Measles Vaccination Narrative in Twitter

A summary of our approach
Continuing our work with respects to GeoSocial analysis we have recently published a paper in JMIR Public Health and Surveillance entitled “The Measles Vaccination Narrative in Twitter: A Quantitative Analysis“. In this paper we explore how social media can be quantitatively studied to explore the narrative behind measles vaccinations. Below you can read the abstract to the paper which includes the background to why we chose to study this topic, the study objective, our methodology, a summary of our results and conclusions. 
Background: The emergence of social media is providing an alternative avenue for information exchange and opinion formation on health-related issues. Collective discourse in such media leads to the formation of a complex narrative, conveying public views and perceptions.

Objective: This paper presents a study of Twitter narrative regarding vaccination in the aftermath of the 2015 measles outbreak, both in terms of its cyber and physical characteristics. The contributions of this work are the analysis of the data for this particular study, as well as presenting a quantitative interdisciplinary approach to analyze such open-source data in the context of health narratives.

Methods: 669,136 tweets were collected in the period February 1 through March 9, 2015 referring to vaccination. These tweets were analyzed to identify key terms, connections among such terms, retweet patterns, the structure of the narrative, and connections to the geographical space.

Results: The data analysis captures the anatomy of the themes and relations that make up the discussion about vaccination in Twitter. The results highlight the higher impact of stories contributed by news organizations compared to direct tweets by health organizations in communicating health-related information. They also capture the structure of the anti-vaccination narrative and its terms of reference. Analysis also revealed the relationship between community engagement in Twitter and state policies regarding child vaccination. Residents of Vermont and Oregon, the two states with the highest rates of non-medical exemption from school-entry vaccines nationwide, are leading the social media discussion in terms of participation.

Conclusions: The interdisciplinary study of health-related debates in social media across the cyber-physical debate nexus leads to a greater understanding of public concerns, views, and responses to health-related issues. Further coalescing such capabilities shows promise towards advancing health communication, supporting the design of more effective strategies that take into account the complex and evolving public views of health issues.

Global distribution of tweets in our data corpus
The paper is open access and can be viewed and downloaded from here.
Full reference:
Radzikowski, J., Stefanidis, A., Jacobsen K.H., Croitoru, A., Crooks, A.T. and Delamater, P.L. (2016). “The Measles Vaccination Narrative in Twitter: A Quantitative Analysis”, JMIR Public Health and Surveillance, 2(1):e1. 

Hashtag associations: clustering based on co-occurrences of hashtags in individual tweets
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Measles Vaccination Narrative in Twitter

A summary of our approach
Continuing our work with respects to GeoSocial analysis we have recently published a paper in JMIR Public Health and Surveillance entitled “The Measles Vaccination Narrative in Twitter: A Quantitative Analysis“. In this paper we explore how social media can be quantitatively studied to explore the narrative behind measles vaccinations. Below you can read the abstract to the paper which includes the background to why we chose to study this topic, the study objective, our methodology, a summary of our results and conclusions. 
Background: The emergence of social media is providing an alternative avenue for information exchange and opinion formation on health-related issues. Collective discourse in such media leads to the formation of a complex narrative, conveying public views and perceptions.

Objective: This paper presents a study of Twitter narrative regarding vaccination in the aftermath of the 2015 measles outbreak, both in terms of its cyber and physical characteristics. The contributions of this work are the analysis of the data for this particular study, as well as presenting a quantitative interdisciplinary approach to analyze such open-source data in the context of health narratives.

Methods: 669,136 tweets were collected in the period February 1 through March 9, 2015 referring to vaccination. These tweets were analyzed to identify key terms, connections among such terms, retweet patterns, the structure of the narrative, and connections to the geographical space.

Results: The data analysis captures the anatomy of the themes and relations that make up the discussion about vaccination in Twitter. The results highlight the higher impact of stories contributed by news organizations compared to direct tweets by health organizations in communicating health-related information. They also capture the structure of the anti-vaccination narrative and its terms of reference. Analysis also revealed the relationship between community engagement in Twitter and state policies regarding child vaccination. Residents of Vermont and Oregon, the two states with the highest rates of non-medical exemption from school-entry vaccines nationwide, are leading the social media discussion in terms of participation.

Conclusions: The interdisciplinary study of health-related debates in social media across the cyber-physical debate nexus leads to a greater understanding of public concerns, views, and responses to health-related issues. Further coalescing such capabilities shows promise towards advancing health communication, supporting the design of more effective strategies that take into account the complex and evolving public views of health issues.

Global distribution of tweets in our data corpus
The paper is open access and can be viewed and downloaded from here.
Full reference:
Radzikowski, J., Stefanidis, A., Jacobsen K.H., Croitoru, A., Crooks, A.T. and Delamater, P.L. (2016). “The Measles Vaccination Narrative in Twitter: A Quantitative Analysis”, JMIR Public Health and Surveillance, 2(1):e1. 

Hashtag associations: clustering based on co-occurrences of hashtags in individual tweets
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New Paper: ABM Applied to the Spread of Cholera

Cholera transmission through the interaction
of host and the environment
We are pleased to announce we have just had a paper published in Environmental Modelling and Software entitled “An Agent-based Modeling Approach Applied to the Spread of Cholera
Research highlights include:
  • An agent-based model was developed to explore the spread of cholera.
  • The progress of cholera transmission is represented through a Susceptible-Exposed-Infected-Recovered (SEIR) model. 
  • The model integrates geographical data with agents’ daily activities within a refugee camp.
  • Results show cholera infections are impacted by agents’ movement and source of contamination. 
  • The model has the potential for aiding humanitarian response with respect to disease outbreaks.
Cholera dynamics when rainfall is introduced.

Spatial spread of cholera over the course of a year.
Study area
If the research highlights have not turned you off, the abstract to the paper is below:
“Cholera is an intestinal disease and is characterized by diarrhea and severe dehydration. While cholera has mainly been eliminated in regions that can provide clean water, adequate hygiene and proper sanitation; it remains a constant threat in many parts of Africa and Asia. Within this paper, we develop an agent-based model that explores the spread of cholera in the Dadaab refugee camp in Kenya. Poor sanitation and housing conditions contribute to frequent incidents of cholera outbreaks within this camp. We model the spread of cholera by explicitly representing the interaction between humans and their environment, and the spread of the epidemic using a Susceptible-Exposed-Infected-Recovered model. Results from the model show that the spread of cholera grows radially from contaminated water sources and seasonal rains can cause the emergence of cholera outbreaks. This modeling effort highlights the potential of agent-based modeling to explore the spread of cholera in a humanitarian context.”
Finally to aide replication, experimentation or just explore how you can link raster and vector data in GeoMason, we have a dedicated website where you can download executables of the model along with the source code and associated data. Moreover we have provide a really detailed Overview, Design concepts, and Details (ODD) Protocol document of the model.

Full Reference:
Crooks, A.T. and Hailegiorgis, A.B. (2014), An Agent-based Modeling Approach Applied to the Spread of Cholera, Environmental Modelling and Software, 62: 164-177
DOI: 10.1016/j.envsoft.2014.08.027 (pdf)

Continue reading »

New Paper: ABM Applied to the Spread of Cholera

Cholera transmission through the interaction
of host and the environment
We are pleased to announce we have just had a paper published in Environmental Modelling and Software entitled “An Agent-based Modeling Approach Applied to the Spread of Cholera
Research highlights include:
  • An agent-based model was developed to explore the spread of cholera.
  • The progress of cholera transmission is represented through a Susceptible-Exposed-Infected-Recovered (SEIR) model. 
  • The model integrates geographical data with agents’ daily activities within a refugee camp.
  • Results show cholera infections are impacted by agents’ movement and source of contamination. 
  • The model has the potential for aiding humanitarian response with respect to disease outbreaks.
Cholera dynamics when rainfall is introduced.

Spatial spread of cholera over the course of a year.
Study area
If the research highlights have not turned you off, the abstract to the paper is below:
“Cholera is an intestinal disease and is characterized by diarrhea and severe dehydration. While cholera has mainly been eliminated in regions that can provide clean water, adequate hygiene and proper sanitation; it remains a constant threat in many parts of Africa and Asia. Within this paper, we develop an agent-based model that explores the spread of cholera in the Dadaab refugee camp in Kenya. Poor sanitation and housing conditions contribute to frequent incidents of cholera outbreaks within this camp. We model the spread of cholera by explicitly representing the interaction between humans and their environment, and the spread of the epidemic using a Susceptible-Exposed-Infected-Recovered model. Results from the model show that the spread of cholera grows radially from contaminated water sources and seasonal rains can cause the emergence of cholera outbreaks. This modeling effort highlights the potential of agent-based modeling to explore the spread of cholera in a humanitarian context.”
Finally to aide replication, experimentation or just explore how you can link raster and vector data in GeoMason, we have a dedicated website where you can download executables of the model along with the source code and associated data. Moreover we have provide a really detailed Overview, Design concepts, and Details (ODD) Protocol document of the model.

Full Reference:
Crooks, A.T. and Hailegiorgis, A.B. (2014), An Agent-based Modeling Approach Applied to the Spread of Cholera, Environmental Modelling and Software, 62: 164-177
DOI: 10.1016/j.envsoft.2014.08.027 (pdf)

Continue reading »

Modeling the outbreak, spread, and containment of tuberculosis

It seems my interest into disease models is growing. While the development of the cholera model is still underway, over the summer I have had been working with a very talented high school student looking at the outbreak, spread and containment of tuberculosis (TB). Why might you ask? TB is a global problem with 1.8 billion people having a TB Infection, 8.8 million people infected with the TB disease, and around 1.5 million annual deaths. It is the second most common form of death from an infectious disease with the majority of cases in developing countries.

So we have been developing a model that explores how TB might manifest itself, spread within an urban setting and the potential to contain the disease. We have chosen as our test case the Kibera slum within Nairobi, Kenya. Agents in this model represent the residents of the Kibera slum. They are mobile and goal-orientated, seeking to fulfill one goal before moving on to the next. Goals are determined based on the agent’s characteristics (age, sex, etc.) as well as their needs (water, food, health etc.). The exact location they choose to go to is also affected by the distance. When agents interact with one another, they can be infected with TB. Infection is determined upon the amount of bacilli absorbed by agents and their immune response. The transition from infection to disease for HIV positive patients is also dependent on the patient’s CD4 cell count.  What you see below is a poster we presented at Krasnow Institute Retreat.

To give a sense of the dynamics of the model, the movie below shows agents moving around the slum and how their health status changes as time progresses.

Continue reading »

Modeling the outbreak, spread, and containment of tuberculosis

It seems my interest into disease models is growing. While the development of the cholera model is still underway, over the summer I have had been working with a very talented high school student looking at the outbreak, spread and containment of tuberculosis (TB). Why might you ask? TB is a global problem with 1.8 billion people having a TB Infection, 8.8 million people infected with the TB disease, and around 1.5 million annual deaths. It is the second most common form of death from an infectious disease with the majority of cases in developing countries.

So we have been developing a model that explores how TB might manifest itself, spread within an urban setting and the potential to contain the disease. We have chosen as our test case the Kibera slum within Nairobi, Kenya. Agents in this model represent the residents of the Kibera slum. They are mobile and goal-orientated, seeking to fulfill one goal before moving on to the next. Goals are determined based on the agent’s characteristics (age, sex, etc.) as well as their needs (water, food, health etc.). The exact location they choose to go to is also affected by the distance. When agents interact with one another, they can be infected with TB. Infection is determined upon the amount of bacilli absorbed by agents and their immune response. The transition from infection to disease for HIV positive patients is also dependent on the patient’s CD4 cell count.  What you see below is a poster we presented at Krasnow Institute Retreat.

To give a sense of the dynamics of the model, the movie below shows agents moving around the slum and how their health status changes as time progresses.

Continue reading »