Mcintoshhensley5580
able groups and the role social media may play.
Overall, the findings contribute to our understanding of health information-seeking behaviors during an outbreak and highlight the importance of paying attention to the information needs of vulnerable groups and the role social media may play.The COVID-19 pandemic has affected all sectors of society, from health and economics to socialization and travel. The level and extent of this impact are unprecedented. Although the cause of COVID-19 was quickly identified to be a new coronavirus (SARS-CoV-2), the world was poorly prepared for preventing its spread. One important pillar of preparedness is surveillance of the sources of emerging pathogens and responding appropriately to prevent their spread in the human population. The ever-increasing interaction between humans and animals is one leading factor in facilitating the emergence of new pathogens. In this viewpoint, we discuss the possibility of the zoonotic origin of SARS-CoV-2, highlight the importance of understanding human-animal interaction to improve preparedness for future outbreaks, and outline recommendations for prevention.
In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease's rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19.
The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations.
We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the pred address privacy issues in the collection and use of big data.
China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.
The COVID-19 pandemic has necessitated an immediate and large-scale uptake of online treatment for mental health care. However, there is uncertainty about what the "new normal" in mental health care will be like in post-COVID-19 times. To what extent will the experiences gained during the pandemic influence a sustainable adoption and implementation of online mental health care treatment in the future?
In this paper, we aim to formulate expectations with regard to the sustainability of online mental health care after COVID-19.
In an interview study, 11 mental health care professionals were asked about their experiences and expectations for the future. Participants were recruited from a mental health care organization in the Netherlands. The interviews took place between April 7-30, 2020, at the peak of the COVID-19 crisis in the Netherlands. The data were analyzed using a thematic coding method.
From the interviews, we learn that the new normal in mental health care will most likely consist of more blein specific situations. There is work to be done before online treatment is inherently part of mental health care.School closures, altered access to health services, and economic stress during the COVID-19 pandemic have likely had an impact on the mental and physical well-being of youth worldwide, particularly among those with chronic health conditions (CHCs). A number of challenges and opportunities have emerged during the COVID-19 pandemic for youth with CHCs. Challenges include heightened anxiety, disrupted routines, academic and social stresses associated with school closure, increased risk of domestic violence and abuse, and reduced access to physical and psychosocial support. On the other hand, opportunities include reduced academic and social stress, increased time with families, reduced access to substances, easier access to health care using technology, and opportunities to build resilience. This viewpoint paper highlights both challenges and opportunities for youth with CHCs during the pandemic and offers recommendations for further research and clinical care.
The estimates of several key epidemiological parameters of the COVID-19 pandemic are often based on small sample sizes or are inaccurate for various reasons.
The aim of this study is to obtain more robust estimates of the incubation period, serial interval, frequency of presymptomatic transmission, and basic reproduction number (R
) of COVID-19 based on a large case series.
We systematically retrieved and screened 20,658 reports of laboratory-confirmed COVID-19 cases released by the health authorities of China, Japan, and Singapore. #link# In addition, 9942 publications were retrieved from PubMed and China National Knowledge Infrastructure (CNKI) through April 8, 2020. To be eligible, a report had to contain individual data that allowed for accurate estimation of at least one parameter. Widely used models such as gamma distributions were fitted to the data sets and the results with the best-fitting values were presented.
In total, 1591 cases were included for the final analysis. The mean incubation period (h potential exposure, but also suggest the need for additional measures. link2 Presymptomatic transmission together with the asymptomatic transmission reported by previous studies highlight the importance of adequate testing, strict quarantine, and social distancing.
As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties.
The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times.
This observational study on the emerging COVID-19 pandemic included 643 patients. An "AI trilogy" of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shortnfectious disease epidemic.
The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible.
We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (R
) as compared to social mobility estimates reported from Google and Apple Maps.
In this observational study, the estimated R
was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. link3 Social media activity was assessed using queries of "social distancing" or "#socialdistancing" on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between R
and social meols in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with Rt when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.
Computed tomography (CT) scans are increasingly available in clinical care globally. They enable a rapid and detailed assessment of tissue and organ involvement in disease processes that are relevant to diagnosis and management, particularly in the context of the COVID-19 pandemic.
The aim of this paper is to identify differences in the CT scan findings of patients who were COVID-19 positive (confirmed via nucleic acid testing) to patients who were confirmed COVID-19 negative.
A retrospective cohort study was proposed to compare patient clinical characteristics and CT scan findings in suspected COVID-19 cases. A multivariable logistic model with LASSO (least absolute shrinkage and selection operator) selection for variables was used to identify the good predictors from all available predictors. The area under the curve (AUC) with 95% CI was calculated for each of the selected predictors and the combined selected key predictors based on receiver operating characteristic curve analysis.
A total of 94 (519. This study suggests that chest CT scans should be more broadly adopted along with nucleic acid testing in the initial assessment of suspected COVID-19 cases, especially for patients with nonspecific symptoms.
Withaferin A purchase -19 outbreak was designated a global pandemic on March 11, 2020. The relationship between vaping and contracting COVID-19 is unclear, and information on the internet is conflicting. There is some scientific evidence that vaping cannabidiol (CBD), an active ingredient in cannabis that is obtained from the hemp plant, or other substances is associated with more severe manifestations of COVID-19. However, there is also inaccurate information that vaping can aid COVID-19 treatment, as well as expert opinion that CBD, possibly administered through vaping, can mitigate COVID-19 symptoms. Thus, it is necessary to study the spread of inaccurate information to better understand how to promote scientific knowledge and curb inaccurate information, which is critical to the health of vapers. Inaccurate information about vaping and COVID-19 may affect COVID-19 treatment outcomes.
Using structural topic modeling, we aimed to map temporal trends in the web-based vaping narrative (a large data set comprising web-mizing treatment of vapers who contract COVID-19, and possibly minimizing instances of inaccurate information. The findings have implications for the management of COVID-19 among vapers and the monitoring of web-based content pertinent to tobacco to develop targeted interventions to manage COVID-19 among vapers.
