COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.
Background
This study was conducted to estimate the distribution of and changes in the global disease burden of ischemic heart disease attributable to smoking between 1990 and 2019.
Methods and Results
Data used in this study come from the GBD 2019 (Global Burden of Disease Study 2019). Age‐standardized rates and estimated annual percentage change of age‐standardized rates were used to describe this burden and its changing trend. Pearson's correlation coefficient was used to evaluate the correlation between the sociodemographic index and changing trend. From 1990 to 2019, the burden of ischemic heart disease attributable to smoking has shown a downward trend globally; estimated annual percentage changes of age‐standardized mortality rates and age‐standardized disability‐adjusted life‐years rates were −2.012 (95% CI, −2.068 to −1.956) and −1.907 (95% CI, −1.975 to −1.838). Nineteen countries experienced an increase in disease burden, and the changes in 17 countries were not statistically significant. In addition, this burden was higher in men and older age groups. Estimated annual percentage change of the age‐standardized rates of this burden were negatively correlated with the sociodemographic index.
Conclusions
Although the burden of ischemic heart disease attributable to smoking has decreased in >80% of countries or regions in the past 30 years, it has remained a significant issue in low‐ and middle‐income countries, particularly among men and elderly populations. Therefore, active tobacco control measures, focusing on key populations, are required to reduce the associated burden of ischemic heart disease, especially in those countries or regions with increasing prevalence and disease burden.
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