A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak.
PLoS Comput Biol
; 13(10): e1005798, 2017 Oct.
Article
em En
| MEDLINE
| ID: mdl-29084216
ABSTRACT
In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Surtos de Doenças
/
Modelos Estatísticos
/
Transmissão de Doença Infecciosa
/
Doença pelo Vírus Ebola
/
Análise Espaço-Temporal
Tipo de estudo:
Etiology_studies
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País como assunto:
Africa
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article