Investigating COVID‐19 transmission in a tertiary hospital in Hanoi, Vietnam using social network analysis

Abstract

Objectives

In March 2020, a COVID‐19 outbreak in a major referral hospital in Hanoi, Vietnam led to 7,664 patients and staff being sent into lockdown for two weeks, and more than 52,200 persons across 49 provinces being quarantined. We assessed SARS‐CoV‐2 transmission patterns during this to‐date largest hospital outbreak in Vietnam using social network analysis (SNA).

Methods

We constructed a directed relational network and calculated network metrics for ‘degree’, ‘betweenness’, ‘closeness’, and ‘eigenvector’ centrality to understand individual‐level transmission patterns. We analysed network components and modularity to identify sub‐network structures with disproportionately big effects.

Results

We detected 68 connections between 46 confirmed cases, of whom 27 (58.7%) were ancillary support staff, seven (15.2%) caregivers, six (13%) patients, and two (4.4%) nurses. Among the ten most important cases selected by each SNA network metric, transmission dynamics clustered in 17 cases, of whom 12 (70.6%) cases were ancillary support staff. Ancillary support staff also constituted 71.1% of cases in the dominant sub‐network and 68.8% of cases in the three largest sub‐communities.

Conclusions

We identified non‐clinical ancillary support staff, who are responsible for room service and food distribution in hospital wards in Vietnam, as a group with disproportionally big impacts on transmission dynamics during this outbreak. Our findings call for a holistic approach to nosocomial outbreak prevention and response that includes both clinical and non‐clinical hospital staff. Our work also shows the potential of SNA as a complementary outbreak investigation method to better understand infection patterns in hospitals and similar settings.