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Open Access Research article

Temporal aggregation impacts on epidemiological simulations employing microcontact data

Mohammad Hashemian1, Weicheng Qian1*, Kevin G Stanley1 and Nathaniel D Osgood12

Author Affiliations

1 Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

2 Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

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BMC Medical Informatics and Decision Making 2012, 12:132  doi:10.1186/1472-6947-12-132

Published: 15 November 2012

Abstract

Background

Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population’s contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes.

Methods

We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks.

Results

No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs – which extrapolate from data collected over a brief period – exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation.

Conclusions

The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.

Keywords:
Infection transmission; Sensor-based data; Aggregation; Simulation model; Dynamic model; Contagion; Epidemiology; Surveillance