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 epidemiologic study used machine learning and time series methods to assess factors that placed people at a higher risk of measles. The study contained the measles incidence in Markazi Province, the center of Iran, from Apr 1997 to Feb 2020. In addition to machine learning, zero-inflated negative binomial regression for time series was utilized to assess development of measles over time.
 Results: The incidence of measles was 14.5% over the recent 24 years and a constant trend of almost zero cases were 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. 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 were almost zero during recent years, age and contact were responsible for non-occurrence of measles. October and May are prone to have new cases for 2021 and 2022.

Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics

“…Therefore, a data-driven approach that involves statistical analysis and machine learning has emerged as a tool that can model spatiotemporal patterns of infectious diseases. The machine learning approach has been used to assess factors that place people at a higher risk of measles [15,16], and researchers have worked on influenza forecasting for a long time using statistical and machine learning methods, such as the autoregressive integrated moving average model and random forest algorithm [17]. Statistical and machine learning models have mainly attempted to simulate the effects of driving factors (i.e., predictive variables) on the spread dynamics of infectious diseases [18][19][20].…”