COVID-19: Modelling Local Transmission and Morbidity effects to provide an estimate of overall Relative Healthcare Resource Impact by General Practice Granularity
Bibliographic record
Abstract
ABSTRACT Introduction Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the name given to the 2019 novel coronavirus. COVID-19 is the name given to the disease associated with the virus. SARS-CoV-2 is a new strain of coronavirus that has not been previously identified in humans. Methods Two key factors were analysed which when multiplied together would give an estimate of relative demand on healthcare utilisation. These factors were case incidence and case morbidity. GP Practice data was used as this provided the most geographically granular source of published public population data. To analyse case incidence, the latest values for indicators that could be associated with infection transmission rates were collected from the Office of National Statistics (ONS) and Quality Outcome Framework (QOF) sources. These included population density, % age >16 at fulltime work/education, % age over 60, % BME ethnicity, social deprivation as IMD 2019, Location as latitude/longitude, and patient engagement as % self-confident in their own long term condition management. Average case morbidity was calculated by applying the international mortality Odds Ratio to the local population relevant age and disease prevalences and then summing and dividing by the equivalent national figure. To provide a comparative measure of overall healthcare resource impact, individual GP practice impact scores were compared against the median practice. Results The case incidence regression is a dynamic situation with the significance of specific factors moderating over time as the balance between external infection, community transmission and impact of mitigation measures feeds through to the number of cases. It showed that currently Urban, % Working and age >60 were the strongest determinants of case incidence. The local population comorbidity remains unchanged. The range of relative HC impact was wide with 80% of practices falling between 20%-250% of the national median. Once practice population numbers were included it showed that the top 33% of GP practices supporting 45% of the patient population would require 68% of COVID-19 healthcare resources. The model provides useful information about the relative impact of Covid-19 on healthcare workload at GP practice granularity in all parts of England. Conclusion Covid-19 is impacting on the utilisation of health and social care resources across the country. This model provides a method for predicting relative local levels of disease burden based on defined criteria and thereby providing a method for targeting limited (and perhaps soon to be scarce) care resources to optimise national, regional and local responses to the COVID-19 outbreak..
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".