Background: Coronavirus disease 2019 (COVID-19) is
one of the updated challenges facing the whole world.
Objective: To identify the characteristics risk factors that
present in humans to be more liable to get an infection
than others.
Methods: A cross-sectional study was conducted for
positively confirmed 35 patients with polymerase chain
reaction in Wasit province at AL-Zahraa Teaching
Hospital from the period of March 13th till April 20th. All
of them full a questionnaire regarded by risk factors and
other comorbidities. Data were analyzed by SPSS version
23 using frequency tables and percentage. For numerical
data, the median, and interquartile range (IQR) were used.
Differences between categorical groups were performed by
fissure exact test.
Results: The median age of the patients was 43 years old
and interquartile range (25-56 years). Majority of the
patients were female (60%) and (51%) of them were from
the same region (AL-ezza). The dominant blood group
among patients was (O) (40%). About 11.4% of patients
had a travel history especially to Islamic Republic of Iran,
while (77.1%) had contact with positive cases. The highest
percentage of comorbidities among patients was
hypertension (40%), and the most presenting symptoms
were cough and fever. About 51% of patients were with
mild symptoms. Diabetes, coronary heart diseases, and
chronic renal diseases were significantly related to disease
severity (P-value=0.02, 0.001, 0.01 respectively).
Conclusion: Being a female, overweight or obese, and
with blood group (O) are the major risk factors among
patients. Comorbidities can play an important role in the
severity of disease especially hypertension, diabetes,
coronary heart diseases, and chronic renal diseases.
This paper investigates an effective computational method (ECM) based on the standard polynomials used to solve some nonlinear initial and boundary value problems appeared in engineering and applied sciences. Moreover, the effective computational methods in this paper were improved by suitable orthogonal base functions, especially the Chebyshev, Bernoulli, and Laguerre polynomials, to obtain novel approximate solutions for some nonlinear problems. These base functions enable the nonlinear problem to be effectively converted into a nonlinear algebraic system of equations, which are then solved using Mathematica®12. The improved effective computational methods (I-ECMs) have been implemented to solve three applications involving nonli
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