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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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Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and the endemic values of cutaneous leishmaniasis were determined by retrospective clusters derived from spatiotemporal permutation modeling on a time-series design. The accuracy of the values was assessed using receiver operating characteristic (ROC) curve. SPSS version 22, ArcGIS, and ITSM 2002 software tools were used for analysis. Results: Nine statistically significant retrospective clusters (P<0.05) resulted in finding seven significant and accurate endemic values (P<0.1). These valid endemic scores were generalized to the other 18 cities based on 6 different climates in the province. Conclusions: Retrospectively detected clusters with the help of ROC curve analysis could help determine cutaneous leishmaniasis endemic values which are essential for future prediction and prevention policies in the area.
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Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis (CL) or for malaria in Fars province, Iran in 2016. Methods: Using time-series data including 29 177 CL cases recorded during 2010-2015 and 357 malaria cases recorded during 2010-2015, CL and malaria cases were predicted in 2016. Predicted cases were used to verify if they followed uniform distribution over time and space using space-time analysis. To testify the uniformity of distributions, permutation scan statistics was applied prospectively to detect statistically significant and non-significant outbreaks. Finally, the findings were compared to determine whether permutation scan statistics worked better for CL or for malaria in the area. Prospective permutation scan modeling was performed using SatScan software. Results: A total of 5 359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied time-series models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria (P
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Objective To assess the spatiotemporal trait of cutaneous leishmaniasis (CL) in Fars province, Iran. Methods Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. Time-series data were recorded from 29 201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using SaTScan software. Results This study resulted in statistically significant spatiotemporal clusters of CL (P < 0.05). The most likely cluster contained 350 cases from 1 July 2010 to 30 November 2010. Besides, 5 secondary clusters were detected in different periods of time. Finally, statistically significant sub-clusters were found within the three large clusters (P < 0.05). Conclusions Transmission of CL followed spatiotemporal pattern in Fars province, Iran. This can have an important effect on future studies on prediction and prevention of CL.
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Measles is a highly contagious viral disease that remains an important cause of mortality among children despite the availability of a safe and effective vaccine around the world. We aimed this study to show a measles out-break occurring. In early 2012, a report of measles outbreak was recorded on center for disease control [CDC] of Fars province, south of Iran. With this case-series study, characteristics of cases and network transition model were described. The primary case was an Afghan refugee who is likely to carry the measles virus from its country or from anywhere in Iran to Koordeh, a small village in south of Iran. Followed by the Afghan case, 6 secondary and tertiary cases including rural health worker, were occurred. Except a case of 35 year old, in which her vaccination status was unknown, all cases has had adherence to vaccination protocol. More immunologic and vaccine quality investigations are needed to find the main cause of the outbreak. Nonetheless, due to the warm weather, probable bad temperature management of cold chain and technical problem in vaccination are the main suspicious reasons of the outbreak
ABSTRACT
Pandemic flu had at least two waves in Iran. Knowing how many of the general population were already exposed to this infection has a major impact on national preventive measures. As of December 30, 2009, a total of 3672 confirmed cases of human infection with a novel Influenza A [2009 H1N1] virus had been reported in Iran with 140 deaths. In this study we aim to measure, as a pilot study, the seroprevalence of positive antibody titer [humoral immunity] against 2009 H1N1 virus in Iranian population in Shiraz, Southern Iran. Through cluster random sampling of families residing in Shiraz, 2553 subjects were selected and after a medical interview blood samples were taken and checked for polyclonal antibody against 2009 H1N1 antigen using hemagglutination inhibition assay. An antibody titer of more than 1:40 dilution was considered positive. Data were analyzed considering the demographic characteristics of the population and were compared among different age groups. 1504 [58.91%] samples were tested positive for the presence of polyclonal antibody against 2009 H1N1 virus. The prevalence of positive titers were significantly higher in 60 to 64 years old group and significantly lower in 20 to 24 years old group [p<0.05]. Data did not differ based on other demographic characteristics or the history of flu like illnesses in the past 6 months. High seroprevalence of antibody against 2009 H1N1 in the sera of our subjects describes either a high level of preexisting immunity against H1N1 in Iranian population or a high rate of asymptomatic infection in our area compared to other countries