Models for individualized COVID-19 diagnostic prediction

Keywords: COVID-19, individual prediction, prediction models, symptoms, detection


Since the COVID-19 pandemic, the world has experienced a large incidence of infections in short periods of time, giving rise to waves of contagion caused by the different variations of SARS-COV-2. Health services, as well as personnel, have been overwhelmed, especially in the poorest countries. Currently and after two years, the pandemic continues and according to experts it is here to stay, which highlights the importance of vaccines and methods of detecting the disease, to curb the number of infections and avoid that the pandemic continues to spread and thus the virus continues to mutate. Detection tests have been scarce and expensive for most of the population, so alternative methods to laboratory ones could be a decisive factor so that people can self-isolate before continuing to infect more people. One of the most effective methods have been statistical predictions of the diagnosis of COVID-19 in a patient, based on certain variables. In this article, it was identified that the most common prediction models were developed from logistic regression and machine-learning, which have shown high percentages of predicting test results for COVID-19. The most important predictor variables in the different models developed in various regions of the world were identified and the opportunities, limitations and perspectives of this prediction method are discussed.


Download data is not yet available.

Author Biographies

Abel Lerma-Talamantes , Instituto Nacional de Cardiología Ignacio Chávez

Autor Abel Lerma Talamantes, investigador académico, psicólogo clínico, postdoctorante en la Universidad de Guadalajara e Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, docente. 

Claudia Lerma , Instituto Nacional de Cardiología Ignacio Chávez

Doctora, investigadora, líneas de investigación en el Instituto Nacional de Cardiología:

  • Análisis no lineal de señales cardiovasculares.
  • Desarrollo de métodos no invasivos para predicción de riesgo de arritmias cardiacas ventriculares y muerte cardiaca súbita
  • Evaluación de la dinámica del control cardiovascular con métodos no invasivos (análisis de electrocardiograma y otras señales fisiológicas y modelos matemáticos)
  • Evaluación y tratamiento de ansiedad, depresión y malnutrición en pacientes con insuficiencia renal crónica.


Berekaa MM. Insights into the COVID-19 pandemic: Origin, pathogenesis, diagnosis, and therapeutic interventions. Front. Biosci. (Elite Ed). 2021;13(1):117–139.

World Health Organization. Coronavirus disease (COVID-19) pandemic. W. H. O. 2021:1-2.

Uddin M, Mustafa F, Rizvi TA, Loney T, Suwaidi HA, Al-Marzouqi A, et al. SARS-CoV-2/COVID-19: Viral Genomics, Epidemiology, Vaccines, and Therapeutic Interventions. Vir. 2020;12(5):526.

Asselah T, Durantel D, Pasmant E, Lau G, Schinazi RF. COVID-19: Discovery, diagnostics, and drug development. J. Hepatol. 2021;74(1):168–184.

He X, Hong W, Pan X, Lu G, Wei X. SARS-CoV-2 Omicron variant: Characteristics and prevention. MedCom. 2021;2(4): 838–845.

Yong SJ. Long COVID or post-COVID-19 syndrome: putative pathophysiology, risk factors, and treatments. J. Infect. Dis. 2021;53(10):737–754.

Kabir MA, Ahmed R, Iqbal S, Chowdhury R, Paulmurugan R, Demirci U, et al. Diagnosis for COVID-19: current status and future prospects. Expert. Rev. Mol. Diagn. 2021;21(3):269–288.

Wong R. COVID-19 testing and diagnosis: A comparison of current approaches. Malays. J. Pathol. 2021;43(1):3–8.

Al-Najjar D, Al-Najjar H, Al-Rousan N. Evaluation of the prediction of CoVID-19 recovered and unrecovered cases using symptoms and patient's meta data based on support vector machine, neural network, CHAID and QUEST Models. Eur. Rev. Med. Pharmacol. Sci. 2021;25(17):5556–5560.

Hemmer CJ, Löbermann M, Reisinger EC. COVID-19: Epidemiologie und Mutationen: Ein Update [COVID-19: epidemiology and mutations: An update]. Radiologe. 2021;61(10):880–887.

Wang R, Hozumi Y, Yin C, Wey GW. Mutations on COVID-19 diagnostic targets. Genom. 2020;112(6):5204–5213.

Solís-Arce JS, Warren SS, Meriggi NF, Scacco A, McMurry N, Voors, et al. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nat. Med. 2021;27(8):1385–1394.

Bose-O'Reilly S, Daanen H, Deering K, Gerrett N, Huynen M, Lee J, et al. COVID-19 and heat waves: New challenges for healthcare systems. Environ. Res. 2021;198:111153.

Centers for Disease Control, 24/7 DBC: We Save Lives. Classifications and definitions of SARS-CoV-2 variants. C. D. C. 2021:1-2.

Cherednik I. Modeling the Waves of Covid-19. Act. Biotheor. 2021;70(1):8.

Lai C, Lam W. Laboratory testing for the diagnosis of COVID-19. Biochem. Biophys. Res. Commun. 2021;538:226–230.

Mohamadian M, Chiti H, Shoghli A, Biglari S, Parsamanesh N, Esmaeilzadeh A. COVID-19: Virology, biology and novel laboratory diagnosis. J. Gene. Med. 2021;23(2):e3303.

Martínez-Chamorro E, Díez-Tascón A, Ibáñez-Sanz L, Ossaba-Vélez S, Borruel-Nacenta S. Radiologic diagnosis of patients with COVID-19. Radiological diagnosis of the patient with COVID-19. Radiolog. 2021;63(1):56–73.

Alizadehsani R, Alizadeh-Sani Z, Behjati M, Roshanzamir Z, Hussain S, Abedini N, et al. Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients. J. Med. Virol. 2021;93(4):2307–2320.

Roland LT, Gurrola JG, Loftus PA, Cheung SW, Chang JL. Smell and taste symptom-based predictive model for COVID-19 diagnosis. Int. Forum. Allergi. Rhinol. 2020;10(7):832–838.

Jehi L, Ji X, Milinovich A, Erzurum S, Rubin BP, Gordon S, Young JB, Kattan MW. Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients. CHEST. 2020;158(4):1364–1375.

Zhang SX, Sun S, Afshar-Jahanshahi A, Wang Y, Nazarian-Madavani A, Li J, et al. Beyond Predicting the Number of Infections: Predicting Who is Likely to Be COVID Negative or Positive. Risk. Manag. Healthc. Policy. 2020;13:2811–2818.

Mamidi T, Tran-Nguyen TK, Melvin RL, Worthey EA. Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data. Fron. Big. Data. 2021;4:675882.

Zoabi Y, Deri-Rozov S, Shomron N. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. N. P. J. Digit. Med. 2021;4(1):3.

Zhang J, Jun T, Frank J, Nirenberg S, Kovatch P, Huang KL. Prediction of individual COVID-19 diagnosis using baseline demographics and lab data. Sci. Rep. 2021;11(1):13913.

Challener DW, Challener GJ, Glow-Lee VJ, Fida M, Shah AS, O'Horo JC. Screening for COVID-19: Patient factors predicting positive PCR test. Infect. Control. Hosp. Epidemiol. 2020;41(8): 968–969.

Galindo-Vázquez O, Ramírez-Orozco M, Costas-Muñiz R, Mendoza-Contreras LA, Calderillo-Ruíz G, Meneses-García A. Symptoms of anxiety, depression and self-care behaviors during the COVID-19 pandemic in the general population. Gac. Med. Mex. 2021;156(4):298–305.

Bender R, Grouven U. Ordinal logistic regression in medical research. Clin. Med. (Lond.). 1997;31(5):546–551.

Iser B, Sliva I, Raymundo VT, Poleto MB, Schuelter-Trevisol F, Bobinski F. Suspected COVID-19 case definition: a narrative review of the most frequent signs and symptoms among confirmed cases. Epidemiol. Serv. Saude. 2020;29(3):1-10

Johansson MA, Quandelacy TM, Kada S, Prasad P. V, Steele M, Brooks JT. SARS-CoV-2 Transmission from People Without COVID-19 Symptoms. J. A. M. A. NETW. OPEN. 2021;4(1):e2035057.

Vila-Muntadas M, Agustí-Sunyer I, Garcia-Navarro A. COVID-19 diagnostic tests: importance of the clinical context. Med. Clin. (Barc). 2021;157(4):185–190.

Chadeau-Hyam M, Bodinier B, Elliott J, Whitaker MD, Tzoulaki I, Vermeulen R, et al. Risk factors for positive and negative COVID-19 tests: a cautious and in-depth analysis of UK biobank data. Int. J. Epidemiol. 2020;49(5):1454–1467.

Halpin S, O'Connor R, Sivan M. Long COVID and chronic COVID syndromes. J. Med. Virol. 2021;93(3):1242–1243.

How to Cite
Victorino-Aguilar , M., Lerma-Talamantes , A., & Lerma , C. (2022). Models for individualized COVID-19 diagnostic prediction. Mexican Journal of Medical Research ICSA, 10(20), 44-50.