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How to Estimate Epidemic Risk from Incomplete Contact Diaries Data?

Mastrandrea R, Barrat A - PLoS Comput. Biol. (2016)

Bottom Line: Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth.The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network.We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.

View Article: PubMed Central - PubMed

Affiliation: Aix Marseille Univ, Univ Toulon, CNRS, CPT, Marseille, France.

ABSTRACT
Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.

No MeSH data available.


Related in: MedlinePlus

Outcome of the spreading processes.Comparison of the distributions of epidemic sizes obtained for 1000 SIR simulations performed on the contact sensors network and on the surrogate contact network with weights randomly drawn from a negative binomial fit of the distribution of contact durations registered by sensors in several environments (). Each process starts with one random infected seed. (a) β/μ = 6, (b) β/μ = 10, (c) β/μ = 14, (d) β/μ = 20, (e) β/μ = 30, (f) β/μ = 40.
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pcbi.1005002.g007: Outcome of the spreading processes.Comparison of the distributions of epidemic sizes obtained for 1000 SIR simulations performed on the contact sensors network and on the surrogate contact network with weights randomly drawn from a negative binomial fit of the distribution of contact durations registered by sensors in several environments (). Each process starts with one random infected seed. (a) β/μ = 6, (b) β/μ = 10, (c) β/μ = 14, (d) β/μ = 20, (e) β/μ = 30, (f) β/μ = 40.

Mentions: For all these datasets, the distributions of cumulated contact durations are broad and, as also discussed in [44], can be modeled by negative binomial functional forms. We show in the Supporting Information that similar parameters of the negative binomial fit are obtained for each dataset and for the combined one. Therefore, to further generalize the procedure and avoid relying on a single dataset, we consider in the following the fit of the five combined datasets. We then assign to the links of the weights drawn at random from this fitted distribution, obtaining . Figs 7 and 6 compare the distributions of epidemic sizes obtained when the SIR model is simulated on the resulting surrogate network and on the CSN (see also the Supporting Information, in which we moreover show the outcome of simulations for different initial seeds, as well as the temporal evolution of the density of infectious individuals in the population when using the .)


How to Estimate Epidemic Risk from Incomplete Contact Diaries Data?

Mastrandrea R, Barrat A - PLoS Comput. Biol. (2016)

Outcome of the spreading processes.Comparison of the distributions of epidemic sizes obtained for 1000 SIR simulations performed on the contact sensors network and on the surrogate contact network with weights randomly drawn from a negative binomial fit of the distribution of contact durations registered by sensors in several environments (). Each process starts with one random infected seed. (a) β/μ = 6, (b) β/μ = 10, (c) β/μ = 14, (d) β/μ = 20, (e) β/μ = 30, (f) β/μ = 40.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4920368&req=5

pcbi.1005002.g007: Outcome of the spreading processes.Comparison of the distributions of epidemic sizes obtained for 1000 SIR simulations performed on the contact sensors network and on the surrogate contact network with weights randomly drawn from a negative binomial fit of the distribution of contact durations registered by sensors in several environments (). Each process starts with one random infected seed. (a) β/μ = 6, (b) β/μ = 10, (c) β/μ = 14, (d) β/μ = 20, (e) β/μ = 30, (f) β/μ = 40.
Mentions: For all these datasets, the distributions of cumulated contact durations are broad and, as also discussed in [44], can be modeled by negative binomial functional forms. We show in the Supporting Information that similar parameters of the negative binomial fit are obtained for each dataset and for the combined one. Therefore, to further generalize the procedure and avoid relying on a single dataset, we consider in the following the fit of the five combined datasets. We then assign to the links of the weights drawn at random from this fitted distribution, obtaining . Figs 7 and 6 compare the distributions of epidemic sizes obtained when the SIR model is simulated on the resulting surrogate network and on the CSN (see also the Supporting Information, in which we moreover show the outcome of simulations for different initial seeds, as well as the temporal evolution of the density of infectious individuals in the population when using the .)

Bottom Line: Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth.The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network.We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.

View Article: PubMed Central - PubMed

Affiliation: Aix Marseille Univ, Univ Toulon, CNRS, CPT, Marseille, France.

ABSTRACT
Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.

No MeSH data available.


Related in: MedlinePlus