Mcqueenanderson7911

As a public health measure during the COVID-19 pandemic, governments around the world instituted a variety of interventions to 'flatten the curve'. The government of Maryland instituted similar measures. We observed a striking decline in paediatric intensive care unit (PICU) admissions during that period, mostly due to a decease in respiratory infections. We believe this decline is multifactorial less person-to-person contact, better air quality and perhaps 'fear' of going to a hospital during the pandemic. We report an analysis of our PICU admissions during the lockdown period and compared them with the same time period during the four previous years.Aging in place (AIP) is a term that is commonly used and defined in a plethora of ways. Multiple disciplines take a different stance on the definition of AIP, and its definition has evolved over time. Such diverse ways to define AIP could be a barrier to reach a shared expectation among multiple stakeholders when formulating research studies, making policy decisions, developing care plans, or designing technology tools to support older adults. We conducted a scoping review for the term AIP to understand specifically how it has been defined across time and disciplines. We collected exemplary definitions of AIP from 7 databases that represent different fields of study; namely, AgeLine, Anthropology Plus, Art and Architecture Source, CINAHL, PsycINFO, PubMed, and SocINDEX. We conducted a thematic analysis to identify the common concepts that emerged across the definitions identified in the scoping review. selleck kinase inhibitor We developed 3 main categories from the themes space, person, and time to illustrate the root of meaning across the definitions. Intersectionality across the categories yielded a comprehensive understanding of AIP, which does not constrain its definition to a place-related phenomenon. We propose that AIP be defined as "One's journey to maintain independence in one's place of residence as well as to participate in one's community." With this shared understanding of the term AIP, policymakers, researchers, technology designers, and caregivers can better support those who aim to age in the place of their choice.The direct relation between the overweight/obesity, MAFLD and the severity SARS-CoV-2 infection. increase number of cases of obesity and MAFLD is an important risk factor for high mortality of COVID-19 patients.In response to the COVID-19 public health emergency, the Tulsa Health Department created local models. This was an iterative process, with the focus predicting all infections (including asymptomatic and mild cases that would not meet testing criteria,) and deaths for the Tulsa area. SEIR-type models were utilized. Developing infectious disease models is challenging due to data issues related to validity, and complex interrelated assumptions, and this was exacerbated with the COVID-19 crisis. Directly related to these data challenges were challenges with communicating without spreading misinformation, and being clear about the model limitations.Query logs include valuable information for understanding user intent and behavior in Web search. In this article, we investigate COVID-19-related query logs by dividing search sessions into different intent and analyzing the user behavior of groups and individuals. We believe it important to learn about the epidemic's influence on users' search behavior and refine search engine to confront similar epidemic outbreaks in the future.In this poster, we report the preliminary results of an inventory of 149 publicly accessible active COVID-19 tracking systems. Key findings include the frequency distribution of the systems' web domain names, the countries where the systems were created, the languages they support, the visual display format, the map platforms, and the data sources. These findings help to advance the knowledge of the data characteristics and design of pandemic surveillance/tracking systems.The COVID-19 outbreak has posed significant threats to international health and the economy. In the absence of treatment for this virus, public health officials asked the public to practice social distancing to reduce the number of physical contacts. However, quantifying social distancing is a challenging task and current methods are based on human movements. We propose a time and cost-effective approach to measure how people practice social distancing. This study proposes a new method based on utilizing the frequency of hashtags supporting and encouraging social distancing for measuring social distancing. We have identified 18 related hashtags and tracked their trends between Jan and May 2020. Our evaluation results show that there is a strong correlation (p  less then  .05) between our findings and the Google social distancing report.COVID-19 has now become a global pandemic. During the widespread of COVID-19, Twitter, as an online social media platform, has been a preferred channel for interaction and communication. As a result, it provides huge amount of information from which latent signals such as sentiments can be mined for a better understanding of COVID-19 transmission patterns. As a preliminary attempt, we reveal a strongly positive zero-order correlation between sentiments of tweets and COVID-19 confirmed cases in U.S. Considering the unique hierarchical structure of the U.S. government, state governments exert their own power to issue public health policies. Indeed, there are different patterns of correlations between sentiments and COVID-19 confirmed cases, affirming that country-level characteristics suppress that of state-level. Diving deeper into the textual content of COVID-19 related tweets, there manifests a diverse set of topics which in turn lead to dispersed sentiments. Our preliminary investigation paves the way for a finer-grained analysis of the COVID-19 transmission and social media activities by considering varying situations across states and topics.During the COVID-19 crisis, fake news, conspiracy theories, and backlash against specific groups emerged and were largely diffused via social media. This phenomenon has been described as an "infodemic," and this study examined that the characteristics of infodemic on Twitter. Typological attributes of the infodemic Twitter network presented the features of "community clusters." The frequently shard domains and URLs demonstrated coherent characteristics within the network. Top domains and URLs were trustworthy information sources, popular blogs, and public health research institutions. Interestingly, the most shard conversational content of the network was a COVID-19 relevant incident occurred at a church in Korea based on misinformation and false belief.