Abstract
INTRODUCTION: Pandemics have historically posed significant threats to public health, with their impact often disproportionately affecting vulnerable populations, including slum dwellers. The convergence of densely populated under-resourced urban areas and the rapid spread of infectious diseases presents unique challenges and heightened risks to the health and well-being of individuals residing in these marginalized communities.
 OBJECTIVES: The study aims to examine how the epidemic has affected slum residents. Additionally, the study intends to measure the stress and resiliency of such residents. For this reason, bibliometric analysis has been used. 
 METHODS: Such analysis has been carried out through the use of procedures like keyword selection, database selection, and research paper collection, search result refinement with selection and rejection criteria, and data collection and analysis. The databases Scopus and Dimension have been chosen to compile papers from 2020 to 2022. When searching for publications, terms like "slum dwellers," "coping strategy," "pandemic stress," and "slum resilience" are often used. Data analysis is done using the R software package Biblioshiny.
 RESULTS: China, Spain, and Switzerland lead in average article citations and overall citations. Dominant themes are Covid, slum, health, and pandemic, with a focus on pandemic effects, health issues, and infection spread. The thematic map highlights "Covid and Slum Dwellers" as a central research theme, with thematic evolution from Covid and Population to themes like Slum, Surveys, and Participants, indicating a focus on data collection via interviews on the Covid impact on slum residents.
 CONCLUSION: Several research papers were published to study the COVID-19 impact on slum dwellers from different dimensions. But their resilience strategy was less emphasised which can be a platform for emerging researchers. More studies are expected in this area. This study will assist policymakers in revising their approach to development and slump transformation.
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