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SUMMARY:Co-infections by non-interacting pathogens are not independent: de
 riving new tests of interaction - Nik Cunnifee\, Department of Plant Scien
 ces
DTSTART:20190510T150000Z
DTEND:20190510T160000Z
UID:TALK107710@talks.cam.ac.uk
CONTACT:Prof. Julia Gog
DESCRIPTION:If pathogen species\, strains or clones do not interact\, intu
 ition suggests the proportion of co-infected hosts should be the product o
 f the individual prevalences. Independence consequently underpins the wide
  range of methods for detecting pathogen interactions from cross-sectional
  survey data. Surprisingly\, however\, the results of the very simplest of
  epidemiological models challenge the underlying assumption of statistical
  independence. Even if pathogens do not interact\, death and/or clearance 
 of co-infected hosts causes net prevalences of individual pathogens to dec
 rease simultaneously. The induced positive correlation between prevalences
  means the proportion of co-infected hosts is expected to be higher than m
 ultiplication would suggest. By modeling the dynamics of multiple non-inte
 racting pathogens\, we develop a pair of novel tests of interaction that p
 roperly account for this hitherto overlooked coupling. Which test is appro
 priate for any application depends on the form of the available data\, as 
 well as the extent to which the co-infecting pathogens are epidemiological
 ly similar. Our tests allow us to reinterpret data from a number of previo
 us studies\, including pathogens of plants and animals\, as well as papill
 omavirus and malaria in humans. We find certain reports of interactions ha
 ve been overstated\, and in some cases are not supported by the available 
 data. Our work demonstrates how methods to identify interactions between p
 athogens can be updated to account for epidemiological dynamics.\n\nhttps:
 //www.biorxiv.org/content/10.1101/618900v1?rss=1
LOCATION:Meeting Room 14\, Centre for Mathematical Sciences
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