Selected Research Projects
COVID-19 Case and Vaccine Forecasting
Developed a model at the city and county level of Virginia that gives a short-term forecast for the total number of COVID-19 cases, COVID-19 vaccines administered and supplied. Available at: https://vmasc.shinyapps.io/va-county-covid-forecast/.
- Provides a 7-day forecast for the number of COVID-19 cases and a 21-day forecast for the COVID-19 vaccines administered and supplied.
- Case forecast for the city/county estimates the age range of individuals with COVID-19 and what their case outcomes are expected to be.
- Attempts to identify tweets from individuals within the city/county that are reporting having COVID-19.
- Designed to be as transparent as possible: for each forecast or piece of insight it provides it tries to describe the methodology behind how it came to that prediction in a straightforward way.
- Has used by the Virginia Department of Health, the Virginia Department of Emergency Management and has over 1,000 different unique users from the public across 75 different cities or counties within Virginia.
Related Publications
- Lynch, C. J., & Gore, R. (2021). Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study. Journal of medical Internet research, 23(3), e24925.
- Lynch, C. J., & Gore, R. (2021). Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods. Data in Brief, 35, 106759.
Selected Media Coverage
Leveraging Data To Better Understand Humanity
A major focus of my research is leveraging data that is an artifact of the way we live to better understand human behavior. Typically, this data takes the form of social media data, anomyized GPS mobility data, and anomyized SMS messages and cell phone records. My aim is to novel insight about human behavior by quantifying and analyzing these data sources.
You Are What You Tweet
We studied the relationship among the obesity rate of urban areas and expressions of happiness, diet and physical activity on social media. We showed that areas with lower obesity rates: (1) have happier tweets and frequently discuss (2) food, particularly fruits and vegetables, and (3) physical activities of any intensity.
Related Publications
Selected Media Coverage
Understanding How Locals And Tourists React Differently To City Attractions
We studied how the time of day and if individuals were locals or tourists can affect the sentiment individuals express towards attractions. We showed that: (1) tourists express more positive sentiment towards attractions than locals and (2) more positive sentiment is expressed about attractions in the morning vs. the afternoon / evening.
Related Publications
- Padilla, J. J., Kavak, H., Lynch, C. J., Gore, R. J., & Diallo, S. Y. (2018). Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter. PloS one, 13(6), e0198857.
Characterizing The Mobile Phone Use Patterns Of Refugee Hosting Provinces In Turkey
We used an anomyized mobile phone data set to understand the experience of refugees throughout Turkey based on their proximity to refugee targeted violent incidents and refugee camps. We showed that: (1) there is more negative sentiment targeted expressed towards refugees in those areas close to refugee camps and (2) the mobile phone behavior (i.e. number of calls made and text messages sent) in the wake of a violent incident for refugees and non-refugees is more similar the close the individuals are to the location of the incident. The research won the "Safety and Security Prize" in the Data For Refugees (D4R) challenge sponsored by Turk Telecom.
Related Publications
- Frydenlund, E., Şener, M. Y., Gore, R., Boshuijzen-van Burken, C., Bozdag, E., & de Kock, C. (2019). Characterizing the Mobile Phone Use Patterns of Refugee-Hosting Provinces in Turkey. In Guide to Mobile Data Analytics in Refugee Scenarios (pp. 417-431). Springer, Cham.
Mobile Data As A Public Health Decision Enabler
We used anomyized cell-phone data from Senegal provided by the Data For Development challenge and joined with with demographic data and the location of hospitals to identify those areas in the country where adding an additional hospital would most reduce the travel time during the critical initial period for patients experiencing an heart attack or stroke. The research won the "Partical Application Prize" in the Data For Development (D4D) challenge sponsored by The Orange Foundation
Related Publications
- Mutafungwa, E., Thiessard, F., Diallo, P., Gore, R., Jouhet, V., Karray, C. & Diallo, G. (2015). Mobile Data as Public Health Decision Enabler: A Case Study of Cardiac and Neurological Emergencies. In Data for Development (D4D) Challenge at Net Mob 2015, Boston, USA, 7-10 April 2015.
Modeling Human Behavior With Agent Based Models
They are diverse in their knowledge and abilities but their behavior is far from random. However, despite often making rational decisions, their behavior is also emotional. My research with agent-based models has attempted to capture these attributes, when they matter, to better understand how humans operate and how the institutions around them operate in a variety of domains.
Related Publications
- Gore, R., Lemos, C., Shults, F. L., & Wildman, W. J. (2018). Forecasting changes in religiosity and existential security with an agent-based model. Journal of Artificial Societies and Social Simulation, 21(1).
- Shults, F. L., Gore, R., Wildman, W. J., Lynch, C., Lane, J. E., & Toft, M. (2017). Mutually escalating religious violence: A generative model. In Proceedings of the Social Science Conference (pp. 1-12).
- Gore, R., Wozny, P., Dignum, F. P., Shults, F. L., Boshuijzen-van Burken, C., & Royakkers, L. (2019, April). A value sensitive ABM of the refugee crisis in the Netherlands. In 2019 Spring Simulation Conference (SpringSim) (pp. 1-12). IEEE.
- Boshuijzen-van Burken, C., Gore, R., Dignum, F., Royakkers, L., Wozny, P., & Shults, F. L. (2020). Agent-based modelling of values: The case of value sensitive design for refugee logistics. JASSS: Journal of Artificial Societies and Social Simulation, 23(4).
Selected Media Coverage
Related Slides
Simulation Debugging, Validation And Verification
The process of developing, verifying and validating models and simulations should be straightforward. Unfortunately, following conventional development approaches can render a model design that appeared complete and robust into an incomplete, incoherent and invalid simulation during implementation. An alternative approach is for subject matter experts (SMEs) to employ formal methods to describe their models. However, formal methods are rarely used in practice due to their intimidating syntax and semantics rooted in mathematics. To address this problem I have developed an approach to gaining insight about unexpected outputs, in some cases bugs, centered around the practice of predicate-based statistical debugging used in software engineering. This approach is realized in a standalone tool published on my Github and in an online web application. We have a small but regular user base (~25-50 users) and we are always looking to grow it.
To address this problem I have developed an approach to gaining insight about unexpected outputs, in some cases bugs, centered around the practice of predicate-based statistical debugging used in software engineering. This approach is realized in a standalone tool published on my Github and in an online web application. We have a small but regular user base (~25-50 users) and we are always looking to grow it.
Related Publications
- Gore, R., Reynolds Jr, P. F., Kamensky, D., Diallo, S., & Padilla, J. (2015). Statistical debugging for simulations. ACM Transactions on Modeling and Computer Simulation (TOMACS), 25(3), 1-26.
- Diallo, S. Y., Gore, R., Lynch, C. J., & Padilla, J. J. (2016). Formal methods, statistical debugging and exploratory analysis in support of system development: Towards a verification and validation calculator tool. International Journal of Modeling, Simulation, and Scientific Computing, 7(01), 1641001.
- Gore, R. J., Lynch, C. J., & Kavak, H. (2017). Applying statistical debugging for enhanced trace validation of agent-based models. Simulation, 93(4), 273-284.
- Gore, R., Diallo, S., Lynch, C., & Padilla, J. (2017). Augmenting bottom-up metamodels with predicates. Journal of Artificial Societies and Social Simulation, 20(1).
Related Slides