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. 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.
