Complexity of hospital demand during the COVID-19 pandemic in Mexico City

The COVID-19 pandemic posed unprecedented challenges to healthcare systems worldwide. In densely populated urban areas such as Mexico City, hospital strain was amplified by high case volumes and limited resources. This study aims to characterize the spatial and temporal organization of hospital demand and its relationship to patient outcomes. We conducted a retrospective analysis of COVID-19 hospitalization data in Mexico City using line-list data from the SISVER surveillance system. Spatial dynamics were examined using the weighted centroid of hospitalizations, and patient–hospital interactions were modeled as a time-resolved bipartite network. The emergence of giant components was used as a proxy for system strain and evaluated in relation to patient outcomes. Hospital demand exhibited marked spatial shifts, including a northward displacement of the hospitalization centroid over time. A small group of 17 hospitals consistently managed the majority of cases. During periods of high demand, the network underwent structural transitions characterized by the emergence of large connected components spanning diverse neighborhoods. These periods of increased system-wide connectivity were associated with higher case fatality rates, particularly among patients over 40. Hospital demand reorganizes at the system level under stress, leading to loss of spatial structure and increased strain. The emergence of large connected components may serve as an early indicator of systemic overload. These findings provide a network-based perspective on healthcare system stress and may inform strategies for resource allocation and emergency response.