail potential infectious locations are not provided for people to avoid traveling or passing by there. Thus, this work presents an open toolbox for generating map of actively “Confirmed” cases in a country, i.e., Vietnam, given a dataset containing their statuses and current locations, detail down to provincial-or state-, city-level. The newly released algorithm reduced approximately 24.41% of processing time of the preceding one. In addition, the algorithm can be easily extended for supporting other countries given suitable datasets.
Using Geographic Information Systems (GIS) to Model the Clustering Effect of ...IJEACS
The purpose of the study was to model the clustering of COVID-19 in Bulawayo, Zimbabwe. A cross-sectional study design was used to provide a snapshot of the occurrence of COVID-19 in Bulawayo at a particular time. About 246 COVID-19 cases were randomly selected from the list of cases that occurred in Bulawayo as of 1 August 2020. The data was analyzed in ArcGIS using spatial autocorrelation and hotspot analysis. From the observed pattern, the results demonstrated a significant overall spatial autocorrelation and clustering of COVID-19 cases in Bulawayo. The hotspot analysis showed hotspot localities around the Western Suburbs such as Nkulumane, Cowdry Park, and Luveve. These are high-density suburbs, endorsing that pattern of COVID-19 infections is related to the population density pattern in Bulawayo. In conclusion, hotspot areas detected in this study can help identify future infectious disease surveillance.
Highlighted notes while preparing for project on Computational Epidemics:
Computational Epidemiology (Review)
By Madhav Marathe, Anil Kumar S. Vullikanti
Communications of the ACM, July 2013, Vol. 56 No. 7, Pages 88-96
10.1145/2483852.2483871
An epidemic is said to arise in a community or region when cases of an illness or other health-related events occur in excess of normal expectancy. Epidemics are considered to have influenced significant historical events, including the plagues in Roman times and Middle Ages, the fall of the Han empire in the 3rd century in China, and the defeat of the Aztecs in the 1500s, due to a smallpox outbreak. The 1918 flu pandemic in the U.S. was responsible for more deaths than those due to World War I. The last 50 years have seen epidemics caused by HIV/AIDS, SARS, and influenza-like illnesses. Despite significant medical advances, according to the World Health organization (WHO), infectious diseases account for more than 13 million deaths a year.
Similar to The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) (20)
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
2 nd International Conference on Soft Computing, Data mining and Data Scienc...rinzindorjej
2
nd International Conference on Soft Computing, Data mining and Data Science (SCDD
2024) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Soft Computing, Data mining, and Data Science.
The Conference looks for significant contributions to all major fields of the Soft Computing,
Data mining, and Data Science in theoretical and practical aspects. The aim of the
Conference is to provide a platform to the researchers and practitioners from both academia
as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the Conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the following areas, but are not limited to
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
International Journal of Computational Science, Information Technology and Co...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
International Journal of Computational Science, Information Technology and Co...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE)
1. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
DOI: 10.5121/ijcsitce.2023.10402 11
MACHINE LEARNING CLASSIFIERS HELP TO
MANAGE COVID-19 DISTRIBUTION IN CHINA
J. Wei1
,Y. Mingxuan1
, Z. Alsahfi2
, P. Norouzzadeh3
, T. Ye2
,
E. Snir1
and B. Rahmani3
1
Washington University, Olin Business School, St. Louis, MO, US
2
Maryville University, Math and Computer Science Department, St. Louis, MO, US
3
Saint Louis University, Computer Science Department, St. Louis, MO, US
ABSTRACT
The coronavirus disease 2019 (Covid-19) first appeared in Wuhan, China in December 2019. It spread
very quickly from Hubei to the rest of China within only 30 days [1]. As of February 14, 2020, 78.91% of
the total confirmed cases in China were in Hubei province which is in midland China and 60.33% of the
total confirmed cases in Hubei were in Wuhan [2]. In this project we use K-Means clustering and
regression analysis to classify the cities in China based on location and infection rate. The goal is
analyzing the demographic and geographic characteristics of the clusters containing less than 5 members
with high infection rate. We found that all the cities in the small groups with high infection rate are from
Hubei province. We conclude that based on K-Means clustering and regression analysis, midland China is
in trouble.
1. INTRODUCTION
Covid-19 is an infectious disease which is caused by the coronavirus. It is currently spreading
worldwide at a faster rate than since it was first reported in Wuhan city in China in 2019 [3].
WHO declared Covid-19 as a world pandemic in March 2020 and as of now, there are almost 108
million confirmed cases and over 2.4 million deaths [4]. It causes a painful breathing disease,
which has taken a large number of lives [3]. Cities in China have managed the spread of this virus
[5]. This regional spread of the virus seems to have had a major effect on the social, political, and
economic environment in different countries that have altered existing interactions [6].
To manage Covid-19 countries have enacted several measures such as travel bans, the use of
masks in public places, and social distance [7]. In China, several measures were placed which
include: active case surveillance, environment sanitation and disinfection, strict quarantine and
follow-up, public awareness through reporting of confirmed cases [8]. There is also monitoring of
people's body temperatures in the railway, airport, freeway toll station, subway entry,
neighborhood entry point, and so on [8].
This research uses K-means clustering to define data subtypes in such a way that datasets are
nearly identical in the same cluster [9].
Section 2 includes a literature review. We describe the data in section 3. Methodology, including
regreesion analysis and k-means analyses, comes in section 4. We show the results and provide a
discussion in section 5. In setion 6 we identify directions for future research. And finaly in section
7 we summarize the paper.
2. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
12
2. LITERATURE REVIEW
Previous research into the intensity and spread of Covid-19 has employed various methodologies
for modeling the disease. Early papers attempted to estimate the transition rates from infected
individuals to others [10]. This early work focused on direct interactions to estimate possible
transmission and exposure within a population. While these allows estimating how quickly
Covid-19 may spread, it doesn’t identify the actual risk to different localities, which is presented
in this paper.
More recent papers focus on the spread of the virus, employing models based on the susceptible
infectious-removed (SIR) model and extensions of these models [11], [12], [14]. These models
explicitly track local mobility to assess the likelihood of transmission from one individual to
another. Extensions to the model evaluate different public policy responses to infected
individuals, [12], with a focus on hospital capacity and public awareness. The data required for
these types of analyses is much more detailed than the proposed methodologies in this paper.
Since detail mobility data from tracking individuals may not be viable, those methods are not
always applicable.
Other papers investigate the spread of Covid-19 within China, similar to ours [13], [14]. Some of
these focus on identifying the trajectory of spread from Wuhan to other cities in China early in
2020, before the disease was declared a pandemic [13]. Competing papers evaluate the impact of
alternative lock-down policies in the spread of the virus. Neither of these classify cities based on
their exposure risk, which is a contribution of this paper.
3. DATA DESCRIPTION
Covid-19 data is from all 329 cities in China from March 2020 to June 2020. The data was
collected from WHO reports on the coronavirus and the National Health Commission (NHC)
[4]. Features included are the population of each city in China, the city’s location, and region.
Total confirmed (Tc) cases data is included. The spread rate is calculated by dividing Tc by the
city’s population:
4. METHODOLOGY
4.1. K-Means Clustering
We build a K-Means clustering model over 329 cities in China. The analysis divides cities into 20
clusters, based on the spread of Covid-19. We then compare these clusters to the geographic
location of cities within clusters. The K-Means clustering algorithm divides the data into k-
clusters such that similar cities are grouped together. Based on the measure of Tc we define the
“distance” between cities. The algorithm minimizes distances within clusters, while maximizing
the distance between clusters . Each cluster represents the mean value of cities within the cluster
[15]. The cost function of the K-Means clustering is the distances between the observations in the
cluster and centroid. The objective function finds the best k value that minimizes the cost function
[16]. KMeans clustering method is applied to maximize the Silhouette values of cities and their
respective cluster centers. The Silhouette is a method to evaluate the validity of clusters. It finds
out the optimum k value by a ratio scale data (as the method of Euclidean distances does), which
identifies well-separated cluster [17]. We measured the Silhouette values of all clusters with
3. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
13
different k values and chose the best k. Higher Silhouette values indicate better clustering. We
found that when k equals 20, the Silhouette values are largest, indicating the best clustering
within the Silhouette method.
K-Means is an unsupervised learning method. Labeling clusters is not important. In this study we
used both supervised and unsupervised learning methods to make our results stronger.
4.2. Linear Regression
One approach to evaluating how geographic, demographic, and economic factors affect the
number of cases of Covid-19 is through supervised learning using linear regression. We
hypothesize that these three factors are related to the number of cases of the virus in by August
2020. The data for 329 cities in 8 geographic regions allows evaluating several such models.
In developing the regression model, we made two decisions. The first involves incorporating
the geographic data. The second focuses on how to view the large heterogeneity in the number
of cases of Covid-19. As seen everywhere in the world, Covid-19 appears to be highly
concentrated in some cities, while other cities may exhibit only a few cases. One approach is to
treat this variation as reasonable. The drawback of this approach is that cities with large
concentration of Covid-19 have a substantial effect on the results of the model. Another view is
that there are numerous outliers in the data. If the objective of the model is to evaluate average
effects, outliers should be removed. Each approach has its merits. We present both sets of
results in section 4.
5. RESULTS AND DISCUSSION
5.1. K-Means Clustering
Table 1 summarizes the 20 clusters of 329 cities. There are 11 clusters with less than 5 members.
Table 1: Information of 20 Clusters and 329 Cities
Cluster
Average
Spread Rate Number of
Cities Cluster
Average Spread
Rate Number of
Cities
0 0.138998 57 10 0.077705 9
1 44.898323 1 11 2.073256 3
2 4.348363 2 12 7.148953 1
3 0.036970 52 13 3.106608 3
4 0.030999 49 14 1.720157 1
5 0.067883 55 15 5.884736 1
6 0.051303 35 16 0.378943 7
7 13.154666 1 17 0.912154 2
8 0.080822 34 18 0.650542 1
9 0.073778 14 19 0.710686 1
The bubble chart of Figure 1 shows the classification result. The diameter of the circle is
proportional to the number of cities in the group. The numbers inside each circle is the spread rate
value for the cities.
4. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
14
There are 6 clusters that have fewer than 5 cities and spread rate higher than 2.8. We would
discuss the demographic and geographic characteristics of these clusters and explain why these
cities have high spread rate. Below is the map of Hubei Province in Midland China (Figure 2),
where most COVID-19 cases were confirmed, the map illustrates the geographic distribution of
cities in Hubei Province [18].
Fig.1: Bubble Chart of Small Clusters (Less Than 5 Members)
Table 2: Clusters with Fewer than 5 Cities with Spread Rate above 2.8 (per Thousand People)
City Province Location
Population
(1,000)
Total Cases
(August
2020) Spread Rate Tourist Cluster
Wuhan Hubei Midland 11,212 50,340 44.89832 1 1
Huanggang Hubei Midland 6,333 2,907 4.590242 0 2
Huangshi Hubei Midland 2,471.7 1,015 4.106485 0 2
Ezhou Hubei Midland 1,059.7 1,394 13.15467 0 7
Xiaogan Hubei Midland 4,921 3,518 7.148953 0 12
Jingzhou Hubei Midland 5,570.1 1,580 2.836574 0 13
Jingmen Hubei Midland 2,897.5 928 3.202761 0 13
Xianning Hubei Midland 2,548.4 836 3.28049 0 13
Suizhou Hubei Midland 2,221 1,307 5.884737 0 15
5. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
15
Fig.2: The Map of Hubei Province in Midland China
Cluster 1: Wuhan
Wuhan is the only city in Cluster 1. Wuhan is the first city in China where Covid-19 was
identified. [19] Wuhan has the largest Spread Rate of around 45 (per ten thousand population).
Wuhan is characterized by a large population of 11 million and high population density, Wuhan is
exposed to the risk of rapid spread of Covid-19 cases. As a transportation center in Midland
China, Wuhan has a well-connected network of transportation. People from nearby cities pour
into Wuhan for employment and education opportunities. Another reason that causes the large
number of cases is that Wuhan is a tourist city. Travelers increase the regional density of
population, thus making it easy for Covid-19 to spread in Wuhan.
Cluster 2: Huangshi and Huanggang
These 2 cities are in the same province as Wuhan (Hubei Province) and both are in Midland
China. Though the population of Huanggang is about three times that of Huangshi, by August 2,
2020, both cities have similar spread rates of 4.1-4.6. Neither of them is a tourist city, so the
transient population of the two cities tend to be small. However, when we look at the geographic
location within the Hubei Province of the two cities, we find that Huangshi and Huanggang lie at
the two sides of the Yangtze river, which is the longest river in China.
Cluster 3 includes 52 cities with similar spread rates, tourism development, and geographic
location.
Cluster 7: Ezhou
What distinguishes Ezhou from other clusters is its middle-sized population and high spread rate.
This may be because Ezhou is geographically close to Wuhan, the capital city of Hubei Province,
and thus many people go to work in Wuhan during the day and return to Ezhou where they live.
As a result, the city has very large number of confirmed cases of Covid-19.
Cluster 12: Xiaogan
As a city of Hubei Province, Xiaogan lies on the northern part of Yangtze River. Xiaogan and
Wuhan are located across the Yangtze River. Xiaogan is a mid-sized city with a population of
around 5 million, but by August 2020, 3,518 cases were confirmed. It has a large spread rate of 7.
6. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
16
Similar to Ezhou in cluster 7, Xiaogan is in Cluster 12 alone because of the change in the infected
population in the short time span and the relatively large spread rate. Just as Ezhou, Xiaogan is
grographically close to Wuhan.
Cluster 13: Jingmen, Jingzhou, Xianning
All three cities are in Hubei Province in Midland China. None of them is a tourist city. Jingmen
and Xianning have similar populations, while Jingzhou has twice the population, at around 1
million people. The number of confirmed cases in Jingzhou is twice that of the other two cities,
yielding a spread rate for the three cities of around 3.
Cluster 15: Suizhou
Suizhou is 200 kilometers away from Wuhan. [20] Compared with Xiaogan, Huanggang and
Huangshi, Suizhou is far away from Wuhan. According to the statistical yearbook of 2019
published by Hubei Province, Suizhou City has a population of 2.22 million, ranking it 12th
in the
province. Suizhou's GDP is 10.11 million yuan, ranking 11th
in the province[21]. The spread rate
in Suizhou was substantial, even early in the pandemic. It’s ranked fourth in Hubei Province.
The characteristics of cities with large number of confirmed cases in China are as follows: 1.
Close to Wuhan; 2. Large population; 3. Frequent contact with Wuhan. Suizhou is a city in Hubei
Province, which does not have all of the above three characteristics, but still has a large number
of confirmed cases. This underscores the complexities of managing a virus such Covid-19, and
the need for statistical modeling to identify drivers of the spread of the virus.
5.2. Linear Regression
The data includes 329 cities in 8 geographical regions. By looking at the distribution of cities to
regions, we chose the Southwest region as the base category. One justification is that it has a
fairly large proportion of the cities at 16%. It also has one of the lowest number of cases at 27.87
in an average city. Our linear regression model forecasts the number of cases as a function of
population (in tens of thousands), a binary variable for whether the city enjoys strong tourism,
and indicator variables for the remaining 7 regions.
We present 2 regression models. Model (a) includes all 329 cities. In Model (b) we use an
iterative approach to removing outliers. Since the number of Covid-19 cases is highly
concentrated in a few cities, these cities can be viewed as outliers. If the purpose of the analysis is
to evaluate average effects of the number of cases of the virus, outliers should be removed. As
seen in Table 3, the differences between the models are substantial.
The iterative method for removing outliers developed several regression models. In each step we
identified those cities with Studentized Residuals more extreme than ±3.5. These observations
are deemed outliers. (This is justified under the assumption of a Normal distribution for the
errors.) These outliers are removed and the regression is developed again. In the first step, only
one observation is removed (Wuhan). In the second step, 5 more observations are removed. In all,
43 cities are removed in 14 iterations.
The two models present very different pictures regarding the number of Covid-19 cases in China.
Both models conclude that Midland China experiences more Covid than the Southwest region.
7. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
17
They also both recognize that tourism is an important factor in the spread of the virus. That’s
about all the models have in common. Even the magnitude of the coefficients is substantially
different in both models.
Based on Table 3, from Model (a) one concludes that the average number of Covid-19 cases in a
city in the Midland region is 1,000 more than in the Southwest region. We reject the null
hypothesis, although only at a p-value a bit below 0.05. Here the null hypothesis evaluates
whether the number of cases is the Midland region is identical to the number of cases in the
Southwest. The conclusion is that there is a statistically significant difference.
In contrast, Model (b) finds that the difference between the Midland region and the Southwest is
only 23.8 cases, in an average city, but it significant even at a very low p-value. The difference is
quite stark. The reason for these differences is that Model (b) does not include the cities with the
highest number of Covid-19 cases, including Wuhan. Removing these outliers may give us a
better identification for the average effects of different factors.
Table 3: Regression Results. Model (b) does not include outliers.
Dependent Variable:
Covid-19 Cases
Independent Variable
Model (a)
Coefficients
Model (b)
Coefficients
Intercept
-151.89515
(419.64372)
-6.66584 *
(2.64409)
Population (Ten Thousand)
0.09701
(0.55463)
0.06062 ***
(0.00463)
Tourist
2,527.18292 ***
(732.29303)
18.48986 ***
(5.17232)
East
-152.77913
(521.0449)
7.90774 *
(3.30545)
Midland
1,000.67316 *
(497.36799)
23.80795 ***
(3.33324)
North
7.57021
(596.1755)
-2.03427
(3.60732)
Northeast
74.44107
(599.31177)
8.7699 *
(3.57386)
Northwest
102.21318
(543.50472)
2.70799
(3.23583)
South
-364.36751
(804.53318)
4.85477
(4.77519)
Northeast
-138.83986
(982.29534)
10.73823
(5.80928)
* p < 0.05, ** p < 0.01, *** p < 0.001 (Standard Errors in parentheses)
Looking at regional differences, Model (b) also identifies that two more regions have
statistically significantly more cases than the Southwest. These are the East, at almost 8 more
cases in an average city, and the Northeast, and almost 9 more cases. It may be interesting to
note that both models do not identify significant differences for other regions in China.
8. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
18
The other common significant factor in both models also emphasizes the differences in the
models. Model (a) suggests that a tourist city has 2,527 more cases than one without tourism.
In model (b) the result is substantially subdued at only 18.5 more cases. Again, removing
Wuhan and a few large outliers is at the heart of these differences.
The one other finding from Model (b) is that population is a statistically significant driver for
the number of cases. Larger cities do have more cases of Covid-19, at approximately 1 more
case per 10,000 population. We now see that the result is significant at a very low p-value. This
result isn’t observed in Model (a), because Wuhan and other moderately-sized cities incur the
vast majority of Covid-19 cases in China. For example, Wuhan has over 50,000 cases, while
Beijing and Shanghai, much larger cities, have less than 2,500 cases each.
From a statistical perspective, Model (b) is much better, which shouldn’t be a surprise. Model
(b) has few outliers. For Model (a) the R2
is 0.072, while the RMSE is 2,722.8. In Model (b)
these are 0.584 and 15.92, respectively. Overall, both models are statistically significant.
6. FUTURE RESEARCH AND IMPLEMENTATION
As Covid-19 continues to be a global pandemic, understanding the prevalence of cases
continuities to be a challenge. In some countries, like China, the reaction to increases in cases is
lock-downs. Other countries still need to be concerned about the impact the pandemic may have
on available hospital beds. This research identifies the heterogeneity in Covid-19 infections. Both
models recognize that infections in China were highly concentrated.
K-Means models identify similarities and differences across regions. It is important to evaluate
the model over multiple time periods. This allows measuring how similarities across regions vary
over time. If the model returns similar results at different time periods, it suggests certain cities
are likely to have recurring outbreaks. More likely, future iterations of the analysis will lead to
different clusters across time. This suggests that real-time monitoring of clusters is essential to
limiting Covid-19 spread. Moreover, through real-time monitoring, policymakers can identify
both current and potential hotspots.
The linear model generates similar results. In addition, the effect of tourism on the spread of
Covid19 is statistically significant. Supervised models facilitate generating inferences, which
support public policy. Continuous reevaluation of the linear model would provide insight into the
importance of outliers in the spread of the virus. Similar to the unsupervised model, identifying
whether the spread of Covid-19 is likely to be localized, or similar across regions, should drive
policy decisions.
7. CONCLUSION
The regression results confirm the need for Cluster Analysis. From the regression, we see that
traditional supervised models identify many cities as outliers. The Cluster Analysis is an effective
way to deal with outliers. The multiple clusters capture substantial differences across cities. Many
of the outliers removed in Model (b) of the regression are clustered into a few different groups.
For example, the extreme number of cases in Wuhan lead to it being in its own cluster. Other
cities, near Wuhan, were also clustered together.
The regression model is effective at identifying average trends in the data. After removing
outliers, we see the importance of city size on the spread of the virus. Tourism, which is
9. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
19
hypothesized to be important in the cluster analysis, is now quantified. Even after removing
outliers, tourism cities have 18 more cases than cities without tourism. The regression model also
accentuates regional differences. Some of the results from the regression model are not
highlighted in the cluster analysis.
In summary, unsupervised and unsupervised learning methods complement each other when
investigating the spread of Covid-19. With frequent “bursts” in the spread of the virus,
unsupervised models provide insights into the outliers. Those cities with very high or very low
spread are identified. Conversely, there is also a need to understand the average spread of
Covid19. For that, a regression model is useful. Together, these types of models help
policymakers effectively allocate limited resources to combat Covid-19.
DECLARATIONS
AUTHORS' CONTRIBUTIONS
Wei, Minxuan, Alsahfi: Modeling, coding, writing the paper
Norouzzadeh, Ye: review and added new section
Snir: review and co-adviser
Rahmani: review and co-advisor
AVAILABILITY OF DATA AND MATERIALS
Data are available to submit if it needed. The data was collected from WHO reports on the
coronavirus and the National Health Commission (NHC)
REFERENCES
[1] The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The
Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19)
— China, 2020. China CDC Weekly (2020).
[2] Zhu ZB, Zhong CK, Zhang KX, Dong C, Peng H, Xu T, Wang AL, Guo ZR. Epidemic trend of
COVID-19 in Chinese mainland. Chinese Journal of Preventive Medicine. (2020)
[3] A. G. Hadi, M. Kadhom, N. Hairunisa, E. Yousif, and S. A. Mohammed, “A review on COVID-
19: Origin, spread, symptoms, treatment, and prevention,” Biointerface Res. Appl. Chem., vol. 10,
no. 6, pp. 7234–7242. (2020)
[4] CCDC, “Distribution of new coronavirus pneumonia,” Oms, vol. 2019, no. February, pp. 1–7 (2020)
[5] M. Azarafza, M. Azarafza, and H. Akgün, “Clustering method for spread pattern analysis of corona-
virus (COVID-19) infection in Iran,” J. Appl. Sci. Eng. Technol. Educ., vol. 3, no. 1, pp. 1–6. (2020)
[6] M. Azarafza, M. Azarafza, and J. Tanha, “COVID-19 Infection forecasting based on Deep Learning
in Iran,” Medrxiv, pp. 3–9. (2020)
[7] O. H. World, "Covid‑19 Strategy Up to Date," no. April, p. 18. (2020)
[8] T. L. Xu et al., “China’s practice to prevent and control COVID-19 in the context of large
population movement,” Infect. Dis. Poverty, vol. 9, no. 1, pp. 1–14. (2020)
[9] V. Chandu, “Identification of spatial variations in COVID-19 epidemiological data using K- Means
clustering algorithm: a global perspective,” medRxiv, p. 2020.06.03.20121194. (2020)
[10] M. Chen, T. Rui, J. Wang, et al. “A mathematical model for simulating the phase-based
transmissibility of a novel coronavirus,” Infect. Dis. Poverty vol 9 no. 24 p. 2 – 8 (2020).
[11] M. Liu, R. Thomadsen, and S. Yao, “Forecasting the spread of COVID-19 under different reopening
strategies,” Sci. Rep., vol. 10, pp. 1-8 2020
[12] A.. Ajbar, R. Alqahtani and M. Boumaza M “Dynamics of an SIR-Based COVID-19 Model with
linear incidence rate, nonlinear removal rate, and public awareness,” Front. Phys., vol 9, no. 13, pp.
1-13 (2021)
10. The International Journal of Computational Science, Information Technology and Control Engineering
(IJCSITCE) Vol.10, No.4, October 2023
20
[13] Z. Du, L. Wang, S. Cauchemez, et al. “Risk for transportation of coronavirus disease from Wuhan
to other cities in China,” Emerging Infectious Diseases, vol. 26, no. 5, pp. 1049- 1052 (2020)
[14] M. Li, G. Sun, J. Zhang, et al. “Analysis of COVID-19 transmission in Shanxi Province with
discrete time imported cases,” Mathematical Biosciences and Engineering, vol. 17, no. 4 pp. 3710-
3720 (2020)
[15] Theodoridis, S., Pikrakis, A., Koutroumbas, K., Cavouras, D. An Introduction to Pattern
Reccognition : A MATLAB Approach. Academic Press, USA. (2010)
[16] J.B. MacQuuen: Some methods for classification and analysis of multivariate observation, in:
Proceedings of the 5th Berkley Symposium on Mathematical Statistics and Probability, pp. 281–
297 (1967)
[17] Thinsungnoen, T., Kaoungku, N., Durongdumronchai, P., Kerdprasop, K., & Kerdprasop, N. The
Clustering Validity with Silhouette and Sum of Squared Errors. The Proceedings of the 2nd
International Conference on Industrial Application Engineering 2015. doi:10.12792/iciae2015.012
(2015)
[18] China Highlights. (2020, September 26). Hubei Map. Retrieved from China Highlights|Travel
Guide: https://www.chinahighlights.com/hubei/map.htm
[19] MA. Shereen, S. K. COVID-19 infection: Origin, transmission, and characteristics of human
coronaviruses. Journal of Advanced Research, 91-98. (2020)
[20] Yan. D, Wang. W, Fang. X: Magnetic levitation in Suizhou, Hubei Province. CHSSCD (2008)
[21] China Statistical Publishing House: Hubei Statistical Yearbook in 2019. (2020)