Design of an intelligent prevention and control platform for major public health emergencies based on a new generation of information technology
Bibliographic record
Abstract
As the COVID-19 has spread over the globe and become a pandemic authority, it has relied heavily on social isolation as a primary strategy for containment. When individuals are confined to their homes, digital technology plays a critical role in supporting their social, professional, and economic activities. There is a pressing need for contemporary healthcare facilities, particularly in developing nations where rural locations have lack high-quality hospitals and medical professionals. For the upcoming years, public health, human civilization, and the global economy will continue to be impacted by this unique coronavirus. Due to the Internet of Things (IoT) health care and automation services, people's health and satisfaction can be preserved when they remain socially isolated. In this paper, the COVID-19 prevention and control using the Internet of Things (CPC-IoT) platform has been suggested to enable social distance in the pandemic. The most crucial metrics for critical care are body temperature, pulse rate, and oxygen saturation, and this research proposes an IoT-based system that uses these variables in real-time. Using the suggested IoT architecture, we offer a short- and long-term approach for managing pandemic situations. Each architectural layer's concerns have been addressed by providing guidelines for design implementation. Covid-19 can be prevented and controlled utilizing an IoT platform that includes symptom diagnosis and quarantine monitoring steps. Compared to other commercially available devices, the system's results are confirmed to be accurate. IoT-based technologies can be useful in the event of a COVID-19 viral epidemic.
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 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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".