User:Marvin O'Neal/OspC: Difference between revisions
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/* Dilution Effect Model <ref> Ostfeld R and Keesing F. 2001. Biodiversity and Disease Risk: the Case of Lyme Disease. Conservation Biology 14.3 (2000): 722-728.[http://dx.doi.org/10.1046/j.1523-1739.2000.99014.x DOI: 10.1046/j.1523-1739.2000.99014.x |
/* Dilution Effect Model <ref> Ostfeld R and Keesing F. 2001. Biodiversity and Disease Risk: the Case of Lyme Disease. Conservation Biology 14.3 (2000): 722-728.[http://dx.doi.org/10.1046/j.1523-1739.2000.99014.x DOI: 10.1046/j.1523-1739.2000.99014.x |
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== Dilution Effect Model <ref> Ostfeld R and Keesing F. 2001. Biodiversity and Disease Risk: the Case of Lyme Disease. Conservation Biology 14.3 (2000): 722-728.[http://dx.doi.org/10.1046/j.1523-1739.2000.99014.x DOI: 10.1046/j.1523-1739.2000.99014.x].</ref> == | == Dilution Effect Model <ref> Ostfeld R and Keesing F. 2001. Biodiversity and Disease Risk: the Case of Lyme Disease. Conservation Biology 14.3 (2000): 722-728.[http://dx.doi.org/10.1046/j.1523-1739.2000.99014.x DOI: 10.1046/j.1523-1739.2000.99014.x].</ref> == | ||
[[Image:Dilution Effect Model.png|200px|right|Dilution effect model]] | [[Image:Dilution Effect Model.png|200px|right|Dilution effect model]] | ||
[[Image:Lyme_Disease_Risk_Map.gif|180px|left|thumb|Map illustrating prevalence of Lyme disease in the Untied States by CDC.# | [[Image:Lyme_Disease_Risk_Map.gif|180px|left|thumb|Map illustrating prevalence of Lyme disease in the Untied States by CDC.#REDIRECT [[http://www.cdc.gov/mmwr/preview/mmwrhtml/rr4807a2.htm]]]] | ||
This conceptual model characterizing the ecological interactions between vertebrate host community and distribution frequency of invasive oMGs determine the cases of human Lyme disease. The principal natural reservoir host for the epidemic of Lyme disease in northeastern and central United States is the presence of only white-footed mice (''Peromyscus leucopus'') population, which has both high frequency distribution in all four human infectious oMGs and high transmission probabilities of oMGs A, B, I and K<ref name="Distribution frequency of particular oMGs">PMID:16606995</ref>. In addition, ticks are least likely to parasitize on inefficient reservoir hosts, thereby increasing high infection prevalence in the tick population, which enhances the risk of exposure of Lyme disease in humans. Therefore, dilution-effect model proposes that maintaining high diversity of vertebrate host community may dilute the power of white-footed mouse by increasing the degree of specialization of ticks on inefficient hosts. This model strongly demonstrates the relationship between species diversity in the community of hosts and the risk of human exposure to Lyme disease. These ecological driving forces described in the model are useful tools in predicting the prevalence and risk of human Lyme disease. | This conceptual model characterizing the ecological interactions between vertebrate host community and distribution frequency of invasive oMGs determine the cases of human Lyme disease. The principal natural reservoir host for the epidemic of Lyme disease in northeastern and central United States is the presence of only white-footed mice (''Peromyscus leucopus'') population, which has both high frequency distribution in all four human infectious oMGs and high transmission probabilities of oMGs A, B, I and K<ref name="Distribution frequency of particular oMGs">PMID:16606995</ref>. In addition, ticks are least likely to parasitize on inefficient reservoir hosts, thereby increasing high infection prevalence in the tick population, which enhances the risk of exposure of Lyme disease in humans. Therefore, dilution-effect model proposes that maintaining high diversity of vertebrate host community may dilute the power of white-footed mouse by increasing the degree of specialization of ticks on inefficient hosts. This model strongly demonstrates the relationship between species diversity in the community of hosts and the risk of human exposure to Lyme disease. These ecological driving forces described in the model are useful tools in predicting the prevalence and risk of human Lyme disease. | ||
