This study verified the effectiveness of Donald Trump's Twitter campaign in guiding agen-da-setting and deflecting political risk and examined Trump's Twitter communication strategy and explores the communication effects of his tweet content during Covid-19 pandemic. We collected all tweets posted by Trump on the Twitter platform from January 1, 2020 to December 31, 2020.We used Ordinary Least Squares (OLS) regression analysis with a fixed effects model to analyze the existence of the Twitter strategy. The correlation between the number of con-firmed daily Covid-19 diagnoses and the number of particular thematic tweets was investigated using time series analysis. Empirical analysis revealed Twitter's strategy is used to divert public attention from negative Covid-19 reports during the epidemic, and it posts a powerful political communication effect on Twitter. However, findings suggest that Trump did not use false claims to divert political risk and shape public opinion.

For more than 30 years, robotics has employed in the healthcare business. These robots range from laboratory robots that deal with medications to surgical robots that do surgeries or procedures on their own to robots that care for patients after surgery. Robots can help humans stay healthy for a long time without the use of medication or hospitalisation. Intel, for example, provides new technologies for the creation of health robots such as surgical robots, modular robots, service robots, mobile robots, and autonomous robots, allowing it to expand its reach in a variety of health-related fields. AI-driven robotic surgery is a mechanical device that allows doctors to focus on the difficult components of surgery by assisting with surgical tool handling and positioning during procedures. Their usage reduces surgeons' instabilities during surgery and assists them in improving their abilities and performing better during interventions, resulting in improved patient outcomes and lower overall healthcare costs.

Since the first coronavirus case was identified in the U.S. on Jan. 21, more than 1 million people in the U.S. have confirmed cases of COVID-19. This infectious respiratory disease has spread rapidly across more than 3000 counties and 50 states in the U.S. and have exhibited evolutionary clustering and complex triggering patterns. It is essential to understand the complex spacetime intertwined propagation of this disease so that accurate prediction or smart external intervention can be carried out. In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease. We further adopt a generative adversarial imitation learning framework to learn the model parameters. In comparison with the traditional likelihood-based learning methods, this imitation learning framework does not need to prespecify an intensity function, which alleviates the model-misspecification. Moreover, the adversarial learning procedure bypasses the difficult-to-evaluate integral involved in the likelihood evaluation, which makes the model inference more scalable with the data and variables. We showcase the dynamic learning performance on the COVID-19 confirmed cases in the U.S. and evaluate the social distancing policy based on the learned generative model.

Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between data points, one can build various distance measures using subsets of these features. Using the fewest features but still retaining sufficient information about the system is crucial in many statistical learning approaches, particularly when data are sparse. We introduce a statistical test that can assess the relative information retained when using two different distance measures, and determine if they are equivalent, independent, or if one is more informative than the other. This in turn allows finding the most informative distance measure out of a pool of candidates. The approach is applied to find the most relevant policy variables for controlling the Covid-19 epidemic and to find compact yet informative representations of atomic structures, but its potential applications are wide ranging in many branches of science.

The recent global outbreak of covid-19 is affecting many countries around the world. Due to the growing number of newly infected individuals and the health-care system bottlenecks, it will be useful to predict the upcoming number of patients. This study aims to efficiently forecast the is used to estimate new cases, number of deaths, and number of recovered patients in Iran for 180 days, using the official dataset of the Iranian Ministry of Health and Medical Education and the impact of control measures on the spread of COVID-19. Four different types of forecasting techniques, time series, and machine learning algorithms, are developed and the best performing method for the given case study is determined. Under the time series, we consider the four algorithms including Prophet, Long short-term memory, Autoregressive, Autoregressive Integrated Moving Average models. On comparing the different techniques, we found that deep learning methods yield better results than time series forecasting algorithms. More specifically, the least value of the error measures is observed in seasonal ANN and LSTM models. Our findings showed that if precautionary measures are taken seriously, the number of new cases and deaths will decrease, and the number of deaths in September 2021 will reach zero.

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.

Sign in to report inappropriate content. Engineers involved in China's artificial intelligence industry who have been developing "smart" robots are trying to come up with ways to use the devices to help fight the Covid-19 epidemic. While some of the robots are blasting the deadly coronavirus that causes the disease, others are being deployed in hospitals, hotels or on the streets to support human health workers and sanitation teams. Follow us on: Website: https://scmp.com