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.
Highlights
Influenza-like illnesses (ILI) are key contributors to mortality rates within the United States (US)
Our results show that parsimonious SIR models are remarkably capable of predicting peak loads, and that calibration yields parameter values similar to those reported in the literature
Maximum 4.840000 outbreak occurs earlier in the simulation runs than in the POC-Data. This is due to the model assumption that the initial infectious population starts to spread the disease at day zero, while the actual onset of the disease-spread could be based on numerous factors
Summary
Influenza-like illnesses (ILI) are key contributors to mortality rates within the United States (US). In spite of extensive surveillance of patients (both inpatients and outpatients) for flu-like symptoms, the reporting lag in most surveillance measures remains between 2 and 4 weeks, which makes it difficult to adopt effective strategies for limiting disease spread and prevention. Traditional surveillance still relies on the use of clinical and laboratory tests for assessing the spread of the disease. Point-of-care (POC) diagnostics for ILI surveillance have been adopted within the US, especially in the context of emerging influenza pandemics. PLOS ONE | DOI:10.1371/journal.pone.0153769 April 20, 2016
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