Journal of Epidemiology and Global Health

Volume 11, Issue 2, June 2021, Pages 200 - 207

Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States

Authors
Olusola S. Makinde1, Abiodun M. Adeola2, 3, *, , Gbenga J. Abiodun4, Olubukola O. Olusola-Makinde5, Aceves Alejandro4
1Department of Statistics, Federal University of Technology, P.M.B. 704, Akure, Nigeria
2Research and Development Department, South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
3School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
4Department of Mathematics, Southern Methodist University, Dallas, TX 75275, USA
5Department of Microbiology, Federal University of Technology, P.M.B. 704, Akure, Nigeria

Centre for Environmental Studies, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0002, South Africa

*Corresponding author. Email: abiodun.adeola@weathersa.co.za
Corresponding Author
Abiodun M. Adeola
Received 25 August 2020, Accepted 2 January 2021, Available Online 22 February 2021.
DOI
10.2991/jegh.k.210215.001How to use a DOI?
Keywords
COVID-19; USA; INGARCH; negative binomial; ARIMA; lockdown policy
Abstract

The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, social stability, and economic development. Since the first case was recorded on December 29, 2019, in Wuhan of China, the disease has rapidly extended to other nations of the world to claim many lives, especially in the USA, the United Kingdom, and Western Europe. To stay ahead of the curve consequent of the continued increase in case and mortality, predictive tools are needed to guide adequate response. Therefore, this study aims to determine the best predictive models and investigate the impact of lockdown policy on the USA’ COVID-19 incidence and mortality. This study focuses on the statistical modelling of the USA daily COVID-19 incidence and mortality cases based on some intuitive properties of the data such as overdispersion and autoregressive conditional heteroscedasticity. The impact of the lockdown policy on cases and mortality was assessed by comparing the USA incidence case with that of Sweden where there is no strict lockdown. Stochastic models based on negative binomial autoregressive conditional heteroscedasticity [NB INGARCH (p,q)], the negative binomial regression, the autoregressive integrated moving average model with exogenous variables (ARIMAX) and without exogenous variables (ARIMA) models of several orders are presented, to identify the best fitting model for the USA daily incidence cases. The performance of the optimal NB INGARCH model on daily incidence cases was compared with the optimal ARIMA model in terms of their Akaike Information Criteria (AIC). Also, the NB model, ARIMA model and without exogenous variables are formulated for USA daily COVID-19 death cases. It was observed that the incidence and mortality cases show statistically significant increasing trends over the study period. The USA daily COVID-19 incidence is autocorrelated, linear and contains a structural break but exhibits autoregressive conditional heteroscedasticity. Observed data are compared with the fitted data from the optimal models. The results further indicate that the NB INGARCH fits the observed incidence better than ARIMA while the NB models perform better than the optimal ARIMA and ARIMAX models for death counts in terms of AIC and root mean square error (RMSE). The results show a statistically significant relationship between the lockdown policy in the USA and incidence and death counts. This suggests the efficacy of the lockdown policy in the USA.

Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Epidemiology and Global Health
Volume-Issue
11 - 2
Pages
200 - 207
Publication Date
2021/02/22
ISSN (Online)
2210-6014
ISSN (Print)
2210-6006
DOI
10.2991/jegh.k.210215.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Olusola S. Makinde
AU  - Abiodun M. Adeola
AU  - Gbenga J. Abiodun
AU  - Olubukola O. Olusola-Makinde
AU  - Aceves Alejandro
PY  - 2021
DA  - 2021/02/22
TI  - Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States
JO  - Journal of Epidemiology and Global Health
SP  - 200
EP  - 207
VL  - 11
IS  - 2
SN  - 2210-6014
UR  - https://doi.org/10.2991/jegh.k.210215.001
DO  - 10.2991/jegh.k.210215.001
ID  - Makinde2021
ER  -