Links between Climate, Malaria, and Wetlands in the Amazon Basin

Abstract

Climate changes are altering patterns of temperature and precipitation, potentially affecting regions of malaria transmission. We show that areas of the Amazon Basin with few wetlands show a variable relationship between precipitation and malaria, while areas with extensive wetlands show a negative relationship with malaria incidence. G lobal models of malaria can be used to forecast the impact of climate change on malaria, a highly climatesensitive disease that causes >1 million deaths worldwide each year, mostly in children. However, a limitation of these models is the application of a uniform malaria-precipitation relationship to geographically diverse regions (1-3). Moreover, the Millennium Ecosystem Assessment has recognized a lack of knowledge about climate-sensitive diseases such as malaria and has called for a "more systematic inventory, by region and country, of current and likely population health impacts of ecosystem change" (4). Understanding malaria-precipitation relationships at regional levels will enhance predictability of ecosystem or climate change impact on population health.Precipitation and surface hydrology are key factors in determining the abundance of Anopheles mosquito vectors for malaria. Mosquitoes require pools of water to complete their life cycle, and malaria models have estimated changing transmission by setting minimum levels of precipitation below which mosquito populations are (theoretically) suppressed. However, using a uniform hydrologic threshold for malaria does not capture critical characteristics of landscape, soil, and rainfall (i.e., intensity, frequency), all known contributors to the abundance, persistence, and spatial distribution of mosquito habitats.In the Amazon Basin, the predominant malaria vector is Anopheles darlingi. Short longitudinal studies show that human-landing catches of An. darlingi, which breeds along the edges and in debris of clear, partially sunlit pools, are closely associated with local malaria rates (5,6). These observations establish that biting rates are elevated in regions of elevated malaria risk. Likewise, biting rates correlate with abundance of larvae and larval habitats and proximity of humans to larval habitats (7,8).Local observations demonstrate the existence of different seasonal patterns for malaria. In a 3-year study in Roraima, 8 municipalities showed increased risk for malaria during the middle of the dry season or shortly after the wet season (9). Other literature on seasonal patterns is limited to local and short (<3 years) longitudinal studies that lack statistical analysis. Although different seasonal patterns emerge in graphs, the collage of different data sources makes formulating a cohesive picture of these patterns in the Amazon region diffi cult.At the regional level, interannual climatologic cycles provide insight into low-frequency malaria patterns. In Columbia, El Niño events (caused by warming sea surface temperatures in the central tropical Pacifi c) are associated with warmer temperatures, higher dew points, and l...

Unstable Malaria Transmission in the Southern Peruvian Amazon and Its Association with Gold Mining, Madre de Dios, 2001–2012

“…There is evidence that climate, including the El Niño Southern Oscillation, is associated with malaria transmission in the Amazon basin, 48 and specifically in Peru. 49 However, no significant associations were found between the monthly reported number of cases and the climatic variables studied in a straightforward analysis, particularly local rainfall and temperature.…”

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

“…Malaria risk mapping is traditionally obtained through various statistical techniques and datadriven modeling [5,12,13,16,[21][22][23][24][25][26]. Such models are often either very specific or very general, as they are generated from data characterizing either local scale contexts at high resolutions, preventing obtaining reproducible results and to describe or predict large scale phenomena [23], or large scale contexts at low resolutions, preventing the ability to precisely describe disease transmission mechanisms [5,12,16].…”

“…Moreover, the species shows a high efficiency in malaria transmission through high biting rates, susceptibility to Plasmodium infection and capacity to easily adapt to environmental changes due to human activities [5,9]. Natural and human-driven environmental changes, such as climate variability [10][11][12][13] and land use and land cover (LULC) changes [13][14][15][16][17][18][19], can determine the malaria distribution by influencing the habitats of adults and larvae. Prevention and treatment measures [20] and mobility of humans infected by Plasmodium [4,15] also influence such distribution.…”

Public health impacts of ecosystem change in the Brazilian Amazon

“…The thin evidence base focuses on specific drivers of specific diseases (6), typically climate (20)(21)(22)(23)(24)(25), biodemography and migration (13,(26)(27)(28)(29)(30), or land-use change (20,(31)(32)(33). Expanding this literature into multifactorial analyses could provide a more comprehensive picture of the human ecology of these diseases, including the role of specific behaviors and policies (8,34).…”