Wind Speed Modeling for Informed Asthma Management in Maiduguri, Nigeria
DOI:
https://doi.org/10.62054/ijdm/0201.15Trefwoorden:
Air pollutants, asthma management, extreme weather, Maiduguri, Weibull model, wind speed dataSamenvatting
This study investigates wind speed modeling in Maiduguri, Nigeria, with the objective of eliciting informed asthma management. Six probability distributions—Weibull, Gumbel, Logistic, Lognormal, Normal, and Gamma—were fitted to monthly wind speed data using the fitdistrplus package in R. Goodness-of-fit was assessed with the Anderson-Darling statistic, while model selection was done using AIC, BIC, and absolute log-likelihood values. The Weibull distribution emerged as the most robust model for 10 months of the year, with Normal and Gamma distributions performing best in April and September, respectively. Results indicated a negative correlation between wind speed and asthma prevalence, r= -0.502,p-value=0.09, emphasizing the influence of pollutants and seasonal conditions on asthma triggers. Findings suggest tailored management strategies, such as protective gear and facemasks during dusty periods and warm clothing during the cold season, to mitigate asthma attacks.
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