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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

Distribution of final size of epidemics.1000 SIR simulations performed on the matched contact sensors network (CSNm), the matched contact diaries network with durations respectively reported by students () and registered by sensors () and the contact diaries network with weights obtained by a negative binomial fit of contact durations reported by students between and within classes (CMDN). Each process starts with one random infected seed. β/μ = 30.
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pcbi.1005002.g002: Distribution of final size of epidemics.1000 SIR simulations performed on the matched contact sensors network (CSNm), the matched contact diaries network with durations respectively reported by students () and registered by sensors () and the contact diaries network with weights obtained by a negative binomial fit of contact durations reported by students between and within classes (CMDN). Each process starts with one random infected seed. β/μ = 30.

Mentions: Fig 2 displays the outcome of SIR simulations performed on the three matched networks. Comparison with Fig 1 shows that the outcomes for and are similar to the cases of CDND and CDNS, which is expected as these networks do not differ strongly (only 12 nodes and 62 links have been removed in the matching procedure). On the other hand, the epidemic risk is strongly underestimated in the CSNm with respect to the CSN: this is due to the strong reduction in the number of nodes and links [40] and hence in the number of potential transmission routes between students and classes. However, the distribution does not exhibit peaks as for the case: the community structure remains indeed weaker in the CSNm with respect to the CDNm, with higher densities of links between different classes. We also note that the underestimation obtained by using CSNm is less strong than in the case of a random removal of the same number of nodes [40] (not shown). This is due to the fact that the students who filled in the diaries tend to be more connected than the others in the CSN: as a result, the CSNm has 970 links while a random removal of the same number of nodes from the CSN leads on average to a network with ≈560 links. Finally, although both CSNm and have the same distributions of weights and lead both to strong underestimations of the epidemic risk, the resulting distributions do not coincide, in particular because the has a much smaller number of links.


How to Estimate Epidemic Risk from Incomplete Contact Diaries Data?

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

Distribution of final size of epidemics.1000 SIR simulations performed on the matched contact sensors network (CSNm), the matched contact diaries network with durations respectively reported by students () and registered by sensors () and the contact diaries network with weights obtained by a negative binomial fit of contact durations reported by students between and within classes (CMDN). Each process starts with one random infected seed. β/μ = 30.
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Related In: Results  -  Collection

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

pcbi.1005002.g002: Distribution of final size of epidemics.1000 SIR simulations performed on the matched contact sensors network (CSNm), the matched contact diaries network with durations respectively reported by students () and registered by sensors () and the contact diaries network with weights obtained by a negative binomial fit of contact durations reported by students between and within classes (CMDN). Each process starts with one random infected seed. β/μ = 30.
Mentions: Fig 2 displays the outcome of SIR simulations performed on the three matched networks. Comparison with Fig 1 shows that the outcomes for and are similar to the cases of CDND and CDNS, which is expected as these networks do not differ strongly (only 12 nodes and 62 links have been removed in the matching procedure). On the other hand, the epidemic risk is strongly underestimated in the CSNm with respect to the CSN: this is due to the strong reduction in the number of nodes and links [40] and hence in the number of potential transmission routes between students and classes. However, the distribution does not exhibit peaks as for the case: the community structure remains indeed weaker in the CSNm with respect to the CDNm, with higher densities of links between different classes. We also note that the underestimation obtained by using CSNm is less strong than in the case of a random removal of the same number of nodes [40] (not shown). This is due to the fact that the students who filled in the diaries tend to be more connected than the others in the CSN: as a result, the CSNm has 970 links while a random removal of the same number of nodes from the CSN leads on average to a network with ≈560 links. Finally, although both CSNm and have the same distributions of weights and lead both to strong underestimations of the epidemic risk, the resulting distributions do not coincide, in particular because the has a much smaller number of links.

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