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ORIGINAL ARTICLE
Year : 2019  |  Volume : 9  |  Issue : 6  |  Page : 232-239

Establishment of an early warning system for cutaneous leishmaniasis in Fars province, Iran


1 Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
2 Colorectal Research Center, Shiraz University of Medical Science, Shiraz, Iran
3 Research Center for Health Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
4 Department of Community Medicine, Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
5 Department of Communicable Diseases, Shiraz university of Medical Science, Shiraz, Iran

Correspondence Address:
Abbas Rezaianzadeh
Colorectal Research Center, Shiraz University of Medical Science, Shiraz
Iran
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Source of Support: This work was funded by Shiraz University of Medical Sciences(12439), Conflict of Interest: None


DOI: 10.4103/2221-1691.260395

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Objective: To establish an early warning system for cutaneous leishmaniasis in Fars province, Iran in 2016. Methods: Time-series data were recorded from 29 201 cutaneous leishmaniasis cases in 25 cities of Fars province from 2010 to 2015 and were used to fit and predict the cases using time-series models. Different models were compared via Akaike information criterion/ Bayesian information criterion statistics, residual analysis, autocorrelation function, and partial autocorrelation function sample/model. To decide on an outbreak, four endemic scores were evaluated including mean, median, mean + 2 standard deviations, and median + interquartile range of the past five years. Patients whose symptoms of cutaneous leishmaniasis began from 1 January 2010 to 31 December 2015 were included, and there were no exclusion criteria. Results: Regarding four statistically significant endemic values, four different cutaneous leishmaniasis space-time outbreaks were detected in 2016. The accuracy of all four endemic values was statistically significant (P<0.05). Conclusions: This study presents a protocol to set early warning systems regarding time and space features of cutaneous leishmaniasis in four steps: (i) to define endemic values based on which we could verify if there is an outbreak, (ii) to set different time-series models to forecast cutaneous leishmaniasis in future, (iii) to compare the forecasts with endemic values and decide on space-time outbreaks, and (iv) to set an alarm to health managers.


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