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Kasey Jones

Research Data Scientist

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Kasey Jones is a data scientist with over four years of experience solving client problems using data analysis techniques in R and Python. He applies predictive modeling, simulation techniques, text analysis, and machine learning to produce impactful solutions. While at RTI, Mr. Jones has developed several modeling algorithms in conjunction with RTI's synthetic population. Projects include: predicting underage drinking rates in D.C., developing a social vulnerability index, and creating an agent-based model that calculates healthcare acquired infection rates for hospitals in North Carolina for the Centers for Disease Control and Prevention. Each project used RTI's synthetic population as the base population in the research. Before joining RTI, Mr. Jones worked as an analytical consultant in Washington, DC, for two years. He was the project lead and main programmer for an application that tracks undocumented immigrants through the United States. The application was presented to the Secretary of the Department of Homeland Security and is being used at the Office of Immigration Statistics.

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Papers

Quiner C, Jones K, Bobashev G. (2022). Impacts of timing, length, and intensity of behavioral interventions to COVID-19 dynamics: North Carolina county-level examples. Infectious Disease Modelling, 7(3)

Preiss A, Hadley E, Jones K, Stoner MCD, Kery C, Baumgartner P, Bobashev G, Tenenbaum J, Carter C, Clement K, Rhea S. (2022). Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic. Infectious Disease Modelling

Jones K, Hadley E, Preiss S, Lofgren ET, Rice DP, Stoner MCD, Rhea S, Adams JW. (2022). Estimate of undetected severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in acute-care hospital settings using an individual-based microsimulation model. Infection control and hospital epidemiology

Hadley E, Rhea S, Jones K, Li L, Stoner M, Bobashev G. (2022). Enhancing the prediction of hospitalization from a COVID-19 agent-based model: A Bayesian method for model parameter estimation. PloS one, 17(3)

Endres-Dighe S, Jones K, Hadley E, Preiss A, Kery C, Stoner M, Eversole S, Rhea S. (2021). Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19's impact on healthcare resources and capacity. PloS one, 16(11)

Rhea S, Jones K, Endres-Dighe S, Munoz B, Weber DJ, Hilscher R, MacFarquhar J, Sickbert-Bennett E, DiBiase L, Marx A, Rineer J, Lewis J, Bobashev G, . (2020). Modeling inpatient and outpatient antibiotic stewardship interventions to reduce the burden of Clostridioides difficile infection in a regional healthcare network. PloS one, 15(6)

Rhea S, Hilscher R, Rineer JI, Munoz B, Jones K, Endres-Dighe SM, DiBiase LM, Sickbert-Bennett EE, Weber DJ, MacFarquhar JK, Dubendris H, Bobashev G. Creation of a Geospatially Explicit, Agent-based Model of a Regional Healthcare Network with Application to Clostridioides difficile Infection. Health security, 17(4)

Kasey Jones, Rob Chew, Allison Witman, Yiyan Liu. (2019). rollmatch: An R Package for Rolling Entry Matching. The R Journal, 11(2)

Robert Chew, Kasey Jones, Jennifer Unangst, James Cajka, Justine Allpress, Safaa Amer, Karol Krotki. (2018). Toward model-generated household listing in low-and middle-income countries using deep learning. ISPRS International Journal of Geo-Information, 7(11)

Chew RF, Amer S, Jones K, Unangst J, Cajka J, Allpress J, Bruhn M. (2018). Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery. International Journal of Health Geographics, 17(1)

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