Application of machine learning for predicting the spread of COVID-19
The spread of diseases has been studied for many years, but it receives a particular focus recently due to the outbreak and spread of COVID-19. Studies show that the spread of COVID-19 can be characterized by the Susceptible-Infectious-Recovered-Deceased (SIRD) model with containment coefficients (due to quarantine and keeping social distance). This project aims to apply the machine learning technique to predict the severity of COVID-19 and the effect of quarantine, keeping social distance, working from home, and wearing masks on the transmission of the disease. This work deepens our understanding of disease transmission and reveals the importance of following policies.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
COVID-19 Probability Prediction Using Machine Learning: An Infectious Approach
The ongoing COVID-19 pandemic continues to pose significant challenges to global public health, despite the widespread availability of vaccines. Early detection of the disease remains paramount in curbing its transmissio…
Symptom-based Machine Learning Models for the Early Detection of COVID-19: A Narrative Review
Despite the widespread testing protocols for COVID-19, there are still significant challenges in early detection of the disease, which is crucial for preventing its spread and optimizing patient outcomes. Owing to the li…
Decision MakingResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images
Automatic disease detection using machine learning-based techniques from X-ray and computed tomography (CT) can play a major role in the frontline to assist medical professionals during the current outbreak of COVID-19. …
BIG-bench Machine LearningComputed Tomography (CT)DiagnosticA Multi-Task Learning Framework for COVID-19 Monitoring and Prediction of PPE Demand in Community Health Centres
Currently, the world seeks to find appropriate mitigation techniques to control and prevent the spread of the new SARS-CoV-2. In our paper herein, we present a peculiar Multi-Task Learning framework that jointly predicts…
Multi-Task LearningModelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives
Since the outbreak of COVID-19, an astronomical number of publications on the pandemic dynamics appeared in the literature, of which many use the susceptible infected removed (SIR) and susceptible exposed infected remove…