Evaluating Measles Incidence Rates Using Machine Learning and Time Series Methods in the Center of Iran; 1997-2020

Abstract

Background: Measles is a feverish condition labeled among the most infectious viral illnesses in the globe. Despite the presence of a secure, accessible, affordable and efficient vaccine, measles continues to be a worldwide concern. Methods: This study uses machine learning and time series methods to assess factors that placed people at a higher risk of measles. This historical cohort study contained the Measles incidence in Markazi Province, the center of Iran, from April 1997 to February 2020. Logistic regression, linear discriminant analysis, random forest, artificial neural network, bagging, support vector machine, and naïve Bayes were used to make the classification. Zero-inflated negative binomial regression for time series was utilized to assess development of measles over time. Results: The prevalence of measles was 14.5% over the recent 24 years and a constant trend of almost zero cases was observed from 2002 to 2020. The order of independent variable importance were recent years, age, vaccination, rhinorrhea, male sex, contact with measles patients, cough, conjunctivitis, ethnic, and fever. Younger age, less probability of contact and no fever is associated with less odds of zero cases. Only 7 new cases were forecasted for the next two years. Bagging and random forest were the most accurate classification methods. Conclusion: Even if the numbers of new cases are almost zero during the recent years, it has been showed that age and contact are responsible for non-occurrence of measles. October and May are prone to have new cases for 2021 and 2022.

Associating Measles Vaccine Uptake Classification and its Underlying Factors Using an Ensemble of Machine Learning Models

“…In contrast, the false-positive and false-negative rates were 5.80 % and 5.10 %, respectively. To evaluate influencing factors that place individuals at a higher risk of measles [22] utilized ML techniques and found that contact with measles patients, age, rhinorrhea, vaccination, male sex, cough, conjunctivitis, ethnicity, and fever were the crucial factors that were associated with measles disease. The authors in [23] adopted the LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression model on the electronic health record to identify message vaccine-resistant families and obtained 72.0 % precision.…”