A Comparative Epidemiology Model for Understanding Mental Morbidity and Planning Health System Response to the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: This particular coronavirus disease is a pandemic giving rise to great global affliction and uncertainty, even among those who have dedicated their lives to health care or the study of disease, or both. Notwithstanding those directly affected, the lives of all people have been turned upside down. Each person has to cope with her or his personal situation and a story is taking shape for everyone on earth. Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 virus, the source of the 2020 pandemic. This paper contains brief highlights from a duplicable PubMed search of the COVID-19 literature published from January 1 to March 31, 2020, as well as a duplicable search of past influenza-related publications. Excerpts from select papers are highlighted. The main focus of this paper is a descriptive analysis of influenza and other respiratory viruses based on a 16-year population-based dataset. In addition, the paper includes analyses based on the presence or absence of mental disorder (MD) in relation to influenza and all other respiratory viruses. Methods: The investigation is descriptive and exploratory in nature. Employing a case-comparison design, a 16-year population-based dataset was analyzed to both understand the present and plan for the future. While not all viral infections are equal, this paper focuses on system responses by describing the epidemiology of respiratory viruses, such as influenza. Influenza is established in the global population and has caused epidemics in the past. Where possible direct comparisons are made between COVID-19, influenza, and other respiratory viruses. Results: Those with MD had a higher rate of viral infection per 100,000 capita compared to those with the viral infection and no MD. Further, the postviral infection MD rate was not higher compared to the MD per capita rate before viral infection. The postinfluenza rate of MD among those who were without mental disorder before influenza represents an estimate of postinfection mental health burden. Conclusions: In summary, those with preinfluenza MD are at greater risk for viral infection. Further, while the postviral infection MD rate was not higher compared to the MD per capita rate before viral infection, this independent estimate may inform the degree to which services may need to undergo a sustained increase to address the bio psychosocial needs of each served population were COVID-19 to persist and become established in the global population.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.001 |
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".