An epidemiological network contains all the organisms involved (types) in the transmission of a parasite. The nodes of the network represent reservoirs, hosts, and vectors, while the links between the nodes represent the strength and direction of parasite movement. Networks that contain @entity1 are of special interest because they are of concern to public health authorities. Under these circumstances, it is possible, in principle, to identify cycles (closed paths in the network) that include @entity1 and select the ones that carry the maximum probability of @entity1 @entity281 . The basic reproduction number R 0 in such a network gives the average number of new infections of any type after the introduction of one individual infected by any type. To obtain R 0 for complex networks, one can use the next-generation matrix (NGM) approach. Every entry in NGM will average the contribution of each link that connects two types. To tease the contribution of every cycle apart, we define the virulence as the geometric mean of the NGM entries corresponding to the links therein. This approach allows for the quantification of specific cycles of interest while it also makes the computation of the sensitivity and elasticity of the parameters easier. In this work, we compute the virulence for the transmission dynamics of @entity1526 for a typical rural area in Colombia incorporating the effect of environmental changes on the vector population size. We concluded that the highest contribution to @entity1 @entity281 comes from @entity1 themselves, which is a surprising and interesting result. In addition, sensitivity analysis revealed that increasing vector population size increases the risk of @entity1 @entity281 .

Environmental changes can produce shifts in XXXX risk.

@entity1 :: ('9606', 'Species') :: ['humans', 'human'] @entity281 :: ('MESH:D007239', 'Disease') :: ['infection'] @entity1526 :: ('MESH:D014355', 'Disease') :: ['Chagas disease']
@entity1526 :: (MESH:D014355,Disease) :: ['chagas disease infection']
