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.
Highlights
The novel Coronavirus Disease 2019 (COVID-19) recently gained attention as the virus continues to claim more lives globally
The daily reported incidence cases in this study have been sourced from the Centre for Disease Control (CDC) and the European CDC (ECDC)
COVID-19 incidence counts are said to exhibit autoregressive conditional heteroscedasticity if mean incidence cases increase with time
Summary
The novel Coronavirus Disease 2019 (COVID-19) recently gained attention as the virus continues to claim more lives globally. At the beginning of the epidemic, elderly people were more susceptible to COVID-19 [1]. An increase in the number of cases among people between 45 and 64 years was recorded, as well as an upsurge in the number of cases among individuals, especially individuals between 18 and 44 years [2]. Reports show that the cases are 2.6 times higher on Black/African American and 2.8 times higher on Hispanic/Latino individuals. COVID-19 induced death is nine times lower on 0–4 years old children and 630 times higher on 85+ years old adults [3]. COVID‐19 poses a severe threat to the health of individuals worldwide; on January 30, 2020, the World Health Organization declared a universal health emergence on COVID-19 [4,5]
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