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DTSTAMP:20260602T224120Z
DESCRIPTION:Title: COVID-19 transmission models in the real world: models\,
  data\, and policy.\n\nAbstract: Simple mathematical models of COVID-19 tr
 ansmission gained prominence in the early days of the pandemic. These mode
 ls provided researchers and policymakers with qualitative insight into the
  dynamics of transmission and quantitative predictions of disease incidenc
 e. More sophisticated models incorporated new information about the natura
 l history of COVID-19 disease and the interaction of infected individuals 
 with the healthcare system\, to predict diagnosed cases\, hospitalization\
 , ventilator usage\, and death. Models also provided intuition for discuss
 ions about outbreaks\, vaccination\, and the effects of non-pharmaceutical
  interventions like social distancing guidelines and stay-at-home orders. 
 But as the pandemic progressed\, complex real-world interventions took eff
 ect\, people everywhere changed their behavior\, and the usefulness of sim
 ple mathematical models of COVID-19 transmission diminished. This challeng
 e forced researchers to think more broadly about empirical data sources th
 at could help predictive models regain their utility for guiding public po
 licy. In this presentation\, I will describe my view of the successes and 
 failures of population-level transmission models in the context of the COV
 ID-19 pandemic. I will outline the evolution of a project to predict COVID
 -19 incidence in the state of Connecticut\, from development of a transmis
 sion model to engagement with public health policymakers and initiation of
  a new data collection effort. I argue that a new data source – passive me
 asurement of close interpersonal contact via mobile device location data –
  is a promising way to overcome many of the shortcomings of traditional tr
 ansmission models. I conclude with a summary of the impact this work has h
 ad on the COVID-19 response in Connecticut and beyond.\n\nJoin Zoom Meetin
 g\n\nhttps://umontreal.zoom.us/j/85105423917?pwd=enM3MGpFNkZKU2daMjRITmo0N
 0JUUT09\n\nMeeting ID: 851 0542 3917\n\nPasscode: 403790\n\n\n	\n	\n	 \n
DTSTART:20220429T193000Z
DTEND:20220429T203000Z
SUMMARY:Forrest Crawford (Yale University)
URL:/mathstat/channels/event/forrest-crawford-yale-uni
 versity-339201
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