Climate Change Effects on Plague and Tularemia in the United States

Nakazawa, Yoshinori; Williams, Richard; Peterson, A. Townsend; Mead, Paul S.; Staples, Erin; Gage, Kenneth L.

doi:10.1089/vbz.2007.0125

Cited by 106 publications

(88 citation statements)

References 42 publications

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“…The SOI, computed from fluctuations in the surface air pressure difference between Tahiti and Darwin, Australia, 26 It had been shown that ENSO was associated with other vector-borne diseases and zoonoses, such as malaria, 38,39 dengue fever, 40,41 Ross River virus infection, 42,43 cutaneous leishmaniasis, and plague. 44,45 Similarly, our results indicate that in addition to relative humidity and maximum temperature, SOI is also associated with HFRS incidence in Heilongjiang Province. Thus, local climate (e.g., temperature and humidity) was more likely to directly affect life cycle dynamics (e.g., reproductive rates and incubation periods) of the disease agents themselves, whereas largerscale factors (e.g., SOI) could also influence broader ecologic processes and have possibly nonlinear impacts on disease transmission dynamics.…”

Section: Discussionsupporting

confidence: 67%

Association between Hemorrhagic Fever with Renal Syndrome Epidemic and Climate Factors in Heilongjiang Province, China

Li¹,

Cui

Li³

et al. 2013

The American Society of Tropical Medicine and Hygiene

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Abstract. The purpose of this study was to quantify the relationship between climate variation and transmission of hemorrhagic fever with renal syndrome (HFRS) in Heilongjiang Province, a highly endemic area for HFRS in China. Monthly notified HFRS cases and climatic data for [2001][2002][2003][2004][2005][2006][2007][2008][2009] in Heilongjiang Province were collected. Using a seasonal autoregressive integrated moving average model, we found that relative humidity with a one-month lag (β = -0.010, P = 0.003) and a three-month lag (β = 0.008, P = 0.003), maximum temperature with a two-month lag (β = 0.082, P = 0.028), and southern oscillation index with a two-month lag (β = -0.048, P = 0.019) were significantly associated with HFRS transmission. Our study also showed that predicted values expected under the seasonal autoregressive integrated moving average model were highly consistent with observed values (Adjusted R 2 = 83%, root mean squared error = 108). Thus, findings may help add to the knowledge gap of the role of climate factors in HFRS transmission in China and also assist national local health authorities in the development/refinement of a better strategy to prevent HFRS transmission.

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Section: Discussionsupporting

confidence: 67%

Association between Hemorrhagic Fever with Renal Syndrome Epidemic and Climate Factors in Heilongjiang Province, China

Li¹,

Cui

Li³

et al. 2013

The American Society of Tropical Medicine and Hygiene

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“…Our results emphasize the importance of selecting the most appropriate and informative environmental data for ecologic niche modeling. Models based on simple environmental data sets may be overly general and lacking detail, owing to the broad interpolation and smoothing inherent in the process of generating the climate coverages (Nakazawa et al, 2007). Refinements such as filtering occurrence localities based on spatial precision can avoid imprecision resulting from an uncertain geolocation (Wieczorek et al, 2004;Peterson, 2008b).…”

Section: Diseases and Niche Modelingmentioning

confidence: 99%

Ecology and Geography of Human Monkeypox Case Occurrences Across Africa

Ellis¹,

Carroll²,

Lash³

et al. 2012

Journal of Wildlife Diseases

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ABSTRACT:As ecologic niche modeling (ENM) evolves as a tool in spatial epidemiology and public health, selection of the most appropriate and informative environmental data sets becomes increasingly important. Here, we build on a previous ENM analysis of the potential distribution of human monkeypox in Africa by refining georeferencing criteria and using more-diverse environmental data to identify environmental parameters contributing to monkeypox distributional ecology. Significant environmental variables include annual precipitation, several temperature-related variables, primary productivity, evapotranspiration, soil moisture, and pH. The potential distribution identified with this set of variables was broader than that identified in previous analyses but does not include areas recently found to hold monkeypox in southern Sudan. Our results emphasize the importance of selecting the most appropriate and informative environmental data sets for ENM analyses in pathogen transmission mapping.

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“…Trends related to increased climatic variability and intensity (Karl et al 2009) due to global climate change are a concern because these factors could exacerbate vector-borne disease outbreaks by affecting a vector's life cycle and improve opportunities for pathogen survival and transmission (Githeko et al 2000, Patz et al 2005. Changing climatic trends may increase the likelihood that F. tularensis will impact new geographic locations (Nakazawa et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Environmental, Climatic, and Residential Neighborhood Determinants of Feline Tularemia

Raghavan

Harrington²,

Anderson³

et al. 2013

Vector-Borne and Zoonotic Diseases

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Background: Tularemia, caused by a Gram-negative bacterium Francisella tularensis, is an occasional disease of cats in the midwestern United States and a public health concern due to its zoonotic potential. Different environmental, climatic, and pet-owner's housing and socioeconomic conditions were evaluated as potential risk factors for feline tularemia using Geographic Information Systems (GIS) in a retrospective case-control study. Methods: The study included 46 cases identified as positive for tularemia based upon positive immunohistochemistry, isolation of F. tularensis using bacterial culture, and 4-fold or greater change in serum antibody titer for F. tularensis. Cats with a history of fever, malaise, icterus, and anorexia but no lesions characteristic of tularemia and/or negative immunohistochemistry, no isolation of bacteria in bacterial culture, and less than 4-fold raise in serum antibody titer for F. tularensis were treated as controls (n = 93). Candidate geospatial variables from multiple thematic sources were analyzed for association with case status. Variables from National Land Cover Dataset, Soil Survey Geographic Database, US Census Bureau, and Daymet were extracted surrounding geocoded case-control household locations. Univariable screening of candidate variables followed by stepwise multivariable logistic modeling and odds ratios were used to identify strengths of variable associations and risk factors. Results: Living in a residence located in newly urbanized/suburban areas, residences surrounded by areas dominated by grassland vegetation, and mean vapor pressure conditions recorded during the 8 th week prior to case arrival at the hospital are significant risk factors for feline tularemia. Conclusions: Prevention strategies such as acaricide applications in residential backyards during spring and early summer periods and any behavior modifications suitable for cats that will prevent them from contracting infection from ticks or dead animals are necessary. Mean vapor pressure conditions recorded during the 8

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Climate Change Effects on Plague and Tularemia in the United States

Cited by 106 publications

References 42 publications

Association between Hemorrhagic Fever with Renal Syndrome Epidemic and Climate Factors in Heilongjiang Province, China

Association between Hemorrhagic Fever with Renal Syndrome Epidemic and Climate Factors in Heilongjiang Province, China

Ecology and Geography of Human Monkeypox Case Occurrences Across Africa

Environmental, Climatic, and Residential Neighborhood Determinants of Feline Tularemia

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