Analysis of Heterogeneous Data Sources for Veterinary Syndromic Surveillance to Improve Public Health Response and Aid Decision Making

The standard technique of implementing veterinary syndromic surveillance (VSyS) is the detection of temporal or spatial anomalies in the occurrence of health incidents above a set threshold in an observed population using the Frequentist modelling approach. Most implementation of this technique also requires the removal of historical outbreaks from the datasets to construct baselines. Unfortunately, some challenges exist, such as data scarcity, delayed reporting of health incidents, and variable data availability from sources, which make the VSyS implementation and alarm interpretation difficult, particularly when quantifying surveillance risk with associated uncertainties. This problem indicates that alternate or improved techniques are required to interpret alarms when incorporating uncertainties and previous knowledge of health incidents into the model to inform decision-making. Such methods must be capable of retaining historical outbreaks to assess surveillance risk. In this research work, the Stochastic Quantitative Risk Assessment (SQRA) model was proposed and developed for detecting and quantifying the risk of disease outbreaks with associated uncertainties using the Bayesian probabilistic approach in PyMC3. A systematic and comparative evaluation of the available techniques was used to select the most appropriate method and software packages based on flexibility, efficiency, usability, ability to retain historical outbreaks, and the ease of developing a model in Python. The social media datasets (Twitter) were first applied to infer a possible disease outbreak incident with associated uncertainties. Then, the inferences were subsequently updated using datasets from the clinical and other healthcare sources to reduce uncertainties in the model and validate the outbreak. Therefore, the proposed SQRA model demonstrates an approach that uses the successive refinement of analysis of different data streams to define a changepoint signalling a disease outbreak. The SQRA model was tested and validated to show the method's effectiveness and reliability for differentiating and identifying risk regions with corresponding changepoints to interpret an ongoing disease outbreak incident. This demonstrates that a technique such as the SQRA method obtained through this research may aid in overcoming some of the difficulties identified in VSyS, such as data scarcity, delayed reporting, and variable availability of data from sources, ultimately contributing to science and practice

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Public Health Monitoring of Behavioural Risk Factors and Mobility in Canada: An IoT-based Big Data Approach

Background: Despite the presence of robust global public health surveillance mechanisms, the COVID-19 pandemic devastated the world and exposed the weakness of the public healthcare systems. Public health surveillance has improved in recent years as technology evolved to enable the mining of diverse data sources, for example, electronic medical records, and social media, to monitor diseases and risk factors. However, the current state of the public health surveillance system depends on traditional (e.g., Canadian Community Health Survey (CCHS), Canadian Health Measures Survey (CHMS)) and modern data sources (e.g., Health insurance registry, Physician billing claims database). While improvement was observed over the past few years, there is still a room for improving the current systems with NextGen data sources (e.g., social media data, Internet of Things data), improved analytical mechanism, reporting, and dissemination of the results to drive improved policymaking at the national and provincial level. With that context, data generated from modern technologies like the Internet of Things (IoT) have demonstrated the potential to bridge the gap and be relevant for public health surveillance. This study explores IoT technologies as potential data sources for public health surveillance and assesses their feasibility with a proof of concept. The objectives of this thesis are to use data from IoT technologies, in this case, a smart thermostat with remote sensors that collect real-time data without additional burden on the users, to measure some of the critical population-level health indicators for Canada and its provinces. Methods: This exploratory research thesis utilizes an innovative data source (ecobee) and cloud-based analytical infrastructure (Microsoft Azure). The research started with a pilot study to assess the feasibility and validity of ecobee smart thermostat-generated movement sensor data to calculate population-level indicators for physical activity, sedentary behaviour, and sleep parameters for Canada. In the pilot study, eight participants gathered step counts using a commercially available Fitbit wearable as well as sensor activation data from ecobee smart thermostats. In the second part of the study, a perspective article analyzes the feasibility and utility of IoT data for public health surveillance. In the third part of this study, data from ecobee smart thermostats from the “Donate your Data” program was used to compare the behavioural changes during the COVID-19 pandemic in four provinces of Canada. In the fourth part of the study, data from the “Donate your Data” program was used in conjunction with Google residential mobility data to assess the impact of the work-from-home policy on micro and macro mobility across four provinces of Canada. The study's final part discusses how IoT data can be utilized to improve policy-level decisions and their impact on daily living, with a focus on situations similar to the COVID-19 pandemic. Results: The Spearman correlation coefficient of the step counts from Fitbit and the number of sensors activated was 0.8 (range 0.78-0.90; n=3292) with statistically significant at P < .001 level. The pilot study shows that ecobee sensors data have the potential to generate the population-level health indicators. The indicators generated from IoT data for Canada, Physical Activity, Sleep, and Sedentary Behaviours (PASS) were consistent with values from the PASS indicators developed by the Public Health Agency of Canada. Following the pilot study, the perspective paper analyzed the possible use of the IoT data from nine critical dimensions: simplicity, flexibility, data quality, acceptability, sensitivity, positive predictive value, representativeness, timeliness, and stability. This paper also described the potential advantages, disadvantages and use cases of IoT data for individual and population-level health indicators. The results from the pilot study and the viewpoint paper show that IoT can become a future data source to complement traditional public health surveillance systems. The third part of the study shows a significant change in behaviour in Canada after the COVID-19 pandemic and work-from-home, stay at home and other policy changes. The sleep habits (average bedtime, wake-up time, sleep duration), average in-house and out-of-the-house duration has been calculated for the four major provinces of Canada (Ontario, Quebec, Alberta, and British Columbia). Compared to pre-pandemic time, the average sleep duration and time spent inside the house has been increased significantly whereas bedtime, and wake-up-time got delayed, and average time spent out-of-the-house decreased significantly during COVID-19 pandemic. The result of the fourth study shows that the in-house mobility (micro-mobility) has been increased after the pandemic related policy changes (e.g., stay-at-home orders, work-from-home policy, emergency declaration). The results were consistent with findings from the Google residential mobility data published by Google. The Pearson correlation coefficient between these datasets was 0.7 (range 0.68-0.8) with statistically significant at P <.001 level. The time-series data analysis for ecobee and google residential mobility data highlights the substantial similarities. The potential strength of IoT data has been demonstrated in the chapter in terms of anomaly detection. Discussion and Conclusion: This research's findings demonstrate that IoT data, in this case, smart thermostats with remote motion sensors, is a viable option to measure population-level health indicators. The impact of the population-level behavioural changes due to the COVID-19 pandemic might be sustained even after policy relaxation and significantly affects physical and mental health. These innovative datasets can strengthen the existing public health surveillance mechanism by providing timely and diverse data to public health officials. These additional data sources can offer surveillance systems with near-real-time health indicators and potentially measure short- and long-term impact policy changes

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining