Digital Suveillance changing the future of Disease and Health-1

.docx

School

University of California, Davis *

*We aren’t endorsed by this school

Course

Subject

Electrical Engineering

Date

Dec 6, 2023

Type

docx

Pages

Uploaded by GeneralLapwing1385

[Vue 1] [Judy Vue] [STS 1] [12/7/2022] Digital Surveillance changing the future of Diseases and Health Introduction to Digital surveillance When we think of Digital Surveillance, most people would think of all the negative connotations it imposes in our society. The term surveillance comes from the French origin definition, which means “to watch.” (Choi, 2018). Digital Surveillance is a highly controversial topic because it mainly has to do with the fact of how our privacy is being exploited, being tracked and watched all the time for all different kinds of purposes like marketing, advertising, and innovation. But have we ever thought about digital surveillance as a useful technology tool, the idea of how life changing Digital surveillance can be when we use it for the purpose of minimizing the spread of diseases and health while finding ways to gain easier access to health treatments from the simple click of our phones. In this research paper, I aim to study Digital Surveillance based on the Uppsala Health Summit organization. As we continue to move forward into the future of the 21st century, without a doubt we will continue to have an increased volume of diverse data. The main challenges that big data can bring is distinguishing which kind of data may or may not be useful while ensuring ethical practices to avoid privacy issues for a more efficient healthcare system. The Uppsala Health Summit is an organization consisting of two hundred delegates from all around the world. The purpose of their presence is to come together and focus on how to make Big data more innovative based on the use of Novel data streams, AI, and machine learning in order to resolve a reliable way to prevent, detect and respond to infectious diseases through a one health approach (Dórea, 2021). Big data is a type of surveillance technology that has replaced the traditional ways of gathering enriched data like

[Vue 2] surveys/ interviews and instead Data is now being modernly collected through tracking of online websites. Reflection on my research processes Before I dive deeper into my research paper, I would like to thank Professor Carlos Barragán and our teaching assistant Alejandro Ponce De Leon for guiding me on how to analyze and reflect my research through the mapping exercises and research diary entries. When writing my draft for this paper, I realized how helpful the Mapping exercises and research diary entries were and it was also useful in organizing my thoughts and ideas for all my resource findings for this research paper. During the first mapping exercise, I struggled a bit trying to understand how to distinguish between socio-cultural dimensions and Actors in the news articles I was finding. The feedback from the mapping exercises were also beneficial in the development of my capacity to analyze and furthering them into arguments for this research paper. For instance, I remember analyzing a majority of my sources, but I wasn’t connecting all of my findings together into a proper narrative. In the Research diaries, I was arguing why the Novel data stream is important because of its mechanism of ensuring privacy of patients while only gathering the data that is needed to improve surveillance. Another different kind of surveillance technology I wanted to include in this paper is internet-based Bio surveillance system. Although I was able to define the technologies of both novel data streams and internet based bio surveillance systems , I lacked the distinct connection of how both of these different technologies could improve the surveillance of diseases. After reviewing all of the work I’ve done for the mapping exercises and research diaries, I realized just as important it is to analyze the sources it is also important to know how to connect back to my argument as to why Novel data streaming technology will be a game changing for disease surveillance and the future of epidemiology.

[Vue 3] How the world is embedded in Digital Surveillance Digital Disease Surveillance in Vet Epidemiology The complexity of Big Data is a type of technology tool that can be used to translate various data that is necessary for understanding animal health (Frontiers Media SA, 2017). Big data is often collected by vet epidemiologists through the use of high-volume datasets in order to identify the different kinds of animal demographics and movement of animals in order to keep track of high risk populations. In order to strategically keep track of animal populations at risk for diseases, currently about 26 different countries use big data as a surveillance technology in order to enforce mandatory animal tracing programs (Frontiers Media SA, 2017). With these programs, large production companies can easily track movement of animals across space and time with movement data. If for whatever reason there is a disease outbreak in a cattle company, rapid responses can be made possible due to the tracing systems implemented for animal health policies. Given the report on animal movement data, another way of how animals can be traced is through the use of GPS tracking. It is more likely that with this kind of method for collecting movement data, it can be utilized for a more real time update which is efficient for reports and influencing decision making policies. For over two decades now, many other streams of data for animal disease tracking can also come from direct monitoring based on veterinarian phone calls, visits to vet hospitals and over the counter drug sales (Dórea, 2021). These methods of data tracking are useful in tracking population patterns and changes that are necessary to detect early signs of health changes for indeterminate clinical symptoms within public health.

[Vue 4] Digital Disease surveillance in Food safety As we have seen the benefits of Big Data in animal disease control, Big data technology can also be used to help ensure food safety and chemical risk assessments as well. Big data in food safety is collected through a food safety platform called “FOSCOLLAB.” (Marvin, 2017). Through the “FOSCOLLAB” platform, many kinds of integrated data can be used to structure animal food safety, agriculture, food hazards and public health. For food safety, data is collected through various forms such as databases, mobiles phones, social media, and the online internet websites (Marvin, 2017). Online databases are used quite often in order to provide an overview of hazardous alerts on food products. An example of how food hazards can be known to the public is through social media. Social media is a powerful technology tool that is used in databases for informing the public of not just food related health hazards but rather how it can be used for all sorts of public health matters. Researchers from International Society for Disease Surveillance were also able to do a study on how certain databases correlate with accurate findings on internet/social media data in order to make public statements for health (Smith LE, 2015). In figure. 1 it is about a Facebook post from May, 2022 warning parents to not buy baby formula due to containing high levels of lead. The contamination of lead in baby formula is what led to the biggest food shortages of baby formula as of this year. Many Foodborne illnesses Fig. 1 “Lead in formula.” have luckily been prevented through the programs of PulseNet. PulseNet is a program implemented in order to reduce national illness. Economically,

[Vue 5] PulseNet has proven to be effective in saving costs for medical and productivity losses (Scharff, 2016) . Digital disease surveillance in Medicine Big data technology also has potential to be used for future drug development processes. Nowadays, researchers may have easier access to large data with the help of the European Bioinformatics Institute, which consists of over 20 petabytes worth of biological data (Leyens, 2016). With the help of enriched biological data, it can effortlessly provide the right analytical tools to help develop research hypotheses with the right associated data and connections for a quicker result of a drug discovery (Leyens, 2016). After a possible drug discovery is made, researchers can run clinical trials through a clinical decision-making supporting system through a machine learning technology that helps track potential patients as candidates for clinical trials (Leyens, 2016). However, Using Big Data in the field of medicine also has its own limitations as well. When it comes to medicine, privacy may become an issue for the healthcare industry (Business compiler. 2022). The bigger data is growing, the more patients are being tracked based on their medical records and lab reports. For instance, “Medical big data experts have said that technology takes away one's privacy for greater good” (Business compiler. 2022). As long as Big data exists, privacy will always be a problem. What is the big idea altogether? What are the challenges? All in all, Big data and machine learning technology can be used innovatively in all different fields that relate to health. Altogether Big data has the most potential to improve health surveillance in Vet epidemiology, food safety and medicine. With Big data, we can track and prevent diseases in animals, track food hazards and track biological data that is necessary for the

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version