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UID:pretalx-foss4g-2022-VKV8BW@talks.staging.osgeo.org
DTSTART;TZID=CET:20220826T152500
DTEND;TZID=CET:20220826T153000
DESCRIPTION:It is essential to understand what future epidemic trends will 
 be\, as well as the effectiveness and potential impact of public health in
 tervention measures. The goal of this research is to provide insight that 
 would support public health officials towards informed\, data-driven decis
 ion making. We present spatialEpisim\, an R Shiny app (https://github.com/
 ashokkrish/spatialEpisim) that integrates mathematical modelling and open-
 source tools for tracking the spatial spread of COVID-19 in low- and middl
 e-income (LMIC) countries.\n\nWe present spatial compartmental models of e
 pidemiology (ex: SEIR\, SEIRD\, SVEIRD) to capture the transmission dynami
 cs of the spread of COVID-19. Our interactive app can be used to output an
 d visualize how COVID-19 spreads across a large geographical area. The rat
 e of spread of the disease is influenced by changing the model parameters 
 and human mobility patterns.\n\nFirst\, we run the spatial simulations und
 er the worst-case scenario\, in which there are no major public health int
 erventions. Next\, we account for mitigation efforts including strict mask
  wearing and social distancing mandates\, targeted lockdowns\, and widespr
 ead vaccine rollout to vaccinate priority groups.\n\nAs a test case Nigeri
 a is selected and the projected number of newly infected and death cases a
 re estimated and presented. Projections for disease prevalence with and wi
 thout mitigation efforts are presented via time-series graphs for the epid
 emic compartments.\n\nPredicting the transmission dynamics of COVID-19 is 
 challenging and comes with a lot of uncertainty. In this research we seek 
 primarily to clarify mathematical ideas\, rather than to offer definitive 
 medical answers. Our analyses may shed light more broadly on how COVID-19 
 spreads in a large geographical area with places where no empirical data i
 s recorded or observed.
DTSTAMP:20260403T233306Z
LOCATION:Modulo 0
SUMMARY:spatialEpisim: an open-source R Shiny app for tracking COVID-19 in 
 low- and middle-income (LMIC) countries - Crystal Wai
URL:https://talks.staging.osgeo.org/foss4g-2022/talk/VKV8BW/
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