Mayaro virus infection in amazonia: a multimodel inference approach to risk factor assessment.

ABSTRACT:

Background

Arboviral diseases are major global public health threats. Yet, our understanding of infection risk factors is, with a few exceptions, considerably limited. A crucial shortcoming is the widespread use of analytical methods generally not suited for observational data--particularly null hypothesis-testing (NHT) and step-wise regression (SWR). Using Mayaro virus (MAYV) as a case study, here we compare information theory-based multimodel inference (MMI) with conventional analyses for arboviral infection risk factor assessment.

Methodology/principal findings

A cross-sectional survey of anti-MAYV antibodies revealed 44% prevalence (n = 270 subjects) in a central Amazon rural settlement. NHT suggested that residents of village-like household clusters and those using clos

SUBMITTER: Abad-Franch F 

PROVIDER: S-EPMC3469468 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature