RESUMO
BACKGROUND: Crimean-Congo hemorrhagic fever virus (CCHFV) is endemic in Iraq, where recurrent epidemics have been constantly observed during the last five years. The present study aimed to determine the factors associated with Crimean-Congo hemorrhagic fever (CCHF) cases in Dhi Qar province during the year 2022. METHODS: A test-negative case-control design was used to analyze 621 CCHF patients, of which 162 were confirmed and 459 were suspected cases. To identify the confirmed and suspected cases, reverse transcriptase polymerase chain reaction (RT-PCR) was used. Suspected patients whose PCR test results were negative were selected as the control group. Data on potential risk factors for CCHF were collected as existing data for previous years for the same geographical locations in Dhi Qar province. Logistic regression analyses were used to determine the correlation between probable risk factors and confirmed CCHF cases. RESULTS: The incidence rate of CCHF was 6.8% per 100,000 people. The total number of deaths was 48 for patients with a case fatality rate of 7.7%. The patients' ages ranged from one year to 65 years, with an overall mean ± SD of 36.08 ± 18.29 years. A total of 98.2% of the patients were between 15 and 65 years of age; 58% of the reported patients were male, and the male-to-female ratio was 1.4:1. Additionally, contact with raw meat, animal contact, and tick bite had the highest percentages for CCHF positivity cases. CONCLUSIONS: Male gender, high-risk jobs like housewives, health staff, shepherds, butchers, animal dealers, slaughterhouse workers, veterinary staff, and farmers, tick bites, and contact with raw meat were statistically significant predictors for increasing CCHF incidence in Dhi Qar province during the year 2022.
RESUMO
OBJECTIVE: Detection of epidemics is a critical issue in epidemiology of infectious diseases which enable healthcare system to better control it. This study is devoted to investigating the 5-year trend in influenza and severe acute respiratory infection cases in Iran. The epidemics were also detected using the hidden Markov model (HMM) and Serfling model. STUDY DESIGN: In this study, we used SARI data reported in the World Health Organization (WHO) FluNet web-based tool from August 2011 to August 2016. METHODS: SARI data in Iran from August 2011 to August 2016 were used. We applied the HMM and Serfling model for indicating the two epidemic and non-epidemic phases. The registered outbreak activity recorded on the WHO website was used as the gold standard. The coefficient of determination was reported to compare the goodness of fit of the models. RESULTS: Serfling models modified by 30% and 35% of the data had a sensitivity of 91.67% and 95.83%, while for 15%, 20% and 25% were 70.83%, 79.17% and 83.33%, respectively. Sensitivity of HMM and autoregressive HMM (AHMM) was 66.67% and 92.86%. All fitted models have a specificity of over 96%. The R2 for HMM and AHMM was calculated 0.73 and 0.85, respectively, showing better fitness of these models, while R2 was around 50% for different types of Serfling models. CONCLUSIONS: Both modified Serfling and HMM were acceptable models in determining the epidemic points for the detection of weekly SARI. The AHMM had better fitness, higher detection power and more accurate detection of the incidence of epidemics than Serfling model and high sensitivity and specificity. In addition to AHMM, Serfling models with 30% and 35% modification can be used to detect epidemics due to approximately the same accuracy but the simplicity of the calculations.
