Analysing and comparing the COVID-19 data: The closed cases of Hubei and South Korea, the dark March in Europe, the beginning of the outbreak in South America

The present work is a statistical analysis of the COVID-19 pandemic. As the number of cases worldwide overtakes one million, data reveals closed outbreaks in Hubei and South Korea, with a new slight increase in the number of infected people in the latter. Both of these countries have reached a plateau in the number of Total Confirmed Cases per Million (TCCpM) residents, suggesting a trend to be followed by other affected regions. Using Hubei’s data as a basis of analysis, we have studied the spreading rate of COVID-19 and modelled the epidemic center for 10 European countries. We have also given the final TCCpM curves for Italy and Lombardia. The introduction of the α-factor allows us to analyse the different stages of the outbreak, compare the European countries amongst each other, and, finally, to confront the initial phase of the disease between Europe and South America.MethodsBy dividing the TCCpM curves in multiple sections spanning short time frames we were able to fit each section to a linear model. By pairing then the angular coefficient (α factor) of each section to the total number of confirmed infections at the center of the corresponding time interval, we have analysed how the spreading rate of Covid-19 changes as more people are infected. Also, by modelling the TCCpM curves with an asymmetrical time integral of a Normal Distribution, we were able to study, by fitting progressively larger data ensembles, how the fitting parameters change as more data becomes available.FindingsThe data analysis shows that the spreading rate of COVID-19 increases similarly for all countries in its early stage, but changes as the number of TCCpM in each country grows. Regarding the modelling of the TCCpM curves, we have found that the fitting parameters oscillate with time before reaching constant values. The estimation of such values allows the determination of better parameters for the model, which in turn leads to more trustworthy forecasts on the pandemic development.InterpretationThe analysis of the oscillating fitting parameters allows an early prediction of the TCC, epidemic center and standard deviation of the outbreak. The α factor and the recovered over confirmed cases ratio can be used to understand the pandemic development in each country and to compare the protective measures taken by local authorities and their impact on the spreading of the disease.FundingCNPq (grant number 2018/303911) and Fapesp (grant numebr 2019/06382-9).

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Analysing and comparing the COVID-19 data: The closed cases of Hubei and South Korea, the dark March in Europe, the beginning of the outbreak in South America (Preprint)

10.2196/preprints.19320 ◽

2020 ◽

Author(s):

Stefano De Leo ◽

Gabriel Gulak Maia ◽

Leonardo Solidoro

Keyword(s):

South America ◽

South Korea ◽

Early Stage ◽

Spreading Rate ◽

European Countries ◽

Time Interval ◽

Angular Coefficient ◽

Infected People ◽

Alpha Factor ◽

Fitting Parameters

BACKGROUND The present work is a statistical analysis of the COVID-19 pandemic. As the number of cases worldwide overtakes one million, data reveals closed outbreaks in Hubei and South Korea, with a new slight increase in the number of infected people in the latter. Both of these countries have reached a plateau in the number of Total Confirmed Cases per Million (TCCpM) residents, suggesting a trend to be followed by other affected regions. OBJECTIVE Using Hubei's data as a basis of analysis, we have studied the spreading rate of COVID-19 and modelled the epidemic center for 10 European countries. We have also given the final TCCpM curves for Italy and Lombardia. The introduction of the $\boldsymbol{\alpha}$-factor allows us to analyse the different stages of the outbreak, compare the European countries amongst each other, and, finally, to confront the initial phase of the disease between Europe and South America. METHODS By dividing the TCCpM curves in multiple sections spanning short time frames we were able to fit each section to a linear model. By pairing then the angular coefficient ( factor) of each section to the total number of confirmed infections at the center of the corresponding time interval, we have analysed how the spreading rate of Covid-19 changes as more people are infected. Also, by modelling the TCCpM curves with an asymmetrical time integral of a Normal Distribution, we were able to study, by fitting progressively larger data ensembles, how the fitting parameters change as more data becomes available. RESULTS The data analysis shows that the spreading rate of COVID-19 increases similarly for all countries in its early stage, but changes as the number of TCCpM in each country grows. Regarding the modelling of the TCCpM curves, we have found that the fitting parameters oscillate with time before reaching constant values. The estimation of such values allows the determination of better parameters for the model, which in turn leads to more trustworthy forecasts on the pandemic development. CONCLUSIONS The analysis of the oscillating fitting parameters allows an early prediction of the TCC, epidemic center and standard deviation of the outbreak. The alpha factor and the recovered over confirmed cases ratio can be used to understand the pandemic development in each country and to compare the protective measures taken by local authorities and their impact on the spreading of the disease. INTERNATIONAL REGISTERED REPORT RR2-doi.org/10.1101/2020.04.06.20055327

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Covid-19 testing strategies and lockdowns: the European closed curves, analysed by “skew-normal” distributions, the forecasts for the UK, Sweden, and the USA, and the ongoing outbreak in Brazil

10.1101/2020.06.01.20119461 ◽

2020 ◽

Cited By ~ 2

Author(s):

Stefano De Leo

Keyword(s):

Early Stage ◽

Spreading Rate ◽

European Countries ◽

Mitigation Measures ◽

Local Authorities ◽

Normal Distributions ◽

Testing Strategies ◽

The Usa ◽

The Uk ◽

Skew Normal

AbstractAs the number of Covid-19 infections worldwide overtakes 6 millions of Total Confirmed Cases (TCC), the data reveal almost closed outbreaks in many European countries. Using the European data as a basis for our analysis, we study the spreading rate of Covid-19 and model the Daily Confirmed Cases and Deaths per Million (DCCpM and DDpM) curves by using “skew-normal” probability density functions. The use of these asymmetrical distributions allows to get a more realistic prediction of the end of the disease in each country and to evaluate the effectiveness of the local authorities strategies in facing the European outbreak. The initial stage of the Brazilian disease is compared with the early phase of the European one. This is done by using the weekly spreading rate of Covid-19. For Sweden, UK, and USA, we shall give a forecast for the end of pandemic and for Brazil the prediction of the peak of DDpM. We also discuss additional factors that could play an important role in the fight against Covid-19, such as the fast response of the local authorities, the testing strategies, the number of beds in the intensive care units, and, last but not least, the measures of isolation adopted. The Brazilian mitigation measures can be placed between the strict lockdown of many European countries and the Swedish approach, but clearly much comparable to the European ones (in particular to the Netherlands).MethodsFor Brazil, the weekly spreading rates of Covid-19, as more people are getting infected, was used to compare the outbreak in these countries with the ones of the European countries when they were at the same stage of infection. In the early stage of the disease, normal distributions have been used to obtain what we call a dynamic prediction of the peaks. After reaching the peak of daily infections and/or deaths, skew-normal distributions are required to correctly fit the asymmetrical DCCpM and DDpM curves and get a realistic forecast of the pandemic end.FindingsThe European data analysis shows that the spreading rate of Covid-19 increased similarly for all countries in its initial stage, but it changed as the number of TCCpM in each country grew. This was caused by the different timely action of the authorities in adopting isolation measures and/or massive testing strategies. The early stage of the outbreak in the USA and Brazil shows for their α factor (DCCpM) a behaviour similar to Italy and Sweden, respectively. For the β factor (DDpM), the American spreading is similar to the one of Switzerland, whereas the Brazilian factor is greater than the ones of Portugal, Germany, and Austria (which showed, in terms of TDpM, the best results in Europe) but, at the moment, it is lower than the other European countries.InterpretationThe fitting skew parameters used to model the DCCpM and DDpM curves allow a more realistic prediction of the end of the pandemic and give us the possibility to compare the mitigation measures adopted by the local authorities by analysing their respective skew normal parameters (mean, mode, standard deviation, and skewness). In Europe, Sweden and the UK show the greatest asymmetries, a kind of marathon instead of the sprint of other European countries (as observed by Swedish authorities). This also happens for the USA. The Brazilian weekly spreading rate for deaths is lower than most of the European countries at the same stage of the outbreak.FundingIndividual grants by CNPq (2018/303911) and Fapesp (2019/06382–9).

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Covid-19 testing strategies and lockdowns: the European closed curves, analysed by ``skew-normal'' distributions, the forecasts for the UK, Sweden, and the USA, and the ongoing outbreak in Brazil (Preprint)

10.2196/preprints.21269 ◽

2020 ◽

Author(s):

Stefano De Leo

Keyword(s):

Early Stage ◽

Spreading Rate ◽

European Countries ◽

Mitigation Measures ◽

Local Authorities ◽

Normal Distributions ◽

Testing Strategies ◽

The Usa ◽

The Uk ◽

Skew Normal

BACKGROUND As the number of Covid-19 infections worldwide overtakes 6 millions of Total Confirmed Cases (TCC), the data reveal almost closed outbreaks in many European countries. Using the European data as a basis for our analysis, we study the spreading rate of Covid-19 and model the Daily Confirmed Cases and Deaths per Million (DCCpM and DDpM) curves by using ``skew-normal'' probability density functions. OBJECTIVE The use of these asymmetrical distributions allows to get a more realistic prediction of the end of the disease in each country and to evaluate the effectiveness of the local authorities strategies in facing the European outbreak. The initial stage of the Brazilian disease is compared with the early phase of the European one. This is done by using the weekly spreading rate of Covid-19. For Sweden, UK, and USA, we shall give a forecast for the end of pandemic and for Brazil the prediction of the peak of DDpM. We also discuss additional factors that could play an important role in the fight against Covid-19, such as the fast response of the local authorities, the testing strategies, the number of beds in the intensive care units, and, last but not least, the measures of isolation adopted. The Brazilian mitigation measures can be placed between the strict lockdown of many European countries and the Swedish approach, but clearly much comparable to the European ones (in particular to the Netherlands). METHODS For Brazil, the weekly spreading rates of Covid-19, as more people are getting infected, was used to compare the outbreak in these countries with the ones of the European countries when they were at the same stage of infection. In the early stage of the disease, normal distributions have been used to obtain what we call a dynamic prediction of the peaks. After reaching the peak of daily infections and/or deaths, skew-normal distributions are required to correctly fit the asymmetrical DCCpM and DDpM curves and get a realistic forecast of the pandemic end. RESULTS The European data analysis shows that the spreading rate of Covid-19 increased similarly for all countries in its initial stage, but it changed as the number of TCCpM in each country grew. This was caused by the different timely action of the authorities in adopting isolation measures and/or massive testing strategies. The early stage of the outbreak in the USA and Brazil shows for their $\boldsymbol{\alpha}$ factor (DCCpM) a behaviour similar to Italy and Sweden, respectively. For the $\boldsymbol{\beta}$ factor (DDpM), the American spreading is similar to the one of Switzerland, whereas the Brazilian factor is greater than the ones of Portugal, Germany, and Austria (which showed, in terms of TDpM, the best results in Europe) but, at the moment, it is lower than the other European countries. CONCLUSIONS The fitting skew parameters used to model the DCCpM and DDpM curves allow a more realistic prediction of the end of the pandemic and give us the possibility to compare the mitigation measures adopted by the local authorities by analysing their respective skew normal parameters (mean, mode, standard deviation, and skewness). In Europe, Sweden and the UK show the greatest asymmetries, a kind of marathon instead of the sprint of other European countries (as observed by Swedish authorities). This also happens for the USA. The Brazilian weekly spreading rate for deaths is lower than most of the European countries at the same stage of the outbreak.

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Novel Indicator to Ascertain the Status and Trend of COVID-19 Spread: Modeling Study (Preprint)

10.2196/preprints.20144 ◽

2020 ◽

Author(s):

Takashi Nakano ◽

Yoichi Ikeda

Keyword(s):

Early Stage ◽

Logarithmic Derivative ◽

Current Status ◽

Difference Approximation ◽

Time Interval ◽

Cumulative Number ◽

Infected People ◽

Linear Decrease ◽

Wide Range ◽

The Status

BACKGROUND In the fight against the pandemic of COVID-19, it is important to ascertain the status and trend of the infection spread quickly and accurately. OBJECTIVE The purpose of our study is to formulate a new and simple indicator that represents the COVID-19 spread rate by using publicly available data. METHODS The new indicator K is a backward difference approximation of the logarithmic derivative of the cumulative number of cases with a time interval of 7 days. It is calculated as a ratio of the number of newly confirmed cases in a week to the total number of cases. RESULTS The analysis of the current status of COVID-19 spreading over countries showed an approximate linear decrease in the time evolution of the K value. The slope of the linear decrease differed from country to country. In addition, it was steeper for East and Southeast Asian countries than for European countries. The regional difference in the slope seems to reflect both social and immunological circumstances for each country. CONCLUSIONS The approximate linear decrease of the K value indicates that the COVID-19 spread does not grow exponentially but starts to attenuate from the early stage. The K trajectory in a wide range was successfully reproduced by a phenomenological model with the constant attenuation assumption, indicating that the total number of the infected people follows the Gompertz curve. Focusing on the change in the value of K will help to improve and refine epidemiological models of COVID-19.

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Influence of countries adopted social distancing policy for COVID-19 reduction under the view of the airborne transmission framework

10.33767/osf.io/2hek4 ◽

2020 ◽

Cited By ~ 1

Author(s):

CHARLES ROBERTO TELLES

Keyword(s):

North America ◽

Middle East ◽

South America ◽

South Korea ◽

East Africa ◽

European Countries ◽

Infection Rates ◽

Airborne Transmission ◽

Geographical Regions ◽

Policy Measures

Cumulative COVID-19 daily new cases dataset during January to April, 2020 were used to search for evidences of SARS-CoV-2 spreading patterns (transmission forms) in the geographical regions with samples of Asia, South America, North America, Middle East, Africa and European countries. In order to comprehend the cause of constant infection rates for some countries, while others present very low daily new cases (China and South Korea), this research investigated possible aerosols forming patterns in the atmosphere and its relation to policy measures adopted by selected countries.

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Predication of Pandemic COVID-19 situation in Maharashtra, India

10.1101/2020.04.10.20056697 ◽

2020 ◽

Cited By ~ 2

Author(s):

Sunny Kumar

Keyword(s):

Public Health ◽

Early Stage ◽

Sir Model ◽

Spreading Rate ◽

Epidemic Spreading ◽

Current Spreading ◽

Infected People ◽

The World ◽

Effective Models ◽

Virus Spreading

AbstractPresently, the world is infected by COVID 19 virus which has created an emergency for public health. For controlling the spreading of the virus, we have to prepare for precaution and futuristic calculation for infection spreading. The coronavirus affects the population of the world including Inia. Here, we are the study the virus spreading rate on the Maharashtra state which is part of India. We are predicting the infected people by the SIR model. SIR model is one of the most effective models which can predict the spreading rate of the virus. We have validated the model with the current spreading rate with this SIR model. This study will help to stop the epidemic spreading because it is in the early stage in the Maharashtra region.

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Precautionary Behavior Practices and Psychological Characteristics of COVID-19 Patients and Quarantined Persons

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18116070 ◽

2021 ◽

Vol 18 (11) ◽

pp. 6070

Author(s):

Yubin Lee ◽

Byung-Woo Kim ◽

Shin-Woo Kim ◽

Hyunjin Son ◽

Boyoung Park ◽

...

Keyword(s):

Public Health ◽

South Korea ◽

Emergency Preparedness ◽

Online Survey ◽

Early Stage ◽

Psychological Characteristics ◽

Social Solidarity ◽

Psychological States ◽

Precautionary Behavior ◽

Public Health Emergency Preparedness

Background: since the coronavirus disease (COVID-19) was first reported in 2019, South Korea has enforced isolation of patients with confirmed cases of COVID-19, as well as quarantine for close contacts of individuals diagnosed with COVID-19 and persons traveling from abroad, in order to contain its spread. Precautionary behavior practices and psychological characteristics of confirmed and quarantined persons were investigated for planning pandemic recovery and preparedness. Methods: this study was conducted with 1716 confirmed patients and quarantined persons in Daegu and Busan, regions where a high number of cases were confirmed during the early stage of the COVID-19 outbreak in South Korea. We collected online survey data from 23 April to 20 May 2020, in Daegu, and 28 April to 27 May 2020, in Busan, in cooperation with Daegu and Busan Infectious Disease Control Centers and public health centers in the regions. COVID-19 symptoms, pre-cautionary behavior practices, psychological states, and the need for improvement in isolation/quarantine environments were examined using an online survey. Results: compared to patients infected with coronavirus, quarantined persons engaged in more hygiene-related behaviors (e.g., hand washing, cough etiquette, and proper mask-wearing) and social distancing. COVID-19 patients had a strong fear of stigma, while quarantined persons had a strong fear of contracting COVID-19. Study participants responded that it was necessary to provide financial support and adequate information during isolation/quarantine. Conclusions: the study highlights the importance of precautionary behavior to prevent COVID-19 infection and the need to provide support (both psychological and financial) to patients and quarantined persons, to reinforce effective communication, social solidarity, and public health emergency preparedness (PHEP) in a pandemic situation.

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Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study

Technology and Health Care ◽

10.3233/thc-218027 ◽

2021 ◽

Vol 29 ◽

pp. 297-309

Author(s):

Xiaohui Chen ◽

Wenbo Sun ◽

Dan Xu ◽

Jiaojiao Ma ◽

Feng Xiao ◽

...

Keyword(s):

Lung Diseases ◽

Early Stage ◽

Temporal Changes ◽

Time Interval ◽

Lesion Area ◽

Quantitative Ct ◽

Diagnosis And Prognosis ◽

Ct Findings ◽

Stage 4 ◽

Stage 1

BACKGROUND: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases. OBJECTIVE: This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results. METHODS: One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups. RESULTS: The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups. CONCLUSIONS: The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportion of HU [-750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.

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The Relationships among Self-Worth Contingency on Others’ Approval, Appearance Comparisons on Facebook, and Adolescent Girls’ Body Esteem: A Cross-Cultural Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18030901 ◽

2021 ◽

Vol 18 (3) ◽

pp. 901 ◽

Cited By ~ 1

Author(s):

Michael Prieler ◽

Jounghwa Choi ◽

Hye Eun Lee

Keyword(s):

South Korea ◽

Cultural Context ◽

Large Scale ◽

Body Esteem ◽

Asian Country ◽

Cross Cultural ◽

European Countries ◽

Self Worth ◽

Appearance Comparison

The present study examined the relationship between appearance-related social comparison on social networking services (SNSs) and body esteem in a cross-cultural context (three European countries, i.e., Austria, Belgium, and Spain, versus one Asian country, i.e., South Korea). The role of self-worth contingency on others’ approval was considered to be a psychological and cultural factor. Utilizing a large-scale cross-national survey of early and middle adolescents in 2017, the responses of female adolescents (N = 981) were analyzed. The results generally support the findings from previous studies but also reveal cultural differences. Appearance comparison on Facebook negatively influenced girls’ body esteem in all European countries, but not in South Korea. Self-worth contingency on others’ approval negatively influenced girls’ body esteem across all four countries. Finally, a positive relationship between self-worth contingency on others’ approval and appearance comparison on Facebook was found in all European countries, but not among Korean girls. These findings suggest the importance of self-worth contingency on others’ approval and cultural contexts can be used to study the effects of body image-related SNS use.

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Sample selection of prognostics validation test based on multi-stage Wiener process

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x18805835 ◽

2018 ◽

Vol 233 (4) ◽

pp. 605-614

Author(s):

Zhiao Zhao ◽

Yong Zhang ◽

Guanjun Liu ◽

Jing Qiu

Keyword(s):

Wiener Process ◽

Process Model ◽

Failure Modes ◽

Early Stage ◽

Sample Selection ◽

Degradation Process ◽

Time Interval ◽

Degradation Data ◽

Multi Stage ◽

Selection Of

Sample allocation and selection technology is of great significance in the test plan design of prognostics validation. Considering the existing researches, the importance of prognostics samples of different moments is not considered in the degradation process of a single failure. Normally, prognostics samples are generated under the same time interval mechanism. However, a prognostics system may have low prognostics accuracy because of the small quantity of failure degradation and measurement randomness in the early stage of a failure degradation process. Historical degradation data onto equipment failure modes are collected, and the degradation process model based on the multi-stage Wiener process is established. Based on the multi-stage Wiener process model, we choose four parameters to describe different degradation stages in a degradation process. According to four parameters, the sample selection weight of each degradation stage is calculated and the weight of each degradation stage is used to select prognostics samples. Taking a bearing wear fault of a helicopter transmission device as an example, its degradation process is established and sample selection weights are calculated. According to the sample selection weight of each degradation process, we accomplish the prognostics sample selection of the bearing wear fault. The results show that the prognostics sample selection method proposed in this article has good applicability.

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