Mapping Environmental Dimensions of Dengue Fever Transmission Risk in the Aburrá Valley, Colombia

Abstract

Dengue fever (DF) is endemic in Medellín, the second largest Colombian city, and surrounding municipalities. We used DF case and satellite environmental data to investigate conditions associated with suitable areas for DF occurrence in 2008 in three municipalities (Bello, Medellín and Itagüí). We develop spatially stratified tests of ecological niche models, and found generally good predictive ability, with all model tests yielding results significantly better than random expectations. We concluded that Bello and Medellín present ecological conditions somewhat different from, and more suitable for DF than, those of Itagüí. We suggest that areas predicted by our models as suitable for DF could be considered as at-risk, and could be used to guide campaigns for DF prevention in these municipalities.

Mean-Field Semantics for a Process Calculus for Spatially-Explicit Ecological Models

“…To establish the initial conditions for the model defined with respect to the human population, we adopted a total population size of 403,235, as recorded for the urban area of Bello (Antioquia) in 2010 by the Colombian administrative department of statistics 2 . The size of the susceptible human population at the beginning of the last registered epidemic was estimated based on the risk map developed by Arboleda et al [5], in which the probability of infection was reported to be 0.3 in 2008 and 2009, with a standard deviation of 0.096; thus, it was determined that the size of the susceptible human population should be between 244,402 and 321,734. The initial condition considered for the infectious human population was the number of cases reported at the beginning of the epidemic.…”

Spatio-Temporal Patterns of Dengue Fever in Cali, Colombia

“…Dengue fever studies have focused in understanding patterns between mosquito infected areas and infected individuals (Chang et al, 2009); identifying causal relationships, in particular weather and vegetation (Tipayamongkholgul et al, 2009;Kolivras, 2006;Arboleda, Jaramillo-O and Peterson, 2009;Braga et al, 2010;Johansson, Dominici & Glass, 2009;Maciel-de-Freitas et al, 2010;Maria & Valencia, 2011;Wu et al 2009); and spatio-temporal patterns of infected individuals (Eisen & Lozano-Fuentes, 2009;Getis et al, 2003;Mammen et al, 2008;Morrison et al, 1998;Rosa-Freitas et al, 2003;Tran et al, 2004;Kan et al, 2008). The disease is known to vary through time and space, due to a number of factors including the human host, the virus, the mosquito vector and the environment (Mammen et al, 2008).…”

“…In particular, focusing on finer scales in space and time is needed to refine dengue surveillance and control strategies (Mammen et al, 2008;Kan et al, 2008). When data on mosquito occurrence is not available using the location of dengue cases can provide a basis for estimating where mosquitoes can be found leading to the identification of transmission patterns (Arboleda et al, 2009;Mammen et al, 2008). Dengue case data can also be of significant value when compared to the prohibitive cost of collecting adult female mosquitoes and the difficulty in identifying the mosquito breeding sites (large artificial water containers located in households (Eisen & Lozano-Fuentes, 2009).…”

A System Dynamic Transmission Model (SYStrans) to Simulate Epidemic Dengue Environment

“…Arboleda et al conducted ecological niche modelling with satellite environmental data and dengue cases (Arboleda, et al, 2009). Peterson et al used ecological niche modelling to predict monthly distribution, dynamics and activity of dengue vector (Peterson, et al, 2005).…”

“…Climate-driven statistical and process based models have been developed to assess the climate change impact on global distribution of dengue (Morin, et al, 2013). These type of models mainly evaluated the geographic distribution of the risk of dengue and have not focused on spatio-temporal risk pattern (Arboleda, et al, 2009).…”