scholarly journals How do Canadian public health agencies respond to the COVID-19 emergency using social media: a protocol for a case study using content and sentiment analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e041818
Author(s):  
Anita Kothari ◽  
Lyndsay Foisey ◽  
Lorie Donelle ◽  
Michael Bauer
Keyword(s):  
Public Health ◽  
Social Media ◽  
Research Ethics ◽  
Sentiment Analysis ◽  
Health Agency ◽  
Message Content ◽  
Public Health Agencies ◽  
The Public ◽  
Board Review

IntroductionKeeping Canadians safe requires a robust public health (PH) system. This is especially true when there is a PH emergency, like the COVID-19 pandemic. Social media, like Twitter and Facebook, is an important information channel because most people use the internet for their health information. The PH sector can use social media during emergency events for (1) PH messaging, (2) monitoring misinformation, and (3) responding to questions and concerns raised by the public. In this study, we ask: what is the Canadian PH risk communication response to the COVID-19 pandemic in the context of social media?Methods and analysisWe will conduct a case study using content and sentiment analysis to examine how provinces and provincial PH leaders, and the Public Health Agency of Canada and national public heath leaders, engage with the public using social media during the first wave of the pandemic (1 January–3 September 2020). We will focus specifically on Twitter and Facebook. We will compare findings to a gold standard during the emergency with respect to message content.Ethics and disseminationWestern University’s research ethics boards confirmed that this study does not require research ethics board review as we are using social media data in the public domain. Using our study findings, we will work with PH stakeholders to collaboratively develop Canadian social media emergency response guideline recommendations for PH and other health system organisations. Findings will also be disseminated through peer-reviewed journal articles and conference presentations.

scholarly journals Public engagement and dialogic accounting through social media during COVID-19 crisis: a missed opportunity?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stefano Landi ◽  
Antonio Costantini ◽  
Marco Fasan ◽  
Michele Bonazzi
Keyword(s):  
Public Health ◽  
Social Media ◽  
New Zealand ◽  
Public Engagement ◽  
Fake News ◽  
Public Agencies ◽  
Content Type ◽  
Public Health Agencies ◽  
The Public ◽  
Health Agencies

PurposeThe purpose of this exploratory study is to investigate why and how public health agencies employed social media during coronavirus disease 2019 (COVID-19) outbreak to foster public engagement and dialogic accounting.Design/methodology/approachThe authors analysed the official Facebook pages of the leading public agencies for health crisis in Italy, United Kingdom and New Zealand and they collected data on the number of posts, popularity, commitment and followers before and during the outbreak. The authors also performed a content analysis to identify the topics covered by the posts.FindingsEmpirical results suggest that social media has been extensively used as a public engagement tool in all three countries under analysis but – because of legitimacy threats and resource scarcity – it has also been used as a dialogic accounting tool only in New Zealand. Findings suggest that fake news developed more extensively in contexts where the public body did not foster dialogic accounting.Practical implicationsPublic agencies may be interested in knowing the pros and cons of using social media as a public engagement and dialogic accounting tool. They may also leverage on dialogic accounting to limit fake news.Originality/valueThis study is one of the first to look at the nature and role of social media as an accountability tool during public health crises. In many contexts, COVID-19 forced for the first time public health agencies to heavily engage with the public and to develop new skills, so this study paves the way for numerous future research ideas.

scholarly journals Information Sharing through Twitter by Public Health care Institution during COVID-19 Pandemic: A Case Study of AIIMS, Raipur

2021 ◽  
Vol 33 (1) ◽  
pp. 189-192
Author(s):  
Shiv Shankar Sharma ◽  
Daljeet Kaur ◽  
Taranjeet Kaur Chawla ◽  
Vaishali Kapoor
Keyword(s):  
Public Health ◽  
Health Care ◽  
Social Media ◽  
Sentiment Analysis ◽  
Public Health Care ◽  
Institutional Image ◽  
Health Care Institutions ◽  
Media Platform ◽  
Two Phases

Background: During the time of COVID 19, public health care institutions have used social media to inform and aware society. Aim & Objective: To analyze how Public Health Care Institutes conveyed the health information and messages through social media platform- Twitter during COVID 19, and analyzing its impact through sentiment analysis of comments. Material & Methods: The Thematic and sentiment analysis method has been used to analyze the data of the Twitter handle of AIIMS, Raipur in two phases; January-March 2020, and April-June 2020.  Results: The analysis shows that the sharing of COVID-19 updates on AIIMS, Raipur Twitter handle increased the followers 15 times from 2,000+ in March 2020 to 30,000+ in June 2020, and the sentiment analysis reflects that COVID related updates received 96.7 % positive comments. Conclusion: The case study finds that transparent and informative message sharing through social media by public health care institutions can create an effective channel of communication. This results in a positive institutional image.

scholarly journals Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study (Preprint)

2020 ◽  
Author(s):  
Aravind Sesagiri Raamkumar ◽  
Soon Guan Tan ◽  
Hwee Lin Wee
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Perceptions ◽  
Perceived Barriers ◽  
Classification Models ◽  
Perceived Severity ◽  
The Public ◽  
Health Authorities ◽  
Public Health Authorities

BACKGROUND Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions should be identified to enable public health authorities to effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, with the aim of understanding the health behaviors of the public. OBJECTIVE This study is aimed at developing and evaluating deep learning–based text classification models for classifying social media content posted during the COVID-19 outbreak, using the four key constructs of the HBM. We will specifically focus on content related to the physical distancing interventions put forth by public health authorities. We intend to test the model with a real-world case study. METHODS The data set for this study was prepared by analyzing Facebook comments that were posted by the public in response to the COVID-19–related posts of three public health authorities: the Ministry of Health of Singapore (MOH), the Centers for Disease Control and Prevention, and Public Health England. The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated data set of 16,752 comments, gated recurrent unit–based recurrent neural network models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used to evaluate the model. Specificity, sensitivity, and balanced accuracy were used to evaluate the classification results in the MOH case study. RESULTS The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91, and 0.94 for the constructs of perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the case study with MOH Facebook comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits, respectively. In addition, sensitivity was 79.6% and 81.5% for perceived susceptibility and perceived barriers, respectively. The classification models were able to accurately predict trends in the prevalence of the constructs for the time period examined in the case study. CONCLUSIONS The deep learning–based text classifiers developed in this study help to determine public perceptions toward physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize the health behaviors of the public through the lens of social media. In future studies, we intend to extend the model to study public perceptions of other important interventions by public health authorities.

scholarly journals Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak

2020 ◽  
Vol 17 (18) ◽  
pp. 6847 ◽  
Author(s):  
Yachao Li ◽  
Sylvia Twersky ◽  
Kelsey Ignace ◽  
Mei Zhao ◽  
Radhika Purandare ◽  
...  
Keyword(s):  
Public Health ◽  
Content Analysis ◽  
Early Stage ◽  
Message Content ◽  
Conspiracy Theories ◽  
Public Health Agencies ◽  
The Public ◽  
Lack Of Information ◽  
Health Agencies ◽  
Public Health Messages

This study focuses on stigma communication about COVID-19 on Twitter in the early stage of the outbreak, given the lack of information and rapid global expansion of new cases during this period. Guided by the model of stigma communication, we examine four types of message content, namely mark, group labeling, responsibility, and peril, that are instrumental in forming stigma beliefs and sharing stigma messages. We also explore whether the presence of misinformation and conspiracy theories in COVID-19-related tweets is associated with the presence of COVID-19 stigma content. A total of 155,353 unique COVID-19-related tweets posted between December 31, 2019, and March 13, 2020, were identified, from which 7000 tweets were randomly selected for manual coding. Results showed that the peril of COVID-19 was mentioned the most often, followed by mark, responsibility, and group labeling content. Tweets with conspiracy theories were more likely to include group labeling and responsibility information, but less likely to mention COVID-19 peril. Public health agencies should be aware of the unintentional stigmatization of COVID-19 in public health messages and the urgency to engage and educate the public about the facts of COVID-19.

scholarly journals Leveraging public health's participation in a Health Information Exchange to improve communicable disease reporting

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Ian Painter ◽  
Debra Revere ◽  
P. Joseph Gibson ◽  
Janet Baseman
Keyword(s):  
Public Health ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Communicable Disease ◽  
Health Agency ◽  
Public Health Agency ◽  
Public Health Agencies ◽  
The Public ◽  
The Mean

Background: Infectious diseases can appear and spread rapidly. Timely information about disease patterns and trends allows public health agencies to quickly investigate and efficiently contain those diseases. But disease case reporting to public health has traditionally been paper-based, resulting in somewhat slow, burdensome processes. Fortunately, the expanding use of electronic health records and health information exchanges has created opportunities for more rapid, complete, and easily managed case reporting and investigation. To assess how this new service might impact the efficiency and quality of a public health agency's case investigations, we compared the timeliness of usual case investigation to that of case investigations based on case report forms that were partially pre-populated with electronic data. Intervention: Between September 2013-March 2014, chlamydia disease report forms for certain clinics in Indianapolis were electronically pre-populated with clinical, lab and patient data available through the Indiana Health Information Exchange, then provided to the patient’s doctor. Doctors could then sign the form and deliver it to public health for investigation and population-level disease tracking. Methods: We utilized a novel matched case analysis of timeliness changes in receipt and processing of communicable disease report forms. Each Chlamydia cases reported with the pre-populated form were matched to cases reported in usual ways. We assessed the time from receipt of the case at the public health agency: 1) inclusion of the case into the public health surveillance system and 2) to close to case. A hierarchical random effects model was used to compare mean difference in each outcome between the target cases and the matched cases, with random intercepts for case. Results: Twenty-one Chlamydia cases were reported to the public health agency using the pre-populated form. Sixteen of these pre-populated form cases were matched to at least one other case, with a mean of 23 matches per case. The mean Reporting Lag for the pre-populated form cases was 2.5 days, which was 2.7 days shorter than the mean Reporting Lag for the matched controls (p = <0.001). The mean time to close a pre-populated form case was 4.7 days, which was 0.2 days shorter than time to close for the matched controls (p = 0.792). Conclusions: Use of pre-populated forms significantly decreased the time it took for the local public health agency to begin documenting and closing chlamydia case investigations. Thoughtful use of electronic health data for case reporting may decrease the per-case workload of public health agencies, and improve the timeliness of information about the pattern and spread of disease.

The Contribution of Residents Who Cooperate With Ring-Vaccination Measures Against Smallpox Epidemic

2012 ◽  
Vol 6 (3) ◽  
pp. 270-276 ◽  
Author(s):  
Hiroki Sato ◽  
Yutaka Sakurai
Keyword(s):  
Public Health ◽  
Health Agency ◽  
The United States ◽  
Public Health Agencies ◽  
The Public ◽  
Level Of Activity ◽  
Health Agencies ◽  
Ring Vaccination ◽  
Containment Measures ◽  
Voluntary Cooperation

ABSTRACTObjectives: Establishing containment measures against the potential spread of the smallpox virus has become a major issue in the public health field since the 2001 anthrax attacks in the United States. The primary objective of the study was to investigate the relationship between the level of activity of public health agencies and the voluntary cooperation of residents with ring-vaccination measures against a smallpox epidemic.Methods: A discrete-time, stochastic, individual-based model was used to simulate the spread of a smallpox epidemic that has become a more pressing topic due to 9/11 and to assess the effectiveness of and required resources for ring-vaccination measures in a closed community. In the simulation, we related sensitive tracing to the level of activity of the public health agency and strict isolation to the level of voluntary cooperation from residents.Results: Our results suggest that early and intensive case detection and contact tracing by public health agencies can reduce the scale of an epidemic and use fewer total resources. In contrast, voluntary reporting by the traced contacts of symptom onset after vaccination had little impact on the scale of epidemic in our model. However, it reduced the total required resources, indicating that citizens' voluntary cooperation would contribute to reducing the burden on public health agencies.Conclusions: We conclude that a combined effort on the part of public health agencies and residents in performing containment measures is essential to quickly ending a smallpox epidemic.(Disaster Med Public Health Preparedness. 2012;6:270–276)

scholarly journals Containing COVID-19 Through Contact Tracing

2020 ◽  
Vol 136 (1) ◽  
pp. 32-38
Author(s):  
Nilesh Kalyanaraman ◽  
Michael R. Fraser
Keyword(s):  
Public Health ◽  
Health Agency ◽  
The United States ◽  
Lessons Learned ◽  
Local State ◽  
Contact Tracing ◽  
Public Health Agencies ◽  
Incident Command ◽  
Department Of Health

Containing coronavirus disease 2019 (COVID-19) through case investigation and contact tracing is a crucial strategy for governmental public health agencies to control the spread of COVID-19 infection in the United States. Because of the recency of the pandemic, few examples of COVID-19 contact-tracing models have been shared among local, state, and federal public health officials to date. This case study of the Anne Arundel County Department of Health (Maryland) illustrates one model of contact-tracing activity developed early in the outbreak. We describe the contact-tracing effort’s place within the broader county health agency Incident Command System, as well as the capabilities needed, team composition, special considerations, and major lessons learned by county health officials. Other local, state, tribal, territorial, and federal health officials and policy makers can use this case study to innovate, iterate, and further refine contact-tracing efforts to prevent the spread of COVID-19 infection and support community members in isolation or quarantine.

scholarly journals Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study

10.2196/20493 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e20493 ◽  
Author(s):  
Aravind Sesagiri Raamkumar ◽  
Soon Guan Tan ◽  
Hwee Lin Wee
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Perceptions ◽  
Perceived Barriers ◽  
Classification Models ◽  
Perceived Severity ◽  
The Public ◽  
Health Authorities ◽  
Public Health Authorities

Background Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions should be identified to enable public health authorities to effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, with the aim of understanding the health behaviors of the public. Objective This study is aimed at developing and evaluating deep learning–based text classification models for classifying social media content posted during the COVID-19 outbreak, using the four key constructs of the HBM. We will specifically focus on content related to the physical distancing interventions put forth by public health authorities. We intend to test the model with a real-world case study. Methods The data set for this study was prepared by analyzing Facebook comments that were posted by the public in response to the COVID-19–related posts of three public health authorities: the Ministry of Health of Singapore (MOH), the Centers for Disease Control and Prevention, and Public Health England. The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated data set of 16,752 comments, gated recurrent unit–based recurrent neural network models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used to evaluate the model. Specificity, sensitivity, and balanced accuracy were used to evaluate the classification results in the MOH case study. Results The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91, and 0.94 for the constructs of perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the case study with MOH Facebook comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits, respectively. In addition, sensitivity was 79.6% and 81.5% for perceived susceptibility and perceived barriers, respectively. The classification models were able to accurately predict trends in the prevalence of the constructs for the time period examined in the case study. Conclusions The deep learning–based text classifiers developed in this study help to determine public perceptions toward physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize the health behaviors of the public through the lens of social media. In future studies, we intend to extend the model to study public perceptions of other important interventions by public health authorities.

scholarly journals The Response of Governments and Public Health Agencies to COVID-19 Pandemics on Social Media: A Multi-Country Analysis of Twitter Discourse

2021 ◽  
Vol 9 ◽  
Author(s):  
Lan Li ◽  
Aisha Aldosery ◽  
Fedor Vitiugin ◽  
Naomi Nathan ◽  
David Novillo-Ortiz ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Health Agency ◽  
Economic Consequences ◽  
Public Health Agencies ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Social Media Platforms ◽  
The Difference ◽  
The Uk

During the COVID-19 pandemic, information is being rapidly shared by public health experts and researchers through social media platforms. Whilst government policies were disseminated and discussed, fake news and misinformation simultaneously created a corresponding wave of “infodemics.” This study analyzed the discourse on Twitter in several languages, investigating the reactions to government and public health agency social media accounts that share policy decisions and official messages. The study collected messages from 21 official Twitter accounts of governments and public health authorities in the UK, US, Mexico, Canada, Brazil, Spain, and Nigeria, from 15 March to 29 May 2020. Over 2 million tweets in various languages were analyzed using a mixed-methods approach to understand the messages both quantitatively and qualitatively. Using automatic, text-based clustering, five topics were identified for each account and then categorized into 10 emerging themes. Identified themes include political, socio-economic, and population-protection issues, encompassing global, national, and individual levels. A comparison was performed amongst the seven countries analyzed and the United Kingdom (Scotland, Northern Ireland, and England) to find similarities and differences between countries and government agencies. Despite the difference in language, country of origin, epidemiological contexts within the countries, significant similarities emerged. Our results suggest that other than general announcement and reportage messages, the most-discussed topic is evidence-based leadership and policymaking, followed by how to manage socio-economic consequences.

scholarly journals Artificial intelligence to predict the risk of mortality from COVID-19: Insights from a Canadian Application

2020 ◽  
Author(s):  
Brett Snider ◽  
Paige Phillips ◽  
Aryn MacLean ◽  
Edward A McBean ◽  
Andrew Gadsden ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Health Agency ◽  
Important Variable ◽  
Training Dataset ◽  
Effective Utilization ◽  
The Public ◽  
Risk Of Mortality ◽  
Key Factor

The Severe Acute Respiratory Syndrome COVID-19 virus (SARS-CoV-2) has had enormous impacts, indicating need for non-pharmaceutical interventions (NPIs) using Artificial Intelligence (AI) modeling. Investigation of AI models and statistical models provides important insights within the province of Ontario as a case study application using patients' physiological conditions, symptoms, and demographic information from datasets from Public Health Ontario (PHO) and the Public Health Agency of Canada (PHAC). The findings using XGBoost provide an accuracy of 0.9056 for PHO, and 0.935 for the PHAC datasets. Age is demonstrated to be the most important variable with the next two variables being Hospitalization and Occupation. Further, AI models demonstrate identify the importance of improved medical practice which evolved over the six months in treating COVID-19 virus during the pandemic, and that age is absolutely now the key factor, with much lower importance of other variables that were important to mortality near the beginning of the pandemic. An XGBoost model is shown to be fairly accurate when the training dataset surpasses 1000 cases, indicating that AI has definite potential to be a useful tool in the fight against COVID-19 even when caseload numbers needed for effective utilization of AI model are not large.

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