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Vector-Borne and Zoonotic Diseases: Climate, Landscape and Transmission IMED 2009 15 Feb. 2009, Vienna, Austria Uriel Kitron Emory University, Atlanta, GA Vector borne zoonoses - arthropods, humans and other animals Many vectorborne diseases are zoonoses Different roles and impacts for wildlife, domestic animals and humans Complex Impact of climate Non-zoonotic: Malaria, Dengue WNV Yellow fever TBE Lyme disease Plague Sleeping sickness, Leishmaniasis Chagas disease CCHF Prerequisites for an active zoonotic VBD focus Vector survival Presence of reservoir hosts Pathogen amplification and transmission Opportunities for human exposure (Some relevant) Components of Climate (weather) Temperature (average, min, max, fluctuations) Rainfall (amount, distribution, pattern, fluctuations) Humidity (relative, absolute, fluctuations) Extreme events (occurrence, frequency, level) Differential impacts, separately and in combination, on VBZD transmission dynamics (vector, pathogen, reservoir, human exposure) Climate variation and change: Temporal and Spatial scales Temporal resolution Long term changes warming variability in weather ocean depth snow pack, melt dates Medium term Cycles ENSO ( El Niño) NAO Short term changes – Weather Storm severity Extremes & Variability Spatial resolution Biosphere Landscape Ecosystem Community Species interactions Species populations Individuals Intra-host and vector Climate Variability vs. Climate Change Climate Variable High Increasing Rate of Increasing Average, Greater Extremes Average Trend (solid line) Actual Measure (dashed line) Low Time Climate, weather and VBZD Temperature Vector survival Rainfall Presence of reservoir hosts Humidity Pathogen transmission Variability Extreme events Opportunities for human exposure Temperature Human Exposure Rainfall Extreme events Human exposure Reservoir Human exposure Rainfall Extreme events Reservoir Temperature Reservoir Rainfall Vector Vector Vector Some associations between climate components and VBZD Temperature Extreme events How will climate change impact transmission – infection and disease? Infection of reservoir hosts Disease of human hosts How can we relate global changes to very local processes? What happens when we oversimplify? Warmer = buggier Do we now enough about climate per se and VBZD? The usual suspects Other Vectors Mosquito borne Malaria Dengue West Nile virus Rift Valley fever Chikungunya Leishmaniases Tick-borne encephalitis Lyme disease Bluetongue Chagas disease An example when only temperature is considered 2050 projection for Malaria (from Martens et al., 1999) DJ Rogers & SE Randolph. 2000. Science 289, 1763 -1766 The Global Spread of Malaria in a Future, Warmer World The frequent warnings that global climate change will allow malaria to spread are based on biological transmission models driven by temperature. In an alternative statistical approach using current multivariate climatic constraints, future distributions showed remarkably few changes, even under the most extreme scenarios. rios Lyme disease in US Projected distribution of climate-based habitat suitability for I. scapularis in US (Brownstein et al, 2005) But he movement of lizards northward could result in the disruption of the enzootic cycle of B. burgdorferi in the North Present and predicted future distribution of tick-borne encephalitis (TBE) virus in Europe Probability = 0.65 - 1.0 = 0.55 - 0.649 = 0.50 - 0.549 = 0.45 - 0.499 = 0.35 - 0.449 = 00 - 0.349 = Observed Based on climate surfaces derived from medium-high scenarios in 2020s. (Redrawn from Randolph & Rogers 2000) Predicted (2020) Network of independent but synergistic biological and non-biological factors Examples of data from Estonia, Latvia, Lithuania, Slovenia and Czech Republic. -2 4 2000 2000 8 1990 0 1980 12 1970 2 1970 2005 2000 1995 1990 1985 16 1990 Mean daily max temp 2000 1990 1980 1980 1970 More hosts for adult ticks CZ 20 15 15 10 10 5 5 0 2000 1990 1980 0 1970 40 LV 2000 Roe deer & wild boar/1000 ha 60 20 1990 2000 1990 1970 1980 LV 80 20 0 2000 Increase in rodent populations (transmission hosts) 100 1990 Regeneration of shrubs 4 21-30.Apr Increased coco-feeding transmission of TBEV More infected ticks 1980 2000 1990 1980 1970 Greater human exposure to ticks in forests? 1970 field crops LV 0 20 SI Adult ticks , May 10 24 20 0 20 ES 6 1980 30 1.Mar-20.Apr 1970 wooded LV 0 CZ 1975 2000 1990 0 1970 20 0 8 1980 1990 2000 LV 20 50 Sudden increase in Spring temperature 40 CZ 5 More wealth & leisure 40 TBE cases/100,000 population 10 Global brightening ?? Higher TBE incidence 60 2000 1990 20 1980 output LV 20 SI 1980 CZ LV 40 15 1970 60 % unemployment SI 80 1970 60 40 Environmental awareness? Reduced industrial pollution Higher unemployment 100 % of total national land area Nationl cattle herd, % of mean for 1986-88 Decline of agriculture LT 80 1980 SocioSocio-economic transition employees 100 1970 Industrial employees, Industrial output, % of pre-1990 level TBE More ticks Randolph SE, Microbes & Infection (2008) (2008) 30, 209209-16 Human population growth is the greatest single factor driving changes at all scales Climate change Urbanization Habitat fragmentation – extended ecotone Loss of habitat complexity and species diversity Pollution/eutrophication Globalization of commerce Some anthropogenic changes Urbanization – demographics, migration, biodiversity Agricultural development – crops, water management, migration Forestation – Both deforestation and reforestation Global warming, other climate changes Rate of Change is on the increase Fine-scale patterns of transmission, amplification, and evolution of West Nile virus in a “hot spot” in suburban Chicago, USA Uriel Kitron1, Tony Goldberg1, Marilyn Ruiz1, Ned Walker2, Jeff Brawn1, Gabe Hamer2, Scott Loss1, and Luigi Bertolotti3 1 University of Illinois, USA 2 Michigan State University, USA 3 University of Illinois and U. of Torino, Italy NSF/NIH Ecology of Infectious Disease West Nile Virus in Illinois WNV appeared in NYC during 1999 (arrived from the old world) 2001 - 123 positive bird specimens, 0 human cases 2002 - 884 human cases, 66 deaths, most in U.S. (4,156/284) Over 680 cases occurred in Chicago and surroundings 2003 - 54 human cases, 1 death (U.S – 9,862/264) 2004 - 60 human cases, 4 deaths (U.S. – 2,539/100) 2005 - 252 human cases, 12 deaths (U.S. – 3000/119) 2006 - 215 human cases, 10 deaths (U.S. – 4269/177) 2007 - 57 human cases, 4 deaths (U.S. – 3,576/115) 2002 2003 WNV cases in Illinois 2002-2006 1000 900 800 700 600 500 400 300 2005 200 2006 100 2004 2003 Date 10/9 10/2 9/25 9/18 9/11 9/4 8/28 8/21 8/14 8/7 7/31 7/24 7/17 7/10 7/3 0 6/26 cumulative cases 2002 Outbreak years were hot and dry Distribution of human WNV cases in the greater Chicago area, 2002 Landscape classification Each different color represents a place with a common set of factors related to housing, vegetation, socioeconomics, and land use. Urban Type 5, dominated by 40s and 50s housing. Mostly white, moderate vegetation and moderate population density. 435 cases (64%) were in this group, 2.27 cases per 10,000 people (RR>3.5). (All other types <0.65 cases per 10,000) Area characterized by many undocumented storm drains In hot dry years standing water with organic matter provide habitat for Culex mosquito larvae High Spatial heterogeneity among sites within the “hot spot” area Mosquito infection July 30 – Aug 19, 2006 Exceptional variation within hot spots Landscape, mosquito vector, avian host , and virus diversity and ecology “Hot spots” within the greater Chicago area result from: Natural landscape (vegetation) Built environment (housing type and density) Climate and weather (rainfall, temperature) Socio-demographics and mosquito control These highly local processes may “coalesce” to create broader, regional patterns of amplification. WNV in Georgia, USA Human cases in Georgia ~10 times lower than in Illinois WNV cases in Georgia 2001-2007 WNV cases in Illinois 2002-2006 1000 100 900 90 2002 800 2002 2003 2004 2005 2006 2007 80 Cumulative cases 700 600 500 400 300 2005 200 2006 70 60 50 40 30 20 2003 2004 100 10 Date 10/9 10/2 9/25 9/18 9/11 9/4 8/28 8/21 8/14 8/7 7/31 7/24 7/17 7/10 7/3 0 6/26 cumulative cases 2001 0 Jun Jul Aug Sep Oct Nov Dec Month Why is WNV transmission in Georgia that low ? Two other examples – Eco-epidemiological studies supported by NIH/NSF EID program Chagas disease – vector distribution, habitat modification and role of various zoonotic hosts T. Cruzi in blood Schistosomiasis in coastal Kenya – patterns of infection and snail dispersal during and after a drought Eco-Epidemiology of Chagas Disease in the Gran Chaco of Argentina R Gurtler, C Cecere, G Vazquez-Prokopec, P Marcet, L Ceballos, M Cardinal, J Guarevitz - Univ. of Buenos Aires, Argentina J Cohen - Rockefeller University, NY S Blanco, D Canale, C Spillmann – National Vector Control Program, Argentina M Lauricella -, Inst. Fatala Chabén, Argentina E Dotson – CDC JP Dujardin - IRD-CNRS, France U Kitron - Univ. of Illinois Supported by NIH/NSF Ecology of Infectious Disease Program (NIH – Fogarty) The Gran Chaco of Argentina, Bolivia, Paraguay and Brazil 1) High levels of poverty 2) Low population density 3) Mostly rural population 4) Subsistence economy 5) Limited health services, political support 6) High disease burden 7) High T. infestans infestation prevalence and transmission of T. cruzi. soybean boom Changes in land use and ownership, deforestation, and accelerated environmental degradation irreversible loss of biodiversity Active dispersal of natural populations of T. (A) Amamá infestans Transects Light trap Vazquez Prokopec et al. 2004, 2006 Flight from peridomestic sources determines the reinfestation of houses after insecticide applications Peak of dispersal in late summer (March) Association with climatic variables 1.4 1.2 Square root (no. adults/no. light traps) Square root (n o . a d u lts/n o . lig h t tra p s) 1.6 1 0.8 0.6 0.4 0.2 0 0 2 4 6 8 Wind speed (km/h) Negative association with wind speed < 5km/h 10 12 Positive association with temperature > 23 ºC and relative humidity Changes in climate/weather and landscape over three decades since 1980: Extensive deforestation resulted in reduced number of refuges for opossums and bugs Cycles of drought and wet years impact bug survival Complex impact on very narrow window of bug dispersal Other examples - concerns Malaria – highlands – return to temperate zones Dengue – spread north and south (esp. in Americas) Other arboviruses: > Rift Valley fever – introduction to US, Europe? > Bluetongue and Chikungunya fever – already in Europe, risk to US Other viruses, bacteria, protozoa – known and unknown Effect of Location – Latitude, Altitude, Distance from coast To understand the impact of climate change on VBZD dynamics, we need: Better tools for vector and reservoir population assessments Long term studies – baseline data and surveillance Study pathogen transmission dynamics Study disease specific and multi-parasite systems Consider Spatial/temporal scale and heterogeneity Improved Global communication - ProMed Improved understanding of associations between (unchanging) climate and the complex components of VBZD Some needed data - Accurate measures of the components of: C Vectorial Capacity – Population size – Blood feeding habit – Survivorship – Vector competence = ma2 pn / (–lnp) Pathogen persistence - Survival - Amplification Human exposure - Behavior - SES Reservoir Host – Distribution in time and space – Behavior – Susceptibility – infectiousness Pathogen transmission - Vector-reservoir - Vector-human - Other routes Conclusions Climate change may enhance (or disrupt) transmission of vector borne zoonoses, But other (mostly anthropogenic) factors can have even more impact on transmission and disease (in synergy or antagonistically), rendering the "warmer=buggier" approach highly simplistic Observational and modeling studies must be interpreted cautiously before they are used to forecast changes in vector and host populations and the risk of outbreaks or spread of disease?