Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data
Abstract
Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnostics data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. As a result, calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.
- Authors:
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1337764
- Alternate Identifier(s):
- OSTI ID: 1250407
- Grant/Contract Number:
- Seed 7280; AC05-00OR22725
- Resource Type:
- Published Article
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Name: PLoS ONE Journal Volume: 11 Journal Issue: 4; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science (PLoS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; H1N1; algorithms; infectious disease epidemiology; census; data processing; diagnostic medicine; influenza; data acquisition
Citation Formats
Özmen, Özgür, Pullum, Laura L., Ramanathan, Arvind, Nutaro, James J., and Sun, ed., Gui-Quan. Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data. United States: N. p., 2016.
Web. doi:10.1371/journal.pone.0153769.
Özmen, Özgür, Pullum, Laura L., Ramanathan, Arvind, Nutaro, James J., & Sun, ed., Gui-Quan. Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data. United States. https://doi.org/10.1371/journal.pone.0153769
Özmen, Özgür, Pullum, Laura L., Ramanathan, Arvind, Nutaro, James J., and Sun, ed., Gui-Quan. Wed .
"Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data". United States. https://doi.org/10.1371/journal.pone.0153769.
@article{osti_1337764,
title = {Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data},
author = {Özmen, Özgür and Pullum, Laura L. and Ramanathan, Arvind and Nutaro, James J. and Sun, ed., Gui-Quan},
abstractNote = {Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnostics data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. As a result, calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.},
doi = {10.1371/journal.pone.0153769},
journal = {PLoS ONE},
number = 4,
volume = 11,
place = {United States},
year = {2016},
month = {4}
}
https://doi.org/10.1371/journal.pone.0153769
Search WorldCat to find libraries that may hold this journalWeb of Science
Works referenced in this record:
Transmission of Novel Influenza A(H1N1) in Households with Post-Exposure Antiviral Prophylaxis
journal, July 2010
- van Boven, Michiel; Donker, Tjibbe; van der Lubben, Mariken
- PLoS ONE, Vol. 5, Issue 7
Epidemiology of 2009 Pandemic Influenza A (H1N1) in the United States
journal, January 2011
- Jhung, Michael A.; Swerdlow, David; Olsen, Sonja J.
- Clinical Infectious Diseases, Vol. 52, Issue suppl_1
Optimization by Simulated Annealing
journal, May 1983
- Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P.
- Science, Vol. 220, Issue 4598
Simulated annealing: A tool for operational research
journal, June 1990
- Eglese, R. W.
- European Journal of Operational Research, Vol. 46, Issue 3
Theory versus Data: How to Calculate R0?
journal, March 2007
- Breban, Romulus; Vardavas, Raffaele; Blower, Sally
- PLoS ONE, Vol. 2, Issue 3
Spatial Transmission of 2009 Pandemic Influenza in the US
journal, June 2014
- Gog, Julia R.; Ballesteros, Sébastien; Viboud, Cécile
- PLoS Computational Biology, Vol. 10, Issue 6
The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus
journal, September 2009
- Yang, Y.; Sugimoto, J. D.; Halloran, M. E.
- Science, Vol. 326, Issue 5953
Estimation of the reproductive number and the serial interval in early phase of the 2009 influenza A/H1N1 pandemic in the USA
journal, November 2009
- White, Laura Forsberg; Wallinga, Jacco; Finelli, Lyn
- Influenza and Other Respiratory Viruses, Vol. 3, Issue 6
Works referencing / citing this record:
Data-driven efficient network and surveillance-based immunization
journal, January 2019
- Zhang, Yao; Ramanathan, Arvind; Vullikanti, Anil
- Knowledge and Information Systems, Vol. 61, Issue 3