Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review.

Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.

Qualitative evaluation of the SMART2 self-management system for people in chronic pain.

PURPOSE: Technology could support the self-management of long-term health conditions such as chronic pain. This article describes an evaluation of SMART2, a personalised self-management system incorporating activity planning and review, feedback on behaviour- and acceptance-based therapeutic exercises. METHOD: The SMART2 system was evaluated over a four-week trial in the homes of people in chronic pain. At conclusion, participants were interviewed to understand the experience of using and living with the SMART2 system as a therapeutic tool. RESULTS: Qualitative analysis of the interviews found that participants liked the system and reported making associated changes to their behaviour. Goal setting and feedback were the most useful elements of the system. A third key and unexpected element was that by simulating some of the functions of a therapist, SMART2 also simulated some of the process of interacting with a therapist. CONCLUSIONS: People in chronic pain may experience positive outcomes when using a self-management system designed for behaviour change. Furthermore, some of the supportive aspects of the therapeutic context can be elicited by self-management technologies. Implications of Rehabilitation Self-management technology has the potential to assist rehabilitation by supporting goal setting and providing feedback. By simulating some of the functions of a therapist, technology can simulate some of the process of therapy during rehabilitation. People in chronic pain liked using the technology in their own home and thought it could augment services delivered by clinical practitioners. Complex programmes of therapeutic exercises delivered by technology had limited success in engaging people in chronic pain.