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

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

Lehmann Type II-Lomax Distribution: Properties and Application to Real Data Set

Communication in Physical Sciences, 2023, 9(1):63 - 72 Alhaji Modu Isa, Aishatu Kaigama, Akeem Ajibola Adepoju and Sule Omeiza Bashiru Received: 15 January 2023/Accepted 25 March 2023 In this paper, we proposed a new compound probability distribution known as the Lehmann Type II-Lomax (LTL) distribution generated from the Lehmann Type II Family of distribution and derived some of its mathematical properties such as entropy, moments, moment-generating functions, and order statistics. Parameters of the new distribution were estimated using a maximum likelihood estimator. One dataset was used to illustrate the usefulness of the model. The newly developed model outperformed its competitor