A dynamic analysis of tuberculosis dissemination to improve control and surveillance

• Autores: Zorzenon dos Santos, R.M.; Amador, A.; de Souza, W.V.; de Albuquerque, M.F.P.M.; Ponce Dawson, S.; Ruffino-Netto, A.; Zárate-Bladés, C.R.; Silva, C.L.
• Año de publicación: 2010
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: PLoS ONE 2010;5(11)
• Materia: BCG vaccine; tuberculostatic agent; antibiotic resistance; article; Brazil; budget; disease course; disease transmission; endemic disease; geographic information system; health care delivery; health care personnel management; health program; human; incidence; Mycobacterium tuberculosis; patient compliance; population research; remote sensing; resource allocation; social status; tuberculosis; tuberculosis control; Brazil; Geography; Humans; Incidence; Population Density; Population Dynamics; Population Surveillance; Socioeconomic Factors; Tuberculosis
• Institución: Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales

Background: Detailed analysis of the dynamic interactions among biological, environmental, social, and economic factors that favour the spread of certain diseases is extremely useful for designing effective control strategies. Diseases like tuberculosis that kills somebody every 15 seconds in the world, require methods that take into account the disease dynamics to design truly efficient control and surveillance strategies. The usual and well established statistical approaches provide insights into the cause-effect relationships that favour disease transmission but they only estimate risk areas, spatial or temporal trends. Here we introduce a novel approach that allows figuring out the dynamical behaviour of the disease spreading. This information can subsequently be used to validate mathematical models of the dissemination process from which the underlying mechanisms that are responsible for this spreading could be inferred. Methodology/Principal Findings: The method presented here is based on the analysis of the spread of tuberculosis in a Brazilian endemic city during five consecutive years. The detailed analysis of the spatio-temporal correlation of the yearly geo-referenced data, using different characteristic times of the disease evolution, allowed us to trace the temporal path of the aetiological agent, to locate the sources of infection, and to characterize the dynamics of disease spreading. Consequently, the method also allowed for the identification of socio-economic factors that influence the process. Conclusions/Significance: The information obtained can contribute to more effective budget allocation, drug distribution and recruitment of human skilled resources, as well as guiding the design of vaccination programs. We propose that this novel strategy can also be applied to the evaluation of other diseases as well as other social processes. © 2010 Zorzenon dos Santos et al.
Fil:Amador, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Ponce Dawson, S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.