Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2021 Jun;11(2):200-207.
doi: 10.2991/jegh.k.210215.001. Epub 2021 Feb 22.

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

Olusola S Makinde  1 , Abiodun M Adeola  2   3 , Gbenga J Abiodun  4 , Olubukola O Olusola-Makinde  5 , Aceves Alejandro  4
Affiliations
Comparative Study

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

Olusola S Makinde et al. J Epidemiol Glob Health. 2021 Jun.

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.

Keywords: ARIMA; COVID-19; INGARCH; USA; lockdown policy; negative binomial.

PubMed Disclaimer

Conflict of interest statement

The authors declare they have no conflicts of interest.

Figures

Figure 1
Figure 1
The plot of counts of COVID-19 (A) incidence and (B) death cases in the USA.
Figure 2
Figure 2
Race distribution of COVID-19 (A) incidence and (B) death cases in the USA.
Figure 3
Figure 3
Age groups distribution of COVID-19 (A) incidence and (B) deaths in the USA.
Figure 4
Figure 4
Comparison between observed counts, fitted values from optimal NB INGARCH (2,2) model and fitted values from optimal ARIMA (1,1,1) model with drift.
Figure 5
Figure 5
Boxplot of (A) COVID-19 incidence cases and (B) the natural logarithm of COVID-19 incidence cases in the USA.
Figure 6
Figure 6
Comparison between observed counts, fitted values from NB, optimal ARIMAX and optimal ARIMA models.
Figure 7
Figure 7
Comparison of predicted values with observed values from August 9 to October 8, 2020.
Figure 8
Figure 8
Comparison of incidence rates and death rates in the USA and Sweden, accounting for efficiency of lockdown policy.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507–13. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed
    1. Zhang J, Litvinova M, Wang W, Wang Y, Deng X, Chen X, et al. Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. Lancet Infect Dis. 2020;20:793–802. doi: 10.1016/S1473-3099(20)30230-9. - DOI - PMC - PubMed
    1. CDC Report Centers for Disease Control and Prevention: Risk for COVID-19 Infection, Hospitalization, and Death By Race/Ethnicity. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-disc... (retrieved on August 8, 2020).
    1. World Health Organization Statement on the Second Meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV) 2020 Available from: https://www.who.int/news-room/detail/30-01-2020-statement-on-the-second-... (retrieved on August 23, 2020).
    1. Fang Y, Nie Y, Penny M. Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: a data-driven analysis. J Med Virol. 2020;92:645–59. doi: 10.1002/jmv.25750. - DOI - PMC - PubMed

Publication types