L Tapak, N Shirmohammadi-Khorram , O Hamidi, Z Maryanaji,
Volume 14, Issue 2 (Vol.14, No.2, 2018)
Abstract
Background and Objectives: Identification of statistical models has a great impact on early and accurate detection of outbreaks of infectious diseases and timely warning in health surveillance. This study evaluated and compared the performance of the three data mining techniques in time series prediction of brucellosis.
Methods: In this time series, the data of the human brucellosis cases and climatology parameters of Hamadan, west of Iran, were analyzed on a monthly basis from 2004 (March/April) to 2017 (February/March). The data were split into two subsets of train (80%) and test (20%). Three techniques, i.e. radial basis function (RBF) and multilayer perceptron (MLP) artificial neural network methods as well as K Nearest neighbor (KNN), were used in both subsets. The root mean square errors (RMSE), mean absolute errors (MAE), mean absolute relative errors (MARE), determination coefficient (R2) and intra-class correlation coefficient (ICC) were used for performance comparison.
Results: Results indicated that RMSE (23.79), MAE (20.65) and MARE (0.25) for MLP were smaller compared to the values of the other two models. The ICC (0.75) and R2 (0.61) values were also better for this model. Thus, the MLP model outperformed the other models in predicting the used data. The most important climatology variable was temperature.
Conclusion: MLP can be effectively applied to diagnose the behavior of brucellosis over time. Further research is necessary to detect the most suitable method for predicting the trend of this disease.
P Maroofi, , Z Cheraghi, L Tapak,
Volume 17, Issue 4 (Vol.17, No.4, Winter 2022 2022)
Abstract
Introduction: Identifying the epidemiological features of reported measles outbreaks including the size, period, and generation of the outbreaks plays a significant role in preventing new outbreaks and estimating effective reproduction number (R) as an indication of measles elimination. This study was conducted to describe the reported measles outbreaks in the world in 2018.
Method: The PubMed, Scopus, and Web of Sciences databases were searched using related keywords to retrieve articles that reported 2018 measles outbreaks. From the full-texts of the articles that met the inclusion criteria, the data including gender, season, age group, country, genotype, and vaccination status as well as shape, size, period of outbreaks and number of generations of each outbreak were extracted and reported using the relevant epidemiological curves.
Results: The search results led to the retrieval of 2806 articles. After screening, 16 studies were used for final analysis. Most outbreaks were reported in the winter (56.25%) with genotypes B3 and D8. The sex female (38.64%, 308 cases) was mostly in Asia and Europe. On average, the minimum and maximum number of outbreaks size was 1 and 23, which spread to 3-4 generations. In terms of death, only one case of death was reported in Ethiopia.
Conclusion: The results of this study are useful for identifying measles outbreaks in other countries according to the at-risk groups. However, publication bias and non-reporting of all outbreaks should be considered as limitations in the generalization of the results.
