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Disease outbreak is a sudden increase in the occurrences of a disease in a particular place and time. For any disease to be coined as outbreak there has to be enough number of people already suffering from that disease. So by the time a news or social media platform inform the public of this outbreak the disease will have spread and people affected by this disease will already have been to the pharmacy for medicines.

Let's assume that if 100 people in a location suffer from a particular disease over a span of time then we can call it disease outbreak. Now let there be 10 pharmacies in an area and each pharmacy having enough medicines for 5 patients. The first 50 patients will be able to get the medicine, however the pharmacies will not be able to immediately provide for the other 49 patients. The purpose of Wellpharma is to solve this problem. But it will fail to do so. This happens because Wellpharma is trying to prevent the problem after the problem has already occurred. Therefore, I feel that Wellpharma fails to solve its purpose and falls short of business value.

How will the web app detect cases of diseases where there is not a large enough population to be noticed? Meaning if a single person gets sick, how will Wellpharma get the data? There may also be situations where there is no mention of a disease outbreak on social media. Then how will WellPharma be able to detect it?

Web Scraping is a program or algorithm which is used to extract and process large amounts of data from web. It looks like that Well Pharma are going to use Python libraries like requests or beautifulsoup4 for Web Scraping. However this may not be the best choice. Most sites have a file called robots.txt which tells us about which parts of the site can be scraped and which parts are not allowed to scrape. Web Scraping on sites like Facebook may be risky as they may require written permission to do any scraping.

See. https://www.facebook.com/robots.txt

There are some ways to bypass the blocks but its a nasty thing to do. An alternative solution could be to use APIs given by sites to access their data. For ex: Twitter is the best example where Well Pharma can use the API instead of scraping. The good thing is that APIs are easier to use than scraping.

MongoDB would improve performance over MySQL because information will be written to the database, the disease data and location, much more often than reading from the database itself. MongoDB allows unstructured documents and you can add new columns without suffering as much in speed as MySQL. See: https://blog.panoply.io/mongodb-and-mysql

Also, Google News API is deprecated so there may be problems later. You may need to use other news sites. Using city and regional newspaper sites may be more helpful since those will be local and close to the pharmacy, but these are unlikely to have any APIs to help with scraping. This will make it more difficult to collect trend data.

One concern is the reliability of user posts on social media. It is unlikely that a normal user will post about diseases on a regular basis, for example weekly. You are more likely to get info from dedicated groups such as a city’s health organizations or groups on social media related to a certain medical condition, for example diabetes or cancer. Then you could more easily spot specific trends. There may be a limited number of such groups in a certain city, however.

Furthermore, misinformation is rampant on social media, and even news sites may make mistakes. For example, there may be a health scare on a measles outbreak which make it look like the disease is happening more often than it actually is. There may be a number of posts with stories or untrue statistics on local diseases. How will the web app deal with this misinformation? Will there be a way to filter the content even further?