The latest outputs from researchers, alumni and friends at the UCL Centre for Advanced Spatial Analysis (CASA).
Lineage distribution of SARS-CoV-2 across geographic regions of Ontario, Canada, Western New York, and New York City over timeIn the past we have posted on using agent-based models for explore the spread of diseases. We have been keeping up with this w…
Continue reading »Often we are building geographically explicit agent-based models we spend a lot of time creating the synthetic population to instantiate our artificial world. We have tired to overcome this with creating methods to generate such populations (see this o…
Continue reading »At this years Association of American Geographers (AAG) Annual Meeting we have a number of presentations ranging from how one can leverage newspaper articles to study cities over time, to that of how people may chose to become vaccinated. These present…
Continue reading »At this years Association of American Geographers (AAG) Annual Meeting we have a number of presentations ranging from how one can leverage newspaper articles to study cities over time, to that of how people may chose to become vaccinated. These present…
Continue reading »When developing geographically explicit agent-based models, one thing we spend a lot of time on is building synthetic populations and then linking the agents in the synthetic population to each other. To overcome this issue we have a new paper pu…
Continue reading »When developing geographically explicit agent-based models, one thing we spend a lot of time on is building synthetic populations and then linking the agents in the synthetic population to each other. To overcome this issue we have a new paper pu…
Continue reading »We have just wrapped up project that created a simple agent-based simulation of urban life as part of DARPA’s Ground Truth Program. To this end we have just published a new paper entitled “Urban life: a model of people and places” published in C…
Continue reading »We have just wrapped up project that created a simple agent-based simulation of urban life as part of DARPA’s Ground Truth Program. To this end we have just published a new paper entitled “Urban life: a model of people and places” published in C…
Continue reading »In the past we have written about the challenges of validation and to some extent the calibration of agent-based models but never really went into much detail about the calibration process. To this end, Jeon-Young Kang, Alexander Michels, Jared Aldstad…
Continue reading »In the past we have written about the challenges of validation and to some extent the calibration of agent-based models but never really went into much detail about the calibration process. To this end, Jeon-Young Kang, Alexander Michels, Jared Aldstad…
Continue reading »Running large scale spatial agent-based models is often a computational challenge. To address this challenge, at the upcoming International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Mode…
Continue reading »Running large scale spatial agent-based models is often a computational challenge. To address this challenge, at the upcoming International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Mode…
Continue reading »Building upon our previous work on Location-Based Social Networks (LBSNs) and how agent-based modeling could provide an alternative to real world data sets, in the latest SIGSPATIAL Special Newsletter, we (Joon-Seok Kim, Hamdi Kavak, Chris Rouly, Hyunj…
Continue reading »Building upon our previous work on Location-Based Social Networks (LBSNs) and how agent-based modeling could provide an alternative to real world data sets, in the latest SIGSPATIAL Special Newsletter, we (Joon-Seok Kim, Hamdi Kavak, Chris Rouly, Hyunj…
Continue reading »The John Snow Cholera Map is world famous as the map that identified the cause of the disease, and was one of the first epidemiological maps created. However, a number of other maps of the location of individuals with the disease were produced at aroun…
Continue reading »
Continuing on our work of exploring health related issues in social media, Xiaoyi Yuan and myself had a paper accepted at the 9th International Conference on Social Media and Society. In our paper entitled: “Examining Online Vaccination Discussion and …
Continue reading »Continuing on our work of exploring health related issues in social media, Xiaoyi Yuan and myself had a paper accepted at the 9th International Conference on Social Media and Society. In our paper entitled: “Examining Online Vaccination Discussion and …
Continue reading »
Continuing our work on geosocial analysis we recently had a paper entitled “Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election” published in World Medical and Health Policy. In the paper we…
Continue reading »
Continuing our work on geosocial analysis we recently had a paper entitled “Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election” published in World Medical and Health Policy. In the paper we…
Continue reading »Continuing our work on geosocial analysis we recently had a paper entitled “Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election” published in World Medical and Health Policy. In the paper we…
Continue reading »
Measles is one of the leading causes of death among young children. In many developed countries with high measles, mumps, and rubella (MMR) vaccine coverage, measles outbreaks still happen each year. Previous research has demonstrated that what underlies the paradox of high vaccination coverage and measles outbreaks is the ineffectiveness of “herd immunity”, which has the false assumption that people are mixing randomly and there’s equal distribution of vaccinated population. In reality, the unvaccinated population is often clustered instead of not equally distributed. Meanwhile, the Internet has been one of the dominant information sources to gain vaccination knowledge and thus has also been the locus of the “anti-vaccine movement”. In this paper, we propose an agent-based model that explores sentiment diffusion and how this process creates anti-vaccination opinion clusters that leads to larger scale disease outbreaks. The model separates cyber space (where information diffuses) and physical space (where both information diffuses and diseases transmit). The results show that cyber space anti-vaccine opinion leaders have such an influence on anti-vaccine sentiments diffusion in the information network that even if the model starts with the majority of the population being pro-vaccine, the degree of disease outbreaks increases significantly.Keywords: Agent-based modeling Information networks Infectious disease transmission.
Full Reference:
Continue reading »Yuan, X. and Crooks, A.T. (2017), From Cyber Space Opinion Leaders and the Spread of Anti-Vaccine Extremism to Physical Space Disease Outbreaks, in Lee, D., Lin, Y., Osgood, N. and Thomson, R. (eds.) Proceedings of the 2017 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Springer, New York, NY., pp. 114-119. (PDF).
Measles is one of the leading causes of death among young children. In many developed countries with high measles, mumps, and rubella (MMR) vaccine coverage, measles outbreaks still happen each year. Previous research has demonstrated that what underlies the paradox of high vaccination coverage and measles outbreaks is the ineffectiveness of “herd immunity”, which has the false assumption that people are mixing randomly and there’s equal distribution of vaccinated population. In reality, the unvaccinated population is often clustered instead of not equally distributed. Meanwhile, the Internet has been one of the dominant information sources to gain vaccination knowledge and thus has also been the locus of the “anti-vaccine movement”. In this paper, we propose an agent-based model that explores sentiment diffusion and how this process creates anti-vaccination opinion clusters that leads to larger scale disease outbreaks. The model separates cyber space (where information diffuses) and physical space (where both information diffuses and diseases transmit). The results show that cyber space anti-vaccine opinion leaders have such an influence on anti-vaccine sentiments diffusion in the information network that even if the model starts with the majority of the population being pro-vaccine, the degree of disease outbreaks increases significantly.Keywords: Agent-based modeling Information networks Infectious disease transmission.
Full Reference:
Continue reading »Yuan, X. and Crooks, A.T. (2017), From Cyber Space Opinion Leaders and the Spread of Anti-Vaccine Extremism to Physical Space Disease Outbreaks, in Lee, D., Lin, Y., Osgood, N. and Thomson, R. (eds.) Proceedings of the 2017 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Springer, New York, NY., pp. 114-119. (PDF).
In the system dynamics model from Shiflet and Shiflet (2014), one person is infected at start. Infected people can infect susceptible people. The population of infected will always increase by (number of infected * number of susceptible * InfectionRate * change in time dt). The infected people may recover. The amount of people that will recover in an iteration is always equal to (number of infected * RecoveryRate * change in time dt). Figure 1 illustrates the system dynamics process while Figure 2 shows the SIR process as a flowchart.
 |
| Figure 1. System Dynamics process (source: Shiflet and Shiflet, 2014) |
 |
| Figure 2. System Dynamics flowchart |
The Agent-based Model
 |
|||
| Figure 3. Agent-based Modeling: agent decision process |
 |
| Figure 4. Display of the ABM. Green = susceptible. Red = infected. Blue = recovered. |
Figure 5 shows the changing process of the cells while Figure 6 shows the display of the CA model.
 |
| Figure 5. Cellular Automata cell changing process |
 |
| Figure 6. Display of the CA model. Green = susceptible. Red = infected. Blue = recovered. |
The Discrete Event Simulation Model
In a Discrete Event Simulation model (aka. queuing model), there are three abstract types of objects: 1) servers, 2) customers, 3) queues, which is quite different from the CA and ABMs.
 |
| Figure 7. Discrete Event Simulation process. |
Results from the Implementations
 |
| Figure 8. Results for the different models. Clockwise from top left: SD model, ABM, DES and CA |
First of all, the SD model has the smallest number of iterations before no one is infected. The number of iterations shown on the graph are the average of the ten runs, since the runs range from smaller to larger numbers (except for the SD model, which only has one run). The SD model only took 17451 iterations to stop, while the ABM took 19145 iterations (on average), the DES model took 18645 iterations (on average). The CA model took the longest time on average for no more individuals to be infected, it took 25680 iterations (on average).
The results of the SD, ABM and DES models while appearing to be very similar to each other. In the sense, that the number of infected people increase fast at first and reaches a peak number of over 1500 at more than 2000 iterations (2272 for SD, 2403 for ABM, 2538 for DES). On the other hand, in the CA model, the number of infected people increases much slower due to the diffusion mechanism of the CA model and never reaches an amount as high as in the former models.
An important characteristic of the SD model is that there is no randomness in the model, so no matter how many times you run this model, you will get the same result. In the other three models, getting infected or recover always depend on a probability function, so there is difference in every run.
Furthermore, people in the SD model and the DES model are homogeneous, and everyone has the same probability to becoming infected or recovering from an infection, although these rates change over time, they do not vary among the different people in the population. On the other hand, in the ABM and the CA model, people (represented by moving agents or static cells) are heterogenous in the sense that they have different locations. Only susceptible people around an infected individual can be infected. It is interesting that when people can move around, like in the ABM, the result is similar to the SD model, though the ABM takes a little more time to recover (19145 iterations in ABM vs. 17451 iterations in SD). When people are static and the number of people on the same space is limited (one cell in one space in this case), like in the CA model, the infection process becomes slower and it takes longer for everyone to recover.
To test how the models are sensitive to a specific parameter we now present what happens if we increase the infection rate in each model from 0.002 to 0.02 and show the results shown in Figure 9. As to be expected as the infection rate increased, the number of susceptible people decrease at a much faster rate. However, the SD, the ABM, and the DES models are still similar to each other, while the infection in the CA model is slower. The average number of iterations for these models are: 15807 (SD), 15252 (ABM), 16937 (CA), 16677 (DES). By increasing the infection rate the total number of iterations of each model has decreased, with the CA model still taking the longest time to converge. The peak of infected people in each model are on average: 2363 people at 255 iterations (SD), 2310 people at 363 iterations (ABM), 2035 people at 1019 iterations (CA), 2340 people at 286 iterations (DES). The CA model takes a longer time and reaches a lower peak.
 |
| Figure 9. Results for the different models with infection rate = 0.02. Clockwise from top left: SD model, ABM, DES and CA. |
These models are only simple examples of how a SIR model can be implemented in different modeling techniques, but in reality, if we were to model disease propagation in more detail we would need to consider many other things such as people could be both moving through space (i.e. traveling to work) and static (i.e. staying at home), and the capacity of each cell is always limited to some amount.
References:
Gilbert, N. and Troitzsch, K.G. (2005), Simulation for the Social Scientist (2nd Edition), Open University Press, Milton Keynes, UK.
Shiflet, A.B. and Shiflet, G.W. (2014), Introduction to Computational Science: Modeling and Simulation for the Sciences (2nd Edition), Princeton University Press, Princeton, NJ.
More information about the models and to download them please visit Yang Zhou’s website.
Continue reading »
In the system dynamics model from Shiflet and Shiflet (2014), one person is infected at start. Infected people can infect susceptible people. The population of infected will always increase by (number of infected * number of susceptible * InfectionRate * change in time dt). The infected people may recover. The amount of people that will recover in an iteration is always equal to (number of infected * RecoveryRate * change in time dt). Figure 1 illustrates the system dynamics process while Figure 2 shows the SIR process as a flowchart.
|
| Figure 1. System Dynamics process (source: Shiflet and Shiflet, 2014) |
|
| Figure 2. System Dynamics flowchart |
The Agent-based Model
|
|||
| Figure 3. Agent-based Modeling: agent decision process |
|
| Figure 4. Display of the ABM. Green = susceptible. Red = infected. Blue = recovered. |
Figure 5 shows the changing process of the cells while Figure 6 shows the display of the CA model.
|
| Figure 5. Cellular Automata cell changing process |
|
| Figure 6. Display of the CA model. Green = susceptible. Red = infected. Blue = recovered. |
The Discrete Event Simulation Model
In a Discrete Event Simulation model (aka. queuing model), there are three abstract types of objects: 1) servers, 2) customers, 3) queues, which is quite different from the CA and ABMs.
|
| Figure 7. Discrete Event Simulation process. |
Results from the Implementations
|
| Figure 8. Results for the different models. Clockwise from top left: SD model, ABM, DES and CA |
First of all, the SD model has the smallest number of iterations before no one is infected. The number of iterations shown on the graph are the average of the ten runs, since the runs range from smaller to larger numbers (except for the SD model, which only has one run). The SD model only took 17451 iterations to stop, while the ABM took 19145 iterations (on average), the DES model took 18645 iterations (on average). The CA model took the longest time on average for no more individuals to be infected, it took 25680 iterations (on average).
The results of the SD, ABM and DES models while appearing to be very similar to each other. In the sense, that the number of infected people increase fast at first and reaches a peak number of over 1500 at more than 2000 iterations (2272 for SD, 2403 for ABM, 2538 for DES). On the other hand, in the CA model, the number of infected people increases much slower due to the diffusion mechanism of the CA model and never reaches an amount as high as in the former models.
An important characteristic of the SD model is that there is no randomness in the model, so no matter how many times you run this model, you will get the same result. In the other three models, getting infected or recover always depend on a probability function, so there is difference in every run.
Furthermore, people in the SD model and the DES model are homogeneous, and everyone has the same probability to becoming infected or recovering from an infection, although these rates change over time, they do not vary among the different people in the population. On the other hand, in the ABM and the CA model, people (represented by moving agents or static cells) are heterogenous in the sense that they have different locations. Only susceptible people around an infected individual can be infected. It is interesting that when people can move around, like in the ABM, the result is similar to the SD model, though the ABM takes a little more time to recover (19145 iterations in ABM vs. 17451 iterations in SD). When people are static and the number of people on the same space is limited (one cell in one space in this case), like in the CA model, the infection process becomes slower and it takes longer for everyone to recover.
To test how the models are sensitive to a specific parameter we now present what happens if we increase the infection rate in each model from 0.002 to 0.02 and show the results shown in Figure 9. As to be expected as the infection rate increased, the number of susceptible people decrease at a much faster rate. However, the SD, the ABM, and the DES models are still similar to each other, while the infection in the CA model is slower. The average number of iterations for these models are: 15807 (SD), 15252 (ABM), 16937 (CA), 16677 (DES). By increasing the infection rate the total number of iterations of each model has decreased, with the CA model still taking the longest time to converge. The peak of infected people in each model are on average: 2363 people at 255 iterations (SD), 2310 people at 363 iterations (ABM), 2035 people at 1019 iterations (CA), 2340 people at 286 iterations (DES). The CA model takes a longer time and reaches a lower peak.
|
| Figure 9. Results for the different models with infection rate = 0.02. Clockwise from top left: SD model, ABM, DES and CA. |
These models are only simple examples of how a SIR model can be implemented in different modeling techniques, but in reality, if we were to model disease propagation in more detail we would need to consider many other things such as people could be both moving through space (i.e. traveling to work) and static (i.e. staying at home), and the capacity of each cell is always limited to some amount.
References:
Gilbert, N. and Troitzsch, K.G. (2005), Simulation for the Social Scientist (2nd Edition), Open University Press, Milton Keynes, UK.
Shiflet, A.B. and Shiflet, G.W. (2014), Introduction to Computational Science: Modeling and Simulation for the Sciences (2nd Edition), Princeton University Press, Princeton, NJ.
More information about the models and to download them please visit Yang Zhou’s website.
Continue reading »
Specifically we combined actors (e.g. twitter uses), locations (e.g. where the tweets originated) and concepts (e.g. emerging narratives such as pregnancy) to gain insights on the mechanisms that drive participation, contributions, and interactions on social media during a disease outbreak. Below you can read a summary of our paper along with some of the figures which highlight our methodology and findings.
 |
| An overview of the Twitter narrative analysis approach, starting with data collection, and proceeding with preprocessing and data analysis to identify narrative events, which can be used to build an event storyline. |
Abstract:
Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts.
Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept- related for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak.
Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks.
Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this.
Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern.
Keywords: Zika Virus; Social Media; Twitter Messaging; Geographic Information Systems.
 |
| Spatiotemporal participation patterns and identifiable clusters over 4 of our twelve week study. The top left panel shows the data during the first week, and time progresses from left to right and from top to bottom towards . |
 |
| Subsets of the full retweet network pertaining to the WHO (left) and CDC (right), and clusters identified within them. Magenta clusters are centered upon health entities, green upon news organizations, orange upon political entities. |
 |
| Visualizing a narrative storyline across locations (blue), actors (red), and concepts (green). |
Full Reference:
Stefanidis, A., Vraga, E., Lamprianidis, G., Radzikowski, J., Delamater, P.L., Jacobsen, K.H., Pfoser, D., Croitoru, A. and Crooks, A.T. (2017). “Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts”, JMIR Public Health and Surveillance, 3 (2): e22. (pdf)
As normal, any feedback or comments are most welcome.
Specifically we combined actors (e.g. twitter uses), locations (e.g. where the tweets originated) and concepts (e.g. emerging narratives such as pregnancy) to gain insights on the mechanisms that drive participation, contributions, and interactions on social media during a disease outbreak. Below you can read a summary of our paper along with some of the figures which highlight our methodology and findings.
|
| An overview of the Twitter narrative analysis approach, starting with data collection, and proceeding with preprocessing and data analysis to identify narrative events, which can be used to build an event storyline. |
Abstract:
Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts.
Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept- related for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak.
Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks.
Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this.
Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern.
Keywords: Zika Virus; Social Media; Twitter Messaging; Geographic Information Systems.
|
| Spatiotemporal participation patterns and identifiable clusters over 4 of our twelve week study. The top left panel shows the data during the first week, and time progresses from left to right and from top to bottom towards . |
|
| Subsets of the full retweet network pertaining to the WHO (left) and CDC (right), and clusters identified within them. Magenta clusters are centered upon health entities, green upon news organizations, orange upon political entities. |
|
| Visualizing a narrative storyline across locations (blue), actors (red), and concepts (green). |
Full Reference:
Stefanidis, A., Vraga, E., Lamprianidis, G., Radzikowski, J., Delamater, P.L., Jacobsen, K.H., Pfoser, D., Croitoru, A. and Crooks, A.T. (2017). “Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts”, JMIR Public Health and Surveillance, 3 (2): e22. (pdf)
As normal, any feedback or comments are most welcome.
We recently co-authored a paper entitled “Lessons from the Ebola Outbreak: Action Items for Emerging Infectious Disease Preparedness and Response” in EcoHealth” with several other researchers from George Mason University. In the paper we discuss t…
Continue reading »
We recently co-authored a paper entitled “Lessons from the Ebola Outbreak: Action Items for Emerging Infectious Disease Preparedness and Response” in EcoHealth” with several other researchers from George Mason University. In the paper we discuss t…
Continue reading »
 |
| A summary of our approach |
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 |
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 |
|
| A summary of our approach |
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 |
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 |
 |
| Cholera transmission through the interaction of host and the environment |
 |
| Cholera dynamics when rainfall is introduced. |
 |
| Spatial spread of cholera over the course of a year. |
 |
| Study area |
“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.”
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)
|
| Cholera transmission through the interaction of host and the environment |
|
| Cholera dynamics when rainfall is introduced. |
|
| Spatial spread of cholera over the course of a year. |
|
| Study area |
“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.”
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)
Over the last few months we have been working on developing a agent-based model which explores the spread and containment of tuberculosis. Today is the first time that we show the model to a academic audience at the AAG Annual meeting. To give a sense …
Continue reading »
Over the last few months we have been working on developing a agent-based model which explores the spread and containment of tuberculosis. Today is the first time that we show the model to a academic audience at the AAG Annual meeting. To give a sense …
Continue reading »
Over the last few months we have been working on developing a agent-based model which explores the spread and containment of tuberculosis. Today is the first time that we show the model to a academic audience at the AAG Annual meeting. To give a sense …
Continue reading »
Dadaab refugee campOne of the projects we are working on at GMU is exploring the the dynamics of refugee camps and the spread of diseases within such camps. Our initial focus is on the the Dadaab refugee camps which are located in Kenya, approxima…
Continue reading »
Dadaab refugee campOne of the projects we are working on at GMU is exploring the the dynamics of refugee camps and the spread of diseases within such camps. Our initial focus is on the the Dadaab refugee camps which are located in Kenya, approxima…
Continue reading »
