Acute respiratory infections and acute bronchial asthma crises addressed by mathematical modeling
1 Department of Forecast, Meteorological Center of Villa Clara, Cuba.
2 Department of Parasitology. Regional High Specialty Hospital (HARE), Dr. Juan Graham Casasús, México.
3 EurAsia Heart Foundation, Switzerland.
4 Department of Hygiene and Epidemiology, Faculty of Health Technology and Nursing (FHTN), University of Medical Sciences of Villa Clara (UMS-VC), Cuba.
5 Department of Nursing, University of Granada, Granada, Spain.
Research Article
Open Access Research Journal of Multidisciplinary Studies, 2024, 07(02), 110–122.
Article DOI: 10.53022/oarjms.2024.7.2.0032
Publication history:
Received on 05 April 2024; revised on 12 May 2024; accepted on 15 May 2024
Abstract:
Acute Respiratory Infections (ARI) and Acute Bronchial Asthma Crises (AABC) are diseases that can be monitored well in advance. The objective of the research consisted in making a prognosis one year in advance of these two variables, obtaining highly significant correlations and small errors. Daily data from the hospital of Sagua La Grande, Villa Clara, Cuba, were used for the period from January 2006 to February 28, 2011. The prediction period or long-term independent sample comprised from March 1, 2010 to March 28, 2011, with a total of 365 cases. A modeling was also performed by first calculating the long-term forecast, where the predicted value was used as a predictor for the short-term model; errors were calculated for the independent sample, obtaining an improvement in the errors in the case of CAAB, where the mean error decreased from 18.7 cases to 1.68. It is concluded that it is possible to predict daily ARI and CAAB cases one year in advance using the Objective Regression Regressive methodology.
Keywords:
Acute Bronchial Asthma Crises; Acute Respiratory Infections; ROR methodology; Mathematical modeling; Villa Clara
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