Network performance assessment for public health emergency response: multi-case study of SARS, H1N1 and COVID-19 in China

Public health emergencies (PHEs) pose significant threats to human health, economic growth and social stability. Network has been regarded as an effective way of dealing with emergencies. Based on the comparative study of multiple cases, this study examined the performance of PHE response network from a three-dimensional perspective of network nodes, node relationships and network structure, and then discussed the reasons affecting the performance of emergency network and finally proposed performance improvement strategies from the perspective of crisis learning. Cases of Beijing's response to SARS in 2003, Shanghai's response to H1N1 in 2009, and Wuhan's response to COVID-19 in 2020 are selected for multi-case comparative study, and social network analysis (SNA) was employed to evaluate the performance of emergency network. The results of this study indicate that there exists is a gap between the actual performance of the emergency network and the target performance set by the planned network. The position of actors, the closeness of actor relationships, and the type of network structure are crucial reasons for this gap. These findings suggest to develop closed-loop emergency network performance improvement strategies: (1) establishing effective response networks through dynamic adjustment of emergency plans; (2) fostering organizational cooperation through open communication, especially strengthen the interactions between governments and NGOs; (3) improving the adaptability of emergency behavior and strengthening the cross-sector dissemination of crisis knowledge through systematic drill training.

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Publication Details

Journal
Scientific Reports
Published
2025-04-04
DOI
https://doi.org/10.1038/s41598-025-92791-2
Citations
1
Primary Topic
Disaster Management and Resilience
Type
article
Field-Weighted Citation Impact
4.20
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article

Network performance assessment for public health emergency response: multi-case study of SARS, H1N1 and COVID-19 in China

Yingbin Feng, Wei Lu, Zijun Wang, Lei Du
1 citations
Scientific Reports
Disaster Management and Resilience
4.20
article

Network performance assessment for public health emergency response: multi-case study of SARS, H1N1 and COVID-19 in China

Yingbin Feng, Wei Lu, Zijun Wang, Lei Du
article en
1 citations

Abstract

Public health emergencies (PHEs) pose significant threats to human health, economic growth and social stability. Network has been regarded as an effective way of dealing with emergencies. Based on the comparative study of multiple cases, this study examined the performance of PHE response network from a three-dimensional perspective of network nodes, node relationships and network structure, and then discussed the reasons affecting the performance of emergency network and finally proposed performance improvement strategies from the perspective of crisis learning. Cases of Beijing's response to SARS in 2003, Shanghai's response to H1N1 in 2009, and Wuhan's response to COVID-19 in 2020 are selected for multi-case comparative study, and social network analysis (SNA) was employed to evaluate the performance of emergency network. The results of this study indicate that there exists is a gap between the actual performance of the emergency network and the target performance set by the planned network. The position of actors, the closeness of actor relationships, and the type of network structure are crucial reasons for this gap. These findings suggest to develop closed-loop emergency network performance improvement strategies: (1) establishing effective response networks through dynamic adjustment of emergency plans; (2) fostering organizational cooperation through open communication, especially strengthen the interactions between governments and NGOs; (3) improving the adaptability of emergency behavior and strengthening the cross-sector dissemination of crisis knowledge through systematic drill training.

Scientific ReportsVol. 15(1)
Xihua University (CN), Southwestern University of Finance and Economics (CN), Western Sydney University (AU)
Good health and well-being
Openalex Percentile: Top 9%
Disaster Management and Resilience
4.20
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