scholarly journals Using photos for public health communication: A computational analysis of the Centers for Disease Control and Prevention Instagram photos and public responses

2020 ◽  
Vol 26 (3) ◽  
pp. 2159-2180 ◽  
Author(s):  
Yunhwan Kim ◽  
Jang Hyun Kim
Keyword(s):  
Public Health ◽  
Disease Control ◽  
The United States ◽  
Public Health Communication ◽  
Cognitive Services ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Control And Prevention ◽  
Centers For Disease Control ◽  
Human Faces

This study aims to explore the use of Instagram by the Centers for Disease Control and Prevention, one of the representative public health authorities in the United States. For this aim, all of the photos uploaded on the Centers for Disease Control and Prevention Instagram account were crawled and the content of them were analyzed using Microsoft Azure Cognitive Services. Also, engagement was measured by the sum of numbers of likes and comments to each photo, and sentiment analysis of comments was conducted. Results suggest that the photos that can be categorized into “text” and “people” took the largest share in the Centers for Disease Control and Prevention Instagram photos. And it was found that the Centers for Disease Control and Prevention’s major way of delivering messages on Instagram was to imprint key messages that call for actions for better health on photos and to provide the source of complementary information on text component of each post. It was also found that photos with more and bigger human faces had lower level of engagement than the others, and happiness and neutral emotions expressed on the faces in photos were negatively associated with engagement. The features whose high value would make the photos look splendid and gaudy were negatively correlated with engagement, but sharpness was positively correlated.

Internal Dissension Grows as CDC Faces Big Threats to Public Health

2005 ◽  
Vol 35 (4) ◽  
pp. 779-782 ◽  
Author(s):  
Rob Stein
Keyword(s):  
Public Health ◽  
Disease Control ◽  
Obesity Epidemic ◽  
Health Authorities ◽  
Political Interference ◽  
Public Health Authorities ◽  
Control And Prevention ◽  
Centers For Disease Control ◽  
The Impact

According to current and former CDC officials and several outside experts, the Centers for Disease Control and Prevention is being roiled by internal dissension as it faces such unprecedented threats as bioterrorism, a potential flu pandemic, and the obesity epidemic. The agency has been thrown into turmoil by a combination of factors, including a drawn-out restructuring, the departure of dozens of its most respected scientists, concerns about political interference, and a pending budget cut of nearly $500 million. The impact remains a matter of debate, but the uproar is causing widespread alarm among public health authorities.

scholarly journals Users’ Beliefs Toward Physical Distancing in Facebook Pages of Public Health Authorities During COVID-19 Pandemic in Early 2020

2021 ◽  
pp. 109019812110144
Author(s):  
Soon Guan Tan ◽  
Aravind Sesagiri Raamkumar ◽  
Hwee Lin Wee
Keyword(s):  
Public Health ◽  
Health Belief Model ◽  
United States Public Health ◽  
Health Belief ◽  
The United States ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Control And Prevention ◽  
Belief Model ◽  
The Health Belief Model

This study aims to describe Facebook users’ beliefs toward physical distancing measures implemented during the Coronavirus disease (COVID-19) pandemic using the key constructs of the health belief model. A combination of rule-based filtering and manual classification methods was used to classify user comments on COVID-19 Facebook posts of three public health authorities: Centers for Disease Control and Prevention of the United States, Public Health England, and Ministry of Health, Singapore. A total of 104,304 comments were analyzed for posts published between 1 January, 2020, and 31 March, 2020, along with COVID-19 cases and deaths count data from the three countries. Findings indicate that the perceived benefits of physical distancing measures ( n = 3,463; 3.3%) was three times higher than perceived barriers ( n = 1,062; 1.0%). Perceived susceptibility to COVID-19 ( n = 2,934; 2.8%) was higher compared with perceived severity ( n = 2,081; 2.0%). Although susceptibility aspects of physical distancing were discussed more often at the start of the year, mentions on the benefits of intervention emerged stronger toward the end of the analysis period, highlighting the shift in beliefs. The health belief model is useful for understanding Facebook users’ beliefs at a basic level, and it provides a scope for further improvement.

scholarly journals Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study

10.2196/25108 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e25108
Author(s):  
Joanne Chen Lyu ◽  
Garving K Luli
Keyword(s):  
Public Health ◽  
Disease Control ◽  
The United States ◽  
Data Set ◽  
Public Discussion ◽  
The Public ◽  
Twitter Data ◽  
The World ◽  
Control And Prevention ◽  
Centers For Disease Control

Background The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion. Objective This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC. Methods Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords—cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov—while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling. Results The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility. Conclusions Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.

scholarly journals Improving Probabilistic Infectious Disease Forecasting Through Coherence

2019 ◽  
Author(s):  
Graham Casey Gibson ◽  
Kelly R. Moran ◽  
Nicholas G. Reich ◽  
Dave Osthus
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Disease Control ◽  
The United States ◽  
Weighted Sum ◽  
Public Health Burden ◽  
Control And Prevention ◽  
Centers For Disease Control ◽  
The U.S ◽  
Geographical Units

AbstractWith an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 90% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy.Author SummarySeasonal influenza causes a significant public health burden nationwide. Accurate influenza forecasting may help public health officials allocate resources and plan responses to emerging outbreaks. The U.S. Centers for Disease Control and Prevention (CDC) reports influenza data at multiple geographical units, including regionally and nationally, where the national data are by construction a weighted sum of the regional data. In an effort to improve influenza forecast accuracy across all models submitted to the CDC’s annual flu forecasting challenge, we examined the effect of imposing this geographical constraint on the set of independent forecasts, made publicly available by the CDC. We developed a novel method to transform forecast densities to obey the geographical constraint that respects the correlation structure between geographical units. This method showed consistent improvement across 90% of models and that held when stratified by targets and test seasons. Our method can be applied to other forecasting systems both within and outside an infectious disease context that have a geographical hierarchy.

scholarly journals Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study (Preprint)

2020 ◽  
Author(s):  
Joanne Chen Lyu ◽  
Garving K Luli
Keyword(s):  
Public Health ◽  
Disease Control ◽  
The United States ◽  
Data Set ◽  
Public Discussion ◽  
The Public ◽  
Twitter Data ◽  
The World ◽  
Control And Prevention ◽  
Centers For Disease Control

BACKGROUND The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion. OBJECTIVE This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC. METHODS Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords—cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov—while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling. RESULTS The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility. CONCLUSIONS Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.

scholarly journals A Decade of Public Health Genomics in the United States: Centers for Disease Control and Prevention 1997–2007

2009 ◽  
Vol 12 (1) ◽  
pp. 20-29 ◽  
Author(s):  
M.J. Khoury ◽  
S. Bowen ◽  
L.A. Bradley ◽  
R. Coates ◽  
N.F. Dowling ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Disease Control ◽  
The United States ◽  
Public Health Genomics ◽  
Control And Prevention ◽  
Centers For Disease Control

Communications and Politics

2018 ◽  
Author(s):  
Joshua M. Sharfstein
Keyword(s):  
Public Health ◽  
Disease Control ◽  
Accurate Information ◽  
Political Judgment ◽  
Political Leaders ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Control And Prevention ◽  
Public Health Officials ◽  
Centers For Disease Control

An effective communications approach starts with a basic dictum set forth by the Centers for Disease Control and Prevention: “Be first, be right, be credible.” Agencies must establish themselves as vital sources of accurate information to maintain the public’s trust. At the same time, public health officials must recognize that communications play out in the context of ideological debates, electoral rivalries, and other political considerations. During a public health crisis, this means that health officials often need to constructively engage political leaders in communications and management. Navigating these waters in the middle of a crisis can be treacherous. Figuring out the best way to engage elected leaders is a core aspect of political judgment.

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