tetano
Editor, Senior Moderator
Disaster Med Public Health Prep
. 2020 Sep 9;1-20.
doi: 10.1017/dmp.2020.323. Online ahead of print.
MODELLING OF COVID-19 OUTBREAK INDICATORS IN CHINA BETWEEN JANUARY AND JUNE
Senol Celik[SUP] 1 [/SUP], Handan Ankarali[SUP] 2 [/SUP], Ozge Pasin[SUP] 3 [/SUP]
Affiliations
Abstract
Objective: The objective of this study is to compare the various nonlinear and time series models in describing the course of the COVID-19 outbreak in China. To this aim, we focus on two indicators including the number of total cases diagnosed with the disease, and the death toll.
Methods: The data used for this study isbased on the reports of China between January 22 - June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model.
Results: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths.Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85589 and 4639, respectively.
Conclusion: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.
Keywords: ARIMA; Coronavirus; Exponential Smoothing; Nonlinear model.
. 2020 Sep 9;1-20.
doi: 10.1017/dmp.2020.323. Online ahead of print.
MODELLING OF COVID-19 OUTBREAK INDICATORS IN CHINA BETWEEN JANUARY AND JUNE
Senol Celik[SUP] 1 [/SUP], Handan Ankarali[SUP] 2 [/SUP], Ozge Pasin[SUP] 3 [/SUP]
Affiliations
- PMID: 32900401
- DOI: 10.1017/dmp.2020.323
Abstract
Objective: The objective of this study is to compare the various nonlinear and time series models in describing the course of the COVID-19 outbreak in China. To this aim, we focus on two indicators including the number of total cases diagnosed with the disease, and the death toll.
Methods: The data used for this study isbased on the reports of China between January 22 - June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model.
Results: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths.Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85589 and 4639, respectively.
Conclusion: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.
Keywords: ARIMA; Coronavirus; Exponential Smoothing; Nonlinear model.