EES Spring 2019 Seminar: Prediction of Cholera: A Novel Paradigm for Health Engineering


11:45 am-12:35 pm
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202 Particle Science & Technology
Gainesville, FL 32611


Title: EES Spring 2019 Seminar: Prediction of Cholera: A Novel Paradigm for Health Engineering

Details: Antar Jutla, Associate Professor, West Virginia University

Abstract: Hippocrates in his book “On Airs, Water and Places” hypothesized role of regional environmental and climatic processes for understanding occurrence of environmentally modulated diseases. Several thousand years later, and despite significant advances in etiology of pathogenesis, we are still not able to predict when and where an outbreak of water-related disease will occur. Part of the reason is the inherent bias in the transfer of domain specific knowledge, that limits testing of new ideas and methodological approaches to water and vector-borne diseases. In addition, data on disease prevalence and infectious pathogens is sparingly collected/available in region(s) where climatic variability and extreme natural events intersect with human population vulnerability.
Using cholera as a signature diarrheal disease, I will show how prediction of this disease was achieved and why it is important for environmental and health engineering. A conceptual health framework was developed that allowed integration of information from microbiology, environmental engineering, water resources and sociology. The causative agent for cholera, Vibrio cholerae, is autochthonous to aquatic media and hence cannot be eradicated from the environment. Occurrence and growth of the bacteria is linked to modalities of hydroclimatic processes. Cholera outbreaks were classified into three modes– epidemic (sudden or seasonal outbreaks), endemic (recurrence and persistence of the disease for several consecutive years) and mixed-mode (combination of certain epidemic and endemic conditions) with significant spatial and temporal heterogeneity. Cholera mode specific innovative algorithms were developed and it includes a time-invariant and adaptive model that was used to predict disease outbreak in Yemen in 2017. This near real-time algorithm was, in part, successful in controlling and preventing disease burden in Yemen in 2018.


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