ABSTRACT
Background. As of May 2, 2021, U.S. nursing homes (NHs) have reported >651,000 COVID-19 cases and >132,000 deaths to CDC's National Healthcare Safety Network. Since U.S. COVID-19 vaccination coverage is increasing, we investigate the role of vaccination in controlling future COVID-19 outbreaks. Methods. We developed a stochastic, compartmental model of SARS-CoV-2 transmission in a theoretical 100-bed NH with a staff of 99 healthcare personnel (HCP) in a community of 20,000 people. We modeled admission and discharge of residents (parameterized with Centers for Medicare & Medicaid Services data), assuming the following: temporary replacement of HCP when tested positive;daily visits to NH residents;isolation of COVID-19 positive residents;personal protective equipment (PPE) use by HCP;and symptom-based testing of residents and staff plus weekly asymptomatic testing of HCP and facility-wide outbreak testing once a COVID-19 case is identified. We systematically varied coverage of an mRNA vaccine among residents and HCP, and in the community. Simulations also varied PPE adherence, defined as the percentage of time in the facility that HCP properly used recommended PPE (25%, 50% or 75% of the time). Infection was initialized in the community with 40 infectious cases, and initial infection in the NH was allowed after 14 days of vaccine dose 1. Simulations were run for 6 months after dose 2 in the NH. Results were summarized over 1000 simulations. Results. At 60% community coverage, expected cumulative symptomatic resident cases over 6 months were ≤5, due to low importation of COVID-19 infection from the community, with further reduction at higher coverage among HCP (Figure 1). Uncertainty bounds narrowed as NH resident coverage or PPE adherence increased. Results were similar if testing of staff and residents stopped. Probability of an outbreak within 4 weeks of dose 2 remained below 5% with high community coverage (Figure 2). An outbreak is defined as an occurrence of 2 or more cases within 4 weeks of dose 2. Probability of no outbreak was calculated by counting how many simulations out of a total of 1000 simulations had ≤1 symptomatic case in NH residents or HCP within 4 weeks after dose 2 was administered in the nursing home. The first vaccine dose in residents and HCP was assumed to be given on day 1, and the second dose 28 days later. A probability value and its 90%-confidence interval (CI) at a given community and HCP coverage was calculated by pooling model outputs for 9 sets (3 PPE adherence values X 3 resident coverage levels) of model simulations. Simulations were performed assuming no asymptomatic testing or facility-wide outbreak testing. Conclusion. Results suggest that increasing community vaccination coverage leads to fewer infections in NH residents. Testing asymptomatic residents and staff may have limited value when vaccination coverage is high. High adherence to recommended PPE may increase the likelihood that future COVID-19 outbreaks can be contained.
ABSTRACT
Social media has become a vital platform for individuals, organizations, and governments worldwide to communicate and express their views. During the coronavirus disease 2019 (COVID-19) pandemic, social media sites play a crucial role in people communicating, sharing, and expressing their perceptions on various topics. Analyzing such textual data can improve the response time of governments and organizations to act on alarming issues. This study aims to perform sentiment analysis on the subject of COVID-19 vaccination, perform temporal and spatial analyses of the textual data, and find the most frequently discussed topics that may help organizations bring awareness to those topics. In this work, the sentiment analysis of tweets was performed using 14 different machine learning classifiers and natural language processing (NLP). Lexicon-based TextBlob and Vader are used for annotating the data. A natural language toolkit is used for preprocessing of textual data. Our analysis observed that unigram models outperform bigram and trigram models for all four datasets. Models using term frequency-inverse document frequency (TF-IDF) have higher accuracy than models using count vectorizer. In the count vectorizer class, logistic regression has the best average accuracy with 91.925%. In the TF-IDF class, logistic regression has the best average accuracy of 92%;logistic regression has the highest average recall, F1-score, and ten cross-validation scores, and a ridge classifier has the highest average precision. The unigram models show a standard deviation (SD) of less than 1 for all classifiers except for the Gaussian Naive Bayes showing 1.18. The experimental results reveal the dates and times in which most positive, negative, and neutral tweets are posted.
ABSTRACT
With high demands for data and processing power, the server industries are proliferating daily. Some hardware can be controlled by servers or Raspberry pi. So, tester and debugger must log in to this server and test and debug this hardware. If these have to be done on 100s of servers, which will be very difficult. So, to overcome this problem we have designed a framework based on SSH with multi-threading though there are various tools, our analytical experimental studies show that SSH with multi-threading or multiprocessing is far better than these tools. This Framework which designed by us will parallelly login into nearly 100s of server or clusters of servers (created using cluster algorithm designed by us.) at once and can run some automated activity like updating software, running some test cases like stressing processors memory or performing some activity which can be controlled by Raspberry pi or get logs, etc. This Framework is very useful for hardware controlled by server or Raspberry pi server industries, especially in the condition like COVID-19. © 2020 IEEE.