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DTSTART:20220313T030000
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DTSTAMP:20220615T150739Z
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DTSTART;TZID=America/New_York:20220615T100000
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DESCRIPTION:Generally speaking\, immunology\, epidemiology\, evolution\, an
 d biophysical processes related to pathogen transmission interact in the c
 ontext of disease control. Particularly for emerging pathogens such as SAR
 S-CoV-2\, minimal data availability combined with modeling / dynamical unc
 ertainties can make correctly accounting for these factors in population-l
 evel disease models challenging. In this talk\, we will present a minimal 
 conceptual modeling framework for Covid-19 to study these interactions. Us
 ing this framework\, we will focus on three primary topics: the impact of 
 the strength and duration of natural and vaccinal immune responses on Covi
 d-19 case burden and timing\, epidemiological and evolutionary impacts of 
 vaccine dose spacing\, and epidemiological and evolutionary consequences o
 f how vaccines are allocated between countries. We will then discuss ongoi
 ng work to better parametrize our models as data become increasingly avail
 able. In particular\, we will introduce experimental methods for studying 
 the biophysical processes underlying viral in-host dynamics as well as the
  impact of seasonality on disease transmission rates.\n\nSpeaker(s): Prof.
  Caroline Wagner\, \n\nMontreal\, Quebec\, Canada\, Virtual: https://event
 s.vtools.ieee.org/m/315368
LOCATION:Montreal\, Quebec\, Canada\, Virtual: https://events.vtools.ieee.o
 rg/m/315368
ORGANIZER:milad.mokhtari@mail.mcgill.ca
SEQUENCE:8
SUMMARY:Accounting for the interaction of immunology\, epidemiology\, evolu
 tion\, and biophysical processes in Covid-19 mathematical models
URL;VALUE=URI:https://events.vtools.ieee.org/m/315368
X-ALT-DESC:Description: <br /><p>Generally speaking\, immunology\, epidemio
 logy\, evolution\, and biophysical processes related to pathogen transmiss
 ion interact in the context of disease control. Particularly for emerging 
 pathogens such as SARS-CoV-2\, minimal data availability combined with mod
 eling / dynamical uncertainties can make correctly accounting for these fa
 ctors in population-level disease models challenging. In this talk\, we wi
 ll present a minimal conceptual modeling framework for Covid-19 to study t
 hese interactions. Using this framework\, we will focus on three primary t
 opics: the impact of the strength and duration of natural and vaccinal imm
 une responses on Covid-19 case burden and timing\, epidemiological and evo
 lutionary impacts of vaccine dose spacing\, and epidemiological and evolut
 ionary consequences of how vaccines are allocated between countries. We wi
 ll then discuss ongoing work to better parametrize our models as data beco
 me increasingly available. In particular\, we will introduce experimental 
 methods for studying the biophysical processes underlying viral in-host dy
 namics as well as the impact of seasonality on disease transmission rates.
 </p>
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