Deciphering the COVID-19 density puzzle: A meta-analysis approach

The COVID-19 pandemic has sparked widespread efforts to mitigate its transmission, raising questions about the role of urban density in the spread of the virus. Understanding how city density affects the severity of communicable diseases like COVID-19 is crucial for designing sustainable, pandemic-resilient cities. However, recent studies on this issue have yielded inconsistent and conflicting results. This study addresses this gap by employing a comprehensive meta-analytic approach, synthesizing data across diverse regions and urban contexts to offer a broader, more nuanced perspective on the impact of city density. A systematic meta-analysis was conducted, initially screening 2,452 studies from Google Scholar, Scopus, and Avery Index databases (up to August 31, 2023), and narrowing down to 63 eligible studies. Using the restricted maximum likelihood (REML) method with a random effects model, the study accounted for variations across different studies. Statistical tests, file drawer analysis, and influence measure analysis were performed, along with assessments of heterogeneity and publication bias through forest and funnel plots. Despite this extensive analysis, the findings indicate that city density has a negligible effect on the severity of COVID-19, challenging the prevailing assumptions in the literature. •This study quantifies how urban density affects communicable diseases, aiding resilient cities.•This study challenges assumptions and lays the groundwork for future infectious disease policies.•We used REML for meta-analysis, generating forest and funnel plots to assess bias and effects.•Rosenthal's file drawer analysis shows 220,723 null studies needed to nullify our findings.•Our analysis challenges past studies, showing density has little impact on COVID-19 severity.