Towards spatially explicit malaria risk models for the Peruvian Amazon

Transcription

1 Towards spatially explicit malaria risk models for the Peruvian Amazon Ben Zaitchik, Beth Feingold, Keith Spangler Margaret Kosek, Pablo Penataro Yori, Weston Anderson Johns Hopkins University Christa Peters-Lidard NASA Goddard Space Flight Center Bill Pan Duke University

2 Motivation Almost 90% of malaria in the Western Hemisphere is located in the Amazon 25% of the malaria burden in the Americas is in 12 municipalities of Peru, Brazil and Venezuela 60% of cases in Peru are in the Department of Loreto

3 Motivation Relationships between land use, mosquito ecology, climate, human activity, and malaria risk are complex # Cases (1000s) High Deforestation P. falciparum P. vivax Total Malaria, Loreto Province Roll Back Malaria Program

4 Motivation Relationships between land use, mosquito ecology, climate, human activity, and malaria risk are complex But strong biophysical links exist, and they can be monitored and addressed through integrated analysis

5 Malaria on the Amazon Frontier Infrastructure Migration, Colonization, and Agriculture Land Use Anopheles Larva Habitat Climate and Hydrology Human Exposure Density of Anopheles Adults Human Malaria Infection

6 Process of Land Use Change Infrastructure Expansion Oil Exploration Highway construction Urban growth

7 Process of Land Use Change Infrastructure Expansion Oil Exploration Highway construction Urbangrowth Annual Crops Perennial Crops Migration, Colonization, and Phases of Agriculture Development Pastures

8 Frontier Malaria Hypothesis Annual Parasite Index Phase 1: EPIDEMIC High density of and exposure to vectors Outdoor transmission Limited knowledge of the disease Phase 2: TRANSITION Poor health infrastructure Declining rate of malaria More deforestation Better infrastructure Low, stable rates of malaria Decreased population mobility Phase 1 Years since settlement Phase 2 Phase 3: ENDEMIC Expanded urbanization High community cohesion Phase 3

9 Research Questions How do physiography, land use, climate and hydrology interact to influence patterns of Anopheles density? How does this differ by species? What is the temporal relationship between land clearance and rates of malaria? What is the spatial structure of association between biophysical conditions and mosquito density? How do human settlement and migration patterns contribute to existing patterns of transmission risk, and how do they drive the evolution of risk zones?

10 Malaria on the Amazon Frontier Infrastructure Migration, Colonization, and Agriculture Land Use Anopheles Larva Habitat Climate and Hydrology Human Exposure Density of Anopheles Adults Human Malaria Infection

11 Malaria on the Amazon Frontier Infrastructure Migration, Colonization, and Agriculture Land Use Anopheles Larva Habitat Climate and Hydrology Human Exposure Density of Anopheles Adults Human Malaria Infection

12 Approach Spatio-temporal Ecological Anopheles model Input 1: Satellite-based land cover maps Input 2: Meteorological data Input 3: Land Data Assimilation System Human Activities and Settlements Map Input 1: Satellite and in situ mapping Input 2: Census and Economic data Eco-epidemiological Malaria Transmission Model Application: Risk monitoring and prediction

13 Methods: Land Cover Mapping Deforestation and forest disturbance in Peru can be subtle Primary tool: Landsat, multi-temporal analysis Supplemented with commercial high resolution imagery Extensive ground truth

14 Methods: Land Data Assimilation System A Land Data Assimilation System (LDAS) is a computational tool that merges observations with numerical models to produce optimal estimates of land surface states and fluxes.

15 Methods: Land Data Assimilation System Update Observations Landscape Information LDAS Output Numerical Model Meteorological Data Hydrological fluxes and storage Localized meteorology Surface energy balance

16 Methods: Spatially explicit Anopheles Model

17 Methods: Human Activities & Settlement Maps

18 Methods: Transmission Risk Monitoring The study is developing four spatially explicit risk factors: Human biting rate, a function of the number of mosquitoes per human and the human feeding rate Sporozoite rate, the % of mosquitoes with sporozoite in their salivary glands Stability index, the expected number of human bites taken by a vector over its lifetime Parasite ratio, the number prevalence of infection in humans

19 Results: Land Cover Analysis 12 class supervised classification Nauta-Iquitos road in 2001and Iquitos-Mazan road in 2009 Distinction between forest and non-forest appears to be adequate Identification of secondary forest is not

20 Results: Anopheles Analysis Mosquito Collection Iquitos-Nauta road: Iquitos-Mazan road: Additional survey of logging camps Iquitos Mazan Nauta

21 Results: Anopheles analysis 40% 35% 30% Vectors Non-Vectors 25% 20% 15% 10% 5% 0% % of sites with An. Present % of site shaded

22 Results: Anopheles analysis 250 m radius 1000 m radius

23 Results: Anopheles analysis Deforestation vs. Scale 90 % Deforested Land Cover Radius of Analysis (m)

24 Results: LDAS Precipitation mm/month MERRA TRMM PERSIANN GPCC R2 = Dec-85 Jun-91 Dec-96 May-02 Nov-07

25 Results: LDAS Soil Surface Temperature NautaRoadS NautaRoadN MazanRoad LoggingCamp Mazan 299 Iquitos Oct-00 Jun-03 Mar-06 Dec-08 Nauta

26 Results: LDAS 45 % Soil Water Content, top 10cm Mazan Iquitos NautaRoadS MazanRoad NautaRoadN LoggingCamp 0 Oct-00 Jun-03 Mar-06 Dec-08 Nauta

27 Conclusions Land Surface Model simulations show strong potential to inform predictions of Anopheles distribution Active data assimilation not yet tested The relationship between land cover and mosquito distribution is robust and species-specific, and it appears to be strongest at 3-5km radius of influence

28 Next Steps Integrate LDAS results to Anopheles distribution models Compile Human settlements and activities map Continued and enhanced mosquito collection and malaria monitoring Work with end-user partners to ensure that the products are taking on a useful form Constant cross-examination of accepted hypotheses regarding what governs malaria risk in this region

29 THANK YOU