We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients.

We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients.

Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modeled human mobility via macro indicators (e.g., average daily travel distance) and then studied the effectiveness of NPIs. In this work, we focus on mobility modeling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a micro perspective prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information). To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph. we propose a model named Deep Graph Diffusion Infomax (DGDI), which jointly models variables including a geometric graph, a set of diffusions and a set of locations.To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods.

World Health Organization (WHO) reported that 80% of patients experienced these symptoms mildly. However, older people ( 60 years old) and persons with co-morbid diseases are at a higher risk for severe symptoms and death (Velavan & Meyer, 2020; World Health Organization, 2020). Besides, younger patients with no underlying disease might also experience severe symptoms or even death (Jahromi, Avazpour, et al., 2020; The Washington Post, 2020; Yousefzadegan & Rezaei, 2020). The first positive case of COVID-19 in the United States was reported in the state of Washington on January 20, 2020. By March 17, 2020, Covid-19 has spread across all US states (Centers for Disease Control and Prevention, 2020; Saad B. Omer et al., 2020). Figure 1 shows the aggregated COVID-19 positive case and death count maps for all US states until November 6, 2020. Reports showed that on November 6, 2020, the top states for positive COVID-19 cases are California, Texas, Florida, New York, and Illinois, while the top 5 states for death cases are New York, Texas, California, New Jersey, and Florida.

A Japanese technology company has developed a new Bluetooth-powered smart mask that uses a speaker to amplify a person's voice. Called'c-mask,' the device can also covert a person's speech into text and then translate it into eight different languages through a smartphone app. The mask was developed by Donut Robotics, which initially raised seven million yen, or around $260,000, to fund its development through the Japanese crowdfunding site Fundinno. Donut Robotics has developed a new smart mask to protect against COVID-19 transmission, which also contains a built-in speaker to amplify a person's voice and connects to a smartphone app that can translate speech into eight languages According to Donut, around 5,000 masks are currently planned to be produced and distributed in Japan this September, where they'll retail for 3,980 yen, or around $37. The company will also charge an additional monthly subscription fee to access translation services, according to a report in Japan Today - though the exact pricing hasn't been announced.