TY - JOUR
T1 - A vaccination model for COVID-19 in Gauteng, South Africa
AU - Edholm, Christina J.
AU - Levy, Benjamin
AU - Spence, Lee
AU - Agusto, Folashade B.
AU - Chirove, Faraimunashe
AU - Chukwu, C. Williams
AU - Goldsman, David
AU - Kgosimore, Moatlhodi
AU - Maposa, Innocent
AU - Jane White, K. A.
AU - Lenhart, Suzanne
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/9
Y1 - 2022/9
N2 - The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time. Analyzing data from the time period before vaccination gives the approximate dates of parameter changes, and those dates are linked to government policies. Unknown parameters are then estimated from available case data and used to assess the impact of each policy. Looking forward in time, possible scenarios give projections involving the implementation of two different vaccines at varying times. Our results quantify the impact of different government policies and demonstrate how vaccinations can alter infection spread.
AB - The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time. Analyzing data from the time period before vaccination gives the approximate dates of parameter changes, and those dates are linked to government policies. Unknown parameters are then estimated from available case data and used to assess the impact of each policy. Looking forward in time, possible scenarios give projections involving the implementation of two different vaccines at varying times. Our results quantify the impact of different government policies and demonstrate how vaccinations can alter infection spread.
KW - COVID-19
KW - Gauteng
KW - ODE epidemiology Model
KW - Parameter estimation
KW - South Africa
KW - Vaccination
UR - http://www.scopus.com/inward/record.url?scp=85133680083&partnerID=8YFLogxK
U2 - 10.1016/j.idm.2022.06.002
DO - 10.1016/j.idm.2022.06.002
M3 - Article
AN - SCOPUS:85133680083
SN - 2468-0427
VL - 7
SP - 333
EP - 345
JO - Infectious Disease Modelling
JF - Infectious Disease Modelling
IS - 3
ER -