Detection of influenza epidemics using hidden Markov and Serfling approaches.

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.

High HIV prevalence in a respondent-driven sampling survey of injection drug users in Tehran, Iran.

Iran is facing unprecedented dual drug use and HIV epidemics. We conducted a cross-sectional survey to obtain HIV prevalence and risk behavior data from injection drug users (IDU) in Tehran. We used respondent-driven sampling (RDS) to recruit IDU through successive waves starting with 24 "seeds," conducted anonymous face-to-face interviews and HIV testing and counseling, and used RDSAT to adjust data. During 44 weeks, 1,726 study referral coupons resulted in 645 (37 %) IDU referrals, of whom 548 (85 %) were enrolled. From those enrolled, 84 % were incarcerated, 47 % employed, 55 % single, 27 % under 30 years of age, and 26 % homeless. The adjusted HIV prevalence was 26.6 % (95 % confidence interval 21.3-32.1), and was higher among certain IDU subgroups (e.g., those who sharing injection paraphernalia). Our estimates of HIV prevalence were higher than some other estimates; however, repeated surveys using similar methodology are needed to monitor the trend of HIV epidemic over time.