Count Data Regression Modelling: An Application to Monkeypox Confirmed Cases

Introduction: With the presence of COVID 19, some countries also faced an increase in number of cases due to Monkeypox virus. The main aim of this research was to investigate whether it is possible to fit count data regression models to predict the daily incidence of Monkeypox confirmed cases. Methods: In this study we have used two types of traditional count regression models like Poisson regression model and Negative binomial regression model using identity and logarithmic link function. Since our data was overdispersed, Negative binomial regression model with logarithmic link function fitted well as compared to other models. The parameters were estimated using SPSS, version 23.0. Results: The Negative Binomial Regression model with logarithm function fits well to the data related to Monkeypox cases. Therefore, the model shows that majority of the countries like Brazil, Canada, France, Germany, Peru, Spain, United Kingdom and United States of America shows significant decrease in number of cases with respect to time. The prediction line was plotted using this model where the line predicts well about the daily Monkeypox cases reported by different countries. Conclusion: From our study, we concluded that the count data regression model can be used widely to predict the incidence of any disease. The countries like Canada and Brazil have largest and smallest slope coefficient which shows maximum and minimum decrease in expected number of cases confirmed each day respectively.  

Link Between Redemption of a Medical Food Pantry Voucher and Reduced Hospital Readmissions

This study investigated the relationship between redeeming a voucher at hospital-based Medical Food Pantry (MFP) and hospital readmissions in Greenville, NC. Admitted patients at Vidant Medical Center identified as food insecure were given a voucher to the MFP. A retrospective chart review identified demographic information, type of insurance, voucher provision, and redemption dates, food bag type and number of subsequent hospital readmissions for all patients issued a voucher (n = 542) between June 21, 2018 and July 1, 2019. Negative binomial regression analysis assessed the relationship between readmissions and voucher redemption. Sixty percent of patients receiving a voucher were minority (African American) with an average age of 55. Nearly half (48 percent) had Medicare. Thirty-eight percent of those vouchers that were issued were redeemed, usually within five days. Regression results indicate that the number of readmissions was higher among women and non-whites in the sample relative to men and whites. Those patients who redeemed a food voucher had a seven percent lower likelihood of being readmitted (CI, 0.05–0.27). Food insecure patients who redeemed MFP vouchers had a comparatively lower likelihood of subsequent readmissions. These findings suggest that programs targeting modifiable social determinants of health like food insecurity could improve health outcomes and reduce utilization of the healthcare system

Count data modelling application.

Masters Degree. University of KwaZulu-Natal, Durban.The rapid increase of total children ever born without a proportionate growth in the Nigerian economy has been a concern and making prediction with count data requires applying appropriate regression model.. As count data assumes discrete, non-negative values, a Poisson distribution is the ideal distribution to describe this data, but it is deficient due to equality of variance and mean. This deficiency results in under/over-dispersion and the estimation of the standard errors will be biased rendering the test statistics incorrect. This study aimed to model count data with the application of total children ever born using a Negative Binomial and Generalized Poisson regression The Nigeria Demographic and Health Survey 2013 data of women within the age of 15-49 years were used and three models applied to investigate the factors affecting the number of children ever born. A predictive count modelling was also carried out based on the performance evaluation metrics (root mean square error, mean absolute error, R-squared and mean square error). In the inferential modeling, Generalized Poisson Model was found to be superior with age of household head (<.0001), age of respondent at the time of first birth (<.0001), urban-rural status (<.0001), and religion (<.0001) being significantly associated with total children ever born. In the predictive modeling, all the three models showed almost identical performance evaluation metrics but Poisson regression was chosen as the best because it is the simplest model. In conclusion, early marriage, religious belief and unawareness of women who dwell in rural areas should be checked to control total children ever born in Nigeria.Supervisor Professor Zewotir prefers using his publications name of Zewotir, Temesgen

Precision Medicine: Viable Pathways to Address Existing Research Gaps

Precision Medicine (PM) seeks to customize medical treatments for patients based on measurable and identifiable characteristics. Unlike personalized medicine, this effort is not intended to result in tailored care for each patient. Instead, this effort seeks to improve overall care within the medical domain by shifting the focus from one-size-fits-all care to optimized care for specified subgroups. In order for the benefits of PM to be expeditiously realized, the diverse skills sets of the scientific community must be brought to bear on the problem. This research effort explores the intersection of quality engineering (QE) and healthcare to outline how existing methodologies within the QE field could support existing PM research goals. Specifically this work examines how to determine the value of patient characteristics for use in disease prediction models with select machine learning algorithms, proposes a method to incorporate patient risk into treatment decisions through the development of performance functions, and investigates the potential impact of incorrect assumptions on estimation methods used in optimization models

Spatial Analysis of Motor Vehicle Theft in Riyadh, Saudi Arabia

Though motor vehicle theft (MVT) has been a major problem in Saudi Arabia (SA) for several decades, particularly in the capital Riyadh, few researchers have investigated this problem. Likewise, understanding the creation of the spatiotemporal patterns of MVT as a key element in tackling crime is also under- researched. This study aims to address this substantial research gap by utilising routine activity theory (RAT) and crime pattern theory (CPT). However, two issues need to be taken into consideration: that RAT and CPT will be applied outside their original context in the West and that few studies have utilised them to model MVT. As such, a contribution of this study is the evaluation of the applicability of these theories to both the Saudi context and MVT in general. The empirical work of this study using RAT and CPT was designed to meet two objectives. First, exploratory spatial analysis techniques were used to determine whether MVTs tended to show high concentrations in certain neighbourhoods and at particular times of the day. Second, regression analysis methods were implemented to identify and predict the factors that contributed to these concentrations of MVTs. The main findings suggest that, due to the substantial difference between contexts, the spatiotemporal patterns of MVT in Riyadh were somewhat different from those in the West. Due to the nature of MVT, the variables associated with RAT explained MVT well at certain times of the day but were insufficient during other periods; however, the variables associated with CPT were not able to explain MVT well at any time of the day. The final chapter of the study addresses the implications for research and police practice. A significant implication of this study is that the explanatory variables varied in their effects on MVT throughout the day and across the areas studied. This allowed for the provision of recommendations for the Saudi police, such as giving priority to tackling MVT in certain areas that experience high MVTs at particular times