We generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include a more full range of shape changing predictive distributions and the ability to handle cycles and conditional heteroscedasticity in the time series. The stationarity conditions and autocorrelation function are derived. The estimation is easily done via a simple EM algorithm and the model selection problem is addressed. The shape changing feature of the conditional distributions makes these models capable of modelling time series with multimodal conditional distributions and with heteroscedasticity. The models are applied to two real data sets and compared with other competing models. The MAR models appear to capture features of the data better than other competing models do.
The coronavirus disease 2019 (COVID-19) pandemic has resulted in millions of patients
infected worldwide and indirectly affecting even more individuals through disruption of
daily living. Long-term adverse outcomes have been reported with similar diseases from
other coronaviruses, namely Middle East Respiratory Syndrome (MERS) and Severe Acute
Respiratory Syndrome (SARS). Emerging evidence suggests that COVID-19 adversely affects
different systems in the human body. This review summarizes the current evidence on the
short-term adverse health outcomes and assesses the risk of potential long-term adverse
outcomes of COVID-19. Major adverse outcomes were found to affect different body systems:
immune system (including but not limited to Guillain-Barré syndrome and paediatric
inflammatory multisystem syndrome), respiratory system (lung fibrosis and pulmonary
thromboembolism), cardiovascular system (cardiomyopathy and coagulopathy), neurological
system (sensory dysfunction and stroke), as well as cutaneous and gastrointestinal
manifestations, impaired hepatic and renal function. Mental health in patients with
COVID-19 was also found to be adversely affected. The burden of caring for COVID-19
survivors is likely to be huge. Therefore, it is important for policy makers to develop
comprehensive strategies in providing resources and capacity in the healthcare system.
Future epidemiological studies are needed to further investigate the long-term impact on
COVID-19 survivors.
Apart from attention to psychosocial risks, health and lifestyle factors are important considerations for mental health promotion. Service utilization for individuals with CMD in Hong Kong remains suboptimal, and would be enhanced by strengthening community primary care.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.