Difference between revisions of "Team:Lambert GA/CALM DISCUSSION"

 
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                             <b><a style="color:black; text-decoration: none; line-height:1.1;" href="#target2">COLOR Q APP</a></b>
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<b>Overview</b>
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Lambert iGEM has developed a mobile application for Android phones called Color Q. Color Q is a color-quantification tool that can be used alongside the Chrome-Q hardware, also developed by Lambert iGEM, in order to determine the relative color value generated by biological indicators, such as chromoproteins and reporters. The samples are loaded into the base of the Chrome-Q hardware and then a smartphone camera is used to take a picture of the base. The app can then use circle detection to determine the color values and this serves as a viable quantification method of the actual cholera present in the sample. This data is then transferred to a database for storage and retrieval. The information from the Color Q app can be used to contribute to the second component of Lambert iGEM's software project.
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Cholera has killed millions of people, and without proper action, could continue to do so for many years. Many have modeled and analyzed cholera outbreaks globally to predict where they will next strike, or to forecast cholera cases and/or risk during one particular outbreak. Lambert iGEM has itself created a system of models, CALM, the Cholera Artificial Learning Model, to forecast exact cholera incidence, with the proof-of-concept forecasting cases in Yemen. Consisting of four XGBoost models working in conjunction, CALM is able to make predictions that are highly accurate and reliable, utilizing a broad range of predictive features, and robust machine learning techniques. In addition, by using a system of four models, CALM is not only able to provide a comprehensive 2 month report with 2-week intervals, but also ensures that none of its models “fall behind” by making a prediction too early or too late. Sudden spikes in the cholera prediction may not be modeled accurately by models of later time frames, but more immediate aspects of the model, such as the predictor for 0-2 weeks, can effectively account for sudden changes in the cholera cases. CALM has been shown, within the data used, to be a reliable, powerful way to reduce the number of people suffering from cholera by providing advance notice of outbreaks and allowing for the distribution of medical supplies to pre-empt an outbreak.
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Our 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 existing code from Michael Koohang’s RatWatch project to distribute cholera surveys and collect data.
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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.
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<b>Color Q App</b>
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<b>CALMWatch- an SMS bot</b>
 
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Color Q is a free mobile application developed in Java for the Google Play Store. The app was developed in the Android Studio v3.2.1 integrated development environment. It works alongside the Chrome-Q hardware also developed by Lambert iGEM in order to effectively quantify the result of a biological reporter, similar to the function of a plate reader. The app is able to use circle detection in order to find the samples on the base of the Chrome-Q hardware and then detect the red, green, and blue values (RGB) of the center of each circle. The way the circles are arranged allow for a range of values to be generated. The first row contains 4 circles. The average RGB values of the first two circles are calculated as the negative control and the average RGB values of the second pair of circles are calculated as the positive control. The distance between the positive and negative control is calculated in the 3D-coordinate plane using the following formula:
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Lambert iGEM has also developed an SMS bot utilizing the Twilio API and the Flask web framework for gathering health and sanitation data from areas affected by a cholera outbreak. This bot, named CALMWatch, allows for a healthcare organization or government agency to distribute an SMS survey to a given population so that affected people can report data related to an ongoing cholera outbreak such as cleanliness of water sources, water storage, and waste management. This data can then be fed into the CALM model in real time to increase the accuracy of the model and increase the size of its databases. This bot is based on RatWatch, an open-source SMS-based rat reporting service for the Atlanta area developed by M. Koohang (Zegura & DiSalvo 2018), who graciously allowed Lambert iGEM to modify it for the purposes of the 2018 project.
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The Hough Circle Transform is used as the method of circle detection found in the app. Open Computer Vision (OpenCV) is a library that can be imported into Android Studio in order to perform image analysis-based methods. The image taken by the smartphone camera is converted into a grayscale photo. This essentially makes the image more readable in terms of edge detection and "round" estimation. There are several parameters that can be modified and calibrated in order to detect an accurate amount of circles:
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<div style="font-size:12px; text-align:center;"><i>Example CALMBot Interaction</i></div>
 
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The 6 by 6 grid located underneath the first row can be loaded with experimental samples and a percentage value can be determined on a scale from the negative control to the positive control. The circle detection process loops through until the maximum radius reaches 120 pixels. If anywhere from 35 to 40 circles are detected in total, then the loop stops. However, if there are fewer circles detected, then the loops restarts to finish through the maximum radius until anywhere from 25 to 40 circles are detected properly. If less than 25 circles are detected, then an error is caught and another picture is requested to be used. Zooming in or zooming out could possibly make the circle detection process easier for the system and more efficient. The following formula is used to calculate the relative percentage values:
 
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The results are then displayed based upon the row in which the circles fall in on the base of the Chrome-Q hardware. The app is able to determine the row in which the circles fall in by comparing y-coordinates. If the y-values are similar to each other, then the circles are classified as being on the same row. The relative values are then transferred to another page within the app where the user is able to enter information that could help contribute to our machine learning model, CALM. The application uses the latitude, longitude, and timestamp values obtained from the phone's GPS to effectively determine where and when the test was run. When the user submits the data, the results are sent to a MySQL database, which is a part of the Relational Database Service (RDS) as a part of the Amazon Web Services (AWS) platform.
 
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<b>CALM
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<b>Further Development
 
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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. In order to make predictions, the machine learning aspect of CALM ___
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While the efficacy of the model has only been proven in Yemen, it is expected that with further development and adaptation CALM will be used to predict disease outbreaks around the world. As development on the project progresses, Lambert iGEM hopes to construct a fully autonomous web-based software platform comprising of data collection bots that collect data from major health and sanitation sources, scripts that are capable of syncing data from the ColorQ app and from CALMWatch surveys with CALM’s databases, and an online platform that coordinates global usage of the model so that users can share and distribute results and model improvements more easily. In terms of CALM itself, Lambert iGEM also hopes to engineer more features for the model by acquiring data for more environmental factors, possibly including algal blooms, migration data, and OCV (oral cholera vaccine) campaign data.  
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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.
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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.
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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. 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.
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Latest revision as of 02:29, 18 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



D I S C U S S I O N




































Summary


Cholera has killed millions of people, and without proper action, could continue to do so for many years. Many have modeled and analyzed cholera outbreaks globally to predict where they will next strike, or to forecast cholera cases and/or risk during one particular outbreak. Lambert iGEM has itself created a system of models, CALM, the Cholera Artificial Learning Model, to forecast exact cholera incidence, with the proof-of-concept forecasting cases in Yemen. Consisting of four XGBoost models working in conjunction, CALM is able to make predictions that are highly accurate and reliable, utilizing a broad range of predictive features, and robust machine learning techniques. In addition, by using a system of four models, CALM is not only able to provide a comprehensive 2 month report with 2-week intervals, but also ensures that none of its models “fall behind” by making a prediction too early or too late. Sudden spikes in the cholera prediction may not be modeled accurately by models of later time frames, but more immediate aspects of the model, such as the predictor for 0-2 weeks, can effectively account for sudden changes in the cholera cases. CALM has been shown, within the data used, to be a reliable, powerful way to reduce the number of people suffering from cholera by providing advance notice of outbreaks and allowing for the distribution of medical supplies to pre-empt an outbreak.


CALMWatch- an SMS bot


Lambert iGEM has also developed an SMS bot utilizing the Twilio API and the Flask web framework for gathering health and sanitation data from areas affected by a cholera outbreak. This bot, named CALMWatch, allows for a healthcare organization or government agency to distribute an SMS survey to a given population so that affected people can report data related to an ongoing cholera outbreak such as cleanliness of water sources, water storage, and waste management. This data can then be fed into the CALM model in real time to increase the accuracy of the model and increase the size of its databases. This bot is based on RatWatch, an open-source SMS-based rat reporting service for the Atlanta area developed by M. Koohang (Zegura & DiSalvo 2018), who graciously allowed Lambert iGEM to modify it for the purposes of the 2018 project.


Example CALMBot Interaction


Further Development


While the efficacy of the model has only been proven in Yemen, it is expected that with further development and adaptation CALM will be used to predict disease outbreaks around the world. As development on the project progresses, Lambert iGEM hopes to construct a fully autonomous web-based software platform comprising of data collection bots that collect data from major health and sanitation sources, scripts that are capable of syncing data from the ColorQ app and from CALMWatch surveys with CALM’s databases, and an online platform that coordinates global usage of the model so that users can share and distribute results and model improvements more easily. In terms of CALM itself, Lambert iGEM also hopes to engineer more features for the model by acquiring data for more environmental factors, possibly including algal blooms, migration data, and OCV (oral cholera vaccine) campaign data.