Research Article
Statistical Analysis of Displacement in Blue Nile State:
A Secondary-Data Analytical Study of Displacement and Humanitarian Indicators
Alshaikh Ahmed Shokeralla*
,
Mohammed Alshaib,
Waleid Alnour Daif
Issue:
Volume 12, Issue 2, June 2026
Pages:
24-35
Received:
20 June 2026
Accepted:
13 July 2026
Published:
28 July 2026
Abstract: Background: Internal displacement in Sudan has escalated dramatically following the April 2023 conflict onset, creating severe humanitarian needs in peripheral states such as Blue Nile State. Objectives: To provide a statistically coherent description of displacement dynamics and humanitarian indicators in Blue Nile State using publicly available secondary data. Methods: A descriptive analytical design was applied using aggregate secondary data from IOM DTM, UNICEF and OCHA. Key statistical procedures included percentage change calculations, annualised growth rate estimation, average flow rate computation, Herfindahl-Hirschman Index (HHI) for locality concentration, and cross-indicator comparison with national benchmarks. All equations are numbered and presented explicitly. Results: The IDP stock increased from 81,640 (April 2023) to 361,000 (March 2025), representing a 342.2% cumulative increase and an annualised growth rate of 117.2%. New displacement flows in early 2026 showed acceleration: the average daily rate increased from 341.7 to 430.5 individuals/day (rate ratio = 1.26). Displacement was geographically concentrated in three localities (HHI = 0.382). Humanitarian indicators reveal critical gaps, with 40% stunting, 57.7% school exclusion, and 57% lacking basic sanitation. Conclusions: Blue Nile State faces a severe and accelerating displacement crisis accompanied by multisectoral humanitarian deficits. The study highlights the need for harmonised monthly locality-level monitoring and multisectoral response prioritisation.
Abstract: Background: Internal displacement in Sudan has escalated dramatically following the April 2023 conflict onset, creating severe humanitarian needs in peripheral states such as Blue Nile State. Objectives: To provide a statistically coherent description of displacement dynamics and humanitarian indicators in Blue Nile State using publicly available s...
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Research Article
Exponential-Gamma Exponential (EGE) Distribution and Its Statistical Properties
Odukoya Elijah Ayooluwa*,
Ilesanmi Anthony Opeyemi,
Aladejana Ayosunkanmi Emmanuel
Issue:
Volume 12, Issue 2, June 2026
Pages:
36-45
Received:
22 June 2026
Accepted:
11 July 2026
Published:
24 August 2026
DOI:
10.11648/j.ijsda.20261202.12
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Abstract: Researchers have developed various models to analyze and assess lifetime data sets across different fields. The need for more applicable and flexible models is highlighted, and statistical distributions emerge as the key to understanding and interpreting these real-world events. However, in many practical situations, these standard distributions do not adequately fit real-life data. As a result, there is a need to develop and modify distributions to increase their flexibility. Statisticians have responded to this need by proposing new families of distributions that extend well-known standard distributions by adding one or more parameters. In this study, a new continuous probability distribution Exponential-Gamma Exponential (EGE) distribution was introduced and investigated. The model was constructed by compounding the exponential baseline distribution with a gamma-generated transformation in order to improve flexibility in modelling complex lifetime and survival data. Many real-world datasets in reliability engineering, biomedical sciences, finance, and hydrology exhibit skewness, heavy tails, and non-monotonic hazard rate behaviours, which are not adequately captured by classical exponential models. The proposed distribution incorporates additional shape parameters that allow greater control over distributional form and hazard behaviour. The statistical properties of the EGE distribution were derived, including the probability density function, cumulative distribution function, survival function, hazard rate function, quantile function, raw moments, mean, variance, coefficient of variation, skewness, kurtosis, moment generating function, characteristic function, Rényi entropy, and maximum likelihood estimation was used to estimate the parameter of the new distribution. The results show that the proposed model is flexible and extends the classical exponential distribution while maintaining analytical tractability. The model can be useful for analyzing lifetime and reliability data, especially in situations involving varying hazard rates and complex data structures.
Abstract: Researchers have developed various models to analyze and assess lifetime data sets across different fields. The need for more applicable and flexible models is highlighted, and statistical distributions emerge as the key to understanding and interpreting these real-world events. However, in many practical situations, these standard distributions do...
Show More
