Spatial modeling of cutaneous leishmaniasis in the Andean region of Colombia

Abstract

The objective of this research was to identify environmental risk factors for cutaneous leishmaniasis (CL) in Colombia and map high-risk municipalities. The study area was the Colombian Andean region, comprising 715 rural and urban municipalities. We used 10 years of CL surveillance: 2000-2009. We used spatial-temporal analysis - conditional autoregressive Poisson random effects modelling - in a Bayesian framework to model the dependence of municipality-level incidence on land use, climate, elevation and population density. Bivariable spatial analysis identified rainforests, forests and secondary vegetation, temperature, and annual precipitation as positively associated with CL incidence. By contrast, livestock agroecosystems and temperature seasonality were negatively associated. Multivariable analysis identified land use - rainforests and agro-livestock - and climate - temperature, rainfall and temperature seasonality - as best predictors of CL. We conclude that climate and land use can be used to identify areas at high risk of CL and that this approach is potentially applicable elsewhere in Latin America.

The role of deforestation on American cutaneous leishmaniasis incidence: spatial‐temporal distribution, environmental and socioeconomic factors associated in the Brazilian Amazon

“…In the Amazon, an overwhelming number of sandflies are reported in forest areas compared to the peridomiciliary environment, with predominance of Nyssomyia umbratilis, the main vector for Leishmania guyanensis, responsible for the majority of ACL cases in the state [29]. The hypotheses previously reported can be confirmed from the findings of P erez-Fl orez et al (2016) [30] who described a positive association between the forest area coverage and the disease in Colombia, a country bordering Amazonas state. Ocampo et al (2012) [31] also reported a negative correlation between abundance of sandflies in the intra and peridomiciliary environments and the distance of houses from dense vegetation, suggesting that these insects need the forest as a place for resting and reproduction.…”

“…The hypotheses previously reported can be confirmed from the findings of Pérez‐Flórez et al . (2016) who described a positive association between the forest area coverage and the disease in Colombia, a country bordering Amazonas state. Ocampo et al .…”

Spatiotemporal Variation and Hot Spot Detection of Visceral Leishmaniasis Disease in Kashi Prefecture, China

“…The results of hot-spot detection and spatiotemporal clustering patterns might offer some useful information to epidemiologists for the control of and to predict VL spread over critical hot-spot areas rather than in an entire region. Multiple causes are possible for the outbreaks of VL, and many previous studies have demonstrated that VL is highly sensitive to environmental factors and that climate affects the spatial and temporal distribution of the disease [15]. For instance, environmental factors, including forest cover, soil type, soil moisture, deforestation, and climate determinants, all play important roles in affecting the distribution of VL transmission [16,17].…”

Forecasting United States heartworm Dirofilaria immitis prevalence in dogs

“…One approach to infectious disease modeling is to use these factors to predict transmission and model the data in both space and time. This has been used successfully to estimate the incidence of malaria during eradication campaigns in Namibia and cutaneous leishmaniasis in high-risk areas of Columbia [ 32 , 33 ].…”