Sensor-based localization of epidemic sources on human mobility networks.
PLoS Comput Biol
; 17(1): e1008545, 2021 01.
Article
em En
| MEDLINE
| ID: mdl-33503024
ABSTRACT
We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Transmissão de Doença Infecciosa
/
Epidemias
/
Vigilância em Saúde Pública
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País como assunto:
Africa
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article