Ensuring access to healthcare is critical to prevent illnesses and deaths from COVID-19 and non-COVID-19 cases in health systems that have deteriorated during the pandemic. This study aims to map the existing literature on healthcare access after the appearance of COVID-19 using an ontological framework. This will help us to formalize, standardize, visualize and assess the barriers to and drivers of access to healthcare, and how to continue working towards a more accessible health system. A total of 131 articles are included and considered for mapping in the framework. The results were also compared to the World Health Organization guidelines on maintaining essential health services to determine the overlapping and nonoverlapping areas. We showed the benefits of using ontology to promote a systematic approach to address healthcare problems of access during COVID-19 or other pandemics and set public policies. This systematic approach will provide feedback to study the existing guidelines to make them more effective, learn about the existing gaps in research, and the relationship between the two of them. These results set the foundation for the discussion of future public health policies and research in relevant areas where we might pay attention.

Surveillance is critical for interrupting transmission of global epidemics. Research has highlighted gaps in the surveillance for tuberculosis that range from failure to collect real-time data to lack of standardization of data for informed decision-making at different levels of the health system. Our research aims to advance conceptual and methodological foundations for the development of a learning surveillance system for Tuberculosis, that involves systematic collection, analysis, interpretation, and feedback of outcome-specific data. It would concurrently involve the health care delivery system, public health laboratory, and epidemiologists. For our study, we systemically framed the cyber environment of TB surveillance as an ontology of the learning surveillance system. We validated the ontology by binary coding of dimensions and elements of the ontology with the metadata from an existing surveillance platform—GPMS TB Transportal. Results show GPMS TB Transportal collects a critical range of data for active case investigation and presumptive case screening for identifying and detecting confirmed TB cases. It is therefore targeted at assisting the Active Case Finding program. Building on the results, we demonstrate enhanced surveillance strategies for GPMS that are enumerated as pathways in the ontology. Our analysis reveals the scope for embedding learning surveillance pathways for digital applications in Direct Benefit Transfer, and Drug Resistance Treatment in National TB Elimination Programme in India. We discuss the possibilities of developing the transportal into a multi-level computer-aided decision support system for TB, using the innumerable pathways encapsulated in the ontology.

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The unprecedented outbreak of the COVID-19 pandemic has forced governments to devise national strategies to curtail its spread. The present study analyzes the national strategies of India and the United States for the COVID-19 vaccine roll out. The paper presents an ontology of COVID-19 vaccine roll out, maps the national strategies, identifies, analyzes the emphases and gaps in them, and proposes corrections to the same. The analysis shows that the national strategies are selective in their focus and siloed in their approach. They must be systematized to address the emerging challenges effectively. Thus, there is need for a systemic understanding and analysis to reinforce the effective pathways to manage vaccine roll out, reposition the ineffective ones, and engineer new ones through feedback and learning.