Using the basic reproduction number to assess the effects of climate change in the risk of Chagas disease transmission in Colombia

• Published in: Acta tropica 2014-01, Vol.129, p.74-82
• Main Authors: Cordovez, Juan M., Rendon, Lina Maria, Gonzalez, Camila, Guhl, Felipe
• Format: Article
• Language: English
• Subjects: Animals; Basic Reproduction Number; Chagas disease; Chagas Disease - epidemiology; Chagas Disease - transmission; Climate Change; Colombia - epidemiology; Humans; Models, Statistical; Next generation matrix; Risk Assessment; Temperature; Trypanosoma cruzi
• ISSN: 0001-706X; 1873-6254
• DOI: 10.1016/j.actatropica.2013.10.003

We computed the next generation matrix for Chagas disease including host and vectors, estimated the parameters that characterize the transmission and used them to estimate the risk of parasite establishment in a climate change context. •We created a Next Generation Matrix for the transmission of T. cruzi in Colombia.•The NGM contains parameters that vary with temperature.•We did elasticity and sensitivity analysis on parameters and transmission routes.•We created a risk map for disease establishment using the basic reproduction number.•Global warming could increase cases in some places while reduce them in others. The dynamics of vector-borne diseases has often been linked to climate change. However the commonly complex dynamics of vector-borne diseases make it very difficult to predict risk based on vector or host distributions. The basic reproduction number (R0) integrates all factors that determine whether a pathogen can establish or not. To obtain R0 for complex vector-borne diseases one can use the next-generation matrix (NGM) approach. We used the NGM to compute R0 for Chagas disease in Colombia incorporating the effect of temperature in some of the transmission routes of Trypanosoma cruzi. We used R0 to generate a risk map of present conditions and a forecast risk map at 20 years from now based on mean annual temperature (data obtained from Worldclim). In addition we used the model to compute elasticity and sensitivity indexes on all model parameters and routes of transmission. We present this work as an approach to indicate which transmission pathways are more critical for disease transmission but acknowledge the fact that results and projections strongly depend on better knowledge of entomological parameters and transmission routes. We concluded that the highest contribution to R0 comes from transmission of the parasites from humans to vectors, which is a surprising result. In addition, parameters related to contacts between human and vectors and the efficiency of parasite transmission between them also show a prominent effect onR0.