About: Inferring person-to-person networks of pathogen transmission: is routine surveillance data up to the task?

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About: Inferring person-to-person networks of pathogen transmission: is routine surveillance data up to the task? Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Inferring person-to-person networks of pathogen transmission: is routine surveillance data up to the task?
Creator	Hsiang, Michelle Nielsen, Rasmus Perkins, T Lerch, Anita Greenhouse, Bryan Dlamini, Nomcebo Huber, John Murphy, Maxwell Nhlabathi, Nomcebo Ntshalintshali, Nyasatu
Source	MedRxiv
abstract	Inference of person-to-person transmission networks using routinely collected surveillance data is being used increasingly to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated. We evaluated the influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum, the pathogen responsible for most human malaria. After developing a new inference framework and applying it to simulated data, we found that these data types have limited utility for inferring transmission networks and, in some combinations, tend to overestimate transmission. Only when outbreaks were highly focal in time or when travel histories were highly accurate was the inference algorithm able to accurately estimate the reproduction number under control, Rc, a key metric of transmission. Applying this approach to surveillance data from Eswatini indicated that inferences of Rc and spatiotemporal patterns therein are sensitive to the choice of data types and assumptions about the accuracy of travel-history data. Taken together, these results suggest that transmission network inferences made with routinely collected surveillance data should be interpreted with caution. As we have done here, future studies inferring transmission networks should apply their algorithm to data simulated under alternative assumptions to assess the robustness of their inferences.
has issue date	2020-08-26(xsd:dateTime)
bibo:doi	10.1101/2020.08.24.20180844
has license	medrxiv
sha1sum (hex)	1cd9b5b362d7776c5a74dab88d52cee9ff8470e5
schema:url	https://doi.org/10.1101/2020.08.24.20180844
resource representing a document's title	Inferring person-to-person networks of pathogen transmission: is routine surveillance data up to the task?
resource representing a document's body	covid:1cd9b5b362d7776c5a74dab88d52cee9ff8470e5#body_text
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