Royal Statistical Society Nigeria Local Group Annual Conference Proceedings

HYBRID GWBURR III SURVIVAL MODEL FOR PROSTATE CANCER: A COMPARATIVE MLE-BASED EVALUATION USING CLINICAL DATA FROM NIGERIA

Mohamed Ismail Noraslinda — 2026-04-14

Accurate modeling of prostate cancer survival data requires flexible statistical distributions that
capture complex hazard structures and account for long-term survivors. In this study, we introduce
a hybrid Generalized Weibull–Burr III (GWBurr III) model and compare its performance with
three widely used flexible parametric alternatives: the Generalized Weibull (GW), Generalized
Log-normal (GL), and Generalized Exponential (GE) distributions. Parameter estimation across
all models was performed using Maximum Likelihood Estimation (MLE) to ensure statistical
efficiency and consistency. Model adequacy and comparative performance were assessed using a
comprehensive suite of evaluation metrics, including the Akaike Information Criterion (AIC),
Bayesian Information Criterion (BIC), log-likelihood values, goodness-of-fit test p-values, and
predictive accuracy measured by the area under the ROC curve. To demonstrate real-world
applicability, we analyzed survival data from oncology patients treated at the Ahmadu Bello
University Teaching Hospital (ABUTH), Zaria, Nigeria, a high-burden setting with substantial
prostate cancer mortality. The results show that the hybrid GWBurr III model provided a
substantially improved fit across all evaluation metrics and greater flexibility in modeling diverse
hazard trajectories. These findings highlight the value of hybrid distributional approaches for
oncology survival analysis and reinforce the methodological relevance of the GWBurr III model

for researchers working with complex, heterogeneous clinical data, particularly in resource-
limited and high-burden environments.

Reliability and Uncertainty Quantification for Pipeline Systems Using Exponentiated- Exponentiated Distributions: A Case Study of Nigerian Oil and Gas Pipelines

Bala Maradun Muhammad — 2026-04-21

This paper introduces a comprehensive framework for modelling the reliability of pipeline systems
in Nigeria’s oil and gas industry, utilizing the Exponentiated-Exponentiated (E-E) distribution.
The E-E distribution, a generalization of the Exponentiated Weibull distribution, affords greater
flexibility to model failure rates that may increase, decrease, or remain constant over time — a
characteristic that renders it particularly suitable for capturing the non-monotonic degradation
patterns commonly observed in pipeline systems. To enhance predictive accuracy and uncertainty
quantification, the E-E distribution is integrated with hybrid neural-statistical models that combine
Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and
Convolutional Neural Networks (CNN). The resulting model provides probabilistic predictions for
pipeline lifespan and Remaining Useful Life (RUL), offering appreciable improvements in
predictive maintenance capabilities. Real-world data from Nigeria’s oil and gas pipelines are used
to validate the model, demonstrating superior performance relative to traditional models such as
the Weibull distribution and ARIMA. The study highlights the potential of this framework to
enhance maintenance planning and decision-making, thereby improving pipeline operations across
the Nigerian oil and gas sector.
Keywords: Reliability Modelling, Exponentiated-Exponentiated (E-E) Distribution, Pipeline
Degradation, Hybrid Models, Predictive Maintenance, Oil and Gas Sector

Hybrid Models for Predictive Maintenance in the Oil and Gas Sector

Bala Maradun Muhammad — 2026-04-17

Nigeria’s oil and gas pipeline network is central to the country’s energy economy; however,
recurring failures due to corrosion, mechanical fatigue, and adverse environmental conditions
impose substantial financial and environmental costs on operators. This study proposes a
predictive maintenance framework for pipeline systems in Nigeria’s oil and gas sector,
employing a hybrid model that integrates a knowledge graph with a neural network to forecast
pipeline corrosion rates. The framework combines Graph Neural Networks (GNNs) with
Generalised Additive Models (GAMs) to predict pipeline lifespan and Remaining Useful Life
(RUL), enabling advanced risk modelling alongside interpretable outputs for decision-makers.
The workflow begins with multi-source data inputs—real-time sensor readings, historical
maintenance logs, and spatial network information—processed through specialised neural
network modules and integrated via a GAM layer that produces interpretable risk forecasts,
estimated lifespan values, and associated uncertainty intervals. The framework leverages a
large, representative dataset from multiple Nigerian operators and introduces explicit
uncertainty quantification via Bayesian inference. Validation against real-world failure records
from three regional operators, using a Bayesian statistical framework under the Weibull
distribution, estimated an expected failure rate of 0.008749 failures per hour (SE = 3.74×10−5)
with a 95% Bayesian prediction interval of [0.00866, 0.00893]. The hybrid model achieved an
F1-score of 0.882 on an independent test set, outperforming all standalone architectures and
traditional baselines. These results demonstrate the practical benefits of integrating advanced
machine learning with interpretable statistical methods for pipeline integrity management.
Keywords: Predictive Maintenance; Hybrid Models; Pipeline Lifespan Prediction; Oil and
Gas Sector; Neural Networks; Generalised Additive Models; Remaining Useful Life (RUL);
Operational Efficiency; Cost Reduction; Nigeria.

THE IMPACT OF BIRTH AND DEATH RATES ON NIGERIA’S POPULATION GROWTH: A STATISTICAL PERSPECTIVE.

B. A. Ogunwole — 2026-04-14

This study examined the impact of birth and death rates on population growth in Nigeria from
2010 to 2024, the data obtained were mainly secondary data sourced from World Bank’s World
Development Indicators (WDI). In this paper, the independent variables are: Birth rate and
Death rate where the response variable is Population Growth. The research employed
descriptive statistics and Multiple Regression analysis to explore the dynamics between
fertility, mortality, and demographic expansion. From the regression model formed through
Multiple regression Analysis, (Y = 1.724 + 0.115 X1 - 0. 294 X2): The model revealed that
Nigeria’s population has grown steadily over the past fifteen years, driven primarily by
persistently high birth rates (0.115) and declining death rates (0.294). The regression findings
indicated that birth rate exerts a strong and significant positive effect on population growth,
while death rate has a weak and negative effect on population growth. The model explained
approximately 93.9% of variations in population growth, suggesting that these demographic
factors are critical determinants of Nigeria’s population trends. Also, the test of
multicollinearity, heteroskedasticity, and autocorrelation revealed that the data obtained were
free of multicollinearity, heteroskedasticity, and autocorrelation. Likewise, the adequacy of the
model was confirmed and it was also confirmed that the parameters of the model formed were
significant at a 0.05 level of significance. Based on these findings, the study recommends
strengthening family planning programs, investing in healthcare, and promoting female
education to achieve sustainable population management and balanced economic
development.

A HYBRID MULTINOMIAL LOGISTIC REGRESSION-NEURAL NETWORK APPROACH TO MODELLING NUTRITIONAL STATUS OF NIGERIAN WOMEN OF REPRODUCTIVE AGE

K. A. Abdulazeez — 2026-04-17

This study focuses on modelling the nutritional status of Nigerian women of reproductive age using
a hybrid approach combining multinomial logistic regression model with a neural network
component. A secondary data extracted from Multiple Indicators Cluster Survey (MICS 6: 2021-
2022) report was the source of data for the study while data analysis was carried out with the aid
of STATA Software version 16.0. Study results show that all coefficients of predictor variables
greater than 3.84 of chi-square distribution are statistically significant in the modified model with
respect to nutritional status of WRA. The study also revealed that all predictor variables with p −
value of less than 0.05 significance level are significantly associated with outcome variables
(underweight, overweight and obesity). By integrating multinomial logistic regression for
interpretability with neural network components for enhanced predictive accuracy, this study
provides a robust modelling framework. It was recommended that establishment of a

comprehensive database on women’s health and nutrition in Nigeria, incorporating socio-
demographic, economic and health-related factors after proper data collection is ensured.

Keywords: Nutritional Status, Women of Reproductive Age, Multinomial Logistic Regression,
Neural Network, Nigeria and Maternal Health.

Comparative Assessment of Random Forest and Support Vector Regression Models for Rainfall Time-Series Forecasting and Flood Risk Implications in Maiduguri, Nigeria.

Maina Abdullahi — 2026-04-21

Rainfall variability is a major driver of hydrological extremes, particularly in semi-arid regions,
which are remarkably sensitive to changes in the planetary climate. Precise rainfall forecasting
is an imperative component in flood risk mapping. This study assessed the applicability of
machine learning models to provide rainfall time series forecasting in Maiduguri, in
northeastern Nigeria, using monthly rainfall data from 1981 to 2023, collected from the
Nigerian Meteorological Agency. Two machine learning models, Random Forest (RF) and
Support Vector Regression (SVR), were evaluated using both regression and classification
frameworks. The performance metrics used in assessing the models in forecasting rainfall were
Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE),
and the coefficient of determination (R2), in addition to classification using a confusion matrix
after binning the rainfall into flood-classes based on a specific threshold. Results indicate a
considerable outperformance of the RF algorithm in forecasting rainfall in Maiduguri, Nigeria,
based on its smaller prediction errors (MSE = 1571.61, RMSE = 39.64 mm, MAE = 21.99 mm,
R2 = 0.80) than those of SVR (MSE = 1956.12, RMSE = 44.23 mm, MAE = 24.80 mm, R2 =
0.77). Also, a confusion analysis on the classification results revealed a higher capability on
the part of the RF algorithm in recognizing events of rainfall that are prone to flooding. The
results demonstrate the superior predictive capability of the RF algorithm in forecasting
rainfall, particularly in flood risk mapping in semi-arid areas sensitive to changes in planetary
climate.

Royal Statistical Society Nigeria Local Group 2026 Conference Proceeding
Keywords: Rainfall forecasting; Machine learning; Random Forest; Support Vector Regression; Flood
risk assessment

An Extended Sine Exponential Distribution with Applications to Reliability Engineering

Omeiza Bashiru Sule — 2026-04-22

This paper presents a new extension of the sine exponential distribution. The main
statistical properties of the proposed model are studied, including the probability density
function, cumulative distribution function, reliability functions, moments, quantile
function, and mean residual life function. The density function of the model is unimodal
and has a positively skewed shape. Four different methods are applied to estimate the
unknown parameters of the model. To assess the performance of the new distribution, two
real datasets from the field of reliability engineering are analyzed. The results from the data
applications show that the proposed model provides better fits than other classical
distributions, including the Marshall–Olkin exponential, sine exponential, and exponential
distributions. These results confirm that the model is flexible and suitable for analyzing
lifetime data in engineering and reliability studies.
Keywords: Exponentiated sine exponential distribution; mean residual life function;
parameter estimation; lifetime data; goodness-of-fit

Evaluating the Black-Scholes Model for Pricing Nigerian Exchange Call Options: Addressing Cost of Carry Bias

C. A. Onyegbuchulem — 2026-04-17

This study examines the Black-Scholes model and its limitations in accurately pricing call options
on five Nigerian stocks listed on the Nigerian Exchange. The model shows systematic pricing
errors, with calculated prices differing from market prices. Observations were made that future
prices are traded at a discount to spot prices, causing a negative cost of carry bias. To address this,
we replace the spot price (S) in the Black-Scholes model with the discounted value of the future
price (DVFP), as suggested by Black. Our results show that the original Black-Scholes model
produces significant pricing errors, but the modified model using DVFP shows improved accuracy.

Machine Learning Algorithms for Survival Classification in Hepatocellular Carcinoma: A Comparative Study

Aishat Oyiza Musa — 2026-04-17

This study conducted a comprehensive comparison of three machine learning algorithms,
Random Forest (RF), Support Vector Machine (SVM), and XGBoost, for predicting survival
outcomes in hepatocellular carcinoma (HCC) patients using clinical data from 204 individuals.
The SVM model demonstrated superior performance with an accuracy of 80% (95% CI: 76–
84%), sensitivity of 80%, and specificity of 80%, outperforming both RF (accuracy: 78.3%,
95% CI: 74–82%; sensitivity: 76.7%; specificity: 80%) and XGBoost (accuracy: 76.7%, 95%
CI: 72–81%; sensitivity: 80%; specificity: 73.3%). All models showed strong discriminative
ability, with the area under the receiver operating characteristic curve (AUC-ROC) ranging
from 0.76 to 0.82, confirming their reliability in binary survival classification. Feature
importance analysis revealed that liver function markers (INR, albumin) and tumour
characteristics (size, AFP levels) were consistently ranked as top predictors across all
algorithms. The SVM's radial kernel effectively handled non-linear relationships in the data,
while RF provided valuable interpretability through its feature importance plots. These results
not only validate SVM as the most robust classifier for HCC survival prediction but also
highlight the broader potential of machine learning in enhancing prognostic accuracy beyond
traditional statistical methods. The study provides a framework for implementing these
algorithms in clinical decision support systems, with SVM particularly suited for settings
requiring high-precision classification, though RF remains advantageous when model
interpretability is prioritised. Future research should focus on integrating these machine
learning approaches with molecular data to develop more comprehensive prognostic tools.
Keywords: Hepatocellular carcinoma, Machine learning, Support Vector Machine, Random
Forest, XGBoost, Survival prediction, Clinical decision support.

DUAL METHODOLOGICAL APPROACH OF RANDOM FOREST REGRESSOR AND ORDINARY LEAST SQUARES IN ASSESSING THE IMPACT OF LABOUR FORCE ON GROSS DOMESTIC PRODUCT (GDP) IN NIGERIA.

Bright Ahuraezemma Nnah — 2026-04-22

This study examines the impact of total labour force on Gross Domestic Product (GDP) in
Nigeria using a comparative methodological framework that integrates Ordinary Least Squares
(OLS) regression and Random Forest regression. While classical economic theory assumes a
linear relationship between labour and output, emerging data science techniques suggest that
macroeconomic relationships may exhibit nonlinear dynamics. The objective of this study is to
determine whether the labour to GDP relationship in Nigeria is predominantly linear or
potentially nonlinear. Annual time series data were analyzed using OLS to estimate the linear
effect of labour force on GDP, followed by Random Forest regression to capture possible
nonlinear structures and improve predictive performance. The OLS results reveal a positive and
statistically significant relationship between labour force and GDP, explaining approximately
79.5% of the variation in economic output. However, the Random Forest model outperformed
OLS, explaining 87.3% of GDP variation and significantly reducing prediction errors (RMSE,
MSE, and MAE). Based on comparative model performance, the findings suggest that although
labour force significantly influences GDP, the relationship is not strictly linear but potentially
nonlinear and structurally complex. The study concludes that integrating machine learning
techniques with traditional econometric methods enhances macroeconomic analysis and policy
interpretation. Policy implications emphasize labour productivity, human capital development,
and data-driven economic planning. This research contributes to the growing literature bridging
applied statistics, machine learning, and economic development analysis in developing
economics.
Keywords: Labour force, GDP, Regression, OLS, Random forest, Machine learning

Threshold Negative Binomial Autoregressive Model for Overdispersed Road Traffic Crash Fatalities Counts with Structural Breaks

Oladimeji M. Damilare — 2026-04-22

Road traffic crashes (RTCs) continue to represent one of the most severe public health challenges
in Nigeria, causing high levels of fatalities and injuries each year. Modeling RTC counts is difficult
because the data typically exhibits overdispersion, nonlinear dependence, and structural breaks
arising from interventions, enforcement, or policy changes. This study sets out to develop a
Threshold Negative Binomial Autoregressive model with Structural Breaks (NB-TAR-SB) inorder
to address the challenges of overdispersion, nonlinear dependence, and structural breaks. The study
extends the work of Liu et al., (2019) and Yang et al. (2018) adapting negative binomial
innovations for overdispersed counts, a threshold mechanism for nonlinear regime switching, and
break adjustments through Bai-Perron multiple breakpoint tests. Parameters are estimated using
conditional maximum likelihood, and thresholds and breakpoints are chosen by minimizing profile
likelihood and BIC. The model's theoretical properties depend on nonlinear stationarity and
ergodicity conditions, which guarantee that the estimators are consistent and asymptotically
normal. Diagnostics including the Box-Pierce test, mean squared error (MSE), and root mean
squared error (RMSE) were used to validate model adequacy. Nigeria Annual RTC fatality data
from 1993 to 2024 was used. The preliminary analysis revealed the presence of overdispersion,
while structural break detection revealed policy-driven shifts, particularly in the early 2000s. The

NB-TAR-SB outperformed benchmark models; it achieved a residual independence of 0.826 (Box-
Pierce pvalue), with the lowest forecast errors of (MSE = 0.968, RMSE = 0.984). These results

demonstrate that incorporating both threshold dynamics and structural breaks yields a more
flexible and powerful tool for modeling RTCs. The proposed NB-TAR-SB model thus offers a
novel methodological and applied framework for evaluating interventions and informing effective
road safety policy in Nigeria.
Keyword: Nonlinear Count Time Series, Threshold Autoregressive model, Structural Breaks,
Conditional Maximum Likelihood, Road Traffic Fatalities.

BRIDGING CLASSICAL ECONOMETRICS AND MACHINE LEARNING: A STATISTICAL STRATEGY FOR NIGERIA’S PREDICTIVE AI IN ECONOMIC PLANNING

T. A. Olominu — 2026-04-22

Nigeria’s economic planning faces growing complexity amid digital transformation, climate
volatility, and post pandemic recovery. Classical econometrics valued for its interpretability has
long guided policy, yet its limitations in handling nonlinear data constrain forecasting precision.
This study proposes a hybrid statistical framework that integrates classical econometric models
with machine learning (ML) techniques to improve predictive analytics in Nigeria's
macroeconomic management. Using a Vector Autoregression (VAR) benchmark and data-driven
ML algorithms (Random Forest, Gradient Boosting, and LSTM), the research empirically
demonstrates the forecasting advantage of hybrid models. The approach not only balances
accuracy and transparency but aligns with Nigeria’s broader vision of AI-driven policymaking and
hybrid approach not only enhances prediction accuracy across key indicators but retains the
interpretability essential for evidence-based policymaking. As Nigeria advances its National AI
Strategy and expands digital infrastructure, integrating statistical and computational methods
becomes a strategic imperative. Empowering institutions with hybrid analytics and training
practitioners in both domains will be critical to transforming data into intelligent, adaptive policy
solutions.
Keywords : Digital transformation; Classical econometrics; Machine learning (ML); Vector Auto
regression (VAR); AI-driven policymaking; Digital modernization

DESIGN-BASED APPROACH TO MODELLING OF MALARIA INCIDENCE IN NIGERIA.

Sunday Adebayo Olawore — 2026-04-22

Most available datasets on malaria in Nigeria used in literatures, were limited to small localised
population or small sample size of cohorts. Hence, inference were not scalable to the entire

country. Interest in malaria modelling is on population estimates and in order to improve cost-
effectiveness and representational accuracy in large-scale surveys, complex sampling designs such

as multistage sampling with uneven selection probabilities are often employed. To avoid biased
estimations, specific, weighted analytical techniques are applied to take into consideration
complex data structures. Such techniques, like resampling or Taylor series linearization, give
estimates about the population, instead of the sample as seen in model-based analysis. The
literature on modelling malaria in Nigeria used survey datasets that were collected from complex
survey designs, yet scarce literature accounted for the complex design in their analyses. The
present study examined the effects of complex design in modelling malaria incidence in Nigeria,
using dataset from the 2021 Nigeria Malaria Indicator Survey (NMIS). The effects of sampling
weights and survey information in the analysis of malaria datasets were examined and compared
with model-based approach. The analysis capturing the survey design produced a different result
from the model-based approach, effectively showing the difference in the sample and population
estimates, for which the latter is the object of surveys such as the 2021 NMIS. Hence, this paper
adds to the literature on country-wise malaria incidence analysis and how survey design is
incorporated in the analysis to meet the objective of such survey.
Keywords: complex design; design-based; malaria incidence; model-based; weights

DIAGNOSTIC TESTS FOR QUANTILE REGRESSION WITH WEIBULL RESPONSE VARIABLES

Okey Onyegbuchulem Besta — 2026-04-22

Quantile regression is a valuable tool for modeling the relationship between covariates and the
conditional quantiles of a response variable. However, the validity of quantile regression
inferences relies heavily on the accuracy of the model assumptions. This study focuses on
developing diagnostic tests for quantile regression models with Weibull-distributed response
variables. We propose a series of diagnostic tests based on cumulative residual processes to assess
the adequacy of the quantile regression model. The proposed tests are evaluated through simulation
studies, and their performance is compared to existing methods. The results demonstrate the
importance of checking model assumptions in quantile regression analysis, particularly when
dealing with Weibull response variables. The proposed diagnostic tests are applied to a real dataset
to illustrate their practical utility.
Keywords: Quantile regression, Weibull distribution, diagnostic tests, cumulative residual
processes, model checking.

A DUAL EMPIRICAL–SIMULATION FRAMEWORK FOR ROBUST PREDICTIVE CLASSIFICATION IN MULTIVARIATE DISCRIMINANT ANALYSIS

Maryjane Nneoma Chika — 2026-04-22

The growing reliance on data-driven decision-making has intensified the demand for statistical
models that combine predictive accuracy with interpretability. Traditional Multivariate
Discriminant Analysis (MDA), notably Linear Discriminant Analysis (LDA) and Quadratic
Discriminant Analysis (QDA), offers a transparent framework for classification but is limited
by its performance on high-dimensional, imbalanced, and non-normal datasets. This study
aimed to evaluate the performance, robustness, and interpretability of classical, regularized,
and robust MDA methods through an integrated empirical–simulation framework. Empirical
analyses were conducted on two secondary health-related datasets, the Centres for Disease
Control and Prevention (CDC), Heart Disease Indicators Dataset (10,000 records) and the
Nigeria Demographic and Health Survey (NDHS) Children Anaemia Dataset (3,856 cases),
purposively sampled, while 1,000 Monte Carlo simulations were performed under varying
sample sizes, covariance structures, and contamination levels. Models were evaluated with
accuracy, precision, recall, F1-score, and Area Under Curve metrics. Findings revealed that
LDA was computationally efficient but had poor recall on imbalanced data (27%), whereas
QDA slightly improved recall (37%) but was unstable under heteroscedasticity. Robust MDA
variants employing Minimum Covariance Determinant (MCD) estimators demonstrated
greater resilience to outliers and violations of normality assumptions, maintaining predictive
stability. The study concludes that robust and regularized MDA models provide dependable
and transparent alternatives for decision-making in imperfect data environments and
recommends simulation-based validation protocols to enhance model reliability across diverse
data conditions.
Keywords: Multivariate Discriminant Analysis, Linear Discriminant Analysis, Quadratic
Discriminant Analysis, Robust Statistics, Monte Carlo Simulation.

HARNESSING GRASSROOT WEARABLE HEALTH DATA ANALYTICS FOR STRENGTHENING NATIONAL HEALTH SYSTEM

Samson Adeola Ajala — 2026-04-17

This paper explores how wearable health data, or devices such as smart-watches, that can be
strengthened on a grassroots scale, can support the national health system of Nigeria. Disease
surveillance, resource allocation and early warning programs could be improved by analyzing
real time measures like heart rate and sleep patterns. An investigation with primary data of a
survey of 200 people at Osun State revealed that the majority of 72% of respondents believe that
wearable can drastically enhance local healthcare monitoring. Nevertheless, affordability and
digital literacy were named as some of the biggest obstacles by 65%. The statistical results
revealed that there was a significant positive correlation (r = 0.78) between adoption of wearable
data and effective community-based healthcare provision. The paper concludes that the
combination of this information can transform preventive care and decrease health disparities on
the national health care. Some of the recommendations to be adopted are government allocation
towards digital infrastructure, subsidization of devices and creation of training programs to
enhance data literacy among healthcare workers and communities.

ROBUST BAYESIAN HIERARCHICAL ESTIMATION OF THE FINITE POPULATION MEAN UNDER UNEQUAL CLUSTER SAMPLING

Nkan Iso-Osene Doris — 2026-04-22

Cluster sampling is widely used for studying populations that are naturally grouped, but classical
estimators can perform poorly when observations contain outliers. In this study, we propose a robust
Bayesian hierarchical estimator for estimating the finite population mean under unequal cluster
sampling. We show that the proposed estimator has a bounded influence function and stable
asymptotic mean-squared error (MSE) under ε-contamination, and we evaluated the performance
of the proposed estimator using both simulated and real datasets. Simulation results show that the
proposed estimator retains efficiency under correct model specification while significantly
improving robustness in contaminated settings, reducing point-estimation MSE by up to 40% and
posterior predictive error by up to 50%. An application to ecological parasite-load data further
demonstrates improved predictive stability and moderated mean estimates relative to the Gaussian
hierarchical model.
Key words: Cluster sampling; Bayesian hierarchical models; contamination; outliers; Student-t
distribution

MODELING MALARIA TRANSMISSION TO OPTIMIZE TREATMENT AND CONTROL STRATEGIES IN RIVERS STATE, NIGERIA

Taiwo Sanusi-Akintunde Aishat — 2026-04-22

Nigeria accounts for 31.3% of global malaria deaths and 26.6% of cases, with low insecticide-
treated net (ITN) coverage, limited artemisinin-based combination therapy (ACT) use due to

financial barriers, and poor preventive chemotherapy uptake. Rivers State, with its high burden
and intervention gaps, is ideal for evaluating transmission dynamics and control.
This study developed a deterministic compartmental model comprises of humans: Susceptible,
Infected, Treated, Recovered (SITR) and vectors: Susceptible, Infected (SI), using parameters
from national reports and literature. Ordinary differential equations assessed intervention impacts
via sensitivity and scenario analyses on the basic (R0) and effective (Re) reproduction number.
Under the current coverage(10% ACT treatment, 15% ITN coverage, 32% IPTp-SP uptake) Re
was estimated at1.90. Further scenario analysis showed that increasing ITN and ACT treatment

coverage to 40% reduced Re to 0.71. Sensitivity analysis identified transmission (0.5) and cost-
driven treatment avoidance (0.08) as key drivers

The findings suggest that scaling ITN and ACT coverage to about 40% could lower the effective
reproduction number towards or below one, indicating reduced malaria transmission.

Policymakers may therefore prioritize expanding affordable access to these interventions in high-
burden settings, while noting that simplifying assumptions in the model limit generalizability.

Keywords: Malaria modeling, SITR–SI model, IPTp-SP, ITN effectiveness, ACT treatment.

ON THE EFFECT OF CONSUMER PRICE INDEX INFLATION ON FOOD SECURITY AND PUBLIC HEALTH OUTCOMES IN NIGERIA: A TIME SERIES APPROACH

O. A. Oyegoke — 2026-04-22

Persistent inflation remains a major macroeconomic challenge in Nigeria, with rising consumer
prices—particularly food prices—posing serious threats to food security and public health
outcomes. This study investigates the dynamic behavior of Nigeria’s Consumer Price Index (CPI)
and examines its implications for food security and public health using a time series approach.
Monthly CPI data were analyzed within the Box–Jenkins framework, employing Autoregressive
Integrated Moving Average (ARIMA) models. Stationarity was assessed using Augmented
Dickey–Fuller (ADF) and KPSS tests, while model adequacy was evaluated through diagnostic
checks including residual autocorrelation, heteroscedasticity, and normality tests. The results
indicate that the CPI series is integrated of order one, I(1), and the ARIMA(3,1,2) model provides
the best fit based on information criteria. Forecasts from the selected model suggest a mild
deceleration in CPI inflation over the forecast horizon, although uncertainty widens over time.
From a food security and public health perspective, persistent CPI inflation—especially food
inflation—erodes household purchasing power, reduces dietary quality, and heightens risks of
malnutrition, maternal and child morbidity, and social vulnerability. The study underscores that
inflation is not merely a macroeconomic indicator but a critical determinant of population health
and human security. It recommends strengthened inflation monitoring, improved agricultural
productivity, and integrated economic–health policy responses to mitigate the adverse welfare
effects of rising prices in Nigeria.
Keywords: Inflation, Consumer Price Index, Food Security, Public Health, Time Series

MODELLING AND FORECASTING MONTHLY MAXIMUM TEMPERATURE IN OGBOMOSO, SOUTH-WEST NIGERIA

O. O. Akinade — 2026-04-22

Temperature variation may have long-lasting effects on environment and economy. This study
examined the maximum temperature in South-West Nigeria using monthly climate variability data
obtained from the Nigeria Meteorological Agency (NiMET) for the period 2013-2023. The data
were statistically analyzed to identify the patterns in maximum temperature. The time plot revealed
a pattern of peaks and troughs, indicates strong seasonality in temperature variations over the study
period. An appropriate Seasonal Autoregressive Integrated Moving Average SARIMA model was
fitted and used to forecast maximum temperature. The forecast results suggest a gradual but steady
rise in maximum temperature, highlighting a potential warming trend in the study area. These

findings provide valuable insights for climate monitoring and environmental planning in South-
West Nigeria.

Keywords: Maximum Temperature; Climate Variability; SARIMA model; Climate Change