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      Disruptions in the Cystic Fibrosis Community’s Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments

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          Abstract

          Background

          The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods.

          Objective

          This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community’s experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases.

          Methods

          We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of “1” was assigned to months in 2020 and “0” otherwise and tested for its statistical significance.

          Results

          A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community’s experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period.

          Conclusions

          There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them.

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          Most cited references37

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          Scikit-learn Machine Learning in Python.

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            The COVID-19 social media infodemic

            We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document} R 0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors’ amplification.
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              Effects of COVID 19 pandemic in daily life

              Dear Editor, COVID-19 (Coronavirus) has affected day to day life and is slowing down the global economy. This pandemic has affected thousands of peoples, who are either sick or are being killed due to the spread of this disease. The most common symptoms of this viral infection are fever, cold, cough, bone pain and breathing problems, and ultimately leading to pneumonia. This, being a new viral disease affecting humans for the first time, vaccines are not yet available. Thus, the emphasis is on taking extensive precautions like extensive hygiene protocol (e.g., regularly washing of hands, avoidance of face to face interaction etc.), social distancing and wearing of masks etc. This virus is spreading exponentially region wise. Countries are banning gatherings of people to the spread and break the exponential curve 1 , 2 . Many countries are locking their population and enforcing strict quarantine to control the spread of the havoc of this highly communicable disease. COVID-19 has rapidly affected our day to day life, businesses, disrupted the world trade and movements. Identification of the disease at an early stage is vital to control the spread of the virus because it very rapidly spreads from person to person. Most of the countries have slowed down their manufacturing of the products 3 , 4 . The various industries and sectors are affected by the cause of this disease; these include the pharmaceuticals industry, solar power sector, tourism, Information and electronics industry. This virus creates significant knock-on effects on the daily life of citizens as well as about the global economy. Presently the impacts of COVID-19 in daily life are extensive and have far reaching consequences. These can be divided into various categories: A) Healthcare • Challenges in the diagnosis, quarantine and treatment of suspected or confirmed cases • High burden of the functioning of the existing medical system • Patients with other disease and health problems are getting neglected • Overload on doctors and other healthcare professionals, who are at a very high risk • Overloading of medical shops • Requirement for high protection • Disruption of medical supply chain B) Economic • Slowing of the manufacturing of essential goods • Disrupt the supply chain of products • Losses in national and international business • Poor cash flow in the market • Significant slowing down in the revenue growth C) Social • Service sector is not being able to provide their proper service • Cancellation or postponement of large-scale sports and tournaments • Avoiding the national and international travelling and cancellation of services • Disruption of celebration of cultural, religious and festive events • Undue stress among the population • Social distancing with our peers and family members • Closure of the hotels, restaurants and religious places • Closure of places for entertainment like movie and play theatres, sports clubs, gymnasiums, swimming pools etc. • Postponement of examinations This COVID-19 has affected the sources of supply and effects the global economy. There are restrictions of travelling from one country to another country. During travelling, numbers of cases are identified positive when tested, especially when they are taking international visits 5 . All governments, health organisations and other authorities are continuously focusing on identifying the cases affected by the COVID-19. Healthcare professional face lot of difficulties in maintaining the quality of healthcare in these days. Declaration of Competing Interest None
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                2023
                20 April 2023
                : 25
                : e45249
                Affiliations
                [1 ] Social Computing Laboratory Nara Institute of Science and Technology Ikoma Japan
                Author notes
                Corresponding Author: Eiji Aramaki aramaki@ 123456is.naist.jp
                Author information
                https://orcid.org/0000-0003-3184-9368
                https://orcid.org/0000-0003-0717-0769
                https://orcid.org/0000-0002-0755-7173
                https://orcid.org/0000-0002-9371-1340
                https://orcid.org/0000-0003-0201-3609
                Article
                v25i1e45249
                10.2196/45249
                10160941
                37079359
                2fa2a2d4-883e-425a-b3b0-f7f9abdacc72
                ©Lean Franzl Yao, Kiki Ferawati, Kongmeng Liew, Shoko Wakamiya, Eiji Aramaki. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.04.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 30 December 2022
                : 26 January 2023
                : 15 March 2023
                : 16 March 2023
                Categories
                Original Paper
                Original Paper

                Medicine
                covid-19,reddit,time series analysis,bertopic,topic modeling,cystic fibrosis
                Medicine
                covid-19, reddit, time series analysis, bertopic, topic modeling, cystic fibrosis

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