Population-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications
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Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology. However, pandemic-related policy decisions cannot be made on health information alone but need to take into account the broader impacts on people and their needs. Quantifying human needs across the population is challenging as it requires high geo-temporal granularity, high coverage across the population, and appropriate adjustment for seasonal and other external effects. Here, we propose a computational framework, based on Maslow's hierarchy of needs, that can characterize a holistic view of relative changes in needs following the pandemic through a difference-in-differences approach that corrects for seasonality and query volume variations. We apply this framework to characterize changes in human needs across physiological, socioeconomic, and psychological realms in the US, based on more than 35 billion search interactions spanning over 36,000 ZIP codes over a period of 14 months. Our analyses reveal that the expression of basic human needs has increased exponentially while higher-level aspirations declined during the pandemic in comparison to the pre-pandemic period. In exploring the timing and variations in statewide policies, we find that the duration of shelter-in-place mandates significantly influenced social and emotional needs. We demonstrate that potential barriers to addressing critical needs such as support for unemployment and domestic violence can be identified through web search interactions. Our approach and results suggest that population-scale monitoring of shifts in human needs can inform policies and recovery efforts for current and anticipated needs.
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