ABSTRACT
After nearly COVID-19 pandemic-mitigation success of 2020, Taiwan experienced its first major domestic outbreak in mid-May, 2021. Taiwan entered a nation-wide level 3 epidemic warning, one step away from a potential national lockdown, on May 19, 2021. Systems thinking is an effective tool to help policymakers better understand the complex COVID-19 crisis. This chapter applies causal loop diagrams to depict the causal connections among different components of the COVID-19 infection and immunization system in Taiwan, including vaccine shortage and hesitancy, medical health resource, and prevention and control measures responding to the COVID-19 outbreak during 2021. First, we described temporal and spatial characteristics of 2021 outbreak and the implementation of control measures in response to this outbreak from Taiwan's Central Epidemic Command Center. Then, we used causal loop diagrams to illustrate the relationship between vaccination and infections how changes in one component potentially affecting the status of the immunization system during a COVID-19 outbreak. © TheEditor(s) (ifapplicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021, 2022.
ABSTRACT
Palliative care is a growing medical specialty. While its value has become particularly evident during the pandemic, it faces various challenges including misconceptions among the general public, a lack of awareness of its benefits, and limited/sporadic access and coverage by Medicare. Given the increasing number of people sharing their health information and seeking healthcare information on social media where misinformation is widely spread, this study examined types of information on palliative care circulated on Twitter. A total of 26,495 English Tweets were collected 01/01/2020 – 12/30/2021 using the keywords "Palliative” and "Covid” or "Corona”. We manually developed a framework for coding/classifying the first 6,000 Tweets. Of those, 5,308 were unrelated Tweets (e.g., advertising palliative care seminars/conferences). Among the remaining Tweets, persistent myths were observed (e.g., palliative care is only for dying people) and were labeled accordingly. In addition, while some people mentioned negative impact of the pandemic on palliative care (e.g., shortage of beds), others found value in palliative care and reported positive changes due to the pandemic (e.g., telehealth in palliative care). Consequently, the following categories were defined for Tweets: i) Recognized benefits (203 Tweets);ii) Positive impact of the pandemic on palliative care (120 Tweets);iii) Negative impact of the pandemic on palliative care (333 Tweets);iv) Myth (63 Tweets). We will use these manually classified Tweets to train machine-learning algorithms to automatically classify the remaining tweets, aiming to obtain a comprehensive understanding of palliative care information circulating on Twitter and seek ways to promote palliative care use.