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Background: Different vaccines have so far been developed and approved to cope with COVID-19 in the world. The aim of this updated network meta-analysis (NMA) was to compare and rank all available vaccines in terms of efficacy and complications simultaneously. Study Design: A systematic review. Methods: Three major international databases, including Web of Science, Medline via PubMed, and Scopus, were searched through September 2023. The transitivity assumption was evaluated qualitatively in terms of epidemiologic effect modifiers. The exposure of interest in this study was receiving any available COVID-19 vaccine, and the primary outcome of interest was the incidence of symptomatic COVID-19. In this NMA, the relative risk of symptomatic COVID-19 was used to summarize the efficacy of vaccines in preventing COVID-19. The data were analyzed using the frequentist-based approach, and the results were reported using a random-effects model. Finally, the vaccines were ranked using a P-score. Results: In total, 34 randomized controlled trials (RCTs) met the eligibility criteria for this systematic review and NMA out of 3682 retrieved references. Based on the results of the NMA, mRNA-1273 was the most effective vaccine in preventing COVID-19 and demonstrated the highest P-score (0.93). The relative risk (RR) for mRNA-1273 versus placebo was 0.07 (95% confidence interval [CI]: 0.03, 0.17). The second and third-ranked vaccines were BNT-162b2 (RR = 0.08; 95% CI: 0.04, 0.15; P-score = 0.93) and Gam-COVID-Vac (0.09; 95% CI: 0.03, 0.25; 0.88). Conclusion: Based on the results of this NMA, it seems that all available vaccines were effective in COVID-19 prevention. However, [ABSTRACT FROM AUTHOR]

Background: The novel influenza A (H1N1) virus was first detected in March 2009 in Mexico and then disseminated to many other countries worldwide. In this study, we assessed the potential risk factors of swine flu as well as the most important clinical manifestations of this infectious disease among confirmed cases during early phase of pandemic H1N1. Methods: Subjects (cases and controls) were selected from those patients with signs and symptoms of respiratory tract infection who referred to health centers of eight cities throughout Hamedan Province, western Iran form July to December 2009. Characteristics of the participants were obtained by interviewers using pre-determined questionnaire. Cases were distinguished by pharyngeal soap specimens positive for influenza A virus using polymerase chain reaction (PCR). Logistic regression model was conducted at 0.05 significance level using Stata 9.1 statistical software to assess the effects of various risk factors on H1N1 influenza infection. Results: Totally, 245 confirmed cases of H1N1 influenza were compared with 388 controls. Case fatality rate of influenza infection was about 2.86%. In comparison with age group of 1-19 yr old, adjusted odds ratio estimates was 1.91 [95% CI: 1.06, 3.46] for age group of 20-39 yr old, 0.94 [0.37, 2.38] for age group of 40-59 yr old, and 0.34 [0.09, 1.37] for age group of 60 -79 yr old. Adjusted odds ratio estimates of influenza A infection was 8.12 [95% CI: 3.11, 21.6] for pregnant women compared to nonpregnant women; 1.84 [95% CI: 1.32, 2.86] for high educated individuals in comparison with low educated individuals; 2.11 [95% CI: 1.25, 3.57] for whose who had close contact with suspected influenza patients; and 2.15 [95% CI: 1.16, 3.98] for individuals with normal body mass index (BMI= 25-30) compared with underweight individuals (BMI< 20). There were no significant differences in clinical manifestations between cases and controls. Conclusion: The risk of influenza A infection is highest among children and adolescents, pregnant women, high educated individuals, and those who had close contact with suspected influenza patients during pandemic phase. In addition, there is no pathogonomonic sign or symptom to distinguish influenza infection clinically from other kinds of respiratory track infections. [ABSTRACT FROM AUTHOR]

Background: Brucellosis is known as the major zoonotic disease. We aimed to compare the performance of some data-mining models in predicting the monthly brucellosis cases in Iran., Study Design: Population-based cohort study., Methods: Three data mining techniques including the Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), and Random Forest (RF) besides to one classic model including Auto-Regressive Integrated Moving Average (ARIMA) was used to predict the monthly incidence of brucellosis in Iran during 2011-2018. We used several criteria (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and intra-class correlation coefficient (ICC) for appraising the accuracy of prediction and performance of our models. All analysis was done using free statistical software of R3.4.0 RESULTS: Overall 118867 cases (with a mean age of 34.01±1.65 yr) of brucellosis were observed and seven-year incidence rate of brucellosis in Iran was 21.78 (95% CI: 21.66, 21.91). The majority of patients (58.84%) were male and 25-29 yr old. The first three provinces with the highest incidence rate of brucellosis included the following; Kurdistan (71.39 per 100000), Lorestan (68.09 per 100000) and Hamadan (56.24 per 100000)., Conclusion: Brucellosis was more common in males, 25-29 aged yr, western provinces and spring months. The disease had a decreasing trend in the last years. MARS model was more appropriate rather than data mining models for prediction of monthly incidence rate of brucellosis.