Abstract
BackgroundDetailed 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 FindingsThe 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/SignificanceThe 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.
Highlights
Despite the implementation of various control and surveillance strategies, infectious diseases remain among the leading causes of worldwide morbidity and mortality [1,2]
The data [14] recorded in Olinda constitute a five-year (1996–2000) data set of cases of pulmonary TB reported monthly, which are annually geo-referenced to the 299 local census tracts (CTs, see methods) from the 2000 Brazilian Census [15]
The method we have presented here is implemented and provides relevant information concerning the spatio-temporal propagation of TB using Geographical Information Systems (GIS) data
Summary
Despite the implementation of various control and surveillance strategies, infectious diseases remain among the leading causes of worldwide morbidity and mortality [1,2] For certain diseases, such as tuberculosis (TB), the number of newly infected people is rising steadily in certain areas [3]. In order to achieve the objectives of the WHO-Stop TB Program [3], these strategies should include a reliable system of information and an efficient method for localizing sources of infection, and an enhanced understanding of the dynamics of disease spreading [2,4] Technologies such as remote sensing and Geographical Information Systems (GIS) have improved the reliability of spatial data related to infectious diseases, but these require new means of analysis, which have the capacity to yield information on the disease’s dissemination dynamics [4,5]. 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
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