Scientists from the Tec’s School of Engineering and Sciences have performed optimal allocation calculations to find the areas of Mexico that are most vulnerable to the pandemic.
Tec data science specialists are working on a project funded by the University of Edinburgh to develop algorithms that contribute to a more equitable distribution strategy for COVID-19 diagnostic tests in Mexico.
Yasmín Águeda Ríos, a researcher at the School of Engineering and Sciences (EIC), indicated that the intention of the project is to visualize the municipalities in the country at highest risk during the pandemic via a web platform.
This was achieved through optimal allocation calculations developed by EIC scientists and posted on the web, taking into account values such as: demographic density, poverty index (Coneval), COVID data, and hospital capacity for each municipality.
“Most tests in Mexico are distributed within large cities. As a result, it is possible that many low-income populations do not have access to this diagnostic method or to a nearby hospital with testing equipment,” said the professor from the Tec’s Monterrey campus.
The project seeks to provide information obtained using a scientific methodology, so it can be used by those decision makers implementing the pandemic strategy at different levels of government in Mexico.
“It is possible that many low-income populations in Mexico do not have access to a diagnostic method.”
“We hope that this tool can help provide people in economically disadvantaged regions with more opportunities to be access protection against COVID-19,” said Yasmín Águeda Ríos.
The researcher from the School of Engineering and Sciences said that the platform was made available to the public on August 10, and is constantly updated. A second stage of the project is involved with the development of statistics and data validation.
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