This article develops nonlinear functional forms for modeling count time series of daily deaths due to the COVID-19 virus. Our models explain the mean levels of the time series while accounting for the time-varying variances. A Bayesian approach using Markov chain Monte Carlo (MCMC) is adopted for analysis, inference and forecasting of the time series under the proposed models. Applications are shown for time series of death counts from several countries affected by the pandemic.
Following the outbreak of COVID-19, various containment measures have been taken, including the use of quarantine. At present, the quarantine period is the same for everyone, since it is implicitly assumed that the incubation period distribution of COVID-19 is the same regardless of age or gender. For testing the effects of age and gender on the incubation period of COVID-19, a novel two-component mixture regression model is proposed. An expectation-maximization (EM) algorithm is adopted to obtain estimates of the parameters of interest, and the simulation results show that the proposed method outperforms the simple regression method and has robustness. The proposed method is applied to a Zhejiang COVID-19 dataset, and it is found that age and gender statistically have no effect on the incubation period of COVID-19, which indicates that the quarantine measure currently in operation is reasonable.
The swift spread of the novel coronavirus is largely attributed to its stealthy transmissions in which infected patients may be asymptomatic or exhibit only flu-like symptoms in the early stage. Undetected transmissions present a remarkable challenge for the containment of the virus and pose an appalling threat to the public. An urgent question is on testing of the coronavirus. In this paper, we evaluate the situation from the statistical viewpoint by discussing the accuracy of test procedures and stress the importance of rationally interpreting test results.
The United States has the highest numbers of confirmed cases of COVID-19 in the world. The early hot spot states were New York, New Jersey, and Connecticut. The workforce in these states was required to work from home except for essential services. It was necessary to evaluate an appropriate date for resumption of business since the premature reopening of the economy would lead to a broader spread of COVID-19, while the opposite situation would cause greater loss of economy. To reflect the real-time risk of the spread of COVID-19, it was crucial to evaluate the population of infected individuals before or never being confirmed due to the pre-symptomatic and asymptomatic transmissions of COVID-19. To this end, we proposed an epidemic model and applied it to evaluate the real-time risk of epidemic for the states of New York, New Jersey, and Connecticut. We used California as the benchmark state because California began a phased reopening on May 8, 2020. The dates on which the estimated numbers of unidentified infectious individuals per 100,000 for states of New York, New Jersey, and Connecticut were close to those in California on May 8, 2020, were June 1, 22, and 22, 2020, respectively. By the practice in California, New York, New Jersey, and Connecticut might consider reopening their business. Meanwhile, according to our simulation models, to prevent resurgence of infections after reopening the economy, it would be crucial to maintain sufficient measures to limit the social distance after the resumption of businesses. This precaution turned out to be critical as the situation in California quickly deteriorated after our analysis was completed and its interventions after the reopening of business were not as effective as those in New York, New Jersey, and Connecticut.