BMC Med Inform Decis Mak - A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data.


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CKGROUND: Dengue is the most common arboviral disease of humans, with more than one third of the world's population at risk. Accurate prediction of dengue outbreaks may lead to public health interventions that mitigate the effect of the disease. Predicting infectious disease outbreaks is a challenging task; truly predictive methods are still in their infancy.METHODS: We describe a novel prediction method utilizing Fuzzy Association Rule Mining to extract relationships between clinical, meteorological, climatic, and socio-political data from Peru. These relationships are in the form of rules. The best set of rules is automatically chosen and forms a classifier. That classifier is then used to predict future dengue incidence as either HIGH (outbreak) or LOW (no outbreak), where these values are defined as being above and below the mean previous dengue incidence plus two standard deviations, respectively.RESULTS: Our automated method built three different fuzzy association rule models. Using the first two weekly models, we predicted dengue incidence three and four weeks in advance, respectively. The third prediction encompassed a four-week period, specifically four to seven weeks from time of prediction. Using previously unused test data for the period 4-7 weeks from time of prediction yielded a positive predictive value of 0.686, a negative predictive value of 0.976, a sensitivity of 0.615, and a specificity of 0.982.CONCLUSIONS: We have developed a novel approach for dengue outbreak prediction. The method is general, could be extended for use in any geographical region, and has the potential to be extended to other environmentally influenced infections. The variables used in our method are widely available for most, if not all countries, enhancing the generalizability of our method.

Resumo Limpo

ckground dengu common arbovir diseas human one third world popul risk accur predict dengu outbreak may lead public health intervent mitig effect diseas predict infecti diseas outbreak challeng task truli predict method still infancymethod describ novel predict method util fuzzi associ rule mine extract relationship clinic meteorolog climat sociopolit data peru relationship form rule best set rule automat chosen form classifi classifi use predict futur dengu incid either high outbreak low outbreak valu defin mean previous dengu incid plus two standard deviat respectivelyresult autom method built three differ fuzzi associ rule model use first two week model predict dengu incid three four week advanc respect third predict encompass fourweek period specif four seven week time predict use previous unus test data period week time predict yield posit predict valu negat predict valu sensit specif conclus develop novel approach dengu outbreak predict method general extend use geograph region potenti extend environment influenc infect variabl use method wide avail countri enhanc generaliz method

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