Team:Lambert GA/CALM RESULTS
C A L M
D A T A & R E S U L T S
D A T A & R E S U L T S
Cholera Epidemiological Data
Cholera case and death statistics are reported by the World Health Organization (WHO) where health experts and researchers work directly with Yemeni health authorities at both the country and local level. Through this direct connection, the WHO is able to record all reported cholera cases and deaths caused by cholera (WHO presence in Yemen, 2018). The data, collected by the WHO, was accessed through the Humanitarian Data Exchange (https://data.humdata.org /group/yem). It provided reports of accumulated new cholera cases and deaths per governorate from up to May 22, 2017, to February 18, 2018.
Past cholera cases and deaths were included with the simple assumption that they would be predictive of future cases. Vibrio cholerae requires aquatic environments and can transfer between humans through the transfer of bodily fluids. Thus, the incidence of cholera in one region can indicate the contamination of several food and water sources and therefore indicate a further spread of cholera (Cholera - Vibrio cholerae infection).
Past cholera cases and deaths were included with the simple assumption that they would be predictive of future cases. Vibrio cholerae requires aquatic environments and can transfer between humans through the transfer of bodily fluids. Thus, the incidence of cholera in one region can indicate the contamination of several food and water sources and therefore indicate a further spread of cholera (Cholera - Vibrio cholerae infection).
Rainfall Data
As Vibrio cholerae is indigenous to aquatic environments, rainfall is a significant predictor of the transmission of cholera. In areas exposed to heavy rainfall, through the collapse of sanitary and health infrastructure, interaction between contaminated water and human activities accelerates, resulting in an epidemic (Jutla et al., 2013). Yemen represents this scenario, where when exposed to heavy rainfall and deterioration of health facilities, there was a surge in cholera cases (Camacho et al., 2018). Global Lancet Researchers analyzing surveillance date for the Yemen Cholera Outbreak from 2016 to 2018 have found a positive and nonlinear association between weekly rainfall and suspected cholera incidence: the relative risk of cholera 10 days after a weekly rainfall of 25 mm is 42% higher than compared with a week without rain (Camacho et al., 2018). Despite the inability to establish that rainfall is causal to the increase in cholera outbreaks, the use of unsafe water sources during the drought season, contamination of water sources during the rainy season, and changing levels of zooplankton and iron in water (which help cholera bacteria survive), may contribute to the increasing levels of cholera during the rainy season (Camacho et al., 2018). These correlations demonstrate the need to measure rainfall in the machine learning model, as rainfall is a predictor for possible climate changes and the corresponding human response and subsequently indicates the spread of cholera in Yemen.
Daily rainfall data for Yemen from January 1st ,2017 to March 30th, 2018 was accessed through NASA’s Goddard Earth Sciences Data and Information Services Center (GES DISC), which provides Global Precipitation Measurement data through the Simple Subset Wizard (SSW) database. The Global Precipitation Measurement mission (GPM), launched on February 27th, 2014, is an international network of satellites that use microwave imagers and precipitation radars to measure the volume of rainfall in several regions of the world (Global Precipitation Measurement, 2011). The rainfall data was initially in a netcdf4 format. The 452 files were then parsed and converted to comma-separated-values (CSV). As there were individual data points for every .25 degrees of both latitude and longitude, Reverse geolocation was performed to match coordinates with corresponding Yemeni governorates.
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.
Daily rainfall data for Yemen from January 1st ,2017 to March 30th, 2018 was accessed through NASA’s Goddard Earth Sciences Data and Information Services Center (GES DISC), which provides Global Precipitation Measurement data through the Simple Subset Wizard (SSW) database. The Global Precipitation Measurement mission (GPM), launched on February 27th, 2014, is an international network of satellites that use microwave imagers and precipitation radars to measure the volume of rainfall in several regions of the world (Global Precipitation Measurement, 2011). The rainfall data was initially in a netcdf4 format. The 452 files were then parsed and converted to comma-separated-values (CSV). As there were individual data points for every .25 degrees of both latitude and longitude, Reverse geolocation was performed to match coordinates with corresponding Yemeni governorates.
- 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.
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.