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UID:pretalx-foss4g2021-YVPFX8@callforpapers.2021.foss4g.org
DTSTART;TZID=America/Argentina/Buenos_Aires:20210930T103000
DTEND;TZID=America/Argentina/Buenos_Aires:20210930T110000
DESCRIPTION:We present a spatial SVEIRD (which stands for Susceptible\, Vac
cinated\, Exposed\, Infectious\, Recovered and Dead compartments) epidemic
model to capture the transmission dynamics of the spread of COVID-19 and
provide insight that would support the Public Health officials towards inf
ormed\, data-driven decision making.\n\nWe use the freely available popula
tion count data downloaded as a gridded raster map from WorldPop.org to as
sess the geographical spread COVID-19. Each grid cell has a population cou
nt\, which is divided into disease compartments. Each grid cell can transm
it disease to its neighbors\, with probabilities that decline exponentiall
y with the Euclidean distance.\n\nPredicting the transmission dynamics of
COVID-19 using mathematical models is challenging and comes with a lot of
uncertainty. First\, we run the spatial simulations under the worst-case s
cenario\, in which there are no major public health interventions. Next\,
we account for mitigation efforts including strict mask wearing and social
distancing mandates\, targeted lockdowns\, and widespread vaccine rollout
to vaccinate priority groups. Predictions for disease prevalence with and
without mitigation efforts are presented via time-series graphs for the e
pidemic compartments. All simulations are carried out using R programming
language.
DTSTAMP:20211121T201543Z
LOCATION:Ushuaia
SUMMARY:Spatiotemporal tracking of COVID-19 using open-source gridded popul
ation rasters and mathematical modelling - Ashok Krishnamurthy
URL:https://callforpapers.2021.foss4g.org/foss4g2021/talk/YVPFX8/
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