  Most work to date on mitigating the COVID-19 pandemic is focused urgently on
biomedicine and epidemiology. Yet, pandemic-related policy decisions cannot be
made on health information alone. Decisions need to consider 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 methodology, building
on Maslow's hierarchy of needs, that can capture a holistic view of relative
changes in needs following the pandemic through a difference-in-differences
approach that corrects for seasonality and volume variations. We apply this
approach 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. The 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 durations of
shelter-in-place mandates have influenced social and emotional needs
significantly. 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.
