Seasonal abundance of anopheline mosquitoes and their association with rainfall and malaria along the Matapí River, Amapí, Brazil

Abstract

Three communities separated by 1.5-7.0 km, along the Matapí River, Amapá State, Brazil, were sampled monthly from April 2003 to November 2005 to determine relationships between seasonal abundance of host-seeking anophelines, rainfall and malaria cases. Out of the 759 821 adult female anophelines collected, Anopheles darlingi Root (Diptera: Culicidae) was the most abundant (56.2%) followed by An. marajoara Galvão & Damasceno (24.6%), An. nuneztovari Gabaldón (12.4%), An. intermedius (Chagas) (4.4%) and An. triannulatus (Neiva and Pinto) (2.3%). Vector abundance, as measured by human landing catches, fluctuated during the course of the study and varied in species-specific ways with seasonal patterns of rainfall. Anopheles darlingi and An. triannulatus were more abundant during the wet-dry transition period in June to August, whereas An. marajoara began to increase in abundance in February in two villages, and during the wet-dry transition in the other village. Anopheles nuneztovari and An. intermedius increased in abundance shortly after the rains began in January to February. A generalized linear mixed model (GLMM) analysis of 32 consecutive months of collections showed significant differences in abundance for each species by village and date (P < 0.0001). Correlations between lagged rainfall and abundances also differed among species. A strong positive correlation of An. darlingi abundance with rainfall lagged by 4 and 5 months (Pearson's r = 0.472-0.676) was consistent among villages and suggests that rainfall may predict vector abundance. Significant correlations were detected between numbers of malaria cases and abundances of suspected vector species. The present study shows how long-term field research may connect entomological and climatological correlates with malaria incidence.

Composition, abundance and aspects of temporal variation in the distribution of Anopheles species in an area of Eastern Amazonia

“…have been implicated as major malaria vectors in the state of Amapá 6,10,11,19 . A similar result was obtained by another study 21 along the Matapí River, an area close to the present study area, where the most abundant species were An. darlingi and An.…”

“…Subsequently, An. marajoara was implicated as an important regional and/or local malaria vector in areas of Amapá 7,19 , including the areas close to the present study 4,11,21 .…”

“…The temperature and humidity varied within the optimal ranges for the vital activities of mosquitoes 17 , exerting their infl uence only at higher intensity variations 36 . Despite the absence of an established correlation, there is clearly an infl uence of precipitation on the density of Anopheles because it ensures the formation and maintenance of water supplies for breeding sites 9,21 . Previous studies conducted in Venezuela 33,37 have also failed to fi nd a signifi cant correlation between these variables and the abundance of anophelines.…”

Climate Change Impacts on Human Health

“…Such mechanistic links have been reported by many studies including for diseases such as malaria (van der Hoek et al 1997;Thomson et al 2005;Galardo et al 2009;Gao et al 2012;Yang et al 2012), dengue fever (Li et al 1985;Su 2008;Johansson et al 2009;Banu et al 2011;Hii et al 2012), West Nile virus (Shaman et al 2005;Soverow et al 2009), and Lyme disease (Ogden et al 2006;Ostfeld et al 2006). …”

Mapping and predicting malaria transmission in the People’s Republic of China, using integrated biology-driven and statistical models

“…Typical examples are the minimum and maximum thermal thresholds for the survival of infectious agents developing in a vector or an intermediate host (Malone, 2005;Peterson, 2006). The mathematical statistical approach, on the other hand, requires the development of a new, weatherdependent mathematical dynamic model, which takes into account known risk factors quantitatively (Yang et al, 2005(Yang et al, , 2008Galardo et al, 2009). The various statistical models rely on various mathematical approaches to quantitatively investigate the linkage between risk factors and disease prevalence or incidence.…”