Abstract
Background: Immunization is a significant public health intervention to reduce child mortality and morbidity. However, its coverage, in spite of free accessibility, is still very low in developing countries. One of the primary reasons for this low coverage is the lack of analysis and proper utilization of immunization data at various healthcare facilities.
Purpose: In this paper, the existing machine learning-based data analytics techniques have been reviewed critically to highlight the gaps where this high potential data could be exploited in a meaningful manner.
Results: It has been revealed from our review that the existing approaches use data analytics techniques without considering the complete complexity of Expanded Program on Immunization which includes the maintenance of cold chain systems, proper distribution of vaccine and quality of data captured at various healthcare facilities. Moreover, in developing countries, there is no centralized data repository where all data related to immunization is being gathered to perform analytics at various levels of granularities.
Conclusion: We believe that the existing non-centralized immunization data with the right set of machine learning and Artificial Intelligence-based techniques will not only improve the vaccination coverage but will also help in predicting the future trends and patterns of its coverage in different geographical locations.
Keywords: Data analytics, expanded program on immunization, vaccination, machine learning, infectious diseases, immunization.
Graphical Abstract
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