Overlooked voices under strict lockdown: mapping humanitarian needs in 2022 Shanghai COVID-19 outbreak

ABSTRACT

The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase would inevitably bring unexpected challenges to vulnerable populations. There is a need to identify the emergent needs of underprepared, vulnerable populations under lockdowns to inform future pandemic management. In this study, the messages posted on an online mutual help-requesting platform during the 2022 Shanghai lockdown were leveraged as the near real-time agent to represent the side effects of the lockdown policy. This work explores the spatiotemporal correlation of the posts to their potential influencing factors and mines knowledge from the textual content of the posts. The results indicate that the help requests were clustered in downtown Shanghai. The access to profuse medical resources and public services may be hindered in districts with more stringent lockdown policies. The help requests’ content unveils a need for medication and groceries under lockdown. It also underscores that the elderly population was affected most by the lockdown. Based on these insights, policymakers can better anticipate and address the urgent residential needs that may arise in future lockdowns. This knowledge can contribute to more effective planning and preparedness efforts to mitigate the impact on vulnerable populations and ensure their well-being during similar crisis situations.

KEYWORDS:

• COVID-19
• emergency management
• lockdown
• humanitarian aid
• LDA

Acknowledgments

Thanks to the nonprofitable organization, Daohouer, for providing the open-source data for this research. This research is supported by the Texas A&M Institute of Data Science (TAMIDS) under the Data Resource Development Program. The statements, findings, and conclusions are those of the authors and do not necessarily reflect the views of the funding agency.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in figshare with the url: https://doi.org/10.6084/m9.figshare.23995800.v1

Additional information

Funding

This work was supported by the Texas A & M Institute of Data Science.