COVID-19 Outbreak Prediction with Machine Learning

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak.

Reliability of accessory motion testing at the carpal joints.

The testing of accessory motion has become a very important part of manual therapy practice. Its value is in assessing whether joint mobility is ideal or impaired. Despite its use, there is little evidence in the literature to support the reliability of such testing. Most of the research carried out on accessory motion testing has focused on the spine. In view of this we decided to evaluate the intra- and interrater reliability of accessory motion testing of carpal joints. Two skilled therapists tested the available motion and the end-feel response of carpal joints in 30 students and 15 patients on two separate occasions. Pain scores were also obtained. In students a moderate to good percentage of agreement [67-97%] was obtained for motion testing. In patients the percentage of agreement ranged from 60% to 100% and weighted kappa values were between 0.33 and 1.0. Intrarater reliability was better than interrater reliability in both groups. Intra- and interrater agreement on end-feel was very good. Overall, the reliability of accessory motion testing of carpal joints was acceptable. The results suggest that this form of testing can be valuable in the training of manual therapists and in clinical practice.