Statistical modeling of the novel COVID-19 epidemic in Iraq
نویسندگان
چکیده
Abstract Objectives This study aimed to apply three of the most important nonlinear growth models (Gompertz, Richards, and Weibull) daily cumulative number COVID-19 cases in Iraq during period from 13th March, 2020 22nd July, 2020. Methods Using least squares method, were estimated addition calculating some related measures this using “nonlinear regression” tool available Minitab-17, initial values parameters deduced transformation simple linear regression equation. Comparison these was made statistics ( F -test, AIC, BIC, AIC c WIC). Results The results indicate that Weibull model is best adequate for studying according criteria such as having highest lowest RMSE, bias, MAE, WIC with no any violations assumptions model’s residuals (independent, normal distribution homogeneity variance). overall test tests showed statistically significant describing data. Conclusions From predictions, confirmed novel coronavirus will increase by a range 101,396 (95% PI: 99,989 102,923) 114,907 112,251 117,566) next 24 days (23rd July 15th August 15, 2020). inflection points curve, peak date when rate be maximum, 7th 2020, at time become 67,338. calculated obtained model.
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ژورنال
عنوان ژورنال: Epidemiologic methods
سال: 2021
ISSN: ['2194-9263', '2161-962X']
DOI: https://doi.org/10.1515/em-2020-0025