There is a large volume of published studies describing the adverse relationship between treatment non-adherence with tuberculosis treatment outcome. Non-adherence could result in increased risks of prolonged infectiousness, drug resistance, relapse cases and poor survival among tuberculosis patients. Nevertheless, few studies are to be found providing detailed on the reason of defaulting treatment among tuberculosis patients in Malaysia. Hence the goal of this paper is to find out the barriers and motivations factors that affect patients’ treatment compliance among our local tuberculosis patients. This is a qualitative study which included 12 in-depth interviews with tuberculosis non-compliance patients who were treated at Institute of Respiratory Medicine, Kuala Lumpur. All the conversations were recorded, transcribed and analysed by using thematic analysis. It was found that low knowledge, self-negative attitudes, traditional believes, negative perceptions towards health caregiver, drug side effects, stigma, financial problems, less family support and work commitments are the barriers that prevent the patients from religiously taking their anti-tuberculosis treatment. Meanwhile, factors that encourage them to continue their treatment were the believes of bad effects of the disease onto their lives and health, good relationship between patient and health caregiver and social support from people around them. In conclusion, non-adherence involved a dynamic influence of individual, socio-economic and treatment-related factors on the patients. The results presented here may facilitate improvement in the activities in promoting compliance among tuberculosis patients in the future which tailored to the patients’ specific needs.
Tuberculosis

Dengue is an increasing threat in Malaysia, particularly in the more densely populated regions of the country. We present an Artificial Intelligence driven model in predicting Aedes outbreak, using predictors of weather variables and vector indices sourced from the Ministry of Health. Analysis and predictions to estimate Aedes populations were conducted, with its results being used to infer the possibility of dengue outbreaks at pre-determined localities around the Klang Valley, Malaysia. A Bayesian Network machine learning technique was employed, with the model being trained using predictor variables such as temperature, rainfall, date of onset and notification, and vector indices such as the Ae. albopictus count, Ae. aegypti count and larval count. The interfaces of the system were developed using the C# language for Server-side configuration and programming, and HTML, CSS and JavaScript for the Client Side programming. The model was then used to predict the population of Aedes at periods of 7, 14, and 30 days. Using the Bayesian Network technique utilising the above predictor variables we proposed a finalised model with predictive accuracy ranging from 79%-84%. This model was developed into a Graphical User Interface, which was purposed to assist and educate the general public of regions at risk of developing dengue outbreak. This remains a valuable case-study on the importance of public data in the context of combating a public health risk via the development of models for predicting outbreaks of dengue which will hopefully spur further sharing of data by all parties in combating public health threats.