Modeling COVID-19 daily cases in Senegal using a generalized Waring regression model

The two most popular models inwell-known count regression models are Poisson and negative binomial regression models. Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. Negative binomial regression is similar to regular multiple regression except that the dependent (Y) variables an observed count that follows the negative binomial distribution. This research studies some factors affecting divorce using Poisson and negative binomial regression models. The factors are unemployment rate, inflation and Gini coefficient. The data were taken from the website of the Statistics Center for the years 2002 to 2023.Under the Poisson regression model, each factor has been reported to have an effect on the divorce rate. The two factors of inflation and unemployment had a direct effect and income inequality factors had an inverse effect on the divorce rate. But, under the negative binomial regression model, only inflation has an effect on the number of divorces. It is worth noting that according to the AIC values, the negative binomial regression model has a better fit than the Poisson regression model because its AIC value is lower.

COVID-19 is currently threatening countries in the world. Presently in Nigeria, there are about 29,286 confirmed cases, 11,828 discharged and 654 deaths as of 6th July 2020. It is against this background that this study was targeted at modeling daily cases of COVID-19’s deaths in Nigeria using count regression models like; Poisson Regression (PR), Negative Binomial Regression (NBR) and Generalized Poisson Regression (GPR) model. The study aim at fitting an appropriate count Regression model to the confirmed, active and critical cases of COVID-19 in Nigeria after 118 days. The data for the study was extracted from the daily COVID-19 cases update released by the Nigeria Centre for Disease Control (NCDC) online database from February 28th, 2020 – 6th, July 2020. The extracted data were used in the simulation of Poisson, Negative Binomial, and Generalized Poisson Regression model with a program written in STATA version 14 and fitted to the data at a 5% significance level. The best model was selected based on the values of -2logL, AIC, and BIC selection test/criteria. The results obtained from the analysis revealed that the Poisson regression could not capture over-dispersion, so other forms of Poisson Regression models such as the Negative Binomial Regression and Generalized Poisson Regression were used in the estimation. Of the three count Regression models, Generalized Poisson Regression was the best model for fitting daily cumulative confirmed, active and critical COVID-19 cases in Nigeria when overdispersion is present in the predictors because it had the least -2log-Likelihood, AIC, and BIC. It was also discovered that active and critical cases have a positive and significant effect on the number of COVID-19 related deaths in Nigeria.

Introduction: The need to model the impact of some demographic indicators on the frequency of household visits to healthcare centres in Nigeria's community is very important for preventing and spreading community diseases. This study aimed to investigate the effect of the patents' age, gender, marital status, type of illness and amount spent on the frequency of visits to community health care centres in Nigeria and to compared Negative Binomial Regression (NBR) and Generalized Poisson Regression (GPR) models to determine the preferred count regression model for the number of household visits to health centres in some communities in Nigeria. Methods: Survey of 132640 households in some Nigeria communities obtained from the 2018/2019 Nigeria Living Standard Survey (NLSS) were extracted from the National Bureau of Statistics (NBS) in collaboration with the World Bank. The Negative Binomial and Generalised Poisson regression models were used to investigate the five demographic variables on the frequency of visit to the community health centres. The performance of the count regression model was assessed using the Chi-square -2log Likelihood Statistic (2logL), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) selection criteria. Results: Findings from the study showed that the type of illness and amount spent has a significantly positive effect on the number of household members' visits to the community health care centres in Nigeria while age, gender, and marital status was discovered to have a negative effect on the number of household members' visits to the community health care centres in Nigeria. Conclusion: The Nigeria Government, health centre management and community healthcare service providers' need to be aware that the amount spent and the nature of illness determines the level of health care services utilisation in the Nigeria community, hence the need for the drastic reduction in charges to encourage a household visit to the community health centres whenever the need arises.

The coronavirus disease 2019 (COVID-19) pandemic was defined by the World Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious disease that threatens public health fatally. In this study, the main aim is to model the impact of various air pollution causes on mortality data due to the COVID-19 pandemic by Generalized Linear Mixed Model (GLMM) approach to make global statistical inferences about 174 WHO member countries as subjects in the six WHO regions. “Total number of deaths by these countries due to the COVID-19 pandemic” until July 27, 2022, is taken as the response variable. The explanatory variables are taken as the WHO regions, the number of deaths from air pollution causes per 100.000 population as “household air pollution from solid fuels,” “ambient particulate matter pollution,” and “ambient ozone pollution.” In this study, Poisson, geometric, and negative binomial (NB) regression models with “country” taken as fixed and random effects, as special cases of GLMM, are fitted to model the response variable in the aspect of the above-mentioned explanatory variables. In the Poisson, geometric, and NB regression models, Iteratively Reweighted Least Squares parameter estimation method with the Fisher-Scoring iterative algorithm under the log-link function as canonical link function is used. In the GLMM approach, Laplace approximation is also used in the prediction of random effects. In this study, six different Poisson, geometric, and NB regression models with fixed and random effects are established for 174 countries all over the world to make global statistical inferences for investigating the relationships between “total number of deaths” by these countries due to the COVID-19 pandemic and “air pollution causes.” As a result of this study, “NB mixed-effects regression model” as the most appropriate GLMM is used to make global statistical inferences about the impact of the various air pollution causes on the mortality data due to the COVID-19 pandemic.

The Coronavirus Disease (COVID-19) pandemic of 2019 has spread to over 200 countries and areas throughout the world. The number of confirmed positive COVID-19 cases in Indonesia is increasing every day, notably in West Java, which has the second largest number of confirmed positive COVID-19 cases in Indonesia, with 141,195 instances. The number of daily Covid-19 cases reveal fluctuations, several seasonal and noise patterns. As the significant severity of this pandemic, estimating the future number of the upper limit of daily Covid-19 cases become a major concern to support information and maintain essential public health services. The estimation of the upper limit is carried out in this study utilizing Vector-AR time series process and examined using Value at Risk based historical simulation. Our simulation studies indicate that Vector-AR and historical simulation provide sharp and well estimation for extreme value with a 99% confidence level, infractions on VaR have a minor violation 0.009.

Count variables are common in behavioral science as an outcome.Count regression models, such as Poisson regression, are recommended when analyzing count variables but can be challenging to interpret given their non-linear functional form.I recommend relative importance analysis as a method to use in interpreting count regression model results.This work extends on past research by describing an approach to determining the importance of independent variables in count regression models using dominance analysis.Herein, dominance analysis is reviewed as a relative importance method, recommend a pseudo-R 2 to use with count regression model-based dominance analysis, and outline the results of an analysis with simulated data that uses the recommended methodology.This work contributes to the literature by extending dominance analysis to count regression models and provides a thoroughly documented example analysis that researchers can use to implement the methodology in their research.

A crash-prediction model for multilane roads

Single-vehicle crashes have been shown to differ from two-plus vehicle crashes. Several studies have discussed the issues with modeling single-vehicle and two-plus vehicle crashes together. However, none of the empirical studies have attempted to study two-vehicle (2V) and multiple-vehicle (MV), that is, three-plus crash groups, to understand their correlation and influencing factors. This study first investigated whether there is a need to develop separate safety performance functions for 2V and MV crashes, in addition to single-vehicle crashes. Then, the correlation and influencing factors of 2V and MV were evaluated. Three regression models—a correlated bivariate negative binomial regression (BNR) model, an uncorrelated bivariate negative binomial regression (NR) model, and a univariate negative binomial regression (UNR) model, were developed and compared. The analysis was based on the 2011–2015 crashes that occurred on I-4 in Florida. Findings indicated that the BNR model significantly outperformed the NR and the UNR models. The model results suggest that disaggregating 2V and MV crashes while allowing correlation between the groups for the latent effects in the model best describes the data. Traffic volume, posted speed limit, and median type were found significant in contributing to the occurrence of both 2V and MV crashes. Additional contributing factors for 2V crashes included the presence of interchange influence area, and for MV crashes, the presence of a vertical curve and the presence of a horizontal curve. Study findings could assist transportation officials in implementing specific safety countermeasures for road segments identified as hotspots for 2V and MV crashes.

Processes that occasionally, but not always, produce extreme values are notoriously difficult to model, as a small number of extreme observations may have a large impact on the results. Existing methods for handling extreme values are often arbitrary and leave researchers without guidance regarding this problem. In this paper, we propose an extreme value and zero-inflated negative binomial (EVZINB) regression model, which allows for separate modeling of extreme and nonextreme observations to solve this problem. The EVZINB model offers an elegant solution to modeling data with extreme values and allows researchers to draw additional inferences about both extreme and nonextreme observations. We illustrate the usefulness of the EVZINB model by replicating a study on the effects of the deployment of UN peacekeepers on one-sided violence against civilians.

This paper introduces a novel modification of the negative binomial distribution, which serves as a generalization encompassing both negative binomial and zero-inflated negative binomial distributions. This innovative distribution offers flexibility by accommodating an arbitrary number of inflation points at various locations. The paper explores key distributional properties associated with this modified distribution. Additionally, this study proposes several estimators designed to obtain estimates for the unknown parameters. Furthermore, the paper introduces a new count regression model that utilizes the modified distribution. To assess the performance of the proposed distribution and the count regression model, a comprehensive Monte Carlo simulation study is conducted. In the final stage of the paper, a real-world dataset is scrutinized to ascertain the superiority of the proposed model. This empirical analysis contributes to validating the practical applicability and effectiveness of the newly introduced distribution in comparison to existing models.

Background: Under-five mortality rate, often known by its acronym U5MR, indicates the probability of dying between birth and five years of age, expressed per 1,000 live births. Globally, 16,000 children under-five still die every day. Especially in Sub-Saharan Africa every 1 child in 12, dying before his or her fifth birthday. This study aims to identify the determinants of under-five mortality among women in child bearing age group of Tach-Armachiho district using count regression models. Methods: For achieving the objective, a two-stage random sampling technique (simple random sampling and systematic random sampling techniques in the first and second stages respectively) was used to select women respondents. The sample survey conducted in Tach-Armachiho district considered a total of 3815 households of women aged 15 to 49 years out of which the information was collected from 446 selected women through interviewer administrated questionnaire. Results: The descriptive statistics result showed that in the district 16.6% of mothers have faced the problem of at least one under-five death. In this study, Poisson regression, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models were applied for data analysis. Each of these count models were compared by different statistical tests. So that, zero-inflated poisson regression model was found to be the best fit for the collected data. Results of the zero-inflated Poisson regression model showed that education of husband, source of water, mother occupation, kebele of mother, prenatal care, place of delivery, place of residence, wealth of house hold, average birth interval and average breast feeding were found to be statistically significant determinants of under-five mortality. Conclusions: In this study, it was found that the factors like average birth interval and average breast feeding were found to be statistically significant factors in both groups (not always zero category and always zero category) with under-five child deathwhereas education of husband, source of water, place of delivery, mother occupation and wealth index of the household have significant effect on under-five mortality under not always zero group. Place of residence, kebele of mother and prenatal care have a significant effect on under-five mortality in TachArmachiho district on inflated group

Nepal has experienced the glacial reduction of under-five mortality in recent years which portends greater challenges to achieve the global target outlined in the Sustainable Development Goals (SDGs) for reducing childhood mortality. To resolve this inevitable adversity and safeguard newborns' lives, additional scientific studies are necessary to plan for evidence-based interventions. This study aimed to develop a suitable statistical model using the associated factors to predict the number of under-five mortality a mother in Nepal encountered throughout her lifetime. The nationally representative Nepal Demographic and Health Survey (NDHS) 2022 data were used to conduct this study. The response variable was the number of under-five mortality a mother has experienced throughout her lifetime. Factors related to different circumstances, including maternal, paternal, socioeconomic, child, environmental, and the utilization of health care services were considered as independent variables. The zero-inflated negative binomial regression (ZINBR) model was fitted as the most plausible model to analyze the number of under-five mortality by its covariates after assessing the different count models including the Poisson regression (PR) model, negative binomial regression (NBR) model, zero-inflated negative binomial regression (ZINBR) model, and hurdle negative binomial regression (HNBR) model. The analysis of the data revealed that 8.6% of the mothers in Nepal have endured at least one under-five mortality. The mean number of deaths before attaining five years was found to be 0.11 (95% CI: 0.10-0.11). The ecological region, smoking habit of mothers, total CEB, source of drinking water, preceding birth interval, and birth order number were obtained as significant covariates of the number of under-five mortality. Mothers from the mountain region (IRR: 1.1756, 95% CI: 1.0095-1.369, p-value = 0.0418) in contrast to hilly region, smoker mothers (IRR: 1.2067, 95% CI: 1.0392-1.4011, p-value = 0.0141) as compared to non-smoker mothers, mothers with total CEB 3 or more (IRR: 1.588, 95% CI: 1.3116-1.9226, p-value = 0.0353) as compared to 2 or less, preceding birth interval less than 24 months (IRR: 1.2061, 95% CI: 1.0788-1.3485, p-value = 0.0018) as compared to 24 months or more, and birth order number 4th or above (IRR: 3.5681, 95% CI: 3.1512-4.0401, p-value < 0.001) as compared to 1st-3rd were associated with the increased incidence of number of under-five mortality. Furthermore, the mothers who drink water from the tube well (IRR: 0.7719, 95% CI: 0.6191-0.9623, p-value = 0.0323) as compared to those who drink water from other sources have a decreased incidence of the number of under-five mortality. The ecological region, mother smokes cigarettes, total CEB, source of drinking water, preceding birth interval, and the birth order number appeared as the important predictors of under-five mortality. The high under-five mortality prevalent in the mountain and terai areas of Nepal presents a significant hurdle in efforts to decrease overall under-five mortality. Thus, improving existing healthcare services, raising the quality of life, and introducing effective health education initiatives are crucial to reducing under-five mortality.

Prelims

Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.

The novel coronavirus causing the COVID-19 pandemic and morbidity and mortality around the world has left unprecedented repercussions on all aspects of life. Estimation of the incidence of coronavirus patients will reimburse planning and preparation for new waves. The link between COVID-19 and air pollution data can allow us to estimate COVID cases. In this study, count data models; Poisson regression and negative binomial regressions models and machine learning methods; support vector regression and random forest are used to investigate the link between new COVID-19 cases and the air pollution data in Bangkok, Thailand. The root means square error and means absolute percentage error are used as the criterions to compare the efficiency from each method. The results from the real data showed that the support vector regression outperforms random forest and also Poisson regression and negative binomial regression models.