Wind Speed Modeling for Informed Asthma Management in Maiduguri, Nigeria
DOI:
https://doi.org/10.62054/ijdm/0201.15Ключевые слова:
Air pollutants, asthma management, extreme weather, Maiduguri, Weibull model, wind speed dataАннотация
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.
Библиографические ссылки
Ajay, K. (2020) Thunderstorm Asthma: Looking Back and Looking Forward, Journal of Asthma and Allergy, 2020:13 293–299, http://doi.org/10.2147/JAA.S265697
Akano, R. O. and Nwosu, S. C. (2001) Analysis of prevalence data on asthma patients: A case study of General Hospital, Hong Local Government Area of Adamawa State, Annals of Borno, volume 17
Daniel H., Hervàs-Masip J., Utrera J. F. and Hervàs J. A. (2013), Can Meteorological Factors Forecast Asthma Exacerbations in Paediatric Population? Article in Allergologia et Immunopathologia, 43(1), October 2013, http://DOI:10.1016/j.aller2013.07.004
Dharmage1, S. C., Perret, J. L. and Custovic, A. (2019) Epidemiology of Asthma in Children and Adults, Front. Pediatr. 7:246. http://doi:10.3389/fped.2019.00246
Gongsin I. E. and Saporu F. W. O. (2016), On the Estimation of Parameters in a Weibull Wind Model and its Application to Wind Speed Data from Maiduguri, Borno, State, Nigeria, Mathematical Modeling and Theory, Vol. 6, No. 7, pp 62 – 76. Available @ www.iiste.org
Laaidi, M (2001) Forecasting the start of the pollen season of Poaceae evaluation of some methods based on meteorological factors. Int J Biometeorol 45(1):1-7
Nwosu S. C., Saporu F. W. O. and Akano R. O. (2012), A Statistical Analysis of the Prevalence Data of Asthma in Borno State of Nigeria, Journal of Mathematical Sciences, vol. 23, No 3 (2012), 303 – 312.
Price, D., Hughes, K. M., Thien, F. and Suphioglu, C. (2020) Epidemic Thunderstorm Asthma: Lessons Learned from the Storm Down-Under, J ALLERGY CLIN IMMUNOL PRACT, https://doi.org/10.1016/j.jaip.2020.10.022
Stephen S. W. (1996), Asthma Exacerbations during Santa Ana Winds in Southern California, Wilderness and Environmental Medicine, 4, 304 – 311, © 1996 Chapman and Hall
Thunderstorm Asthma: Predicting Outbreaks; Paradigm Shifter, April 20, 2017. Available @
www.immunitytales.com
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