Mobility network reveals the impact of spatial vaccination heterogeneity on COVID-19
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Abstract
Mass vaccination is one of the most effective epidemic control measures. Because one’s vaccination decision is shaped by social processes, the pattern of vaccine uptake tends to show strong social and spatial heterogeneity, such as urban-rural divide and clustering. Examining through network perspectives, we develop a framework for estimating the impact of spatial vaccination heterogeneity on epidemic outbreaks. Leveraging fine-grained mobility data and computational models, we investigate two network effects—the “hub effect” (vaccinating mobility hubs reduces transmission) and the “homophily effect” (stronger homophily in vaccination rates increases transmission). Applying Bayesian deep learning and fine-grained epidemic simulations, our study suggests a negative effect of homophily and a positive effect of highly vaccinated hubs on reducing case counts for both the synthetic network and the U.S. mobility network. Our framework enables us to evaluate outcomes from various hypothetical spatial vaccine distributions and to study a hypothetical vaccination campaign strategy that targets a small number of regions with the largest gain in protective power using the data from January 2022. Our simulation suggests that our strategy can potentially prevent about 2.5 times more cases than a uniform strategy with an additional 1% of the population vaccinated. Notably, our simulation also shows that this strategy could even better protect vulnerable or disadvantaged communities through network effects than strategies that directly target them. Our study suggests that we need to examine the interplay between vaccination patterns and mobility networks beyond the overall vaccination rate, and that understanding geographical pattern of vaccine uptake could be just as important as improving the overall vaccination rate.
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SciScore for 10.1101/2021.10.26.21265488: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank…
SciScore for 10.1101/2021.10.26.21265488: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a protocol registration statement.
Results from scite Reference Check: We found no unreliable references.
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