scholarly journals Mapping Individual Earthquake Preparedness in China

2017 ◽  
Author(s):  
Guochun Wu ◽  
Ziqiang Han ◽  
Weijin Xu ◽  
Yue Gong
Keyword(s):  
Disaster Preparedness ◽  
Regression Models ◽  
Current Knowledge ◽  
Mainland China ◽  
National Sample ◽  
Public Affairs ◽  
Earthquake Risk ◽  
Linear Regression Models ◽  
Earthquake Preparedness ◽  
Material Preparation

Abstract. Disaster preparedness is critical for reducing potential impact. This paper contributes to current knowledge of disaster preparedness using a representative national sample data from China, which faces high earthquake risks in many areas of the country. The adoption of earthquake preparedness activities by the general public, including five material preparation, and five awareness preparation were surveyed, and 3,245 respondents from all of the 31 provinces of mainland China participated in the survey. Linear regression models and Logit regression models were used to analyze the effects of potential influencing factors. Overall, the preparedness levels are not satisfied, with a material preparation score of 3.02 (1–5), and awareness preparation score of 2.79 (1–5), nationally. Meanwhile, residents from west China where have higher earthquake risk have higher preparedness degrees. The concern of disaster risk reduction, the concern of building safety and participation in public affairs are consistent positive predictors of both material and awareness preparedness. The demographic and socioeconomic variables' effects, such as gender, age, education, income, urban/rural division, and occupied building type, vary according to different preparedness activities. Finally, the paper concludes with a discussion of the theoretical contribution and potential implementation.

scholarly journals Mapping individuals' earthquake preparedness in China

2018 ◽  
Vol 18 (5) ◽  
pp. 1315-1325 ◽  
Author(s):  
Guochun Wu ◽  
Ziqiang Han ◽  
Weijin Xu ◽  
Yue Gong
Keyword(s):  
Disaster Preparedness ◽  
Regression Models ◽  
Current Knowledge ◽  
Mainland China ◽  
National Sample ◽  
Public Affairs ◽  
Western China ◽  
Earthquake Risk ◽  
Linear Regression Models ◽  
Earthquake Preparedness

Abstract. Disaster preparedness is critical for reducing potential impact. This paper contributes to current knowledge of disaster preparedness using representative national sample data from China, which faces high earthquake risks in many areas of the country. The adoption of earthquake preparedness activities by the general public, including five indicators of material preparedness and five indicators of awareness preparedness, were surveyed and 3245 respondents from all 31 provinces of Mainland China participated in the survey. Linear regression models and logit regression models were used to analyze the effects of potential influencing factors. Overall, the preparedness levels are not satisfied, with a material preparation score of 3.02 (1–5), and awareness preparation score of 2.79 (1–5), nationally. Meanwhile, residents from western China, which has higher earthquake risk, have higher degrees of preparedness. The concern for disaster risk reduction (DRR) and the concern for building safety and participation in public affairs are consistent positive predictors of both material and awareness preparedness. The demographic and socioeconomic variables' effects, such as gender, age, education, income, urban/rural division, and building size, vary according to different preparedness activities. Finally, the paper concludes with a discussion of the theoretical contribution and potential implementation.

scholarly journals Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020

Epidemiologia ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 95-113 ◽  
Author(s):  
Isaac Chun-Hai Fung ◽  
Xiaolu Zhou ◽  
Chi-Ngai Cheung ◽  
Sylvia K. Ofori ◽  
Kamalich Muniz-Rodriguez ◽  
...  
Keyword(s):  
Regression Models ◽  
Doubling Time ◽  
Metropolitan Areas ◽  
Mainland China ◽  
Linear Regression Models ◽  
Case Count ◽  
Daily Time Series ◽  
The Mean ◽  
Doubling Times ◽  
Daily Time

To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran’s I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (−0.012, 95% CI, −0.017, −0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time.

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy (Preprint)

2021 ◽  
Author(s):  
Alessandro Rovetta ◽  
Akshaya Srikanth Bhagavathula
Keyword(s):  
Regression Models ◽  
Current Knowledge ◽  
Statistical Significance ◽  
National Level ◽  
Ordinary Least Squares ◽  
Linear Regression Models ◽  
Mortality Trends ◽  
Male Mortality ◽  
Definitive Answer ◽  
Significant Difference

BACKGROUND COVID-19 mortality was associated with several reasons, including conspiracy theories and infodemic phenomena. However, little is known about the potential endogenous reasons for the increase in COVID-19 associated mortality in Italy. OBJECTIVE This study aimed to search the potential endogenous reasons for the increase in COVID-19 mortality recorded in Italy during the year 2020 and evaluate the statistical significance of the latter. METHODS We analyzed all the trends in the timelapse 2011-2019 related to deaths by age, sex, region, and cause of death in Italy and compared them with those of 2020. Ordinary least squares (OLS) linear regressions and ARIMA (p, d, q) models were applied to investigate the predictions of death in 2020 as compared to death reported in the same year. Grubbs and Iglewicz-Hoaglin tests were used to identify the statistical differences between the predicted and observed deaths. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. RESULTS Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (95% confidence intervals range: 620,000 – 695,000) and these values were far from the observed deaths reported (above 750,000). Significant difference in deaths at national level (P = 0.003), and higher male mortality than women (+18% versus +14%, P < 0.001 versus P = 0.01) was observed. Finally, higher mortality was strongly and positively correlated with latitude (R = 0.82, P < 0.001). CONCLUSIONS Our findings support the absence of historical endogenous reasons capable of justifying the increase in deaths and mortality observed in Italy in 2020. Together with the current knowledge on the novel coronavirus 2019, these findings provide decisive evidence on the devastating impact of COVID-19 in Italy. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses. Moreover, given the marked concordance between the predictions of the ARIMA and OLS regression models, we suggest that these models be exploited to predict mortality trends.

scholarly journals Dying from COVID-19 or with COVID-19: a definitive answer through a retrospective analysis of mortality in Italy

2021 ◽  
Author(s):  
Alessandro Rovetta ◽  
Akshaya Srikanth Bhagavathula
Keyword(s):  
Regression Models ◽  
Current Knowledge ◽  
Statistical Significance ◽  
National Level ◽  
Ordinary Least Squares ◽  
Linear Regression Models ◽  
Mortality Trends ◽  
Male Mortality ◽  
Definitive Answer ◽  
Significant Difference

Background: COVID-19 mortality was associated with several reasons, including conspiracy theories and infodemic phenomena. However, little is known about the potential endogenous reasons for the increase in COVID-19 associated mortality in Italy. Objective: This study aimed to search the potential endogenous reasons for the increase in COVID-19 mortality recorded in Italy during the year 2020 and evaluate the statistical significance of the latter. Methods: We analyzed all the trends in the timelapse 2011-2019 related to deaths by age, sex, region, and cause of death in Italy and compared them with those of 2020. Ordinary least squares (OLS) linear regressions and ARIMA (p, d, q) models were applied to investigate the predictions of death in the year 2020 as compared to death reported in 2020. Grubbs and Iglewicz-Hoaglin tests were used to identify the statistical differences between the predictors and observed death during the year 2020. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Results: Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during the year 2020 is between 640,000 and 660,000 (95% confidence intervals range: 620,000 - 695,000) and these values were far from the observed deaths reported (n = 750,000). Significant difference in deaths at national level (P = 0.003), and higher male mortality than women (+18% versus +14%, P < 0.001 versus P = 0.01) was observed. Finally, higher mortality was strongly and positively correlated with latitude (R = 0.82, P < 0.001) Conclusions: Our findings suggest that the absence of historical endogenous reasons capable of justifying the increase in deaths and mortality observed in Italy in 2020. Together with the current knowledge on the novel coronavirus 2019, these findings provide decisive evidence on the devastating impact of COVID-19 in Italy. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy theorists. Moreover, given the marked concordance between the predictions of the ARIMA and OLS regression models, we suggest that these models be exploited to predict mortality trends.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Income-related health inequality among Chinese adults during the COVID-19 pandemic: evidence based on an online survey

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Peng Nie ◽  
Lanlin Ding ◽  
Zhuo Chen ◽  
Shiyong Liu ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Health Outcomes ◽  
Family Member ◽  
Health Inequalities ◽  
Online Survey ◽  
Mainland China ◽  
Decomposition Analysis ◽  
National Sample ◽  
Vulnerable Groups

AbstractBackgroundPartial- or full-lockdowns, among other interventions during the COVID-19 pandemic, may disproportionally affect people (their behaviors and health outcomes) with lower socioeconomic status (SES). This study examines income-related health inequalities and their main contributors in China during the pandemic.MethodsThe 2020 China COVID-19 Survey is an anonymous 74-item survey administered via social media in China. A national sample of 10,545 adults in all 31 provinces, municipalities, and autonomous regions in mainland China provided comprehensive data on sociodemographic characteristics, awareness and attitudes towards COVID-19, lifestyle factors, and health outcomes during the lockdown. Of them, 8448 subjects provided data for this analysis. Concentration Index (CI) and Corrected CI (CCI) were used to measure income-related inequalities in mental health and self-reported health (SRH), respectively. Wagstaff-type decomposition analysis was used to identify contributors to health inequalities.ResultsMost participants reported their health status as “very good” (39.0%) or “excellent” (42.3%). CCI of SRH and mental health were − 0.09 (p < 0.01) and 0.04 (p < 0.01), respectively, indicating pro-poor inequality in ill SRH and pro-rich inequality in ill mental health. Income was the leading contributor to inequalities in SRH and mental health, accounting for 62.7% (p < 0.01) and 39.0% (p < 0.05) of income-related inequalities, respectively. The COVID-19 related variables, including self-reported family-member COVID-19 infection, job loss, experiences of food and medication shortage, engagement in physical activity, and five different-level pandemic regions of residence, explained substantial inequalities in ill SRH and ill mental health, accounting for 29.7% (p < 0.01) and 20.6% (p < 0.01), respectively. Self-reported family member COVID-19 infection, experiencing food and medication shortage, and engagement in physical activity explain 9.4% (p < 0.01), 2.6% (the summed contributions of experiencing food shortage (0.9%) and medication shortage (1.7%),p < 0.01), and 17.6% (p < 0.01) inequality in SRH, respectively (8.9% (p < 0.01), 24.1% (p < 0.01), and 15.1% (p < 0.01) for mental health).ConclusionsPer capita household income last year, experiences of food and medication shortage, self-reported family member COVID-19 infection, and physical activity are important contributors to health inequalities, especially mental health in China during the COVID-19 pandemic. Intervention programs should be implemented to support vulnerable groups.

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