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SUMMARY:What kind of distance underlies influenza transmission in the US? 
 - Maria Tang\, University of Cambridge
DTSTART:20220511T150000Z
DTEND:20220511T160000Z
UID:TALK174269@talks.cam.ac.uk
CONTACT:Dr Ciara Dangerfield
DESCRIPTION:For directly transmitted infectious diseases such as influenza
 \, understanding how best to incorporate human mobility into spatial trans
 mission models is needed for better epidemic prediction and control. The g
 ravity model is a popular spatial framework that assumes the force of infe
 ction from one population to another decays with the distance between them
 \, but the choice of distance metric can affect the predicted dynamics. Co
 nventionally\, great-circle distance is used\, but humans don’t generall
 y travel in a straight line. In this talk\, we evaluate driving distance a
 nd driving time against great-circle distance as gravity model distance me
 trics by testing their ability to predict influenza dynamics in the US wit
 h fine-scale influenza-like-illness medical claims data. I will show that 
 driving distance metrics can offer better model fits to infectious disease
  spread compared to great-circle distance\, but that simulated predictions
  remain similar. However\, the choice of distance metric can matter more d
 epending on the terrain and the nature of the disease spread.
LOCATION:Zoom
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