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

Author:

Victorino-Aguilar Mariel1ORCID,Lerma Abel2ORCID,Badillo-Alonso Humberto3ORCID,Ramos-Lojero Víctor Manuel4ORCID,Ledesma-Amaya Luis Israel2ORCID,Ruiz-Velasco Acosta Silvia5ORCID,Lerma Claudia67ORCID

Affiliation:

1. Master’s Program in Biomedical Sciences, Institute of Health Sciences, Autonomous University of the State of Hidalgo, San Agustín Tlaxiaca 42160, Mexico

2. Area of Psychology, Institute of Health Sciences, Autonomous University of the State of Hidalgo, San Agustín Tlaxiaca 42160, Mexico

3. Jalalpa el Grande Health Center, Mexico City Health Secretariat, Mexico City 01377, Mexico

4. Health Jurisdiction Alvaro Obregon, Mexico City Secretary of Health, Mexico City 01470, Mexico

5. Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México, Mexico City 04510, Mexico

6. Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México Campus Norte, Huixquilucan Edo. de Mexico 52786, Mexico

7. Department of Molecular Biology, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City 04480, Mexico

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.

Publisher

MDPI AG

Reference31 articles.

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2. World Health Organization (2023, February 08). Coronavirus Disease (COVID-19). Coronavirus Disease (COVID-19) PANDEMIC. World Health Organization. Available online: https://www.who.int/es/emergencies/diseases/novel-coronavirus-2019?gclid=EAIaIQobChMIqYKjrvnw_gIVcCutBh0a-AYkEAAYASAAEgL5BvD_BwE.

3. Gobierno de México (2023, March 20). Sistema Nacional de Vigilancia Epidemiológica, Dirección de Vigilancia Epidemiológica. Ciudad de México: SINAVE, Available online: https://datos.covid-19.conacyt.mx/.

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5. Salud Digna (2023, May 10). ¿Síntomas de COVID? Realizate una Prueba. México: Salud Digna. Available online: https://www.salud-digna.org/informativa-covid-19/.

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