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

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).

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.

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.