Establishment of an early warning system for cutaneous leishmaniasis in Fars province, Iran
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摘要
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.
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.
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.
引文
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[11]Rodríguez EM,Díaz F,Pérez MV.Spatio-temporal clustering of American Cutaneous Leishmaniasis in a rural municipality of Venezuela.Epidemics 2013;5(1):11-19.
[12]Khosravani M,Nasiri Z,Keshavarz D,Rafat-Panah A.Epidemiological trend of cutaneous leishmaniasis in two endemic focus of disease,south of Iran.J Parasit Dis 2016;40(4):1609-1613.
[13]Reithinger R,Dujardin JC,Louzir H,Pirmez C,Alexander B,Brooker S.Cutaneous leishmaniasis.Lancet Infect Dis 2007;7(9):581-596.
[14]Sharafi M,Ghaem H,Tabatabaee HR,Faramarzi H.Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province,Iran using seasonal ARIMA time series method.Asian Pac JTrop Med 2017;10(1):79-86.
[15]Levin SA.The problem of pattern and scale in ecology:The Robert H.Mac Arthur award lecture.Ecology 1992;73(6):1943-1967.
[16]Clark JS,Carpenter SR,Barber M,Collins S,Dobson A,Foley JA,et al.Ecological forecasts:An emerging imperative.Science 2001;293(5530):657-660.
[17]Levins R,Awerbuch T,Brinkmann U,Eckardt I,Epstein P,Makhoul N,et al.The emergence of new diseases.Am Sci 1994;82(1):52-60.
[18]Royama T.Analytical population dynamics.Vol.10.Springer Science&Business Media;2012.
[19]Turchin P.Complex population dynamics:A theoretical/empirical synthesis.Vol.35.Princeton University Press;2003.
[2]Hotez PJ,Molyneux DH,Fenwick A,Ottesen E,Sachs SE,Sachs JD.Incorporating a rapid-impact package for neglected tropical diseases with programs for HIV/AIDS,tuberculosis,and malaria.PLo S Med 2006;3(5):e102.
[3]Lainson R,Shaw JJ.Epidemiology and ecology of leishmaniasis in LatinAmerica.Nature 1978;273(5664):595-600.
[4]Francis F,Ishengoma DS,Mmbando BP,Rutta AS,Malecela MN,Mayala B,et al.Deployment and use of mobile phone technology for real-time reporting of fever cases and malaria treatment failure in areas of declining malaria transmission in Muheza district north-eastern Tanzania.Malar J 2017;16(1):308.
[5]Ali-Akbarpour M,Mohammadbeigi A,Tabatabaee SHR,Hatam G.Spatial analysis of eco-environmental risk factors of cutaneous leishmaniasis in southern Iran.J Cutan Aesthet Surg 2012;5(1):30.
[6]Gálvez R,Descalzo M,MiróG,Jiménez M,Martín O,Dos SantosBrandao F,et al.Seasonal trends and spatial relations between239environmental/meteorological factors and leishmaniosis sand fly vector abundances in Central Spain.Acta Trop 2010;115(1):95-102.
[7]Hanafi‐Bojd A,Rassi Y,Yaghoobi‐Ershadi M,Haghdoost A,Akhavan A,Charrahy Z,et al.Predicted distribution of visceral leishmaniasis vectors(Diptera:Psychodidae;Phlebotominae)in Iran:A niche model study.Zoonoses Public Health 2015;62(8):644-654.
[8]Karimi A,Hanafi-Bojd AA,Yaghoobi-Ershadi MR,Akhavan AA,Ghezelbash Z.Spatial and temporal distributions of phlebotomine sand flies(Diptera:Psychodidae),vectors of leishmaniasis,in Iran.Acta Trop2014;132:131-139.
[9]Mollalo A,Khodabandehloo E.Zoonotic cutaneous leishmaniasis in northeastern Iran:A GIS-based spatio-temporal multi-criteria decisionmaking approach.Epidemiol Infect 2016;144(10):2217-2229.
[10]Norouzinezhad F,Ghaffari F,Norouzinejad A,Kaveh F,Gouya MM.Cutaneous leishmaniasis in Iran:Results from an epidemiological study in urban and rural provinces.Asian Pac J Trop Biomed 2016;6(7):614-619.
[11]Rodríguez EM,Díaz F,Pérez MV.Spatio-temporal clustering of American Cutaneous Leishmaniasis in a rural municipality of Venezuela.Epidemics 2013;5(1):11-19.
[12]Khosravani M,Nasiri Z,Keshavarz D,Rafat-Panah A.Epidemiological trend of cutaneous leishmaniasis in two endemic focus of disease,south of Iran.J Parasit Dis 2016;40(4):1609-1613.
[13]Reithinger R,Dujardin JC,Louzir H,Pirmez C,Alexander B,Brooker S.Cutaneous leishmaniasis.Lancet Infect Dis 2007;7(9):581-596.
[14]Sharafi M,Ghaem H,Tabatabaee HR,Faramarzi H.Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province,Iran using seasonal ARIMA time series method.Asian Pac JTrop Med 2017;10(1):79-86.
[15]Levin SA.The problem of pattern and scale in ecology:The Robert H.Mac Arthur award lecture.Ecology 1992;73(6):1943-1967.
[16]Clark JS,Carpenter SR,Barber M,Collins S,Dobson A,Foley JA,et al.Ecological forecasts:An emerging imperative.Science 2001;293(5530):657-660.
[17]Levins R,Awerbuch T,Brinkmann U,Eckardt I,Epstein P,Makhoul N,et al.The emergence of new diseases.Am Sci 1994;82(1):52-60.
[18]Royama T.Analytical population dynamics.Vol.10.Springer Science&Business Media;2012.
[19]Turchin P.Complex population dynamics:A theoretical/empirical synthesis.Vol.35.Princeton University Press;2003.