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The European Air Transportation Network was significantly impacted by the COVID-19 pandemic, resulting in an unprecedented loss of flight connections. Utilizing a combination of graph representation learning and time series analysis, this paper studies the evolution of both the global connectivity as well as the structure of the European Air Transportation Network from January 2020 to December 2022. Specifically, it finds strong differences in recovery rates for flights across six different market segments. In terms of network structure, the study finds that structural roles that are present in the pre-covid network have seen a loss in performance over the course of the pandemic, but have recovered to pre-covid levels. Using regional changes in structural roles, this study identifies Italy as the region with the strongest increase and the United Kingdom as the region with the strongest decrease in structural role, finding substantial differences in recovery rates per market segment. Lastly, this study pays special attention on the effect of the Russia-Ukrainian war on the European Air Transportation Network. © 2023 IEEE.
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In this paper has been proposed a methodology for ensuring the financial security of enterprises in the context of recession caused by the COVID-19 pandemic. Based on pre-crisis data related to the new coronavirus infection pandemic and multi-component modeling of the dynamics of industrial production in the Republic of Uzbekistan during the "corona crisis,"this study seeks to identify the dynamics of growth by economic activity type and recovery rate in order to identify areas of state support for industrial production. In this paper has been investigated issues of financial security management of textile enterprises. On the basis of secondary statistics, the growth of textile production in the regions of the Republic of Uzbekistan in 2008-2020 was analyzed and the factors influencing it were identified. By the author have been presented the main tasks and conditions for the financial security of enterprises, as well as developed scientific and practical recommendations for eliminating factors affecting the financial security of textile enterprises. © 2022 Owner/Author.
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All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.
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Coronavirus disease 2019 is a new infectious respiratory disease as named by the World Health Organization. This virus is affecting different individuals in diverse manners. Consequently, studies are going on to identify the factors and parameters disturbing predominantly. According to various studies, the immunity of a person determines the effect of the virus on that individual's health. Thus, immunity is determined by multiple factors like climate, population, geographical location, sanitation facilities. In existing studies, the effect of various climatic factors, such as temperature, relative humidity of diverse countries and areas, on COVID-19 spread is taken. To extend these studies, this paper is an effort to consider almost all the topological parameters of significant countries and different states of India for analysing their effects on the recovery rate due to COVID-19. Finally, these parameters are ranked/sorted as per their impact on recovery rates. © 2023 The authors.
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When using the traditional SEIR infectious disease model to predict the trend of novel coronavirus pneumonia epidemic, numerous initial parameters need to be tuned, and the parameters cannot change over time during the prediction process, which reduces the accuracy of the model. Firstly, thesis used a logistic model to preprocess the SEIR model parameters and proposed a SEIR model based on time series recovery rate optimization with a new parameter of effective immunity rate. Secondly, the model was trained with epidemic data from domestic and foreign provinces and cities, and the usability of the model was demonstrated experimentally, and the mean absolute percentage error (MAPE) and goodness of fit (R2) were used to compare with other models, which proved the superiority of the model prediction and indicated further research directions. © 2022 IEEE.
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In this paper, the time dependent carrier-borne epidemic model defined by Weiss in 1965 has been adopted into a Bayesian framework for the estimation of its parameters. A complete methodological structure has been proposed for estimating the relative infection rate and probability of survival of k out of m susceptibles after time t from the start of the epidemic. The methodology has been proposed assuming a single carrier to simplify the study of the behavioral validity of the fitted Bayesian model with respect to time and relative infection rate. Further, the proposed model has been implemented on two real data sets the typhoid epidemic data from Zermatt in Switzerland and the Covid-19 epidemic data from Kerala in India. Results show that the proposed methodology produces reliable predictions which are consistent with those of the maximum likelihood estimates and with expected epidemiological patterns. © 2023 Deo et al
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Background: COVID-19 is an international public health emergency in the world. Objectives: The aim of the present study is to determine the geographic pattern and temporal trend of Coronavirus disease 2019 incidence, fatality, and recovery rates worldwide. Methods: The present ecological study is a mixed exploratory study. The study population included Patients with COVID-19, recov-ered individuals, and deaths from COVID-19 from October 1, 2019, until June 30, 2021, worldwide. Descriptive analysis included the calculation cumulative incidence rate (CIR), case fatality rate (CFR), and case recovery rate (CRR) of COVID-19. Global Moran's I and Anselin Local Moran's I tests were used for spatial analysis. The joinpoint regression analysis was used to examine the time trend by ArcGIS, Joinpoint, and SPSS software. Results: The average cumulative incidence rate was 1077 in 106 individuals;also, the average case recovery rate and average case fatality rate were %72.81 and %3.21, respectively. Global Moran's I index measured for CIR was 0.159. The results of Anselin's local Moran's I, high-high cluster, consists of some countries in South America and in southern and Western Europe and central and western Asia. The temporal trend of changes in the incidence rate and CRR of COVID-19 were incremental, and the average annual percentage change from October 2019 to June 2021 increased by 44.4% and 3.2%, respectively (P < 0.001), but CFR decreased by-0.3% and was not significant (P > 0.05). Conclusions: As regards the specific spatial pattern of fatality and recovery rate of COVID-19, it seems essential to consider spatial conditions and environmental factors which are related to the incidence and fatality of COVID-19 in different regions, as well as the necessity of upgrading the care system in high-risk areas, in order to have better management and control of the pandemic and optimal function in early diagnosis, proper treatment, and high vaccination coverage. © 2023, Author(s).
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The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R-squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people's lives in different sectors were also discussed in this paper.
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The spread of COVID-19 results in a significant drop in traffic levels worldwide. Quantifying the impact of multiple COVID-19 outbreaks on traffic systems is critical to developing differentiated policies in the future. This paper proposes a novel COVID-19 multiple outbreak analysis method (NCMOA), dividing the impact scope and degree under multiple COVID-19 disturbances, and using the recovery rate and accumulated loss to quantify the impacts on air passenger flow. A case study based on Chinese national air traffic flow is executed, and the recovery patterns and the differentiated disturbances are analyzed. Results show that air passenger flow recovers with a similar pattern after the first outbreak, and subsequent outbreaks cause local effects and cannot affect the overall recovery pattern. Further, the heterogeneous influence factors and trends on the epi-centers (EC) and the nation are analyzed. In addition, the methods and results of this paper quantify the impact of COVID-19 on air passenger flow at a more detailed level under multiple disturbances. They could provide a basis for differentiated policy formulation of airlines and government in the future.
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Background: COVID-19 is an international public health emergency in the world. Objectives: The aim of the present study is to determine the geographic pattern and temporal trend of Coronavirus disease 2019 incidence, fatality, and recovery rates worldwide. Methods: The present ecological study is a mixed exploratory study. The study population included Patients with COVID-19, recov-ered individuals, and deaths from COVID-19 from October 1, 2019, until June 30, 2021, worldwide. Descriptive analysis included the calculation cumulative incidence rate (CIR), case fatality rate (CFR), and case recovery rate (CRR) of COVID-19. Global Moran's I and Anselin Local Moran's I tests were used for spatial analysis. The joinpoint regression analysis was used to examine the time trend by ArcGIS, Joinpoint, and SPSS software. Results: The average cumulative incidence rate was 1077 in 106 individuals;also, the average case recovery rate and average case fatality rate were %72.81 and %3.21, respectively. Global Moran's I index measured for CIR was 0.159. The results of Anselin's local Moran's I, high-high cluster, consists of some countries in South America and in southern and Western Europe and central and western Asia. The temporal trend of changes in the incidence rate and CRR of COVID-19 were incremental, and the average annual percentage change from October 2019 to June 2021 increased by 44.4% and 3.2%, respectively (P < 0.001), but CFR decreased by-0.3% and was not significant (P > 0.05). Conclusions: As regards the specific spatial pattern of fatality and recovery rate of COVID-19, it seems essential to consider spatial conditions and environmental factors which are related to the incidence and fatality of COVID-19 in different regions, as well as the necessity of upgrading the care system in high-risk areas, in order to have better management and control of the pandemic and optimal function in early diagnosis, proper treatment, and high vaccination coverage. © 2023, Author(s).
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Purpose: This study aims to evaluate the efficiency of the Indian state's health care systems in increasing the recovery rate of the Corona virus by using data envelopment analysis. Methodology: The Data envelopment analysis (Output Oriented CRS Model) has been applied to evaluate the efficiency scores of Indian states concerning the recovery rate of people affected by COVID -19. The tobit regression model is used to find the efficiency drivers. The required corona data has been collected during the period of time Feb 2019 - May 2020 from the government official websites like Worldometer, MoHFW, Corona Tracker etc,. Finding(s): The efficient states were found along with the factor that increases the state's efficiency in increasing the recovery rate. The study reveals that out of 36 State/UT, only 5 State/UT were efficient in increasing the recovery rate. In addition the study found that there is an input slack in the number of hospitals for inefficient states/UTs. Copyright © 2022 Authors. All rights reserved.
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Objectives: Documentation the risk of the COVID-19 in Iraq is occurred by interpretation of the case fatality rate (CFR) across a period of time. and different regions. This could highlight the reason behind the rapid spreading of COVID-19 among Iraqi population. Methods: in this work we introduced and applied a protocol to evaluate and elucidate the behavior of the case fatality rate (CFR) of COVID-19 at different regions in Iraq. This evaluation was performed across 6 months (27th March - 27th September). All data of COVID-19 pandemic in Iraq were obtained from the websites of Health Ministry in Iraq (https://moh.gov.iq/). Results: During the study period, Baghdad score is the highest fatality rate compering with other cities flowed by Basara then Babylon. Baghdad has the highest CFR value (8.5) compared with Arbil and Mosil have 2.5 and 3.19 respectively, in which are better than (Baghdad, Basra and Babil) there was more than 50% less than Baghdad. Furthermore, Babylon city ranks the first in terms of the death rate among population (0.25%) and (0.0017%) to Iraq population. The reason behind that could be related to the population density in the rural area, and the lag during transfer COVID-19 patients to specialized tertiary. On the other hand, Mosil had the lowest death rate population (0.012%) compared with other cities in this study. Conclusion: Comparing fatality rate and death rate of COVID-19 pandemic across different Iraqi cities would help to illustrate the strength and spreading rate of the pandemic. © 2022 American Institute of Physics Inc.. All rights reserved.
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This paper uses prognosticative machine learning models that predict corona positives and deaths as a result of the crisis, and the recovery rate from the pandemic. This method aids in diagnosing the contours of an individual's presumption in data transmission based on medical knowledge and calculates the unfolding virus's socioeconomic impact. It examines the Covid-19's spread technique with the help of machine learning models. It also identifies the approaching prophecy and recessive presumption of the crisis at the same time, and as a result, this applicable analysis aids similar countries in making decisions. This paper also considers the global prevalence of the plague. Within the first phase of the irruption, eight supervised classification epidemiologic models are used to estimate the day-to-day and monomer incidents of coronavirus throughout the world, as well as the vital replica variety, growth rate, and increasing time. Calculations are also made for the more intricate efficacious replica variety, which reveals that since the predominant cases are confirmed to the specific countries, the severity has decreased. Machine learning models' prognosticative capabilities are found to provide an additional satisfactory match, and simple estimates of daily incidents around the world. © 2022 IEEE.
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Introduction. The development of SARS-COV-2 (COVID-19) pandemic creates certain analytical challenges with respect to both examining the spread of the disease itself and investigating reasons and regularities in the epidemic propagation under different conditions. This article dwells on the least examined issue related to the necessity to establish peculiarities and reasons for occurring differences in the coronavirus infection spread in RF regions with different socioeconomic and social-hygienic status. This is necessary for substantiating relevant actions, which are to compensate for preventable modifying influence exerted by environmental factors and lifestyle-related ones. Materials and methods. The study involved analyzing regularities in regional differentiation of parameters in a classic SIR model describing the epidemic process in RF regions. We analyzed data on more than two hundred fifty various indicators describing levels of infection, vaccination, hospital admission and mortality among population collected in 85 RF regions in 2020–2021. All the data were taken as average values over a week. Results. We assessed parameters of mathematical models for RF regions. The assessments gave grounds for analyzing peculiar development of the epidemic process and for detecting basic regularities in the territorial distribution of parameters describing rates of infection, recovery and mortality rate and the basic reproductive number for SARS-COV-2 virus. Limitations. The results are limited by data aggregation performed only at a regional level and a simplified model of the developing epidemic process applied in the present study. Another limitation is insufficient coverage of environmental factors reflecting peculiarities in the infection spread. The latter is considered a promising trend in future research. Conclusion. The study made it possible to trace basic peculiarities and regularities in the spread of the disease and to spot out regions where the epidemic process was the most acute and accompanied with the highest burdens on regional social security services. These trends and regularities indicate to the occurring regional differentiation detected at various stages in the development of epidemic process of the new coronavirus infection (COVID-19) spread due to the Delta strain caused by complex interactions and influence exerted by modifying factors creating a certain multi-level and multi-component structure. © 2022 Izdatel'stvo Meditsina. All rights reserved.
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This paper attempts to model and to predict the outbreak of COVID-19 through the susceptible-infectious-removed (SIR) epidemic model with a specific focus on India. The impact of lockdown on transmission rate has been exponentially decaying the transmission rate that is very significant in order to control the spread of the disease. The proposed model can approximately predict the newly infected cases and recovered cases of COVID-19. This paper is based on SIR-based mathematical model, designed to predict the newly infected cases and recovered cases of COVID-19. The transmission rate and recovery rate parameters studied are with the impact of a lockdown situation. The simulation result predicts that the epidemic grows on August-October 2020, and after that, it started to shrink with the assumed constrained scenario. This study measures the impact of lockdown on various organisations such as the health sector, e-commerce, IT sector, etc. in India.
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A compartmental epidemiological model with distributed recovery and death rates is proposed. In some particular cases, the model can be reduced to the conventional SIR model. However, in general, the dynamics of epidemic progression in this model is different. Distributed recovery and death rates are evaluated from COVID-19 data. The model is validated by the epidemiological data for different countries, and it shows better agreement with the data than the SIR model. The time-dependent disease transmission rate is estimated.
Subject(s)
COVID-19 , Epidemics , COVID-19/epidemiology , Humans , Mathematical Concepts , Models, BiologicalABSTRACT
We develop a new data-driven immuno-epidemiological model with distributed infectivity, recovery and death rates determined from the epidemiological, clinical and experimental data. Immunity in the population is taken into account through the time-dependent number of vaccinated people with different numbers of doses and through the acquired immunity for recovered individuals. The model is validated with the available data. We show that for the first time from the beginning of pandemic COVID-19 some countries reached collective immunity. However, the epidemic continues because of the emergence of new variant BA.2 with a larger immunity escape or disease transmission rate than the previous BA.l variant. Large epidemic outbreaks can be expected several months later due to immunity waning. These outbreaks can be restrained by an intensive booster vaccination.
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COVID-19 is a highly contagious disease that has infected over 136 million people worldwide with over 2.9 million deaths as of 11 April 2021. In March 2020, the WHO declared COVID-19 as a pandemic and countries began to implement measures to control the spread of the virus. The spread and the death rates of the virus displayed dramatic differences among countries globally, showing that there are several factors affecting its spread and mortality. By utilizing the cumulative number of cases from John Hopkins University, the recovery rate, death rate, and the number of active, recovered, and death cases were simulated to analyse the trends and patterns within the chosen countries. 10 countries from 3 different case severity categories (high cases, medium cases, and low cases) and 5 continents (Asia, North America, South America, Europe, and Oceania) were studied. A generalized SEIR model which considers control measures such as isolation, and preventive measures such as vaccination is applied in this study. This model is able to capture not only the dynamics between the states, but also the time evolution of the states by using the fourth-order-Runge-Kutta process. This study found no significant patterns in the countries under the same case severity category, suggesting that there are other factors contributing to the pattern in these countries. One of the factors influencing the pattern in each country is the population's age. COVID-19 related deaths were found to be notably higher among older people, indicating that countries comprising of a larger proportion of older age groups have an increased risk of experiencing higher death rates. Tighter governmental control measures led to fewer infections and eventually reduced the number of death cases, while increasing the recovery rate, and early implementations were found to be far more effective in controlling the spread of the virus and produced better outcomes.
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The Philippines is one of the countries where the coronavirus has spread. The virus has infected almost every Filipino individual;coronavirus affects people of all ages, from children to adults, and as a result, recovery rate is unknown. This research aims to develop a predictive model using random forest algorithms to predict the high and low recovery rate by age. Based on the descriptive analysis of the data set, the age range of 20 to 29 has a 99.3 percent recovery rate compared to other age groups. The Random Forest Predictive Model was able to predict the high recovery rate with an accuracy rate of 93%. © 2022 ACM.
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Covid19 pandemic is infecting a large community across the globe. Nearly 29 million got affected due to covid in India. The cases are increasing day by day. There are confirmed cases along with recovery and fatality rate. Prediction of the growth /fatality rate is a challengeable one. This paper implements an Artificial Intelligence (AI) strategy for analyzing and predicting the covid cases across the nation on daily basis at various rates. It includes (a) Analysis of growth rate, (b) Prediction of Confirmed rate, (c) Prediction of Deceased Rate, (d) Analysis of Recovery rate, etc. Logistic regression (LR) is a classification problem that performs well on medical data. The proposed work implements logistic regression along with prophet methods for analyzing the time-based covid cases across India. This proposed work analyzes the active cases and perform them effectively with an accuracy of 0.96. © 2021 IEEE.