Population Informatics

DBMI’s has a long-standing history in population informatics efforts, including nearly 20 years of experience in infectious disease modeling and developing methods for real-time detection and assessment of disease outbreaks. Historically, the RODS biosurveillance laboratory is home to three large projects that work with health departments to create surveillance systems: the RODS Open Source Project, the Pennsylvania RODS, and the National Retail Data Monitor (NRDM). These projects benefit the public and also benefit the research by grounding our work in actual public health practice and by collecting surveillance data for algorithm validation and investigations into the value of different types of novel data for outbreak detection.

Examples of projects include algorithm development, assessment of novel types of surveillance data, grid computing, natural language processing, and analyses of detectability. DBMI’s responses to the COVID-19 pandemic include development of (1) a Bayesian outbreak detection and characterization system capable of modeling outbreaks of novel diseases; (2) transfer learning approaches for applying

outbreaks models trained in one geographic region to predict outbreaks in other; (3) Bayesian networks to identify likely cases of COVID-19; and (4) COVID-19 infection fatality rate estimates based on death counts.

 

Sample Publications:

Le TT, Gutiérrez-Sacristán A, Son J, Hong C, South AM, Beaulieu-Jones BK, Loh NHW, Luo Y, Morris M, Ngiam KY, Patel LP, Samayamuthu MJ, Schriver E, Tan ALM, Moore J, Cai T, Omenn GS, Avillach P, Kohane IS; Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Visweswaran S, Mowery DL, Xia Z. Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19. Sci Rep. 2021 Oct 12;11(1):20238. doi: 10.1038/s41598-021-99481-9. PMID: 34642371; PMCID: PMC8510999.

Weber GM, Zhang HG, L’Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan AL, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García-Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, M Liu, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera-Jiménez M, Prudente RA, Samayamuthu MJ, Sanz J, Schriver ER, Schubert P, Serrano-Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, The Consortium For Clinical Characterization Of COVID-19 By EHR (4CE), Kohane IS, Cai T, South AM, Brat GA. International changes in COVID-19 clinical trajectories across 315 hospitals and 6 countries: Retrospective cohort study. Journal of Medical Internet Research. 2021 Oct 10. 2021;23(10):e31400. PMID: 34533459 PMCID: PMC8510151

Aronis JM, Ferraro JP, Gesteland PH, Tsui F, Ye Y, Wagner MM, Cooper GF. A Bayesian approach for detecting a disease that is not being modeled. PloS one. 2020 Feb 28;15(2):e0229658.

Millett NE, Aronis JM, Wagner MM, Tsui F, Ye Y, Ferraro JP, Haug PJ, Gesteland PH, Cooper GF. The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza. Online J Public Health Inform. 2019 Sep 19;11(2):e6. doi: 10.5210/ojphi.v11i2.9952. PMID: 31632600; PMCID: PMC6788888.

Tsui F, Ye Y, Ruiz V, Cooper GF, Wagner MM. Automated influenza case detection for public health surveillance and clinical diagnosis using dynamic influenza prevalence method. J Public Health (Oxf). 2018 Dec 1;40(4):878-885. doi: 10.1093/pubmed/fdx141. PMID: 29059331; PMCID: PMC6676953.

Our Population Informatics Team

Shyam Visweswaran
Work Phone: 
412-648-7119
Work Fax: 
412-648-9118
Gerald Douglas
Work Phone: 
412-648-9323
Gregory Cooper
Work Phone: 
412-624-3308
Work Fax: 
412-624-5310

gfc@pitt.edu

Harry Hochheiser
Work Phone: 
412-648-9300
Work Fax: 
412-624-5310
Ye Ye