A Data-Driven Optimization Model for Medical Resource Allocation during the Pandemic
Bibliographic record
Abstract
The outbreak of Covid-19 in recent years has once again brought the critical issue of medical resource allocation during a pandemic to the forefront of research and public attention. The dynamic and rapid nature of the pandemic has posed significant challenges in accurately predicting the demands for medical resources and developing effective strategies for their distribution. In this study, we aim to address these challenges by studying the medical resource allocation problem during a pandemic and proposing a data-driven optimization methodology that combines mathematical programming and machine learning techniques. \nTo tackle the problem of demand prediction, we utilize a Long Short-Term Memory(LSTM) model to predict medical resource demand using historical pandemic time series data. Building upon the demand predictions, we develop a linear programming model to optimize the allocation of medical resources. The objective is to maximize the total accessibility of hospitals within each region while also ensuring a balanced distribution of accessibility across all regions. We also conducted a case study on the application of this framework to the Quebec, Canada, pandemic hospitalization case scenarios. The dataset we utilized consisted of hospitalization case numbers from 16 regions in Quebec, along with the geographical locations of 15 regions and their corresponding healthcare facilities. The prediction performance is evaluated by mean absolute error(MAE) and root mean square error(RMSE), which yielded average values of 3.079 and 5.491, respectively. And after optimizing, the total accessibility of all regions is 4.503. The results indicate the effectiveness of our proposed method in accurately predicting future hospitalization numbers and determining the necessary increase in bed capacity for each region, showcasing its potential to assist in resource planning and allocation during a pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".