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

References

Almagro-Moreno, S., & Taylor, R. K. (2014). Cholera: Environmental Reservoirs and Impact on Disease Transmission. ASM Press, 1(2), 149- 165. https://doi.org/10.1128/microbiolspec.OH-0003-2012

Al Jazeera. (2017, August 27). Cholera outbreak in yemen [Photograph]. Retrieved from https://www.aljazeera.com/mritems/Images/2017/9/5/d4be60b186c14c4a9f6c8ef2ba7cdff0_7.jpg

Azman, D. A. S., Parker, L. A., Rumunu, J., Tadesse, F., Grandesso, F., Deng, L. L., … Luquero, F. J. (2016). Effectiveness of one dose of oral cholera vaccine in response to an outbreak: a case-cohort study. The Lanclet, 4(11), 856–863.

Camacho, A., Bouhenia, M., Alyusfi, R., Alkohlani, A., Naji, M. A. M., Radiguès, X., … Luquero, F. J. (2018). Cholera epidemic in Yemen, 2016–18: an analysis of surveillance data. The Lancelet Global Health, 6,680–690. https://doi.org/10.1016/S2214109X(18)30230-4

Chen, T., Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Unpublished manuscript, University of Washington, Seattle, WA.

Cholera - Vibrio cholerae infection. (2018). Retrieved from https://www.cdc.gov/cholera/general/index.html

Cole, S. (2018, August 28). NASA Investment in Cholera Forecasts Helps Save Lives in Yemen [Press Release]. Retrieved from https://www.nasa.gov/press-release/nasa-investment-in-cholera-forecasts-helps-save-lives-in-yemen

Doldersum, T. (2013). The role of water in cholera diffusion Improvements of a cholera diffusion model for Kumasi, Ghana. University of Twente: Water Engineering and Management Geo-Information Processing. Retrieved from . Retrieved from https://essay.utwente.nl/63456/1/DoldersumT_0138827_openbaar.pdf

Emch, M., Feldacker, C., Islam, M. S., & Ali, M. (2008). Seasonality of cholera from 1974 to 2005: a review of global patterns. International Journal of Health Geographics, 7, 31. http://doi.org/10.1186/1476-072X-7-31

Grad, Y. H., Miller, J. C., & Lipsitch, M. (2012). Cholera Modeling: Challenges to Quantitative Analysis and Predicting the Impact of Interventions. National Center for Biotechnological Information, 23(4), 523–530. https://doi.org/10.1097/EDE.0b013e3182572581

Jutla, A. S., Akanda, A. S., & Islam, S. (2010). Satellite remote sensing-based forecasting of cholera outbreaks in the Bengal Delta. AHS-AISH publication, 241-243. Retreived from https://iahs.info/uploads/dms/15080.65-241-243-Jutla-et-al.pdf

Jutla, A., Akanda, A., Unnikrishnan, A., Huq, A., & Colwell, R. (2015). Predictive Time Series Analysis Linking Bengal Cholera with Terrestrial Water Storage Measured from Gravity Recovery and Climate Experiment Sensors. The American Journal of Tropical Medicine and Hygiene, 93(6), 1179–1186. http://doi.org/10.4269/ajtmh.14-0648

Jutla, A., Whitcombe, E., Hasan, N., Haley, B., Akanda, A., Huq, A., … Colwell, R. (2013). Environmental Factors Influencing Epidemic Cholera. The American Journal of Tropical Medicine and Hygiene, 89(3), 597–607. https://doi.org/0.4269/ajtmh.12-0721

London School of Hygiene & Tropical Medicine. (2018, May 03). Upcoming rainy season likely to trigger renewed cholera outbreak in Yemen. Retrieved from https://www.lshtm.ac.uk/newsevents/news/2018/upcoming-rainy-season-likely-trigger-renewed-cholera-outbreak-yemen

Nishiura, H., Tsuzuki, S., & Asai, Y. (2018). Forecasting the size and peak of cholera epidemic in Yemen, 2017. Future Medicine, 13(4), 399–402. https://doi.org/060-8638

Nishiura, H., Tsuzuki, S., Yuan, B., Yamaguchi, T., & Asai, Y. (2017). Transmission dynamics of cholera in Yemen, 2017: a real time forecasting. Theoretical Biology & Medical Modelling, 14, 14. http://doi.org/10.1186/s12976-017-0061-x

Pezeshki, Z., Shadpour-Tafazzoli, M., Nejadgholi, I., Mansourian, A., & Rahbar, M. (2016). Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran. International Journal of Enteric Pathogens, 4(1), e31445. https://doi.org/10.17795/ijep31445

Raleigh, C., & Dowd, C. (2017). Armed Conflict Location and Event Data Project (ACLED) Codebook. Retrieved from https://www.acleddata.com/wpcontent/uploads/2017/01/ACLED_Codebook_2017.pdf

The Forgotten War: The Ongoing Disaster in Yemen. (2018). The Soufan Center. doi:10.1186/isrctn06195297

Yemen. (2014, February 21). Retrieved October 16, 2018, from http://www.citypopulation.de/Yemen.html

Yemen's Cholera Crisis: Fighting Disease During Armed Conflict. (n.d.). Retrieved from https://gheli.harvard.edu/news/yemens-cholera-crisis-fighting-disease-during-armed-conflict

Yemen Situation Reports. (n.d.). Retrieved from http://www.emro.who.int/yem/yemeninfocus/situation-reports.html Zegura, E., & DiSalvo, C. (2018). RatWatch. Retrieved from https://ratwatch.lmc.gatech.edu/

Color Q App

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:


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:

• Maximum Radius: The smallest value for the radius of a detected circle
• Minimum Radius: The largest value for the radius of a detected circle
• Minimum Distance: The smallest distance between the centers of any two detected circles
• Edge Gradient Value: The roundness of each detected circle
• Threshold Value: The amount of memory the system has to store the detected circles

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:


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.

CALM

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 ___ 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.

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.

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.