Attend the C3.ai Digital Transformation Institute Colloquia on Digital Transformation Science Thursday, March 11 at 3:00 p.m. U.S. Central time. Presenting "Using Data Science to Understand the Heterogeneity of SARS-COV-2 Transmission & COVID-19 Clinical Presentation in Mexico" will be Stefano Bertozzi, University of California, Berkeley, and Juan Pablo Gutierrez, National Autonomous University of Mexico.
Registration is required to attend this event.
Abstract: In 2020, Mexico confirmed 1.5M cases of COVID-19, with 128,000 deaths—an 8.8 percent fatality rate that is among the highest worldwide. The positivity rate for those tested is 42 percent (WHO target = 5 percent). The pandemic is likely to become the main cause of death in 2020, and in 2021—even with the vaccine—mortality is expected to rise. Almost half of the Mexican population receives its medical care from the Mexican Social Security Institute (IMSS). Our team from UCB, IMSS, and UNAM aims to harness the massive patient-level clinical and socio-demographic data from the IMSS to better predict susceptibility to infection and serious complications among those who are infected. The advantages of working with the IMSS are clear – the disadvantage is that it has taken many months to get approval from the relevant human subjects and research committees. The IMSS comprises many poorly integrated data systems, so there is significant work involved in relating the disparate databases to each other. We now have 2.5 years of utilization data (outpatient visits [>300M], hospitalizations, prescriptions [almost 500M], and COVID tests). We will study variability by employer, by state and neighborhood, by household structure, by clinic, by provider (and provider behavior), by current and prior health conditions, by degree of control of chronic health conditions, by any drugs that they have been prescribed, as well as by the usual demographic and socioeconomic characteristics. The priority will be to identify modifiable factors that the IMSS can use to reduce population risk.
