How can risk of COVID-19 transmission be minimised in domiciliary care for older people: development, parameterisation and initial results of a simple mathematical model. (17th December 2022)

• Record Type: Journal Article
• Authors: Kiss, István Z.; Blyuss, Konstantin B.; Kyrychko, Yuliya N.; Middleton, Jo; Roland, Daniel; Bertini, Lavinia; Bogen-Johnston, Leanne; Wood, Wendy; Sharp, Rebecca; Forder, Julien; Cassell, Jackie A.
• Is Part Of: Epidemiology and infection. Volume 150(2022)
• Publication Date: 2022-12-17
• Subjects: care workers -- domiciliary care -- networks -- SEIR/D; Communicable diseases -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1017/S0950268821002727
• Languages: English
• ISSNs: 0950-2688

Abstract

Abstract: 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.