The Economic Impact of Pandemics on Individuals, Families and Communities
Bibliographic record
Abstract
The Coronavirus disease 2019 (COVID-19) pandemic has dramatically changed systems, routines, practices, and beliefs. This pandemic will have a number of adverse outcomes which will continue to be felt for years to come. Understanding the economic impact on individuals, families, businesses, and communities is essential for developing strategies that reduce long-term negative outcomes. However, we are unaware of any evidence synthesis describing the range of economic or financial impacts associated with pandemics. In this paper, we analyze data from a large scoping review of previous pandemics to identify the various economic and financial impacts of global disease outbreaks on families, businesses, and economic systems. We found that individuals and families around the world experienced a reduction or loss of income associated with losing their job or having to work fewer hours, which increased their psychological stress. At the same time, the pandemic has negatively affected the financial outcomes of small and medium-sized businesses due to reduced economy activity, disruptions in the supply chain, and weakened infrastructure. We examine these findings in the light of two topics. First, we discuss how vulnerable and minority communities experience the various financial and economic impacts of global outbreaks to a greater degree compared to the general public. We also discuss the concepts of flexibility and resilience in order to understand how businesses respond to the changes brought forth by the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".