TIME SERIES ANALYSIS FOR THE TREATMENT OF TYPHOID (ENTERIC) FEVER IN MAIDUGURI: USING ARIMA MODEL

This paper titled time series analysis for treatment of typhoid (etteric) fever in Maiduguri; using Arima model, the paper has noticed that the data displayed both a trend and seasonality; the trend indicates a reduction in the most recent year's data. Using autocorrelation and partial autocorrelation function (ACF and PACF), the data can also be utilized to determine the model's order. The model obtained is subjected to model diagnostics in order to determine its efficiency and the model is used to forecast the typhoid fever. From the forecast graph shows that there may be a decrease in future years due to the pattern of the series the impression, we obtain from the graph is that predicted series seems to be trend upward and then downward. ARIMA (1,0,0) has the minimum value of AIC therefore it found to be best model. Hence, the model to fit the typhoid fever based on diplomatic test, which is LJung Box test from the family of Box Janks procedure, then our P-value is less than 0.05 level of significant, we reject the null hypothesis and conclude that the typhoid fever is statistically significant at 5% level of significant. Forecast of typhoid fever from February to December 2025 we also conclude that the typhoid fever is stable. Improve Sanitation and Hygiene: Implement measures to improve sanitation and hygiene practices, especially in areas with high disease prevalence. This may include promoting access to clean water, proper waste management, and hygiene education campaigns

Time Series Analysis for The Treatment of Typhoid (Enteric) Fever in Maiduguri: Using Arima Model

This research employs the ARIMA model to conduct a thorough time series analysis on the treatment of typhoid (enteric) fever in Maiduguri. The study reveals the presence of both trend and seasonality in the data, with the trend indicating a recent reduction in the recorded data. Using autocorrelation and partial autocorrelation function (ACF and PACF), the data can also be utilized to determine the model's order. The model obtained is subjected to model diagnostics to determine its efficiency and the model is used to forecast the typhoid fever. From the forecast graph shows that there may be a decrease in future years due to the pattern of the series the impression, we obtain from the graph is that predicted series seems to be trend upward and then downward. ARIMA (1,0,0) has the minimum value of AIC therefore it found to be best model. Hence, the model to fit the typhoid fever based on diplomatic test, which is LJung Box test from the family of Box Janks procedure, then our P-value is less than 0.05 level of significant, we reject the null hypothesis and conclude that the typhoid fever is statistically significant at 5% level of significant. Forecast of typhoid fever from February to December 2025 we also conclude that the typhoid fever is stable. Improve Sanitation and Hygiene: Implement measures to improve sanitation and hygiene practices, especially in areas with high disease prevalence. This may include promoting access to clean water, proper waste management, and hygiene education campaigns.Keyword: ARIMA Model, Typhoid fever, Box janks, WHO, Model diagnostic

A Bayesian estimation on right censored survival data with mixture and non-mixture cured fraction model based on beta-Weibull distribution

Models for survival data that includes the proportion of individuals who are not subject to the event under study are known as a cure fraction models or simply called long-term survival models. The two most common models used to estimate the cure fraction are the mixture model and the non-mixture model. in this work, we present mixture and the non-mixture cure fraction models for survival data based on the beta-Weibull distribution. This four parameter distribution has been proposed as an alternative extension of the Weibull distribution in the analysis of lifetime data. This approach allows the inclusion of covariates in the models, where the estimation of the parameters was obtained under a Bayesian approach using Gibbs sampling methods

Cure models based on Weibull distribution with and without covariates using right censored data

In this paper we use a methodology based on the Weibull distributions covariates in the presence of cure fraction models, censored data and covariates. Objective: The objective of the study is to check the performance of mixture and non-mixture cure models based on LPML. Methods/Analysis: Two models were explored here in which are the mixture and non-mixture cure fraction models. Inferences for the models are obtained under the Bayesian approach via Markov Chain Monte Carlo (MCMC) where the posterior estimates were obtained by using Metropolis-Hastings sampling methods in the presence of covariates and without covariates considering a real life time dataset and comparing the two cure models using the Log Pseudo Maximum Likelihood estimates (LPML) and some related special cases of the distribution. Findings/ Conclusion: We observed that the Weibull distribution has the least LPML value while its special cases where the two models are quite similar having the highest values on the other hand, the Mixture fits better than the non-mixture having the highest (LPML) based on the results obtain from all the models suggesting that the standard parametric cure (mixture) model fits the AML data which shows a great indication of similarity with the covariates and flexibility of the models

A bayesian via laplace approximation on log-gamma model with censored data

Log-gamma distribution is the extension of gamma distribution which is more flexible, versatile and provides a great fit to some skewed and censored data. Problem/Objective: In this paper we introduce a solution to closed forms of its survival function of the model which shows the suitability and flexibility towards modelling real life data. Methods/Analysis: Alternatively, Bayesian estimation by MCMC simulation using the Random-walk Metropolis algorithm was applied, using AIC and BIC comparison makes it the smallest and great choice for fitting the survival models and simulations by Markov Chain Monte Carlo Methods. Findings/Conclusion: It shows that this procedure and methods are better option in modelling Bayesian regression and survival/reliability analysis integrations in applied statistics, which based on the comparison criterion log-gamma model have the least values. However, the results of the censored data have been clarified with the simulation results

Growth Performance and Carcass Merit of Japanese Quails (Coturnix japonica) Fed with Sorghum as an Energy Source Substitute for Maize in North Western Nigeria

A feeding trial was conducted to determine the growth performance and carcass characteristics of Japanese quails fed diets containing sorghum. A total of ninety of one-week old Japanese quail chicks were used for the experiment. Five diets were formulated in which sorghum was included at graded levels 0, 15, 30, 45 and 60% dietary levels designated as treatment 1, 2, 3, 4 and 5 respectively. The experimental period was five weeks when the birds reach six weeks of age.  The results of the growth performance showed significant differences (P<0.05) in the final body weight (161.70 – 180.10 g) and daily feed intake (21.34–25.12 g). Significant (P<0.05) differences was also observed in all carcass parameters measured except for dressing percentage. Liver weight and large intestine with T5 (65% total replacement) recorded the highest means value of 6.00 g and 6.66 g. Non-significant high values of feed conversion ratio were also obtained among the treatments. Treatment 1 (0%) and Treatment 5 (60%) showed better results in all parameters measured compared to other treatments. In conclusions, sorghum grain can play a significant role in formulating quail feed and can completely (60%) be used to replace maize without affecting the performance and carcass yield of the quails

Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates

In the last few years, many attempts have been made to define new models that extend well known Beta Kumaraswamy-G (BK-G) and Kumaraswamy-G (K-G) families of distribution that can provide a greater flexibility in modeling real-life data. Further-more, one of the weakness of Beta distribution is that it is not fairly tractable and in a particular case, its cumulative distribution function (CDF) involves the incomplete beta function ratio. Kumaraswamy distribution has a closed form of probability density function (PDF) and CDF, which makes it tractable. This motivated us to extend the BK-G family which has four shape parameters and K-G family which has two shape parameters. The Burr Type X (BX) distribution was chosen because of its PDF and CDF are of a closed form. Asa consequence of this, it can be used suitably for censored data. Based on the problem stated, we develop a new model using the method of confounding the existing parametric models by adopting the BX model as the baseline distribution. This proposed model is called Beta Kumaraswamy Burr-Type X (BKBX) distribution with six parameters. Due to the intricacy and non-close form solution of the BKBX model, we provide the modified better version of the model by reducing its parameters to four and called this as Kumaraswamy Burr-Type X (KBX) distribution. In this thesis, we considered two methods via the classical maximum likelihood estimation (MLE) and the Bayes estimation using the Gibbs sampling (G-S) algorithm to estimate the parameters of BKBX, KBX and Beta-Weibull (BWB) models. We obtained the posterior summaries considering the cure models with covariates by the method of Gibbs sampling of the Markov Chain Monte Carlo (MCMC). Series of simulation studies were conducted to evaluate the performance of the proposed estimation approaches. The two common types of cure fraction models, namely; mixture and the non-mixture models for the survival data based on the BKBX, KBX and BWB distributions were provided. Hence, to obtain effective results for the cure models with censored data and covariates, the estimation of the parameters was done under a Bayesian approach using G-S method. The comparison was done between the BKBX, KBX and BWB models to validate the usefulness of the modified distributions. The KBX signifies and proves to be less time consuming, have a close form solution of both its survival and hazard function unlike BKBX and BWB models and yet have similar features as the Kumaraswamy Weibull (KWB) distribution. Based on the results of the cure models, with or without covariates for the censored dataset used at all levels of comparison, the KBX model okto be the best choice. The application of real datasets which are uncensored and not cure model of right skewed, left skewed and approximate symmetry were considered. Based on the results obtained, the KBX distribution has provided a better fit compared to the BKBX, the baseline BX, and non-nested models based on the model selection criteria using the MLE

A Bayesian Parametric Estimation of Beta Kumaraswamy Burr Type X (Beta Kum-BX) Distribution Based on Cure Models with Covariates

In statistical models for censored survival data which includes a proportion of individuals who are not subject to the event of interest under study are known as the long-term survival cured models. It has two most adopted and common models used in estimating the cure fraction namely: the mixture (standard cure) and the non-mixture models. In this research work, we introduce a Bayesian approach using the two models for survival data based on the beta Kumaraswamy Burr Type X distribution with six parameters and compared with two existing models: beta-Weibull and beta-generalized exponential distributions in analyzing a real-life dataset. The proposed approach allows the inclusion of covariates in the model. The parameter estimation was obtained by maximum likelihood and Bayesian analysis methods. The win Bugs and MCMC pack library in R softwares were employed for the Gibbs sampling algorithm in other to obtain the posterior summaries of interest and also the trace plots by the applying of real data sets and a simulation study was done based on cure models to compare the performance of both models relating to actual sense of motivation and novelty which clarifies the usefulness of the proposed methodologies