Mid-Atlantic Section Meeting 2021
Volume 66, Number 18
Friday–Sunday, December 3–5, 2021;
Rutgers University, New Brunswick, New Jersey
Session E04: Biophysics III
2:00 PM–3:36 PM,
Saturday, December 4, 2021
Room: 202B
Chair: Gyan Bhanot, Rutgers University
Abstract: E04.00001 : A spherical cow model of Covid-19 epidemiology and applications*
2:00 PM–2:36 PM
Preview Abstract
Abstract
Author:
Gyan Bhanot
(Rutgers University)
In late 2019, a coronavirus called SARS-CoV-2 appeared in Wuhan, China. This
virus has since caused a worldwide pandemic, which is still ongoing. The
associated respiratory illness, called COVID-19, ranges in severity from a
symptomless infection, to common-cold like symptoms, to viral pneumonia,
organ failure, neurological complications and sometimes, death. While
mortality rates from SARS-CoV-2 infections are significantly lower than from
the earlier pandemic in 2003 from the SARS-CoV-1 virus, it has more
favorable transmission characteristics, a higher reproduction number, and a
long incubation period, when the patient may be asymptomatic but infective.
Each country/region instituted varying measures to reduce the rates of
infections using lockdown, quarantine, use of masks, reduced movement of
people etc. In this paper, after a brief introduction to the origins and
spread of the virus, I will describe a simple mathematical epidemiological
SIR model for the pandemic, where S $=$ Susceptible, I $=$ Infected, R $=$
Removed: Recovered or Dead. This model accurately describes the initial rise
of cases in a pandemic, up to and beyond the initial peak in daily cases. I
will then discuss two applications of the model, using public data on
caseloads and deaths. The first application was to understand daily
caseloads and deaths in the United Kingdom and eight European counties:
Norway, Sweden, Denmark, The Netherlands, Italy, France, Germany, and Spain.
The results can be used to determine where mitigation effects worked and
where they did not. In the second application I will show how to use Change
of Address data to understand how the virus spread from its epicenter in the
five boroughs of New York City into counties of the tri-state area of New
Jersey, Connecticut and New York to cause a second wave in cases because of
movements of households. This analysis shows that tracking household
movements may be a simple way to predict where new cases are likely to
appear. I will end with some caveats on the limitations of the model and
prospects for future work.
*partly supported by grants from M2GEN/ORIEN, DoD/ KRCP (KC180159) and NIH/NCI (1R01CA243547-01A1)