Abstract

Nationwide disease surveillance at a high spatial resolution is desired for many infectious diseases, including Visceral Leishmaniasis. Statistical and mathematical models using data collected from surveillance activities often use a spatial resolution and scale either constrained by data availability or chosen arbitrarily. Sensitivity of model results to the choice of spatial resolution and scale is not, however, frequently evaluated. This study aimed to determine if the choice of spatial resolution and scale are likely to impact statistical and mathematical analyses, using Visceral Leishmaniasis in Brazil as a case study. Probabilistic characteristics of disease incidence were compared across spatial resolutions and scales. Best fitting distributions were fit to annual incidence from 2004 to 2014 by municipality and by state, with Gamma and Poisson distributions providing the best fits both among individual states and nationwide. Comparisons using Kullback-Leibler divergence showed that incidence by state and by municipality do not follow distributions that provide equivalent information. These results demonstrate empirically how the choice of spatial resolution and scale can impact mathematical and statistical models used in disease surveillance.