Infection Point Algorithm

No other time than now has our modern world been reminded of the importance of health and safety. Moving forward, we need to protect ourselves from the spread of another international pandemic. Our program is designed to identify common risk factors faced by travelers and alert health centers to stop the spread of infectious diseases before they are able to reach pandemic status. By classifying data to pinpoint the origin of a specific health risk, it will be easier to discern legitimate risks from isolated cases. Using predictive machine learning techniques, we will be able to accurately define pressing world health matters. Infectious disease suppression will become more effective, as our frontline disease control organizations will be informed of all potential risks as they become relevant rather than after the outbreak has escalated into pandemic.

Delayed national responses to emerging pandemics makes it more difficult to contain the spread of infectious diseases later on. Prior to the COVID-19 pandemic, 70% of countries were not prepared to respond to a public health emergency, which led to a scarcity of necessary supplies in many societies.

There is a lack of coordinated and accurate detection technology implemented, leaving the door wide open for the spread of infectious diseases across international borders. Information collection to confirm the magnitude of a pandemic takes time, so many nations wait to act.

During the COVID-19 pandemic, Germany had one of the world’s quickest and most efficient responses, causing it to reach only one half of its projected total death toll. In comparison, Italy had one of the slowest responses to the pandemic, causing it to face one of the highest case fatality rates (13.22% as of April 2020) in the world. Similar correlations between response times and CFRs were reported in nations like Mexico and Hungary.

Particularly at risk are people who live in developed parts of the world, where travelling is common practice, especially in densely populated areas – an estimated 1.17 billion people.

Our solution uses predictive machine learning models to quantify the risks of certain health anomalies. The program focuses on cross-border contamination. 

Data collection will occur in three major ways:

1. Database information from the World Health Organization and immigration and customs (or equivalent) 

2. Virtual health declaration forms for foreign visitors, streamlined through the usage of QR code scanning.

3. Online Reporting

Not only does the program collect data of travel history, it can also collect contact information to be used only when an individual may be posing a significant health safety concern. Our models will not only be able to locate the source of an infectious disease, it can also predict the locations most at risk of spreading, based on traveler information. The program is also malleable enough to satisfy any and all data inputs that can be provided by our clients.

The user interface of this program concisely presents information and highlights the most vital data.

Our pandemic detection program will benefit those who live in areas of the world that are particularly at risk for the spread infectious diseases. These are people who live in more developed areas of the world, where travelling is a common practice- an estimated 1.17 billion people. Those particularly at risk are those who live in urban and other densely populated areas.

By enabling these regions to collect outbreak data efficiently, governments will be able to begin acting quicker. They will be able to target the most likely affected areas and contain the spread of infectious disease before the situation escalates, saving an innumerable amount of lives and sparing billions of dollars in government spending and overall toll on national economies.

The stakeholders (the clients, travelers, and at-risk citizens) each play their role in providing input for the program to function effectively. Our clients will be international ports of entry, and the program will be fitted to match specific data sources available to a given client. Once implemented, the program will service the thousands of international travelers that pass through an average airport in a single day while also protecting the millions of citizens in surrounding areas most at risk. 

In the early stages of an outbreak, not much information is known, and data is not collected as diligently as it should be. For those reasons, governments seldom take them seriously and fail to act as quickly as they should. The world is in need of improved solutions for accurate detection of emerging health security threats in order to enable rapid response from governments. The Infection Point Algorithm is exactly that solution. Designed to adapt to individual environments, the program will allow for individual communities to better understand the health risks they are facing through the use of innovative technology.

As of right now, there is no marketed software quite like ours. Our algorithm is flexible enough to fit any and all information that can be provided, and it performs comprehensive searches across all inputs to generate the most accurate data possible. Not only does it update in real time, providing concise, factual data, but it also generates predictive machine learning models to gauge the potential threat level of a specific risk. The predictive models are compared to those of previous pandemics of similar diseases to determine the probability of a potential major health security situation.

The Inflection Point Algorithm is deeply rooted in big data analytics. Big data analytics is the process of collecting and analyzing a large volume of data sets (hence “big data”) to uncover patterns, correlations, and other useful insights. Our program is advanced enough to recognize almost any data set that is inputted, making it adaptive enough for use in any international port in the world. Thanks to big data, we are able to work with our clients to collect as much data as can be provided. We recommend that clients implement virtual health declaration forms to allow for the program to capture the most specific and up-to-date data as possible.

Once the data is collected, the algorithm searches for red flags in the data: shared symptoms among travelers from the same point of origin, shared symptoms from those who declare similar goods at customs, travelers who have been in regions facing infectious disease outbreaks, etc. Beyond identifying the presence of a health security threat, the algorithm goes one step further and predicts the most likely risk areas based on the declarations of exposed persons. Overall, the usage of big data analytics will allow for accurate detection of emerging health security threats and enable the rapid response of governments. They will be able to allocate resources in an efficient manner by pinpointing and identifying the risk before it has spread across entire regions. 

During the coronavirus pandemic, Taiwan is easily one of the most at-risk destinations, being only 81 miles off the coast of China, where the virus originated. Yet, as of May 1, 2020, Taiwan has had only 6 deaths. 

Taiwan's response to the coronavirus pandemic is among the best in the world. The nation is not receiving help from the World Health Organization and is not allowed to participate in global health discussions due to political reasons, which makes their low mortality rate even more impressive. 

A huge part of the island's success in suppressing the virus is their effective data collection techniques, which caused them to quickly impose travel bans. The Taiwanese government tracked foreign health concerns by having foreign visitors scan QR codes that took them to an online health declaration form. There, they provided their contact information and symptoms. Taiwan also leveraged its national health insurance database and integrated it with its immigration and customs database to begin the creation of big data for analytics; it generated real-time alerts during a clinical visit based on travel history and clinical symptoms to aid case identification. Their response is what our program is modeled after. 

Our software streamlines data from multiple sources to generate comprehensive inputs for our algorithm that is updated as new information is collected. Then, using big data analysis, the algorithm detects prominent health security threats within a client jurisdiction. The immediate results that the program provides our clients are quick and reliable detection of health security risks, as well as projections for the potential spread of infectious disease. This helps to efficiently gauge the magnitude of a health security threat and better prepare for its containment. In the long run, this saves lives and economic toll on entire societies.

We know that the implementation of data science technologies is highly effective in outbreak detection because of similar implementations that have been used around the world. A Canadian startup, BlueDot, uses artificial intelligence to analyze news reports, social media, and government documents and find infectious disease risks. The company warned of the threat of COVID-19 several days before the WHO and CDC issued public warnings. Faster detection and response times very obviously leads to less overall deaths, but if proven evidence is needed, here it is: an analysis from Columbia University reported that the United States could have prevented an estimated 36,000 deaths during the COVID-19 pandemic had social distancing measures been put in place just one week earlier. The total number of cases and hospitalized cases also would have significantly decreased. The economic toll of a single symptomatic case would cost a median $3,045 to treat. An inpatient case costs a median of $14,366. By reducing the impact of a pandemic, we are saving individuals thousands of dollars each.

Currently, our solution is in the concept phase, and we plan on dedicating  the next year, or maybe two, to research and development, as well as creating a solid financial base and business strategy. After this phase is completed, we will take the Infection Point Algorithm to market. 

In 5 years, we will ideally have built a stable client base and begun vertical scaling of the program in these key cities. For every client that we gain, the average number of people who will be serviced by our solution is about 900,000.

Our goal is to devise a business and development strategy that is stable and will be able to take us to new heights. Within the next year, development should be well underway, and all logistics should be worked out. We also want to generate funding for our program and have a lasting financial plan. 

Within the next 5 years, we hope that our program will generate partnerships and investors, and we will be able to have a marketing strategy and sales. 

• Experience
  • While all of our team members are knowledgeable in our respective fields, we lack the professional experiences of your typical innovators
• Financial
  • Our project is currently in the conceptual stages of its design. We have not pitched our idea to anybody, and we have not generated funding that has not come out of pocket.

• Experience
  • Each and every member of our team takes initiative. We are all determined to learn more about how we can create the most successful business model possible, and how we can develop this product to be as comprehensive as it can be. We plan to reach out to mentors and learn as much as we can on our own time. 
• Financial
  • Our first step is to build a prototype. From there, we should be able to gauge an estimate of our costs for developing the product. At that point, we will be able to apply for grants and pitch to investors to generate funding.

As of right now, there are 3 members of the team, who all work part-time. We have every intention of expanding the team. We already have generated a lot of interest from prospective team members, but we are holding off until the project gains serious financial backing.

Every member of our team has a background in electrical and computer engineering. We are familiar with the concepts required to put together the project, even our market strategist and financial analyst. This is what makes our team so unique. Through education, our team has bonded within an environment that is STEM and innovation-driven. We are young and hardworking, and are in a position to take on an arduous task like developing the Infection Point Algorithm. Over the next year, each of our team members and prospective team members is dedicated to getting the company off the ground.

None

As a company that is still in the concept phase, in the early stages of development we will be entirely funded by grants and donations. Once we enter the marketplace, we will generate revenue from clients in the form of installation fees, one-time licensing fees, and subsequent maintenance fees.

Solve is dedicated to improving the world by recognizing the greatest idea, no matter who comes up with it. The biggest challenge that our team has always faced is not being taken seriously due to our professional inexperience. Solve can provide us with the mentorship and funding to take our idea to the next level. Our motivation and passion for this project is unstoppable, and with Solve backing us up, I'd have a very hard time believing that our idea won't be an absolute success.

Our idea is currently still in the concept phase. While we believe that we will be able to assemble a team that is well equipped to take on the task, we will require much guidance to help us develop a long-term plan.