scholarly journals A Model to Estimate the Effect of International Traffic on Malaria Cases: The Case of Japan from 1999 to 2021

2022 ◽  
Vol 19 (2) ◽  
pp. 880
Author(s):  
Hiroyuki Noda
Keyword(s):  
Regression Model ◽  
Malaria Case ◽  
Confidence Limit ◽  
Time Trend ◽  
Future Trends ◽  
Potential Reduction ◽  
Using Data ◽  
Risk Ratios ◽  
Generalized Regression Model ◽  
International Traffic

Aiming to identify the potentially reduced malaria cases by stagnation of international traffic after the COVID-19 pandemic, a longitudinal analysis of malaria cases as well as entries of Japanese and foreigners was conducted using data from 5 April 1999 to 30 September 2021 in Japan. Multivariable risk ratios were calculated with the Poison regression model as a predictive model of malaria cases by the number of entries for Japanese and foreigners. A generalized regression model was used to examine an association of time trend with entries for Japanese and foreigners using data before 2019, to estimate the potentially reduced number of entries after 2020. The potentially reduced number of malaria cases was estimated by the potentially reduced number of entries for Japanese and foreigners after 2020 using a multivariable Poison regression model. The multivariable risk ratio (95% confidence intervals) of malaria case numbers per 100,000 persons increment of entries per day was 3.41 (1.50–7.77) for Japanese and 1.47 (0.92–2.35) for foreigners. During 2020, a potential reduction of 28 (95% confidence limit: 22–34) malaria cases was estimated, which accounted for 58% (52–63%) of malaria cases in Japan. These finding suggest that the stagnation of international traffic during the COVID-19 pandemic reduced the number of malaria cases in Japan. This model may be helpful for countries without indigenous malaria to predict future trends of imported malaria cases.

scholarly journals A Skellam regression model for quantifying positional value in soccer

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Konstantinos Pelechrinis ◽  
Wayne Winston
Keyword(s):  
Regression Model ◽  
Video Game ◽  
Soccer Player ◽  
Performance Metrics ◽  
Optical Tracking ◽  
General Managers ◽  
Annual Salary ◽  
Using Data ◽  
English Premier League ◽  
Positional Value

Abstract Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players’ performance. Metrics applied successfully in other sports, such as the (adjusted) +/− that allows for division of credit among a basketball team’s players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team’s winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over eight seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team’s salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.

scholarly journals The Impact of Public Debt on the Economic Growth of Jordan: An Empirical Study (2000- 2015)

2017 ◽  
Vol 6 (2) ◽  
pp. 114 ◽  
Author(s):  
Tawfiq Ahmad Mousa ◽  
Abudallah. M. LShawareh
Keyword(s):  
Economic Growth ◽  
Empirical Study ◽  
Regression Model ◽  
Least Squares ◽  
Public Debt ◽  
Least Squares Method ◽  
Negative Impact ◽  
External Debt ◽  
Using Data ◽  
The Impact

In the last two decades, Jordan’s economy has been relied on public debt in order to enhance the economic growth. As such, an understanding  of the dynamics between public debt and economic growth is very important in addressing the obstacles to economic growth. The study investigates the impact of public debt on economic growth using data from 2000 to 2015. The study employs least squares method and regression model to capture the impact of public debt on economic growth. The results of the analysis indicate that there is a negative impact of total public debt, especially the external debt on economic growth. 

scholarly journals Modeling a Bridge When Survey Questions Change: Evidence from the Current Population Survey Health Insurance Redesign

2019 ◽  
Vol 35 (1) ◽  
pp. 189-202
Author(s):  
Brett O’Hara ◽  
Carla Medalia ◽  
Jerry J. Maples
Keyword(s):  
Health Insurance ◽  
Current Population Survey ◽  
Time Trend ◽  
Population Survey ◽  
The United States ◽  
Community Survey ◽  
Historical Time ◽  
Current Population ◽  
Using Data ◽  
The Impact

Abstract Most research on health insurance in the United States uses the Current Population Survey Annual Social and Economic Supplement. However, a recent redesign of the health insurance questions disrupted the historical time trend in 2013. Using data from the American Community Survey, which has a parallel trend in the uninsured rate, we model a bridge estimate of the uninsured rate using the traditional questions. Also, we estimate the effect of changing the questionnaire. We show that the impact of redesigning the survey varies substantially by subgroup. This approach can be used to produce bridge estimates when other questionnaires are redesigned.

scholarly journals Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

Revisiting the Relationship Between Marketing Education and Marketing Career Success

2017 ◽  
Vol 39 (2) ◽  
pp. 109-123 ◽  
Author(s):  
Donald R. Bacon
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Career Success ◽  
Academic Major ◽  
Marketing Education ◽  
Classic Article ◽  
Using Data ◽  
The Relationship

In a replication of a classic article by Hunt, Chonko, and Wood, regression analysis was conducted using data from a sample of 864 marketing professionals. In contrast to Hunt, Chonko, and Wood, an undergraduate degree in marketing was positively related to income in marketing jobs, but surprisingly, respondents with some nonmarketing majors earned about the same as marketing majors in marketing jobs. Satisfaction with a marketing career was not significantly related to academic major. The income regression model explained 30% of the variance in marketing income, which is an improvement over the earlier study, but also indicates that most of the variance in marketing success is not explained by education. Implications are discussed.

MODELING THE PHILLIPS CURVE IN CHINA: A NONLINEAR PERSPECTIVE

2016 ◽  
Vol 21 (2) ◽  
pp. 439-461 ◽  
Author(s):  
Lingxiang Zhang
Keyword(s):  
Regression Model ◽  
Inflation Rate ◽  
Phillips Curve ◽  
Smooth Transition ◽  
Short Term ◽  
Smooth Transition Regression ◽  
Using Data ◽  
Smooth Transition Regression Model ◽  
Economic Growth Rates ◽  
The Relationship

This paper investigates the nonlinear dynamics of the inflation–output type of Phillips curve based on a multiple-regime smooth transition regression model using data from China. The empirical results indicate significant nonlinearities in China's Phillips curve. The relationship between inflation and output can be modeled by a four-regime smooth transition regression model in which the responses of inflation to output depend on both inflation and economic growth rates. The inflation–output type Phillips curve may be positively sloped, negatively sloped, or even vertical in the short term, depending on different business cycles. Furthermore, we analyze business cycle fluctuations based on the nonlinear Phillips curve, indicating a coexisting zone of stable inflation rate and rapid growth rate.

scholarly journals Stagnation in Decreasing Gastric Cancer Incidence and Mortality in Quito: Time Trend Analysis, 1985–2013

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Wilmer Tarupi ◽  
Esther de Vries ◽  
Patricia Cueva ◽  
José Yépez
Keyword(s):  
Gastric Cancer ◽  
Latin American ◽  
Time Trend ◽  
Mortality Rates ◽  
Joinpoint Regression ◽  
Period Effects ◽  
Incidence And Mortality ◽  
Latin American Region ◽  
Using Data ◽  
The City

Background. Despite the significant global decline in mortality and incidence, gastric cancer (GC) remains a very common cause of illness and death in the Latin American region. This article seeks to describe, in depth, the time trend of incidence and mortality of GC in the city of Quito, from 1985 to 2013. Methods. Using data from the Quito Cancer Registry, annual sex-specific age-standardized incidence and mortality rates were calculated. The analysis included all types of GC together, as well as by histological subtype. Joinpoint regression analysis was performed to estimate the annual percentage change (EAPC). To evaluate cohort and period effects, Age-Period-Cohort (APC) modeling was performed. Results. Over time, incidence rate decreased from 30.4 to 18.8 cases in men and from 20.1 to 12.9 cases in women. The mortality rate decreased from 17.5 to 14.4 deaths in men and from 14.2 to 10.9 deaths in women. The incidence trend was composed of a first period (1986-1999) of strong decline (EAPC Men= -2.6, 95% Confidence Interval [CI]: -4.2, -0.9; EAPC Women= -3.2, 95% CI: -4.6, -1.9), followed by a less important decrease in men (EAPC= -0.8, 95% CI:-2.5, 0.9) and a slight increase in women (EAPC= 0.7, 95% CI: -1.4; 2.8). Mortality rates were constantly decreasing in both men (EAPC= -0.5, 95% CI: -0.9, -0.1) and women (EAPC= -0.9, 95% CI: -1.7, -0.1) throughout the period of analysis. Conclusions. The declines in incidence and mortality rates are stagnating. It is important to take measures to further reduce the high burden of GC.

scholarly journals Factors Influencing the Frequency of Consumers’ Purchases of Locally-Produced Rice in Indonesia: A Poisson Regression Analysis

Agriculture ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 117 ◽  
Author(s):  
Apri Wahyudi ◽  
John K. M. Kuwornu ◽  
Endro Gunawan ◽  
Avishek Datta ◽  
Loc T. Nguyen
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Marketing Strategy ◽  
Poisson Regression ◽  
Poisson Regression Model ◽  
Poisson Regression Analysis ◽  
Empirical Results ◽  
Factors Influencing ◽  
Using Data

This study assessed the factors influencing the frequency of purchases of locally-produced rice using data collected from a sample of 400 consumers in Jakarta Province in Indonesia. The empirical results of a Poisson regression model revealed that socio-economic characteristics of the consumers (i.e., gender, age, occupation, education, and income), characteristics of the product (i.e., label and color), and the product’s price and promotion significantly influenced consumers’ frequency of purchasing locally-produced rice. The implication is that increasing the quality of locally-produced rice, applying an appropriate marketing strategy such as offering a relatively lower-priced product compared to the price of imported rice, and product promotion are necessary for increasing the frequency of consumers’ purchases of locally-produced rice.

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