Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598. Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J.
Watson Research Center, Yorktown Heights, NY 10598. Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598. Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598.
Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598. Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598.
Parmenter.W. Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598.
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Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598. Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J.
Watson Research Center, Yorktown Heights, NY 10598. Chung-Sheng Li.W. Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598. Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598.
Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598.
Harry Feinstone Department of Molecular Microbiology and Immunology and ∥Department of Environmental Health Sciences, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; ‡Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131; § Special Pathogens Branch, Division of Viral and Rickettsial Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333; and ¶IBM T.J. Watson Research Center, Yorktown Heights, NY 10598.
In 1993 a disease outbreak marked by rapidly progressing pulmonary symptoms and a high likelihood of mortality reawakened interest in diseases caused by hantaviruses. These agents cause zoonotic diseases and are primarily associated with the rodent family Muridae. The viruses and rodent reservoir species are tightly linked so that a single rodent species tends to be infected by a single viral species. Thus, in general, at-risk areas for people are defined by the rodent reservoirs' geographic distributions. However, within these regions, risk varies based on the ecology of the rodent species and the availability of habitats (, ). Recently, Glass et al. used Landsat Thematic Mapper (TM) satellite data to identify environments associated with human risk of hantaviral disease caused by Sin Nombre virus (SNV).
This retrospective study of human disease in the southwestern United States introduced an epidemiologic risk algorithm that combined several TM bands, taken nearly a year before the outbreak, and elevation, to estimate human risk in 1993, when disease was reported from nearly 30 locations. When the study was repeated using TM imagery from 1995 to predict risk in 1996 the extent of high-risk areas was substantially reduced and only a single human case was observed. The hantaviral pulmonary syndrome (HPS) outbreak led to a series of studies of hantavirus–rodent reservoir systems examining SNV and its main reservoir, Peromyscus maniculatus, the deer mouse (, ).
Three key observations emerged from this research. First, as with other hantaviruses, SNV was maintained within a single reservoir species. Although spillover to other mammalian species occurred, viral persistence in the ecosystem relied on the deer mouse. Second, SNV appeared to be transmitted horizontally, so that the prevalence of infection increased with age class (or its surrogate, body mass) (, ). Thus, the prevalence of infection in deer mouse populations tended to peak with an aging population rather than with the absolute size of the population (, ).
Consequently, HPS risk could be higher in areas where populations showed a preponderance of adult animals (, ). Third, there was a sex bias in infection; more adult male than adult female mice were infected (, ).
surveyed 50 locations in the southwestern United States and found a crude prevalence of infection in female deer mice of 6%, compared with 14% in males. Among adult deer mice the difference was more striking with. Satellite Imagery and Risk Classification. Landsat 5 TM imagery from mid-June in 1997 and 1998 was obtained for a 105,200-km 2 study area in the southwestern United States where HPS was initially recognized in 1993.
Imagery was obtained from the Earth Resources Data Center Earth Resources Observation Systems (EROS) Distributed Active Archive Center (DAAC); U.S. Geological Society (USGS), Sioux Falls, SD. The images were processed and annual predicted HPS risk maps were generated by using described methods.
Logistic regression was used to estimate risk for each year by using the digital numbers in each of three TM bands (bands 1, 5, and 7) and elevations at 28 HPS cases and 170 randomly selected control sites from the original 1993 outbreak. Risk at each pixel within the study region was assigned by weighting the digital numbers for each band, and elevation by the respective coefficients from the logistic models.
Pixels were categorized into three risk categories. Areas of low, moderate, or high human risk were aggregated by using a receiver operator characteristic (ROC) function. Low-risk areas were those with risk values accounting for ≤10% of cases, moderate-risk areas had values with 10% but. Rodent Faunal Sampling. In 1998, 40 sites encompassing a wide range of predicted HPS risk (0.05–0.86 from the 1997 images) within the study area were selected. Sites were chosen by an individual unfamiliar with the region to reduce selection bias.
The criteria used in site selection were that the sites were within relatively homogeneous patches of risk categories and that they were within 300 m of a road to allow access. For the field surveys, sites were dichotomized into those above and below the high-risk threshold, and two sites above and two sites below predicted high risk were surveyed at a time. This methodology was devised to control for environmental conditions (e.g., meteorological and lunar conditions) that could affect trap success. The field crews were blinded to the predicted human risk level to further reduce sampling bias. These sites were resampled in the spring of 1999 by using the same protocols. The 1998 TM images used to generate the risk map for 1999 did not include two of the original survey sites in moderate-risk areas, and these were excluded from further analyses.
A standardized trapping protocol was used to sample the local small mammal fauna (, ). One hundred single-capture small mammal live traps were set for two nights as four parallel lines of 25 traps each.
Traps were spaced at ≈10-m intervals. Longitude and latitude were recorded at the contralateral corners of the two outer trap lines by using global positioning system (GPS) receivers. The data were used to locate the sampling sites on the satellite images. The major vegetation in the overstory, understory, and herbaceous layers and the extent of cover, as well as the amount of cover by litter and bare soil or rock, were recorded by using a standardized protocol. These data were used to qualitatively compare sites for similarities in plant community structure. Standard measures of faunal composition and abundance were generated from the trapping data.
Trap success for selected species was measured as the number of animals captured divided by the number of trap nights (maximally 200) at each study site and used as an indication of abundance. Corrections were made for traps that were lost or sprung but empty.
The age, sex, body mass, and species identification were recorded for each animal captured. These data were used to characterize the age and sex structure of populations for selected species.
Collected animals were processed according to protocols for SNV detection. All animals were accessioned to the Museum of Southwestern Biology (MSB), University of New Mexico, and retained as voucher specimens NK0 (1998) and NK0 (1999). Frozen tissues and blood samples from voucher specimens were deposited in the Division of Genomic Resources, MSB. Serological Testing. Sera from captured rodents were tested by ELISA for antibodies to hantaviruses by using described methods. Briefly, recombinant SNV nucleocapsid antigen cloned into a vector was expressed and coated onto ELISA plates. As control antigen, protein from the vector alone was expressed and coated onto plates.
Sera were screened at a dilution of 1:100 in PBS–Tween-20 (0.1%) and 5% skim milk. Sera were incubated in duplicate wells and detected by using a combination of goat anti- Peromyscus IgG and goat anti- Rattus IgG conjugated with horseradish peroxidase followed with 2,2′-azinodi(3-ethyl)benzthiazoline-6-sulfonic acid (ABTS).
Optical densities were read at 410 nm and the difference between the pair of SNV antigen wells and the pair of control wells for each sample was calculated. Statistical Analysis.
The human risk for HPS at each trapping site was determined by averaging the risk for all of the pixels incorporated within the area enclosed by the trapping lines. Prevalence of SNV antibody at the small mammal sampling locations was estimated by dividing the number of individuals that were antibody positive by the numbers of individuals tested. Previously identified risk factors for hantavirus infection in small mammals (species, sex, and age composition) were used to compare risk sites.
We tested the hypothesis that the predicted HPS risk derived from the satellite imagery identified biologically relevant features associated with hantavirus transmission by predicting characteristics of local rodent populations. First, we examined the relationship between P. Maniculatus abundance at the 38 trapping sites and the HPS risk by using multiple linear regression. The number of deer mice captured (logarithmically transformed captures + 1) was the dependent variable. The HPS risk obtained from the previous June's satellite image and the change in the risk from the prior year, for 1999, were used as the independent variables. Satellite images from 1996 were not available to allow us to calculate changes in risk from 1997 to 1998, so the analysis for 1998 was performed as a simple linear regression. We predicted that there should be a significant relationship between the abundance of deer mice captured at a site and the predicted risk of HPS because deer mice are the principal reservoir of SNV.
As a second test, the age and sex structures of deer mouse populations at high-risk sites were compared with low-risk sites. Adult male deer mice are disproportionately infected with SNV compared with females and juveniles of either sex. Thus, we hypothesized that high-risk sites would have a population sampled that was biased toward adult male deer mice. Finally, we examined the prevalence of SNV infection, as measured by the presence of specific antibodies in individual animals, in high- and low-risk sites. We predicted that the prevalence of SNV infection should be higher in high-risk sites, especially in the year after the ENSO, as this was the time lag associated with the previous HPS outbreak in 1993. Sampling Locations.
Three types of trapping sites were identified on the risk maps for 1998 and 1999. The categorization was based on the annual stability of local risk (Fig.
Low- and moderate-risk sites were combined because few low-risk sites were available in 1998 and because of the small contributions that both categories made to the number of human cases. Eleven sites were in low–moderate-risk categories during both years, 7 sites were classified as high-risk areas in 1998, but as low-risk areas in 1999, and 20 sites were in high-risk areas during both years (Fig. No sites categorized as low–moderate risk in 1998 (during an ENSO) became high risk in 1999 (after an ENSO). Sites that changed from high risk in 1998 to low risk in 1999 were locations where the field crew could not access the originally provided coordinates. The crew selected nearby sites.
These sites were adjacent to low–moderate-risk areas. Trapping Results. A total of 15,042 trap nights were completed during sampling at the 38 trapping sites (7,527 trap nights and 7,515 trap nights in 1998 and 1999, respectively).
Thirteen species of small mammals were captured, with the preponderance (77.8%; n = 826) being members of the genus Peromyscus. Small mammal captures were more common in 1999 (494 animals = 6.4% trap success), the year after the ENSO, than during 1998 (332 animals = 4.41% trap success; P. Numbers of Peromyscus species captured in low–moderate (Low), high, and change sites during 1998 ( Upper) and 1999 ( Lower). Maniculatus was the most common member of the genus captured. Its abundance decreased in low and change sites in 1999, but its abundance increased in high sites in 1999. After the start of the ENSO, in 1998, deer mice were found at most trapping sites but generally were not abundant.
Despite the low overall trap success, there was a statistically significant association between the numbers of P. Maniculatus captured at a site in 1998 and the human risk predicted from the 1997 satellite images ( r = 0.76; F = 53.38; df = 1,37; P. Overall, the abundance of deer mice varied between years and was related to the predicted human risk (Fig.
At least one deer mouse was captured at 10/11 low–moderate-risk sites in 1998 ( x ̄ = 8.0 mice per site; SD = 16.06; range, 0–56 mice). However, the average abundance was skewed by results from a single location. When that one moderate-risk site was excluded, capture statistics were substantially lower ( x ̄ = 3.2; SD = 2.20; range, 0–6 mice).
Deer mice were captured at only 4/7 sites that changed from high risk in 1998 to low–moderate risk in the following year ( x ̄ = 2.0 mice per site; SD = 2.08; range, 0–5 mice). Deer mice were trapped at 17/20 sites in persistently high-risk areas in 1998 ( x ̄ = 5.4 mice per site; SD = 5.19; range, 0–17 mice). The differences in average local deer mouse abundance by risk category in 1998 were not statistically significant (Kruskal–Wallis; H = 1.37; df = 2,35; P = 0.27). In 1999, the average abundance of deer mice in low–moderate risk sites was not significantly different from that observed in 1998 ( x ̄ = 2.1; SD = 4.50; range, 0–15). However, deer mice were captured at fewer low–moderate risk sites (4/11 sites vs.
10/11 sites). Sites that had changed from high risk to low risk in 1999 had marginally lower numbers of deer mice ( x ̄ = 0.9; SD = 0.90; range, 0–2) and deer mice were captured at only four of seven sites. In contrast, all high-risk sites had at least two deer mice and the numbers of deer mice increased in high-risk areas ( x ̄ = 9.4; SD = 0.5.79; range, 2–24). The average local abundance of deer mice at high-risk sites was significantly greater than at other sites (Kruskal–Wallis; H = 24.42; df = 2,35; P. Age structure of P. Maniculatus captured in low–moderate (Low), high, and change sites during 1998 ( Upper) and 1999 ( Lower). The y axis shows the number of individuals captured.
High-risk sites were biased toward adults. Age classes were juvenile, ≤10 g; subadult, 10 to. Was supported by an Intergovernmental Personnel Agreement from the Centers for Disease Control and Prevention (CDC), the United States Environmental Protection Agency (EPA), and National Aeronautics and Space Administration (NASA) Grant NCC5-305. Were also supported by EPA ORD Grant 96-NCERQA-1B.
Received additional support from a cooperative agreement from the EPA. Satellite imagery, and supplies were provided through Cooperative Agreement CR823143 and Grant 96-NCERQA-1B from the EPA and from NASA Grant NCC5-305. Field studies were supported by National Oceanic and Atmospheric Administration Grant NA96gp0419 and the Museum of Southwestern Biology.
The CDC provided funding through a cooperative agreement (to T.L.Y.).