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Spatial modelling of the potential temperature-dependent transmission of vector-associated diseases in the face of climate change: main results and recommendations from a pilot study in Lower Saxony (Germany)

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Abstract

The sustained climate change is going to modify the geographic distribution, the seasonal transmission gate and the intensity of the transmission of vector-borne diseases such as malaria or the bluetongue disease. These diseases occur nowadays at higher latitudes or altitudes. A further rise in ambient temperature and rainfall will extend the duration of the season in which mosquito vectors are transmitting pathogens. The parasites transmitted by the vectors also benefit from increasing temperatures, as both their reproduction and development are then accelerated, too. Thus, it seemed prudent to examine potential effects on the seasonal transmission gate due to the ongoing and predicted climate changes. Lower Saxony (northwest Germany) is a former malaria region with highest incidences of Anopheles atroparvus and tertian malaria along the coastal zones before malaria had finally become extinct in the early 1950s. Nevertheless, the Anopheles mosquitoes which transmit the malaria pathogens have still been present in Lower Saxony up to now. This together with the climate change-related implications gave reason to investigate whether a new autochthonous transmission could take place if the malaria pathogen is introduced again in Lower Saxony. Thus, the potential spatial and temporal structure of temperature-driven malaria transmissions was mapped using the basic reproduction rate (R 0) and measured and predicted air temperatures (1947–1960, 1961–1990, 1985–2004, 2020, 2060, 2100, each best case and worst case scenario). This paper focuses on both the summarizing of the results from this risk modelling approach and on the conclusions to be drawn. The recommendations highlight the need to link vector monitoring as one of the key elements of an epidemiological monitoring with the environmental monitoring.

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References

  1. Anderson RM, May RM (1991) Infectious diseases of humans: Dynamics and control. Oxford University Press, Oxford

  2. Bailey NTJ (1982) The biomathematics of malaria. Biomathematics of diseases 1. Griffin, London

  3. Baylis M, Bouayoune H, Touti J, Hasnaoui HEL (1998) Use of climatic data and satellite imagery to model the abundance of Culicoides imicola, the vector of African horse sickness virus, in Morocco. Med Vet Entomol 12:255–266

  4. Becker K, Müssig-Zufika M, Conrad A, Lüdecke A, Schulz C, Seiwert M, Kolossa-Gehring M, (2008) German environmental survey for children 2003/06—GerES IV—human biomonitoring. levels of selected substances in blood and urine of children in Germany. WaBoLu-Hefte 01/08 (ISSN 1862-4340. Environmental research of Federal Ministry of the Environment, Nature Conservation and Nuclear Safety, Research Report 202 62 219, UBA-FB 001026 by Federal Environment Agency/Umweltbundesamt, Dessau-Roßlau, and Robert Koch-Institut/RKI, Berlin. On behalf of the Federal Environment Agency)

  5. Brookmeyer R, Stroup D (2004) Monitoring the health of populations: statistical principles and methods for public health surveillance. Oxford University Press, New York

  6. Davis AJ, Jenkinson LS, Lawton JH, Shorrocks B, Wood S (1998) Making mistakes when predicting shifts in species range in response to global warming. Nature 391:783–786

  7. Dietz K (1993) The estimation of the basic reproduction number for infectious diseases. Stat Methods Med Res 2:23–41

  8. Dreesmann M (2004) Geoservices. Kurzdokumentation von OGC basierten Geoservices, GIB, Potsdam

  9. Eritja R, Aranda C, Padrós J, Goula M, Lucientes J, Escosa R, Marquès E, Cáceres F (2000) An annotated checklist and bibliography of the mosquitoes of Spain (Diptera: Culicidae). Eur Mosq Bull 8:11–42

  10. Estrada-Pena A (1998) Geostatistics and remote sensing as predictive tools of tick distribution: a cokriging system to estimate Ixodes scapularis (Acari: Ixodidae) habitat suitability in the United States and Canada from advanced very high resolution radiometer satellite imagery. J Med Entomol 35:989–995

  11. Gemperli A, Vounatsou P, Sogoba N, Smith T (2006) Malaria mapping using transmission models. Application to survey data from Mali. Am J Epidemiol 163:289–297

  12. Gill CA (1921) The role of meteorology on malaria. Indian J Med Res 8:633–693

  13. Gill CA (1923) The prediction of malaria epidemics. Indian J Med Res 10:1136–1143

  14. Gimnig JE, Hightower AW, Hawley WA (2005) Application of geographic information systems to the study of the ecology of mosquitoes and mosquito-borne diseases. In: Takken W, Martens P, Bogers RJ (eds) Environmental change and malaria risk. Global and local implications. Springer, Dordrecht, pp 15–26

  15. Glass GE, Amerasinghe FP, Morgan JM, Scott TW (1994) Predicting Ixodes scapularis abundance on white-tailed deer using geographic information systems. Am J Trop Med Hyg 51:538–544

  16. Hay SI, Cox J, Rogers DJ, Randolph SE, Stern DI, Shanks GD, Myers MF, Snow RW (2002) Climate change: regional warming and malaria resurgence-reply. Nature 420:628–628

  17. Hendrickx G, Biesemans J, de Deken R (2004) The use of GIS in veterinary parasitology. In: Durr PA, Gatrell AC (eds) GIS and spatial analysis in veterinary science. CABI, Wallingford

  18. Hoshen MB, Morse AP (2005) A model structure for estimating malaria risk. In: Takken W, Martens P, Bogers RJ (eds) Environmental change and malaria risk: global and local implications. Springer, Dordrecht, pp 41–50

  19. IPCC (Intergovernmental Panel of Climate Change) (2001) Climate change. The scientific basis. Cambridge University Press, Cambridge

  20. Jetten TH, Takken W (1994) Anophelism without malaria. Agricultural Univ. Papers 94, Wageningen

  21. Kampen H, Kiel E, Schröder W (2007) Blauzungenkrankheit in Deutschland 2006. Epizootiologischer Hintergrund, entomologische Analyse und notwendige Konsequenzen. UWSF Z Umweltchem Ökotox 19:37–46

  22. Killeen GF, McKenzie FE, Foy BD, Schieffelin C, Billingsley PF, Beier JC (2000) A simplified model for predicting malaria entomologic inoculation rates based on entomologic and parasitologic parameters relevant to control. Am J Trop Med Hyg 62:535–544

  23. Kitron U (2000) Risk maps: transmission and burden of vector-borne diseases. Parasitol Today 16:324–325

  24. Kitron U, Pener H, Costin C, Orshan L, Greenberg Z, Shalom U (1994) Geographic information system in malaria surveillance: mosquito breeding and imported cases in Israel, 1992. Am J Trop Med Hyg 50:550–556

  25. Kleinschmidt I, Bagayoko M, Clarke GPY, Craig M, Le Sueur D (2000) A spatial statistical approach to malaria mapping. Int J Epidemiol 29:355–361

  26. Korduan P, Zehner ML (2008) Geoinformation im Internet. Technologien zur Nutzung raumbezogener Informationen im WWW. Wichmann, Heidelberg

  27. Krüger A, Rech A, Su XZ, Tannich E (2001) Two cases of autochthonous Plasmodium falciparum malaria in Germany with evidence for local transmission by indigenous Anopheles plumbeus. Trop Med Int Help 6:983–985

  28. Kubica-Biernat B (1999) Distribution of mosquitoes (Diptera: Culicidae) in Poland. Eur Mosq Bull 5:1–17

  29. Lindsay SW, Parson L, Thomas CJ (1998) Mapping the ranges and relative abundance of the two principal African malaria vectors, Anopheles gambiae sensu stricto and An. arabiensis, using climate data. Proc Roy Soc Lond B Biol Sci 265:847–854

  30. Lindsay SW, Thomas CJ (2001) Global warming and risk of vivax malaria in Great Britain. Glob Change Hum Health 2:80–84

  31. Maier WA, Grunewald J, Habedank B, Hartelt K, Kampen H, Kimmig P, Naucke T, Oehme R, Vollmer A, Schöler A, Schmitt C (2003) Mögliche Auswirkungen von Klimaveränderung auf die Ausbreitung von primär humanmedizinisch relevanten Krankheitserregern über tierische Vektoren sowie auf die wichtigen Humanparasiten in Deutschland. Climate Change 05/03, Umweltbundesamt, Berlin

  32. Martens P, Kovats RS, Nijhof S, de Vries P, Livermore MTJ, Bradley DJ, Cox J, McMichael AJ (1999) Climate change and future population at risk of malaria. Glob Environ Change 9:89–107

  33. Martens P, Thomas C (2005) Climate change and malaria risk: complexity and scaling. In: Takken W, Martens P, Bogers RJ (eds) Environmental change and malaria risk. Global and local implications. Springer, Dordrecht, pp 3–14

  34. Martin PH, Lefebvre MG (1995) Malaria and climate: sensitivity of malaria potential transmission to climate. Ambio 24:200–207

  35. Mühlberger N, Jelinek T, Gascon J, Probst M, Zoller T, Schunk M, Beran J, Gjørup I, Behrens RH, Clerinx J, Björkman A, McWhinney P, Matteelli A, Lopez-Velez R, Bisoffi Z, Hellgren U, Puente S, Schmid ML, Myrvang B, Holthoff-Stich ML, Laferl H, Hatz C, Kollaritsch H, Kapaun A, Knobloch J, Iversen J, Kotlowski A, Malvy DJM, Kern P, Fry G, Siikamaki H, Schulze MH, Soula G, Paul M, Gómez i Prat J, Lehmann V, Bouchaud O, da Cunha S, Atouguia J, Boecken G (2004) Epidemiology and clinical features of vivax malaria imported to Europe: Sentinel surveillance data from TropNetEurop. Malaria J 3:5

  36. Mühlens P (1930) Malaria. Neue Deutsche Klinik. Handwörterbuch der Praktischen Medizin mit besonderer Berücksichtigung der Inneren Medizin, der Kinderheilkunde und ihrer Grenzgebiete VII (31):122–149

  37. Müller M, Augstein B (2005) Das Hamburger Umweltinformationssystem HUIS—integration von Umweltdaten auf Basis eines GDI-Ansatzes. In: Fischer-Stabel P (Hrsg) Umweltinformationssysteme. Wichmann, Heidelberg, pp. 246–263

  38. Patz JA, Hulme M, Rosenzweig C, Mitchell TD, Goldberg RA, Githeko AK, Lele S, McMichael AJ, Le Sueur D (2002) Climate change: regional warming and malaria resurgence. Nature 420:627–628

  39. Peng ZR, Tsou MH (2003) Internet GIS: Distributed geographic information services for the internet and wireless networks. Wiley, Hoboken, NJ

  40. Reiter P (2000) Malaria and global warming in perspective? Emerg Infect Dis 6:438–439

  41. Rogers DJ, Randolph SE (2000) The global spread of malaria in a future, warmer world. Science 289:1763–1766

  42. Romi R, Pierdominici G, Severini C, Tamburo A, Cocchi M, Menichetti D, Pili E, Marchi A (1997) Status of malaria vectors in Italy. J Med Entomol 34:263–271

  43. Schaffner F (1998) A revised checklist of French mosquitoes. Eur Mosq Bull 2:1–9

  44. Schmidt G, Schröder W (2007): Flächenhafte Szenarien zur potenziellen Ausbreitung von Malaria vivax in Niedersachsen in Abhängigkeit steigender Lufttemperaturen. In: Strobl J, Blaschke Th, Griesebner G (Hrsg): Angewandte Geoinformatik 2007. Beiträge zum 19. Agit-Symposium: 670–680

  45. Schröder W (2006) GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology. Int J Med Microbiol 296(Suppl 40):23–36

  46. Schröder W, Schmidt G (2007) Vektorassoziierte Krankheiten im Klimawandel: Risiken in einem ehemals endemischen Malariagebiet Nordwest-Deutschlands? GIS Business 10:12–20

  47. Schröder W, Bast H, Pesch R, Schmidt G, Kiel E (2007a) Flächenhafte Modellierung der potenziellen Reproduktionsrate des Malaria-Erregers Plasmodium vivax in Anopheles atroparvus auf Grundlage gemessener und prognostizierter Lufttemperaturen in Niedersachsen. UWSF – Z Umweltchem Ökotox 19:115–122

  48. Schröder W, Schmidt G, Bast H, Pesch R, Kiel E (2007b) Pilot-study on GIS-based risk modelling of a climate warming induced tertian malaria outbreak in Lower Saxony (Germany). Environ Monit Assess 133:483–493

  49. Schröder W, Schmidt G, Hasenclever J (2005) Bioindication of climate change by means of mapping plant phenology on a regional scale. A geostatistically based correlation analysis of data on air temperature and phenology by the example of Baden-Württemberg (Germany). Environ Monit Assess 130:27–43

  50. Schröder W, Schmidt G, Hornsmann I (2006) Landschaftsökologische Raumgliederung Deutschlands. In: Fränzle O, Müller F, Schröder W (Eds) Handbuch der Umweltwissenschaften. Grundlagen und Anwendungen der Ökosystemforschung. ecomed, München, Kap. V-1.9, 17. Erg.Lfg.:1–100

  51. Small J, Goetz SJ, Hay SI (2003) Climatic suitability for malaria transmission in Africa 1911–1995. Proc Natl Acad Sci USA 100:15341–15345

  52. Smith DL, McKenzie FE (2004) Statics and dynamics of malaria infection in Anopheles mosquitoes. Malaria J 3:13

  53. Snow RW, Gouws E, Omumbo J, Rapuoda B, Craig MH, Tanser FC, le Suer D, Ouma J (1998) Models to predict the intensity of Plasmodium falciparum transmission: applications to the burden of disease in Kenya. Trans Roy Soc Trop Med H 92:601–606

  54. Snow RW, Ikoku A, Omumbo J, Ouma J (1999) The epidemiology, politics and control of malaria epidemics in Kenya: 1900–1998. Roll Back Malaria. Resource network on epidemics. World Health Organisation, Nairobi

  55. Spath D, Günther J (2005) Open Source Software—Strukturwandel oder Strohfeuer?—Eine empirische Studie zu Trends und Entwicklungen zum Einsatz von Open Source Software in der öffentlichen Verwaltung und IT-Unternehmen in Deutschland. http://www.iao.fraunhofer.de/d/oss_studie.pdf

  56. Takken W, Martens P, Bogers R J (Eds) (2005) Environmental change and malaria risk. Global and local implications. Springer, Dordrecht

  57. Teutsch SM, Churchill RE (1994) Principles and practice of public health surveillance. Oxford University Press, New York

  58. Wakefield JC, Best NG, Waller L (2000) Bayesian approaches to disease mapping. In: Elliott P, Wakefield JC, Best NG, Briggs DG (eds) Spatial epidemiology: methods and applications. Oxford University Press, Oxford, pp 104–127

  59. Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley, New York

  60. Waller LA, Goodwin BJ, Wilson ML, Ostfeld RS, Marshall SL, Hayes EB (2007) Spatio-temporal patterns in county-level incidence and reporting of Lyme disease in the northeastern United States, 1990–2000. Environ Ecol Stat 14:83–100

  61. Webster R, Oliver MA (2001) Geostatistics for environmental scientists. John Wiley & Sons, Ltd., Chichester, New York

  62. Weyer F (1956) Bemerkungen zum Erlöschen der ostfriesischen Malaria und zur Anopheles-Lage in Deutschland. Z Tropenmed Parasitol 7:219–228

  63. WHO (World Health Organistion) (2004) Using climate to predict infectious disease outbreaks. A review. Geneva

  64. Wilke A, Kiel E, Schröder W, Kampen H (2006) Anophelinae (Diptera: Culicidae) in ausgewählten Marschgebieten Niedersachsens: Bestandserfassung, Habitatbindung und Interpolation. Mitt Dtsch Ges Allg Angew Ent 15:357–362

  65. Wilson ML (1998) Distribution and abundance of Ixodes scapularis (Acari: Ixodidae) in NorthAmerica: ecological processes and spatial analysis. J Med Entomol 35:446–457

  66. Williams S (2002) Free as in freedom. Richard Stallman’s crusade for free software. O’Reilly, Sebastopol, Cambridge, pp. 240

  67. Wilson ML, Ducey AM, Litwin TS, Gavin TA, Spielman A (1990) Microgeographic distribution of immature Ixodes dammini ticks correlated with that of deer. Med Vet Entomol 4:151–159

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Correspondence to Winfried Schröder.

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Schröder, W., Schmidt, G. Spatial modelling of the potential temperature-dependent transmission of vector-associated diseases in the face of climate change: main results and recommendations from a pilot study in Lower Saxony (Germany). Parasitol Res 103, 55 (2008). https://doi.org/10.1007/s00436-008-1051-z

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Keywords

  • Malaria
  • Geographic Information System
  • Malaria Transmission
  • Early Warning System
  • Rift Valley Fever