"What should be computed" for supporting post-pandemic recovery policymaking? A life-oriented perspective

Area of Science:

• Computational Urban Science
• Behavioral Science
• Public Health

Background:

• The COVID-19 pandemic significantly impacted daily life, with reciprocal effects on the spread of SARS-CoV-2.
• Existing research often overlooks the interconnectedness of behavioral changes across various life domains.
• A life-oriented approach is essential for understanding complex human behavior systems.

Purpose of the Study:

• To review and highlight inconsistencies in behavioral factors influencing COVID-19 transmission.
• To emphasize the necessity of analyzing co-changes in behavior across multiple life domains.
• To identify key computational challenges for post-pandemic urban recovery and Sustainable Development Goals (SDGs).

Main Methods:

• Literature review of behavioral factors impacting COVID-19 spread.
• Analysis of the interplay between life domain changes and pandemic mitigation.
• Identification of computational needs for addressing post-pandemic societal challenges.

Main Results:

• Inconsistent evidence exists regarding specific behavioral factors affecting COVID-19.
• Current studies inadequately address the co-evolution of behaviors across different life domains.
• Uncertain trends in post-pandemic life require further investigation.

Conclusions:

• There is a critical need to compute behavioral co-changes within multiple life domains for pandemic management.
• Addressing digital divides and e-society dilemmas is crucial for equitable recovery.
• Computational Urban Science must focus on understanding these complex dynamics to support SDG catch-up and inform policymaking.