Risk calculators during COVID-19 pandemic Four innovative examples from Wrocław


  • Andrzej Jarynowski Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Königsweg 67, 14163 Berlin, Germany; Interdisciplinary Research Institute Oriona 15/8, 67-200 Głogów, Poland https://orcid.org/0000-0003-0949-6674
  • Alexander Semenov Herbert Wertheim College of Engineering, University of Florida, 1949 Stadium Rd, Gainesville, FL 32611, USA https://orcid.org/0000-0003-2691-4575
  • Vitaly Belik Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Königsweg 67, 14163 Berlin, Germany https://orcid.org/0000-0003-3748-0071




risk calculator, Adverse Event (AE), Machine Learning (ML), COVID-19, infection probability, risk of severe disease


Aim. The Internet and e-health solutions have become an integral part of daily life due to the pandemic. We are exploring the most impactful and positive innovations such as risk calculators or dashboards with forecasts and current situations aimed at providing information to the public.

Concept. We analysed four innovative, Wrocław-based risk calculators which allow users to better understand transmission dynamics, pathogenesis process or infection control.  

Result. Practical application: We show that: 1) Polish COVID-19 symptom checker for self-diagnosis is among the leading products providing similar services around the world; 2) predicting disease course at its beginning is one of the main challenges of future medicine due to the availability of various kinds of data; 3) analysis of spatio-temporal transmission patterns based on digital surveillance for a given community can help with managing infection control locally; and 4) Sputnik V risk calculator enables patients to estimate probabilities of having given adverse events (probably the first app of this kind) following a given individual's variables (age, gender and dose).

Conclusion. There are already thousands of disseminated e-health solutions related to the coronavirus pandemic which will shape medicine for the next decade. Risk calculators can impact both individual decisions as well as community public health service.



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Adamik, B., Bawiec, M., Bezborodov, V., Biecek, P., Bock, W., Bodych, M., ... & MOCOS International Research Group. (2020). Estimation of the severeness rate, death rate, household attack rate and the total number of COVID-19 cases based on 16 115 Polish surveillance records [Preprint]. MedRxiv. https://doi.org/10.1101/2020.10.29.20222513.

Brockmann, D. (2018). Human mobility, networks and disease dynamics on a global scale. In A. Bunde, J. Caro, J. Kärger, G. Vogl (Eds.), Diffusive Spreading in Nature, Technology and Society (pp. 375–396). Springer. https://doi.org/10.1007/978-3-319-67798-9_19.

Brown, V. J. (2014). Risk perception: It's personal. Environmental health perspectives, 122(10), A276–A279. https://doi.org/10.1289/ehp.122-A276.

Clift, A. K, Coupland, C. A., Keogh, R. H., Diaz-Ordaz, K., Williamson, E., Harrison, E. M., Hayward, A., Hemingway, H., Horby, P., Mehta, N., Benger, J. (2020, October 20). Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study. British Medical Journal, 371, 3731. https://doi.org/10.1136/bmj.m3731.

Czekaj, Ł., Domaszewicz, J., Radziński, Ł., Jarynowski, A., Kitłowski, R., & Doboszyńsla, A. (2020). Validation and usability of AIDMED-telemedical system for cardiological and pulmonary diseases. E-methodology, 7(7), 125–139. https://doi.org/10.15503/emet2020.125.139.

Duplaga, M. (2020). The acceptance of key public health interventions by the Polish population is related to Health Literacy, but not eHealth Literacy. International journal of environmental research and public health, 7(15), 54–59.

Gigerenzer, G., & Edwards, A. (2003). Simple tools for understanding risks: from innumeracy to insight. British Medical Journal, 327(7417), 741–744.

HcraCoV. (2021). Hack4Med CraCoV. https://www.hack4med.com

IBI. (2021). Sputnik Vaccine Adverse Events (Aes) risk calculator. https://infodemia-koronawirusa.shinyapps.io/sputnik/.

Infermedica. (2020). Sympomate. https://symptomate.com.

Jarynowski, A., & Belik, V. (2018). Choroby przenoszone drogą płciową w dobie Internetu i e-zdrowia: kalkulatory ryzyka [Sexually transmitted diseases in the age of the Internet and e-health: risk calculators]. Wydawnictwo UJ.

Jarynowski, A., & Skawina, I. (2020). E-contact tracing of hospital infections–validation of SIRSZ automatic risk assessment. E-methodology, 7(7), 51–70. https://doi.org/10.15503/emet2020.51.70.

Jarynowski, A., Wójta-Kempa, M., Płatek, D., & Czopek, K. (2020). Attempt to understand public-health relevant social dimensions of COVID-19 outbreak in Poland. Society Register, 4(3), 7–44. https://doi.org/10.14746/sr.2020.4.3.01.

Jarynowski, A., Wójta-Kempa, M., & Krzowski, Ł. (2020). An attempt to optimize human resources allocation based on spatial diversity of the first wave of COVID–19 in Poland. E-methodology, 7(7), 100–122. https://doi.org/10.15503/emet2020.100.122.

Jarynowski, A., Semenov, A., Kamiński, M., & Belik, V. (2021). Mild adverse events of sputnik V vaccine in Russia: social media content analysis of telegram via deep learning. Journal of Medical Internet Research, 23(11), e30529. https://doi.org/10.2196/30529.

Jarynowski, A., & Belik, V. (2022). Access to healthcare as an important moderating variable for understanding geography of immunity levels for COVID-19-preliminary insights from Poland. European Journal of Translational and Clinical Medicine. https://doi.org/10.1101/2021.12.08.21267167.

Kamiński, M., Skonieczna-Żydecka, K., Nowak, J. K., & Stachowska, E. (2020). Global and local diet popularity rankings, their secular trends, and seasonal variation in Google Trends data. Nutrition, 79, 110759. https://doi.org/10.1016/j.nut.2020.110759.

Knop, K., Smolak, K., Kasieczka, B., Rohm, W., Smolarczyk, T., & Zyga, M. (2021). Mobility modelling for simulation of spatial spread of infectious diseases. In EGU General Assembly Conference Abstracts. https://doi.org/10.5194/egusphere-egu21-13112.

Kwaśniewski, M., Korotko, U., Chwiałkowska, K., Niemira, M., Jaroszewicz, J., Sobala-Szczygiel, B., ... & Moniuszko, M. (2022). Implementation of the User-Friendly Odds Ratio Calculator for Unvaccinated Individuals in a Country with a High COVID-19 Death Toll. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4099428.

Logunov, D. Y., Dolzhikova, I. V., Shcheblyakov, D. V et al. (2021). Safety and efficacy of an rAd26 and rAd5 vector-based heterologous prime-boost COVID-19 vaccine: an interim analysis of a randomised controlled phase 3 trial in Russia. Lancet, 397(10275), 671–681. https://doi.org/10.1016/s0140-6736(21)00234-8.

Ministerio Salud. (2021). Campaña Nacional de Vacunación contra la COVID-19. 10° Informe de vigilancia de seguridad en vacunas 2 de abril code 2021 [National Vaccination Campaign against COVID-19. 10th Vaccine Safety Surveillance Report 2 April 2021]. Ministerio de Salud Argentina. https://bancos.salud.gob.ar/recurso/10deg-informe-de-seguridad-en-vacunas.

Mocos. (2021). COVID-19 risk calculator. https://crs19.pl

Munsch, N., Martin, A., Gruarin, S., Nateqi, J., Abdarahmane, I., Weingartner-Ortner, R., & Knapp, B. (2020). Diagnostic accuracy of web-based COVID-19 symptom checkers: comparison study. Journal of medical Internet research, 22(10), e21299. https://doi.org/10.2196%2F21299.

Public Health and Medical Professionals for Transparency. (2022). Pfizer’s documents. https://phmpt.org/pfizers-documents/.

Romaszko-Wojtowicz, A., Maksymowicz, S., Jarynowski, A., Jaśkiewicz, Ł., Czekaj, Ł., & Doboszyńska, A. (2022). Telemonitoring in Long-COVID Patients-Preliminary Findings. International Journal of Environmental Research and Public Health, 19(9), 5268. https://doi.org/10.3390/ijerph19095268.

Schwarzinger, M., Watson, V., Arwidson, P., Alla, F., & Luchini, S. (2021). COVID-19 vaccine hesitancy in a representative working-age population in France: A survey experiment based on vaccine characteristics. The Lancet Public Health, 6(4), e210–e221. https://doi.org/10.1016/S2468-2667(21)00012-8.

Semenov, A., Mantzaris, A. V., Nikolaev, A. et al. (2019). Exploring social media network landscape of post-soviet space. IEEE Access, 7, 411–426. https://doi.org/10.1109/ACCESS.2018.2885479.

Shimabukuro, T. T. et al. (2021). Preliminary findings of mRNAna covid-19 vaccine safety in pregnant persons. New England Journal of Medicine, 384, 2273-2282. DOI: 10.1056/NEJMoa2104983.

Spyrosoft. (2021). Meet the most committed people in IT. https://spyro-soft.com/about-us.

Statista. (2021). Leading social media platforms in Russia as of 3rd quarter of 2020, by penetration rate. https://www.statista.com/statistics/867549/ top-active-social-media-platforms-in-russia/.

Telegram. (2021). Narodnye otcety o vakcinacii [People's reports on vaccination]. https://t.me/Sputnik_results

Tebeje, T. H., & Klein, J. (2021). Applications of e-health to support person-centered health care at the time of COVID-19 pandemic. Telemedicine and e-health, 27(2), 150–158. https://doi.org/10.1089/tmj.2020.0201.

Vlassov, V. V. (2017). Russian medicine: trying to catch up on scientific evidence and human values. The Lancet 390(10102), 1619–1620. https://doi.org/10.1016/s0140-6736(17)32382-6.

Zagorecki, A., Orzechowski, P., & Hołownia, K. (2013). Online diagnostic system based on Bayesian networks. In N. Peek, R. Marín Morales, M. Peleg (Eds.), Conference on Artificial Intelligence in Medicine in Europe (pp. 145–149). Springer. https://doi.org/10.1007/978-3-642-38326-7_22.




How to Cite

Jarynowski, A., Semenov, A., & Belik, V. (2022). Risk calculators during COVID-19 pandemic Four innovative examples from Wrocław. E-Methodology, 8(8), 112–124. https://doi.org/10.15503/emet.2021.112.124



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