Currently in the position of Software Steward at the University of Twente. He developed his Ph.D. research entitled Spatiotemporal Modeling for Wastewater Surveillance and Epidemiology. As a former consultant at UNU-FLORES (Dresden, Germany), Mr. De la Paz developed tasks applying transdisciplinary approaches with a perspective on Geomatics, sustainability, and integrated water management.
During his postgraduate studies at CentroGeo (Mexico City), Mr. De la Paz has participated in applied research to address social needs in the design, development, and implementation of Geomatics solutions supported by Geospatial Information Systems technologies and spatial analysis.
Likewise, his current interest focuses on open science, research software, open-source code, modelling, wastewater-based epidemiology, digital twins, and monitoring systems on water quality, environment, and health with ABM and ML models.
Worldwide, there is a lack of Domestic Wastewater (DWW) Quality and Quantity (QQ) models and data to help decision-makers to improve sanitation and health. Modelling DWWQQ variations with a spatiotemporal approach can yield new knowledge and promotes the study of DWW. However, day-to-day variations of DWW at high spatial resolutions (e.g., neighborhoods) are hard to model. Data on DWWQQ variations (in space and time) is rarely monitored and mapped across neighborhoods. DWWQQ variations are influenced by hours of using toilets, taking showers, using kitchens, cleaning activities, chemical products used by people, people’s diet, etc., and the combination of all these factors are specific to each household.
The topic of the current proposal can be mistakenly assigned to the field of water sciences. The truth is that there is no unique and well-defined scientific field for this transdisciplinary research. This proposal combines an approach of spatiotemporal analytics, wastewater (WW) characterization, artificial intelligence with ML, complex systems with ABM, and the perspective of the empiric knowledge from local stakeholders.
Scan the QR code or