1921
Volume 75, Issue 1
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

Marburg virus represents one of the least well-known of the hemorrhagic fever-causing viruses worldwide; in particular, its geographic potential in Africa remains quite mysterious. Ecologic niche modeling was used to explore the geographic and ecologic potential of Marburg virus in Africa. Model results permitted a reinterpretation of the geographic point of infection in the initiation of the 1975 cases in Zimbabwe, and also anticipated the potential for cases in Angola, where a large outbreak recently (2004–2005) occurred. The geographic potential for additional outbreaks is outlined, including in several countries in which the virus is not known. Overall, results demonstrate that ecologic niche modeling can be a powerful tool in understanding geographic distributions of species and other biologic phenomena such as zoonotic disease transmission from natural reservoir populations.

Loading

Article metrics loading...

/content/journals/10.4269/ajtmh.2006.75.1.0750009
2006-07-01
2017-11-21
Loading full text...

Full text loading...

/deliver/fulltext/14761645/75/1/0750009.html?itemId=/content/journals/10.4269/ajtmh.2006.75.1.0750009&mimeType=html&fmt=ahah

References

  1. Murphy FA, Kiley MP, Fisher-Hoch SP, 1990. Filoviridae: Marburg and Ebola viruses. Fields BN, Knipe DM, eds. Virology. New York: Raven Press, Ltd., –942.
  2. Peters CJ, Johnson ED, Jahrling PB, Ksiazek TG, Rollin PE, White J, Hall W, Trotter R, Jaax N, 1993. Filoviruses. Morse SS, ed. Emerging Viruses. Oxford, United Kingdom: Oxford University Press, 159–175.
  3. Miranda ME, Yoshikawa Y, Manalo DL, Calaor AB, Miranda NL, Cho F, Ikegami T, Ksiazek TG, 2002. Chronological and spatial analysis of the 1996 Ebola Reston virus outbreak in a monkey breeding facility in the Philippines. Expl Anim 51: 173–179.
  4. Feldmann H, Wahl-Jensen V, Jones SM, Stroher U, 2004. Ebola virus ecology: a continuing mystery. Trends Microbiol 12: 433–437.
  5. Leroy EM, Rouquet P, Formentry P, Souquiere S, Kilbourne A, Froment JM, Bermejo M, Smit S, Karesh W, Swanepoel R, Zaki SR, Rollin PE, 2004. Multiple Ebola virus transmission events and rapid decline of central African wildlife. Science 303: 387–390.
  6. Peterson AT, Bauer JT, Mills JN, 2004. Ecologic and geographic distribution of filovirus disease. Emerg Infect Dis 10: 40–47.
  7. Pinzon JE, Wilson JM, Tucker CJ, Arthur R, Jahrling PB, Formenty P, 2004. Trigger events: enviroclimatic coupling of Ebola hemorrhagic fever outbreaks. Am J Trop Med Hyg 71: 664–674.
  8. Conrad JL, Isaacson M, Smith EB, Wulff H, Crees M, Geldenhuys P, Johnston J, 1978. Epidemiologic investigation of Marburg virus disease, southern Africa, 1975. Am J Trop Med Hyg 27: 1210–1215.
  9. Zeller H, 2000. Lessons from the Marburg virus epidemic in Durba, Democratic Republic of the Congo (1998–2000). Med Trop 60: 23–26.
  10. Bausch DG, Borchert M, Grein T, Roth C, Swanepoel R, Libande ML, Talarmin A, Bertherat E, Muyembe-Tamfum JJ, Tugume B, Colebunders R, Konde KM, Pirard P, Olinda LL, Rodier GR, Campbell P, Tomori O, Ksiazek TG, Rollin PE, 2003. Risk factors for Marburg hemorrhagic fever, Democratic Republic of the Congo. Emerg Infect Dis 9: 1531–1537.
  11. Monath TP, 1999. Ecology of Marburg and ebola viruses: speculations and directions for future research. J Infect Dis 179: S127–S138.
  12. Smith DH, Johnson BK, Isaacson M, Swanepoel R, Johnson KM, Killey M, Bagshawe A, Siongok T, Koinange Keruga W, 1982. Marburg-virus disease in Kenya. Lancet 1: 816–820.
  13. Johnson ED, Johnson BK, Silverstein D, Tukei P, Geisbert TW, Sanchez AN, Jahrling PB, 1996. Characterization of a new Marburg virus isolated from a 1987 fatal case in Kenya. Arch Virol Suppl 11: 101–114.
  14. Tucker CJ, Wilson JM, Mahoney R, Anyamba A, Linthicum K, Myers MF, 2002. Climatic and ecological context of the 1994– 1996 Ebola outbreaks. Photogrammetric Engineering Remote Sensing 68: 147–152.
  15. Soberón J, Peterson AT, 2005. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics 2: 1–10.
  16. Csuti B, 1996. Mapping animal distribution areas for gap analysis. Scott JM, Tear TH, Davis FW, eds. Gap Analysis: A Landscape Approach to Biodiversity Planning. Bethesda, MD: American Society for Photogrammetry and Remote Sensing, 135–145.
  17. Gottfried M, Pauli H, Reiter K, Grabherr G, 1999. A fine-scaled predictive model for changes in species distribution patterns of high mountain plants induced by climate warming. Diversity-Distributions 5: 241–251.
  18. Manel S, Dias JM, Ormerod SJ, 1999. Comparing discriminant analysis, neural networks, and logistic regression for predicting species distributions: A case study with a Himalayan river bird. Ecol Modelling 120: 337–347.
  19. Manel S, Dias JM, Buckton ST, Ormerod SJ, 1999. Alternative methods for predicting species distribution: An illustration with Himalayan river birds. J Appl Ecol 36: 734–747.
  20. Miller RI, 1994. Mapping the Diversity of Nature. London: Chapman and Hall.
  21. Tucker K, Rushton SP, Sanderson RA, Martin EB, Blaiklock J, 1997. Modeling bird distributions: a combined GIS and Bayesian rule-based approach. Landscape Ecol 12: 77–93.
  22. Stockwell DR, 1999. Genetic algorithms II. Fielding AH, ed. Machine Learning Methods for Ecological Applications. Boston: Kluwer Academic Publishers, 123–144.
  23. Stockwell DR, Peters DP, 1999. The GARP modelling system: Problems and solutions to automated spatial prediction. Int J Geogr Information Systems 13: 143–158.
  24. Stockwell DR, Noble IR, 1992. Induction of sets of rules from animal distribution data: A robust and informative method of analysis. Mathematics Computers Simulation 33: 385–390.
  25. Peterson AT, Stockwell DR, Kluza DA, 2002. Distributional prediction based on ecological niche modeling of primary occurrence data. Scott JM, Heglund PJ, Morrison ML, eds. Predicting Species Occurrences: Issues of Scale and Accuracy. Washington, DC: Island Press, 617–623.
  26. Stockwell DR, Peterson AT, 2002. Effects of sample size on accuracy of species distribution models. Ecol Modelling 148: 1–13.
  27. Stockwell DR, Peterson AT, 2002. Controlling bias in biodiversity data. Scott JM, Heglund PJ, Morrison ML, eds. Predicting Species Occurrences: Issues of Scale and Accuracy. Washington, DC: Island Press, 537–546.
  28. Peterson AT, Cohoon KC, 1999. Sensitivity of distributional prediction algorithms to geographic data completeness. Ecol Modelling 117: 159–164.
  29. Peterson AT, Ball LG, Cohoon KC, 2002. Predicting distributions of Mexican birds using ecological niche modelling methods. Ibis 144: e27–e32.
  30. Peterson AT, 2001. Predicting species’ geographic distributions based on ecological niche modeling. Condor 103: 599–605.
  31. Peterson AT, Soberon J, Sanchez-Cordero V, 1999. Conservatism of ecological niches in evolutionary time. Science 285: 1265–1267.
  32. Peterson AT, Vieglais DA, 2001. Predicting species invasions using ecological niche modeling. BioScience 51: 363–371.
  33. Anderson RP, Lew D, Peterson AT, 2003. Evaluating predictive models of species’ distributions: Criteria for selecting optimal models. Ecol Modelling 162: 211–232.
  34. Anderson RP, Gómez-Laverde M, Peterson AT, 2002. Geographical distributions of spiny pocket mice in South America: Insights from predictive models. Global Ecol Biogeography 11: 131–141.
  35. Anderson RP, Peterson AT, Gómez-Laverde M, 2002. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 93: 3–16.
  36. Holt RD, Gaines MS, 1992. Analysis of adaptation in heterogeneous landscapes: Implications for the evolution of fundamental niches. Evol Ecol 6: 433–447.
  37. Grinnell J, 1917. Field tests of theories concerning distributional control. Am Naturalist 51: 115–128.
  38. Austin MP, Nicholls AO, Margules CR, 1990. Measurement of the realized qualitative niche: Environmental niches of five Eucalyptus species. Ecol Monogr 60: 161–177.
  39. Walker PA, Cocks KD, 1991. HABITAT: A procedure for modelling a disjoint environmental envelope for a plant or animal species. Global Ecol Biogeography Lett 1: 108–118.
  40. Nix HA, 1986. A biogeographic analysis of Australian elapid snakes. Longmore R, ed. Atlas of Elapid Snakes of Australia. Canberra, Australia: Australian Government Publishing Service, 4–15.
  41. Scott JM, Tear TH, Davis FW, 1996. Gap Analysis: A Landscape Approach to Biodiversity Planning. Bethesda, MD: American Society for Photogrammetry and Remote Sensing.
  42. Scott JM, Heglund PJ, Morrison ML, 2002. Predicting Species Occurrences: Issues of Accuracy and Scale. Washington, DC: Island Press.
  43. Scott JM, Davis F, Csuti B, Noss R, Butterfield B, Groves C, Anderson H, Caicco SL, D’Ericha F, Edwards TC Jr, Ullman J, Wright RG, 1993. Gap analysis: a geographic approach to the protection of biological diversity. Wildl Monogr 23: 1–41.
  44. Leirs H, Mills JN, Krebs JW, Childs JE, Akaibe D, Woollen N, Ludwig G, Peters CJ, Ksiazek TG, 1999. Search for the ebola virus reservoir in Kikwit, Democratic Republic of the Congo: Reflections on a vertebrate collection. J Infect Dis 179: S155–S163.
  45. Costa J, Peterson AT, Beard CB, 2002. Ecological niche modeling and differentiation of populations of Triatoma brasiliensis Neiva, 1911, the most important Chagas disease vector in northeastern Brazil (Hemiptera, Reduviidae, Triatominae). Am J Trop Med Hyg 67: 516–520.
  46. Peterson AT, Martínez-Campos C, Nakazawa Y, Martínez-Meyer E, 2005. Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases. Trans R Soc Trop Med Hyg 99: 647–655.
  47. Peterson AT, Shaw JJ, 2003. Lutzomyia vectors for cutaneous leishmaniasis in southern Brazil: Ecological niche models, predicted geographic distributions, and climate change effects. Int J Parasitol 33: 919–931.
http://instance.metastore.ingenta.com/content/journals/10.4269/ajtmh.2006.75.1.0750009
Loading
/content/journals/10.4269/ajtmh.2006.75.1.0750009
Loading

Data & Media loading...

  • Received : 31 May 2005
  • Accepted : 21 Dec 2005

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error