Title of the Essay, Author, and Date

T he Perils of Predicting Epidemics by Nikki Williams (April 10, 2015)

Title of the Reflection

D anger of Widespread

First Impression

A n Essay that explains the advantages or disadvantages of predicting epidemics

Quote

There should also be some level of participatory agreement or consent before data of a private nature is shared and participants’ identity should be protected.

Reflection paper

In the last 10-years or so, a new type of disease surveillance has been evolving – we call it Digital Disease Detection, or DDD. We are trying to accomplish the same goals as before, like finding out what type of disease are out there and how severe they are, but we’re trying to do it much, much faster. Like, instantly. In real-time.

The ability to rapidly recognize and respond to both global and local health threats remains a critical public health priority. The ever-growing digital world represents an unprecedented opportunity to harvest for new solutions and tools to face these emergencies. This digital means of disease detection has been made possible by the growing influence of Internet technology, which has significantly changed the landscape of public health surveillance and epidemic intelligence gathering.

Disease and outbreak data is now disseminated not only through formal online announcements by government agencies, but also through other informal digital channels such as social networking sites, blogs, chat rooms, Web searches, local news media, crowdsourcing platforms. These informal data streams have been credited with decreasing the time between an outbreak and formal recognition of an outbreak, allowing for an expedited response to the public health threat.

The DDD is new and exciting, and offers huge potential to help untold millions of people, it’s not infallible, and it’s not yet at a stage where it can replace traditional surveillance. While DDD continues to evolve and get better, the key is to combine the best of traditional disease surveillance with the best of digital disease detection to get the clearest picture possible of the state of health of our world. 

5 Things That I've learned

1. Digital disease detection (DDD) has been gaining momentum over the last fifteen years. The internet has become a resource for clinicians and health officials looking for new ways to determine the strength and breadth of diseases and communicating this information to the general public.
2. The idea of using Twitter-based data mining methodology to track disease spread introduces the possibility of labeling and tracking individuals based on casual public comments. This type of tracking raises critical ethical questions due to the stigma of HIV and other sensitive diseases.
3. A holistic approach to these issues is needed. Initially, there should be a global governance system that ensures the privacy of all individuals whose data is used for public health reasons, as well as an emphasis on preserving the data for public health use only.
4. DDD relies on spatial analysis of cases in both data collection and outbreak reporting, but spatial event data has been found to be widely inaccurate due to the geocoding process. Spatial analysis entails monitoring cases within a given geographic space and looks for discernable patterns from which disease spread probability can be extrapolated. 
5. DDD has a lot of structure and regulation to undergo before it can be considered a real, rather than rogue, technology.

5 Integrative Questions

1. Where it goes after disease-related data is being dispersed and collected through both formal and informal channels, from chatrooms and blogs to web-search analyses?
2. Why what was the reason by using Twitter-based data mining methodology to track disease spread introduces the possibility of labeling and tracking individuals based on casual public comments?
3. Where can this be reconciled among nations if the agency collecting and disseminating the data resides in a country that does not place a high value on protecting personal information?
4. What supplements spatial data by matching geographic coordinates with an address, a postal code or other location identifier in order to pinpoint specific outbreak locations? What if the data accuracy varies based on the population density and data quality?
5. Why a watchdog organization should be create to oversee the correct and private handling of personal data? Why there must also be significant oversight and monitoring, including checks and rechecks, before warnings and predictions are released publicly?