# Individualized Prediction of SARS-CoV-2 Infection in Mexico City Municipality during the First Six Waves of the Pandemic

## TL;DR

This study developed models to predict SARS-CoV-2 infection in Mexico City using patient data, showing good accuracy and highlighting changing factors during the pandemic.

## Contribution

The study introduces individualized prediction models for SARS-CoV-2 infection in Mexico City, adapting to changing pandemic dynamics across six waves.

## Key findings

- The models achieved an overall accuracy of 73% in predicting SARS-CoV-2 infection.
- Prediction variables varied significantly across different pandemic waves.
- The models showed 84% specificity but only 52% sensitivity in identifying positive cases.

## Abstract

After COVID-19 emerged, alternative methods to laboratory tests for the individualized prediction of SARS-CoV-2 were developed in several world regions. The objective of this investigation was to develop models for the individualized prediction of SARS-CoV-2 infection in a large municipality of Mexico. The study included data from 36,949 patients with suspected SARS-CoV-2 infection who received a diagnostic tested at health centers of the Alvaro Obregon Jurisdiction in Mexico City registered in the Epidemiological Surveillance System for Viral Respiratory Diseases (SISVER-SINAVE). The variables that were different between a positive test and a negative test were used to generate multivariate binary logistic regression models. There was a large variation in the prediction variables for the models of different pandemic waves. The models obtained an overall accuracy of 73% (63–82%), sensitivity of 52% (18–71%), and specificity of 84% (71–92%). In conclusion, the individualized prediction models of a positive COVID-19 test based on SISVER-SINAVE data had good performance. The large variation in the prediction variables for the models of different pandemic waves highlights the continuous change in the factors that influence the spread of COVID-19. These prediction models could be applied in early case identification strategies, especially in vulnerable populations.