Satellite imagery characterizes local animal reservoir populations of Sin Nombre virus in the southwestern United States

Gregory E. Glass, Terry L. Yates, Joshua B. Fine, Timothy M. Shields, John B. Kendall, Andrew G. Hope, Cheryl A. Parmenter, C. J. Peters, Thomas G. Ksiazek, Chung Sheng Li, Jonathan A. Patz, James N. Mills

Research output: Contribution to journalArticlepeer-review

89 Scopus citations

Abstract

The relationship between the risk of hantaviral pulmonary syndrome (HPS), as estimated from satellite imagery, and local rodent populations was examined. HPS risk, predicted before rodent sampling, was highly associated with the abundance of Peromyscus maniculatus, the reservoir of Sin Nombre virus (SNV). P. maniculatus were common in high-risk sites, and populations in high-risk areas were skewed toward adult males, the subclass most frequently infected with SNV. In the year after an El Niño Southern Oscillation (ENSO), captures of P. maniculatus increased only in high-risk areas. During 1998, few sites had infected mice, but by 1999, 18/20 of the high-risk sites contained infected mice and the crude prevalence was 30.8%. Only 1/18 of the low-risk sites contained infected rodents, and the prevalence of infection was lower (8.3%). Satellite imagery identified environmental features associated with SNV transmission within its reservoir population, but at least 2 years of high-risk conditions were needed for SNV to reach high prevalence. Areas with persistently high-risk environmental conditions may serve as refugia for the survival of SNV in local mouse populations.

Original languageEnglish (US)
Pages (from-to)16817-16822
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume99
Issue number26
DOIs
StatePublished - Dec 24 2002

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Satellite imagery characterizes local animal reservoir populations of Sin Nombre virus in the southwestern United States'. Together they form a unique fingerprint.

Cite this