biostudies-literatureUnknown10(3)Gonzalez-Escamilla MAn early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the RHealthcare (Basel, Switzerland)462https://www.ebi.ac.uk/biostudies/studies/S-EPMC8950794biostudies-literatureEpidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center.PMC8950794Rodriguez-Gutierrez HFVidal-Gutierrez OPineiro-Retif RGarza-Rodriguez MLGonzalez-Escamilla MOrtiz-Murillo VNAlcorta-Nunez FPerez-Ibave DCRamirez-Correa GAGonzalez-Guerrero JFBurciaga-Flores CHRodriguez-Nino PfalseEpidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center.An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R2022-01-01T00:00:00Z2022 Mar2025-04-21T15:39:43.783Z2025-04-21T15:39:43.783ZS-EPMC89507943532694010.3390/healthcare10030462