Information differences across spatial resolutions and scales for disease surveillance and analysis: The case of Visceral Leishmaniasis in Brazil
PLoS ONE publish this investigation article
July 17th, 2020
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 aims to determine if the choice of spatial resolution and scale are likely to impact statistical and mathematical analyses. Visceral Leishmaniasis in Brazil is used as a case study. Probabilistic characteristics of disease incidence, representing a likely outcome in a model, are compared across spatial resolutions and scales. Best fitting distributions were fit to annual incidence from 2004 to 2014 by municipality and by state. Best fits were defined as the distribution family and parameterization minimizing the sum of absolute error, evaluated through a simulated annealing algorithm. Gamma and Poisson distributions provided best
fits for incidence, both among individual states and nationwide. Comparisons of distributions using Kullback-Leibler divergence shows that incidence by state and by municipality do not follow distributions that provide equivalent information. Few states with Gamma distributed
incidence follow a distribution closely resembling that for national incidence. These results demonstrate empirically how choice of spatial resolution and scale can impact mathematical and statistical models
Servadio JL., Machado A., Alvarez J., de Ferreira Lima FE., Vieira Alves R. and Convertino M.
 | Environmental Health Sciences. School of Public Health. University of Minnesota (UMM). |
 | Department of Population Health and Pathobiology. College of Veterinary Medicine. North Carolina State University (NCSU). |
 | Centro de Vigilancia Sanitaria Veterinaria (VISAVET). Universidad Complutense (UCM). |
| Departamento de Sanidad Animal. Facultad de Veterinaria. Universidad Complutense (UCM). |
 | Secretaria de Vigilância em Saúde. Ministério da Saúde. Governo do Brasil. |
 | Nexus Group. Graduate School of Information Science and Technology and GI-CoRE Station for Big-Data and Cybersecurity. University Hokkaido. |
