TY - JOUR
T1 - How can risk of COVID-19 transmission be minimised in domiciliary care for older people
T2 - development, parameterisation and initial results of a simple mathematical model
AU - Kiss, István Z.
AU - Blyuss, Konstantin B.
AU - Kyrychko, Yuliya N.
AU - Middleton, Jo
AU - Roland, Daniel
AU - Bertini, Lavinia
AU - Bogen-Johnston, Leanne
AU - Wood, Wendy
AU - Sharp, Rebecca
AU - Forder, Julien
AU - Cassell, Jackie A.
N1 - Publisher Copyright:
Copyright © The Author(s), 2021. Published by Cambridge University Press
PY - 2022/12/17
Y1 - 2022/12/17
N2 - This paper proposes and analyses a stochastic model for the spread of an infectious disease transmitted between clients and care workers in the UK domiciliary (home) care setting. Interactions between clients and care workers are modelled using specially generated networks, with network parameters reflecting realistic patterns of care needs and visit allocation. These networks are then used to simulate a susceptible-exposed-infected-recovered/dead (SEIR/D)-type epidemic dynamics with different numbers of infectious and recovery stages. The results indicate that with the same overall capacity provided by care workers, the minimum peak proportion of infection and the smallest overall size of infection are achieved for the highest proportion of overlap between visit allocation, i.e. when care workers have the highest chances of being allocated a visit to the same client they have visited before. An intuitive explanation of this is that while providing the required care coverage, maximising overlap in visit allocation reduces the possibility of an infectious care worker inadvertently spreading the infection to other clients. The model is generic and can be adapted to any directly transmitted infectious disease, such as, more recently, corona virus disease 2019, provided accurate estimates of disease parameters can be obtained from real data.
AB - This paper proposes and analyses a stochastic model for the spread of an infectious disease transmitted between clients and care workers in the UK domiciliary (home) care setting. Interactions between clients and care workers are modelled using specially generated networks, with network parameters reflecting realistic patterns of care needs and visit allocation. These networks are then used to simulate a susceptible-exposed-infected-recovered/dead (SEIR/D)-type epidemic dynamics with different numbers of infectious and recovery stages. The results indicate that with the same overall capacity provided by care workers, the minimum peak proportion of infection and the smallest overall size of infection are achieved for the highest proportion of overlap between visit allocation, i.e. when care workers have the highest chances of being allocated a visit to the same client they have visited before. An intuitive explanation of this is that while providing the required care coverage, maximising overlap in visit allocation reduces the possibility of an infectious care worker inadvertently spreading the infection to other clients. The model is generic and can be adapted to any directly transmitted infectious disease, such as, more recently, corona virus disease 2019, provided accurate estimates of disease parameters can be obtained from real data.
KW - SEIR/D
KW - care workers
KW - domiciliary care
KW - networks
UR - http://www.scopus.com/inward/record.url?scp=85121633187&partnerID=8YFLogxK
U2 - 10.1017/S0950268821002727
DO - 10.1017/S0950268821002727
M3 - Article
AN - SCOPUS:85121633187
SN - 0950-2688
VL - 150
JO - Epidemiology and Infection
JF - Epidemiology and Infection
M1 - e13
ER -