This study aimed to design and test a COVID-19 surveillance system model for community-industry population. A prospective cohort study was conducted from May to December, 2020. Researchers designed a COVID-19 surveillance system and presented it to stakeholders from the community-industry setting in Lamphun and Chiang Mai provinces, Thailand. The model was adjusted following feedback and tested. The model was an Active surveillance for early Alert and rapid Action using Big data and mobile phone application technology for a Community-industry setting (3ABC model). The major components were active surveillance, community-based surveillance, event-based surveillance, and early warning and rapid response. A drive-thru testing unit was operated to enable early detection. Alerts and recommended action on individual and administrative levels were sent via an application and networks. In the testing of the model, risk assessment was initially conducted with regard to COVID-19 transmission in the factories. Researchers provided recommendations based on findings. The improvements included human resource management, systems, and structure. The 3ABC model work well as designed. The participants actively reported events daily including prevention and control activities, animal diseases (foot-and-mouth disease in buffalos and hog cholera), human diseases (dengue and chikungunya), and absent of COVID-19 outbreak. Only five quarantined COVID-19 cases whom were monitored. Daily reports of no abnormal event was also high (70.2% to 71.1%). It is practical and feasible to implement the 3ABC model in a community-industry setting. A further study for a longer period to verify its level of effectiveness should be done. Keywords: Infectious disease, Epidemic model, Surveillance, Mobile application, Model evaluation
Sentiment analysis of Twitter data is quite valuable for determining the market opinion. Twitter sentiment analysis is more challenging than generic sentiment analysis owing to slang and misspellings. The techniques utilized for evaluating the sentiment of tweets that have the greatest importance for the success of an Initial Coin Offering (ICO) are machine learning approaches. In this study, we examined market sentiment and used Expert Ratings to predict the success of ICOs in the Australian and Singapore markets. Based on 68,281 tweets from 57 ICOs across four industries: business services, cryptocurrency, entertainment, and platform. Several classification methods were investigated, including Support Vector Machines (SVMs), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB). The outcomes indicated that sentiment analysis of tweets and expert ratings may be used to forecast the success of an initial coin offering. The results indicate that the suggested model is capable of accurately assessing the tweets of the ICO Successful with a maximum accuracy of about 94.7 % when implementing the Support Vector Machines (SVMs) classifier.
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