Leveraging H1N1 infection transmission modeling with proximity sensor microdata

Abstract

Background: The contact networks between individuals can have a profound impact on the evolution of an infectious outbreak within a network. The impact of the interaction between contact network and disease dynamics on infection spread has been investigated using both synthetic and empirically gathered micro-contact data, establishing the utility of micro-contact data for epidemiological insight. However, the infection models tied to empirical contact data were highly stylized and were not calibrated or compared against temporally coincident infection rates, or omitted critical non-network based risk factors such as age or vaccination status.

Methods: In this paper we present an agent-based simulation model firmly grounded in disease dynamics, incorporating a detailed characterization of the natural history of infection, and 13 weeks worth of micro-contact and participant health and risk factor information gathered during the 2009 H1N1 flu pandemic.

Results: We demonstrate that the micro-contact data-based model yields results consistent with the case counts observed in the study population, derive novel metrics based on the logarithm of the time degree for evaluating individual risk based on contact dynamic properties, and present preliminary findings pertaining to the impact of internal network structures on the spread of disease at an individual level.

Conclusions: Through the analysis of detailed output of Monte Carlo ensembles of agent based simulations we were able to recreate many possible scenarios of infection transmission using an empirically grounded dynamic contact network, providing a validated and grounded simulation framework and methodology. We confirmed recent findings on the importance of contact dynamics, and extended the analysis to new measures of the relative risk of different contact dynamics. Because exponentially more time spent with others correlates to a linear increase in infection probability, we conclude that network dynamics have an important, but not dominant impact on infection transmission for H1N1 transmission in our study population.

Figure 1

Simulation structure and flow.

Figure 2

Flunet Findings.a) Contact histogram by hour of day, b) CCDF of contact duration, c) Connectivity graph with threshold of 18 minutes per day average contact. Black nodes represent stationary nodes associated with a location, and are included in this graph for illustrative purposes only. d) Network span for close and all contacts.

Figure 3

Weekly laboratory-confirmed H1N1 cases reported in Saskatchewan.

Figure 4

Observed attack rate based on endogenous and exogenous infection pressure. Attack rate (fraction of endogenous population infected) according to different assumptions about endogenous and exogenous infection pressure. In the left-hand panel, vaccination effect is incorporated, while in the right hand panel no vaccination is considered.

Figure 5

Number of infections per week. The number of exogenous and endogenous infections per week without vaccination (left), and with vaccination (right) over the course of 100,000 runs.

Figure 6

Depth of infection spread. Depth of infection spread for scenarios with consideration of vaccination (x’s) and without consideration (o’s).

Figure 7

Impact of LTD on endogenous infection probability. Impact of a node’s log-transformed TD centrality (LTD) and immunization on endogenous infection probability. Results from two simulation scenarios are shown: One where the vaccination effect is considered (x’s), and another where this effect is ignored (o’s). The red line indicates the least squares fit for the case without vaccination. Dashed lines represent outliers; solid lines denote a single identified subnet.

Figure 8

Infection transmission across network. Representations of infection rates across the network without (a) and with (b) vaccination. Node size represents participant LTD, node edge color and width represent infection events.