A reliable livestock disease surveillance system should detect changes in health events whenever they occur. Such a system ought to be evaluated regularly to ensure it provides valuable information in an efficient manner. Thus, a cross-sectional study was carried out in 2017 to assess eight attributes of the livestock disease surveillance systems in Pallisa and Kumi districts, Uganda. A total of 772 livestock farmers were interviewed to evaluate the surveillance system at their level, using a structured questionnaire. Guided interviews were also carried out with 13 key informants who included all veterinary staff at the districts and sub-county administrative units, as well as two officials at the Ministry of Agriculture Animal Industry and Fisheries (MAAIF). The stakeholders interviewed at the three different levels of the livestock diseases surveillance system perceived the system as useful, with ability to detect epidemics and initiate their control if they occurred. The surveillance system was perceived to be considerably representative, sensitive and acceptable, with the ability to generate data of good quality. However, key emerging issues that need improvement were noted. These included poor laboratory diagnostic services, inability to work within the means of available resources, slow data transmission and feedback, and nonspecific surveillance forms leading to poor quality of data collected. Poor communication along the surveillance system chain and inadequate staffing were noted as the major challenges faced by the surveillance system in the two districts. Although perceived to be functional, the livestock surveillance requires improvements for efficient disease detection and control. For better performance, the surveillance system could be strengthened by establishing and equipping laboratories for efficient confirmatory diagnosis of diseases; adjusting to work within the means of available resources; improving the reporting process through quick data transmission and quick feedback and designing precise surveillance form to improve quality of data collected.
This paper focuses on assessing the behavior of a patient over time periods for managing type 2 Diabetes. In some cases, patients with type 2 diabetes not only behave differently from other patients, but the severity of a given health problem varies even for an individual patient. We focus on understanding how and when patients differ from other patients. In addition, we also look at the diversity that exists within an individual patient especially over time-periods throughout the day. Our aim is to identify such time intervals when a patient may need more targeted care. Thus, for type 2 Diabetes we identify which time-periods exhibit a mismatch in terms of the blood glucose readings and the insulin doses. For instance, if the blood glucose readings fluctuate and the insulin doses are fixed it may indicate a poor management of the insulin doses and therefore a poor management of Diabetes. Based on such findings a number of factors can be taken into consideration when drawing out a care plan for example diet, lifestyle, and type of treatment, among others. Our study uses a data mining approach, particularly clustering to study the measurements in blood glucose and doses of regular insulin for a selected number of patients. We look at their behavior on an overall days' basis, which we refer to as large-scale binning. Additionally, we study their behavior at specified time intervals throughout the day, which we refer to as small-scale binning. Our findings indicate that we are clearly able to see the trends in blood glucose readings as compared to the insulin doses for different patients indicating a well managed or a poorly managed plan.
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