Bayesian zero-inflated spatio-temporal modelling of scrub typhus data in Korea, 2010-2014

Bayesian zero-inflated spatio-temporal modelling of scrub typhus data in Korea, 2010-2014

Blog Article

Scrub typhus, a bacterial, febrile disease commonly occurring in the autumn, can easily be cured if diagnosed early.However, it can develop serious complications and even lead to death.For this reason, it is an important issue to find the risk factors and thus be able to prevent outbreaks.
Text Clustering Algorithm Based on Random Cluster Core
.

We analyzed the monthly scrub typhus data over the entire areas of South Korea from 2010 through 2014.
Et si les jeux vidéo servaient à comprendre la géographie ?
.A 2-stage hierarchical framework was considered since weather data are covariates and the scrub typhus data have different spatial resolutions.At the first stage, we obtained the administrative-level estimates for weather data using a spatial model; in the second, we applied a Bayesian zero-inflated spatio-temporal model since the scrub typhus data include excess zero counts.

We found that the zero-inflated model considering the spatio-temporal interaction terms improves fitting and prediction performance.This study found that low humidity and a high proportion of elderly people are significantly associated with scrub typhus incidence