article · Journal of Statistical Modelling and Analytics
This research evaluates the infection trends of human immunodeficiency virus, tuberculosis, and viral hepatitis among farmers in Benue State using monthly serologically confirmed data collected from January 2010 to December 2022. By applying a first-order Poisson autoregressive model alongside statistical tests, the investigation examined longitudinal patterns and non-Gaussian characteristics across the datasets. The findings reveal that all three infections exhibited upward trajectories across the twelve-year span, reaching their lowest levels in 2010 and peaking between 2017 and 2019. The statistical modelling accounted for roughly 79 to 83 percent of data variability and established significant monthly increases across the diseases, with viral hepatitis growing fastest at 12.08 percent monthly, followed by HIV at 2.25 percent and tuberculosis at 1.22 percent.
Identifying infection trends among agricultural populations is critical for targeted health interventions. By demonstrating that HIV, tuberculosis, and viral hepatitis are consistently climbing over time rather than declining, this statistical modelling highlights an urgent need for public health authorities to strengthen ongoing surveillance and design focused healthcare initiatives aimed at reversing infection trajectories in rural farming communities.
The abstract does not indicate an application pathway or a direct commercial application, presenting early-stage statistical modelling for public health surveillance and policy planning.
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The aim of this work is to model the infection rates of some infectious diseases among farmers in Benue state using Poisson autoregressive model. The study utilizes monthly secondary data on serologically confirmed infection cases of Human Immunodeficiency Virus (HIV), tuberculosis (TB), and viral hepatitis (VHP)—the data span from January 2010 to December 2022. The study employs summary statistics and the Anderson-Darling normality test, time plots, bar graphs, and the Poisson autoregressive model as the principal methods of investigation. Results show that HIV, TB, and VHP have positive and increasing trends over time with non-Gaussian tendencies. All three infections peaked between 2017 and 2019 and had their lowest occurrence in 2010, indicating a potential common relationship among them. The results of the PAR (1) model indicate positive time trends in the HIV, TB, and VHP infection rates, suggesting that infections are increasing over time. The results further revealed a significant monthly increase of 2.25% in HIV, 1.22% in TB, and 12.08% in VHP respectively over the study period. The coefficient of determinations of the models explained 83.3%, 78.8%, and 80.8% of the variability in the HIV, TB, and VHP data indicating better fit for all the fitted models. The positive time trend suggests that monitoring the infection rates over time is crucial. Public health strategies need to account for this increasing trend and aim to reverse it.
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DOI: 10.22452/josma.vol7no1.2
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