Dataset Information

Continuing Orthohantavirus Circulation in Deer Mice in Western Montana.

ABSTRACT: Hantavirus pulmonary syndrome (HPS) is an often-fatal disease caused by New World hantaviruses, such as Sin Nombre orthohantavirus (SNV). In the US, >800 cases of HPS have been confirmed since it was first discovered in 1993, of which 43 were reported from the state of Montana. The primary cause of HPS in the US is SNV, which is primarily found in the reservoir host Peromyscus maniculatus (deer mouse). The reservoir host covers most of the US, including Montana, where multiple studies found SNV in local deer mouse populations. This study aimed to check the prevalence of SNV in the deer mice at popular recreation sites throughout the Bitterroot Valley in Western Montana as compared to previous studies in western Montana. We found high prevalence (up to 20%) of deer mice positive for SNV RNA in the lungs. We were unable to obtain a SNV tissue culture isolate from the lungs but could passage SNV from lung tissue into naïve deer mice. Our findings demonstrate continuing circulation of SNV in western Montana.

SUBMITTER: Williamson BN

PROVIDER: S-EPMC8226622 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Continuing Orthohantavirus Circulation in Deer Mice in Western Montana.

Williamson Brandi N BN Meade-White Kimberly K Boardman Kristin K Schulz Jonathan E JE Telford Carson T CT Figueroa Acosta Dania M DM Bushmaker Trenton T Fischer Robert J RJ Rosenke Kyle K Feldmann Heinz H

Viruses 20210527 6

Hantavirus pulmonary syndrome (HPS) is an often-fatal disease caused by New World hantaviruses, such as <i>Sin Nombre orthohantavirus</i> (SNV). In the US, >800 cases of HPS have been confirmed since it was first discovered in 1993, of which 43 were reported from the state of Montana. The primary cause of HPS in the US is SNV, which is primarily found in the reservoir host <i>Peromyscus maniculatus</i> (deer mouse). The reservoir host covers most of the US, including Montana, where multiple stud ...[more]

PMID: 34072112

Similar Datasets

Project description:Background/aimsWildfire air pollution is a growing public health concern as wildfires increase in size, intensity, and duration in the United States. The public is often encouraged to stay indoors during wildfire smoke events to reduce exposure. However, there is limited information on how much wildfire smoke infiltrates indoors at residences and what household/behavioral characteristics contribute to higher infiltration. We assessed fine particulate matter (PM2.5) infiltration into Western Montana residences during wildfire season.MethodsWe measured continuous outdoor and indoor PM2.5 concentrations from July-October 2022 at 20 residences in Western Montana during wildfire season using low-cost PM2.5 sensors. We used paired outdoor/indoor PM2.5 data from each household to calculate infiltration efficiency (Finf; range 0-1; higher values indicate more outdoor PM2.5 infiltration to the indoor environment) using previously validated methods. Analyses were conducted for all households combined and for various household subgroups.ResultsMedian (25th percentile, 75th percentile) daily outdoor PM2.5 at the households was 3.7 μg/m3 (2.1, 7.1) during the entire study period and 29.0 μg/m3 (19.0, 49.4) during a 2-week period in September impacted by wildfire smoke. Median daily indoor PM2.5 at the households was 2.5 μg/m3 (1.3, 5.5) overall and 10.4 μg/m3 (5.6, 21.0) during the wildfire period. Overall Finf was 0.34 (95 % Confidence Interval [95%CI]: 0.33, 0.35) with lower values during the wildfire period (0.32; 95%CI: 0.28, 0.36) versus non-wildfire period (0.39; 95%CI: 0.37, 0.42). Indoor PM2.5 concentrations and Finf varied substantially across household subgroups such as household income, age of the home, presence of air conditioning units, and use of portable air cleaners.ConclusionsIndoor PM2.5 was substantially higher during wildfire-impacted periods versus the rest of the study. Indoor PM2.5 and Finf were highly variable across households. Our results highlight potentially modifiable behaviors and characteristics that can be used in targeted intervention strategies.

Project description:Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data (e.g., juveniles:adult females) as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates (e.g., per capita recruitment and population growth) that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk (Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate (mean = 0.25, 90% CI = 0.21, 0.29), temporal variation in hunting-district-specific recruitment rates (minimum = 0.09; 90% CI = [0.07, 0.11], maximum = 0.43; 90% CI = [0.38, 0.48]), and annual population growth rates (minimum = 0.83; 90% CI = [0.78, 0.87], maximum = 1.20; 90% CI = [1.11, 1.29]). We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics.

Dataset Information

Continuing Orthohantavirus Circulation in Deer Mice in Western Montana.

Publications

Continuing Orthohantavirus Circulation in Deer Mice in Western Montana.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets