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SUMMARY:A novel method to quantify the impact of weather on Lyme disease  
 - Gianni Loiacono (PHE)
DTSTART:20170310T160000Z
DTEND:20170310T170000Z
UID:TALK70796@talks.cam.ac.uk
CONTACT:Prof. Julia Gog
DESCRIPTION:Many infectious diseases exhibit seasonal changes driven by th
 e weather\, socio-economic\, behavioral and other environmental factors. S
 eparating and quantifying the contribution of each driver is extremely dif
 ficult as often some of the explanatory variables are related (collinearit
 y).\n\nLyme Borreliosis is a tick-borne bacterial infection\, particularly
  relevant for public health. The incidence is strongly influenced by the w
 eather and other environmental variables such as the presence of vertebrat
 e host and land use. The 11-year study dataset of human cases reported to 
 PHE in England and Wales was linked with weather data (air temperature\, r
 ainfall\, relative humidity and wind speed) at diagnostic laboratory postc
 ode and specimen date. Then we statistically analyzed the subsets of epide
 miological cases when all variables\, except one\, were within the same na
 rrow range\; in this way\, we could detect the relevant explanatory variab
 les\, and remove the problem of collinearity. We also developed a novel me
 thod to define and quantify the time-lag between relevant changes in the w
 eather variables and the risk of Lyme disease.\n\nWe provide an estimation
  of the probability of acquiring a disease conditional on the relevant wea
 ther variables. The risk of an infection increases non-linearly with the t
 emperature\, but decreases with relative humidity\, probably due to vector
  and human behavioral factors. Other variables\, such as daylight duration
  which affects diapause in ticks\, might play important roles in the risk 
 of disease.\n\nWe developed a robust\, yet conceptually simple\, method to
  identify the relevant weather variables involved in the disease transmiss
 ion\, and disentangled their relative contribution.  By allowing the memor
 y of past events in the model\, this method could be generalized to a wide
 r class of diseases for which human-to-human transmission and depletion of
  susceptible population are significant\, such as respiratory diseases.\n
LOCATION:Meeting room 4\, Centre for Mathematical Sciences
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