期刊
PLOS ONE
卷 17, 期 9, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0273906
关键词
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资金
- Spanish Ministry of Science and Innovation
- Centro de Excelencia Severo Ochoa
- CERCA Programme/Generalitat de Catalunya
- Indiana University Bloomington Mexico Gateway
- Indiana University UITS Research Technologies computing resources
- DGAPA-UNAM postdoctoral program
- Mexican science council (CONACYT) [A1-S-14615]
- National Heart, Lung, and Blood Institute (NHLBI) [HL137338-03S1, HL12614602]
Preventive and modeling approaches for COVID-19 pandemic often neglect the heterogeneity in population contact structure and individual connectivity, which significantly affect the effectiveness of interventions.
Preventive and modeling approaches to address the COVID-19 pandemic have been primarily based on the age or occupation, and often disregard the importance of heterogeneity in population contact structure and individual connectivity. To address this gap, we developed models based on Erdos-Renyi and a power law degree distribution that first incorporate the role of heterogeneity and connectivity and then can be expanded to make assumptions about demographic characteristics. Results demonstrate that variations in the number of connections of individuals within a population modify the impact of public health interventions such as lockdown or vaccination approaches. We conclude that the most effective strategy will vary depending on the underlying contact structure of individuals within a population and on timing of the interventions.
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