Dissertation

Satellite Based Predictability of Water Sensitive Infectious Diseases

Khan, Md Rakibul Hassan

2018

Overview

The global human population remains at heightened risk of diarrheal diseases after natural disasters, such as hurricanes, earthquakes, floods or droughts. The uncertainties in timing and magnitude of natural disasters impact the hydroclimatic baseline, and/or access to safe drinking water and sanitation infrastructure (WASH). Also, data on disease prevalence and infectious pathogens is sparingly available in the region(s) where climatic variability and extreme natural events intersect with population vulnerability. Therefore, traditional time series modeling approach of calibration and validation of a model is inadequate and predictions of diarrheal infections remain a challenge. From this context, it is pivotal to understand the role of hydroclimatic processes in creating seasonality and inter-annual variability in environmental conditions favorable for exposure to pathogenic agents (e.g. bacteria) that lead to outbreaks of environmentally modulated water-related diseases. Here, using cholera as one of the signature diarrheal diseases, a framework is proposed that can be used to assess the impact of natural disasters with response to an outbreak of cholera, providing an assessment of short-term and long-term influence of climatic processes on disease outbreaks is human. Cholera, a deadly waterborne diarrheal disease is transmitted by drinking water contaminated with Vibrio cholerae, an autochthonous bacterium. Prediction of cholera, using earth observations, especially for regions where hydroclimatic and disease surveillance data are not routinely collected, is a critical tool to prioritize prevention and mitigation strategies, such as the distribution of oral rehydration solutions, strengthening WASH infrastructure, and increasing the availability of antibiotics and vaccines. A new algorithm was developed that integrates satellite derived data on several hydroclimatic and ecological processes into a framework that can determine high resolution cholera risk on global scales. Using satellite-derived hydroclimatic data and information on WASH, the algorithm tracks the changing environmental conditions conducive to the growth of pathogenic vibrios. The algorithm was applied following hurricane Matthew in 2016 in Haiti, two consecutive earthquakes in 2015 in Nepal and recent civil unrest in Yemen with realistic accuracy in forecasting the risk of a cholera outbreak in the human population. A software version (KJ Cholera Forecast, V 1.0) of the algorithm with a user-friendly graphical user interface (GUI) was also developed to generate near real time cholera forecast on a global scale. Also, the abundance, distribution and environmental linkages of vibrio species were explored and modeled to understand the potential risk of emergence in the coast. Prediction systems when incorporated with vaccine protocols and long term strategies for development of civil infrastructure, can provide the capacity needed to eradicate the burden of cholera in a human population, if not the disease itself.

• Publisher: ProQuest Dissertations & Theses
• Subject: Civil engineering / Engineering
• ISBN: 9780438723702, 0438723708