Difference between revisions of "Team:Lambert GA/Software"

Line 220: Line 220:
 
     <div id="overlay"></div>
 
     <div id="overlay"></div>
 
           <div id="bigtitle">
 
           <div id="bigtitle">
−
                 C A L M  
+
                 C A L M O V E R V I E W
 
           </div>
 
           </div>
 
</div>
 
</div>
Line 251: Line 251:
 
<div id="target3"></div>
 
<div id="target3"></div>
 
<div id="subheading3">
 
<div id="subheading3">
−
<b>CALM
+
<b>OVERVIEW
 
</b>
 
</b>
 
</div>
 
</div>
 
<br><br>
 
<br><br>
 
<div id="content3">
 
<div id="content3">
−
The 2018 project includes a software subproject known as CALM, the Cholera Artificial Learning Model. CALM is a machine learning based platform which predicts cholera outbreaks in advance and uses SMS notifications to notify healthcare organizations as well as locals so that they can prepare for outbreaks. SMS-based surveys are a key component of CALM, as health and sanitation data can be collected from a population to be used in the machine learning model, making it more accurate. For 2018, Lambert iGEM has built the machine learning aspect of CALM and has modified the existing code from Michael Koohang’s RatWatch project to distribute cholera surveys and collect data.
+
The ongoing Yemeni cholera outbreak has been deemed one of the worst cholera outbreaks in history, resulting in thousands of deaths. Triggered by a civil war, the outbreak has been shaped by various political, environmental, and epidemiological factors and has impacted to over a million Yemenis. Limited access to healthcare infrastructure deters citizens from receiving widely available treatments for the disease. To solve this issue, various mathematical models have been made to track the Yemeni outbreak and identify governorates (administrative divisions) at risk of an outbreak by providing advance notice of incidence. These models, while useful, are not powerful enough to accurately and consistently forecast exact cholera cases per governorate over multiple timeframes. To address the need for a complex, reliable model, the Lambert iGEM team presents CALM, the Cholera Artificial Learning Model; a system of four extreme-gradient-boosting (XGBoost) machine learning models that forecast the exact number of cholera cases a Yemeni governorate will experience from a time range of  2 weeks to 2 months. CALM provides a novel machine learning approach that makes use of rainfall data, past cholera cases and deaths data, civil war fatalities, and inter-governorate interactions represented across multiple timeframes. Additionally, the use of machine learning, along with extensive feature engineering, allows CALM to easily learn complex non-linear relations apparent in an epidemiological phenomenon.
−
<br><br>
+
−
Diseases similar to cholera are especially damaging because even though they are curable (i.e., a cure exists), they still cause horrific amounts of damage to millions of people. What cholera treatment projects require more than anything are preventative measures to ensure a population does not become infected. CALM fulfills this need as a predictive actor, providing healthcare organizations the information required to pinpoint where a cholera outbreak is going to occur so that they can send medicines and supplies before the outbreak begins, thus preventing it. As a prediction and notification-based platform, CALM also satisfies the needs of locals who can take preventative measures (such as boiling water) to prevent cholera but can’t afford to do it constantly - thus making timely notifications critical for locals.
+
−
<br><br>
+
−
There are two main components of the CALM platform; the SMS component and the machine learning component. The entirety of the platform is written in Python 3.6+, and several libraries, including the pandas, numpy, scikit-learn, beautifulsoup, xgboost, and flask libraries are utilized. To distribute SMS notifications, Michael Koohang graciously allowed Lambert iGEM to modify his RatWatch project (developed at Georgia Tech) to create CALM’s SMS component. The code for SMS distribution is located on a server using the Flask microframework for logic and computation. The Flask server interacts with Twilio’s (an SMS-survey provider) Python API in order to send out text messages to a specified population. The population’s survey results are aggregated and stored on the Flask server using pandas.
+
−
 
+
−
 
+
−
<br><br>
+
−
We hope to see CALM in use throughout the cholera field within the next few years as medical organizations begin using it to prevent outbreaks and better distribute medical supplies. As cholera already has a cure, a machine-learning based approach to predicting and preventing cholera, especially one that is open-source and free to use, will drastically reduce the time, energy, and money required to treat an infected population. Finally, we believe the CALM project will not only treat millions of people affected with cholera but will also begin efforts to use CALM’s foundation to predict other diseases such as malaria and parasitic infections.
+
−
<br><br>
+
−
CALM began as a subcomponent of Lambert’s 2018 project and rapidly developed throughout the beginning of the 2018 season. In late May Lambert participated in the Day One Challenge, an Atlanta-based AI competition, and won the Grand Prize. Through further collaboration and outreach with the Day One organization Lambert has been able to receive feedback and advice from professionals in a variety of fields, such as epidemiology, computer science, machine learning, and business. As CALM develops further, we hope to not only see other teams adopt the platform to address other issues but also for healthcare organizations across the world to utilize CALM and adapt it to other diseases.
+
−
<br><br>
+
−
 
+
 
</div>
 
</div>
  

Revision as of 17:46, 17 October 2018

CALM ELECTROPEN
BACKGROUND DATA & METHODS RESULTS DISCUSSION COLOR Q SUPPLEMENTARY REFERENCES
COLOR Q CHROME Q REFERENCES
PRINCIPLES & DESIGN MODEL & THEORY PRODUCT DESIGN REFERENCES
INTEGRATED HUMAN PRACTICES EDUCATION & ENGAGEMENT COLLABORATIONS
BASIC PARTS COMPOSITE PARTS
DESCRIPTION BACKGROUND DESIGN EXPERIMENTS LAB NOTEBOOK INTERLAB RESULTS DEMONSTRATE THE VISION
TEAM MEMBERS ATTRIBUTIONS

C A L M O V E R V I E W



































OVERVIEW


The ongoing Yemeni cholera outbreak has been deemed one of the worst cholera outbreaks in history, resulting in thousands of deaths. Triggered by a civil war, the outbreak has been shaped by various political, environmental, and epidemiological factors and has impacted to over a million Yemenis. Limited access to healthcare infrastructure deters citizens from receiving widely available treatments for the disease. To solve this issue, various mathematical models have been made to track the Yemeni outbreak and identify governorates (administrative divisions) at risk of an outbreak by providing advance notice of incidence. These models, while useful, are not powerful enough to accurately and consistently forecast exact cholera cases per governorate over multiple timeframes. To address the need for a complex, reliable model, the Lambert iGEM team presents CALM, the Cholera Artificial Learning Model; a system of four extreme-gradient-boosting (XGBoost) machine learning models that forecast the exact number of cholera cases a Yemeni governorate will experience from a time range of 2 weeks to 2 months. CALM provides a novel machine learning approach that makes use of rainfall data, past cholera cases and deaths data, civil war fatalities, and inter-governorate interactions represented across multiple timeframes. Additionally, the use of machine learning, along with extensive feature engineering, allows CALM to easily learn complex non-linear relations apparent in an epidemiological phenomenon.