Using Data Science to Understand the Heterogeneity of SARS-COV-2 Transmission and COVID-19 Clinical Presentation in Mexico

In late April 2020, Mexico confirmed 15,529 positive cases of COVID-19 within its borders and the Mexican government announced the start of "Phase 3" of the pandemic, acknowledging widespread community transmission, thousands of cases of infection, and increased numbers of patients requiring hospitalization. The epidemic continues to grow rapidly in Mexico. There is a critical need in the country to use data science to advance COVID-19 prevention and treatment, and to support policy response. Country-level data and cooperation is essential to curbing the global pandemic and to support binational/cross-border relations with the U.S. and its Latin American neighbors. The health data available to our team via the Mexican Social Security Institute/Instituto Mexicano del Seguro Social (IMSS) are vast. These assets combined with the computational and data management strengths of the C3.ai tools and data lake-and the disciplinary strengths of the binational team-create an unprecedented opportunity to analyze a multitude of clinical, individual, facility and structural determinants of exposure and susceptibility to SARS-CoV-2, and the determinants of effective health services responses to the pandemic. This research project will reveal predictors of SARS-COV-2 infection and severity that will help guide prevention efforts, allowing Mexico to focus treatment on those at greatest risk. However, they should also generate hypotheses about mechanisms that might improve COVID-19 patient outcomes. We can also estimate disease burden and economic impact, and provide a best practice model for other countries with similar data systems to guide their policy decisions around pandemic management.