Reflection on contextual factors associated with burden of infectious diseases in multi-country modelling approach on the example of Eastern Europe


  • 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



COVID-19, mathematical modelling, e-health, methodology


Thesis. The use of mathematical models to nowcast and forecast allows to improve predictive understanding of epidemiological targets and indexes among various populations during infectious disease epidemics. Such models enable trend predictions of various scenarios of the pandemic and guiding epidemic prevention and control.

Concept.  In order to avoid the influence of low-quality studies (for a given region) on the interpretation and decision making, a critical analysis based on experience in field epidemiology should be carried out. For the sake of transparency and quality of scientific discourse, such observations should indeed be collected and discussed by the scientific and medical community. Selected global modelling studies have been assessed according to their epidemiological outcomes such as cases and deaths.

Results and Conclusions: I show that the discrepancy between reported and predicted epidemiological features varies more significantly than the order of magnitude between the countries in selected models. My findings highlight that models’ results; readers should prefer locally developed models over multi-country models, even those being published in prestigious journals. Thus, agent based models should be prioritised against system dynamics or machine learning models. I suggest that future epidemiological models should adopt healthcare access as a factor of so-called dark figure of infections, especially in Eastern Europe.


Download data is not yet available.


Barber, R. M., Sorensen, R. J. D., Pigott, D. M., Bisignano, C., Carter, A., Amlag, J. O., Collins, J. K., Abbafati, C., Adolph, C., Allorant, A., Aravkin, A. Y., Bang-Jensen, B. L., Castro, E., Chakrabarti, S., Cogen, R. M., Combs, E., Comfort, H., Cooperrider, K., Dai, X., … Murray, C. J. L. (2022). Estimating global, regional, and national daily and cumulative infections with SARS-CoV-2 through Nov 14, 2021: A statistical analysis. The Lancet, 399(10344), 2351–2380.

Bollyky, T. J., Hulland, E. N., Barber, R. M., Collins, J. K., Kiernan, S., Moses, M., Pigott, D. M., Reiner Jr, R. C., Sorensen, R. J. D., Abbafati, C., Adolph, C., Allorant, A., Amlag, J. O., Aravkin, A. Y., Bang-Jensen, B., Carter, A., Castellano, R., Castro, E., Chakrabarti, S., … Dieleman, J. L. (2022). Pandemic preparedness and COVID-19: An exploratory analysis of infection and fatality rates, and contextual factors associated with preparedness in 177 countries, from Jan 1, 2020, to Sept 30, 2021. The Lancet, S0140673622001726.

Danilova, I., Shkolnikov, V. M., Jdanov, D. A., Meslé, F., & Vallin, J. (2016). Identifying potential differences in cause-of-death coding practices across Russian regions. Population Health Metrics, 14(1), 8.

Duszyński, J., et al. (2020). Zrozumieć COVID-19 [Understand COVID-19].

ECDC. (2022). European COVID-19 Forecast Hub. Forecast scores, Poland.

Ferguson, N. M., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z., Cuomo-Dannenburg, G., & others. (2020). Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College, London Doi.Org/10.25561/77482.

Herby, J., Jonung, L., & Hanke, S. H. (2022). A Literature Review and Meta-Analysis of the Effects of Lockdowns on COVID-19 Mortality. 200, 1–62.

Ioannidis, J. P. (2005). Why most published research findings are false. PLoS medicine, 2(8), 124.

Jarynowski, A. (2021). Phenomenon of participatory “guerilla” epidemiology in post-communist European countries. Baltic Rim Economies, 3, 25–26.

Jarynowski, A., & Belik, V. (2021). 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 (accepted).

Jarynowski, A., Paradowski, M. B., & Buda, A. (2019). Modelling communities and populations: An introduction to computational social science. Studia Metodologiczne, 39, 117–139.

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.

Jarynowski, A., Semenov, A., Wójta-Kempa, M., & Belik, V. (2021). Social Cohesion During the Stay-at-Home Phase of the First Wave of the COVID-19 Pandemic on Polish-Speaking Twitter. In D. Mohaisen & R. Jin (Eds.), Computational Data and Social Networks (Vol. 13116, pp. 361–370). Springer International Publishing.

Jarynowski, A., & Skawina, I. (2021). Attempt at profiling and regionalisation of COVID-19 vaccine campaigns in Poland—Preliminary results. European Journal of Translational and Clinical Medicine, 4(1), 13–21.

Jarynowski, A., & Wójta-Kempa. (2021). Zróżnicowanie geograficzne szczepień p/COVID-19 w Polsce—Nierówności społeczne i peryferyjność, a możliwe środki zaradcze [Geographical variation in COVID-19 vaccination in Poland -social inequalities, peripherality and possible interventions].óżnicowanie_geograficzne_szczepień_p_COVID_19_w_Polsce_nierówności_społeczne_i_peryferyjność_a_możliwe_środki_zaradcze

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.

Jarynowski, A., Wójta-Kempa, M., Płatek, D., & Belik, V. (2020). Social values are significant factors in control of COVID-19 pandemic–preliminary results.

Jr Reiner, R. C., Collins, J. K., & Murray, C. J. (2022). Forecasting the Trajectory of the COVID-19 Pandemic under Plausible Variant and Intervention Scenarios: A Global Modelling Study [Preprint]. SSRN Electronic Journal.

Kossarova, L., Holland, W., & Mossialos, E. (2013). ‘Avoidable’ mortality: A measure of health system performance in the Czech Republic and Slovakia between 1971 and 2008. Health Policy and Planning, 28(5), 508–525.

Niedzielewski, K., Nowosielski, J., Bartczuk, R., Dreger, F., Górski, Ł., Gruziel-Słomka, M., Kaczorek, A., Kisielewski, J., Krupa, B., Moszyński, A., Radwan, M., Semeniuk, M., Zieliński, J., & Rakowski, F. (2022). The Overview, Design Concepts and Details Protocol of ICM Epidemiological Model (PDYN 1.5) [Preprint]. SSRN Electronic Journal.

Our World in data. (2022, February 4). Coronavirus (COVID-19) Cases.

Rakowski, F. (2020). Predicting the course of the COVID-19 epidemic in Poland.

Rechel, B. (2010). HIV/AIDS in the Countries of the Former Soviet Union: Societal and Attitudinal Challenges. Central European Journal of Public Health, 18(2), 110–115.

Shankar, S., Mohakuda, S. S., Kumar, A., Nazneen, P. S., Yadav, A. K., Chatterjee, K., & Chatterjee, K. (2021). Systematic review of predictive mathematical models of COVID-19 epidemic. Medical Journal Armed Forces India, 77, 385–392.

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F., & Gilbert, N. (2020). Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. Journal of Artificial Societies and Social Simulation, 23(2), 10.

Stochmal, M., Jarynowski, A., & Maciejewski, J. (2021). Review of social research in Poland on the Sars-Cov-2 pandemic in the light of the fuzzy reasoning paradigm. Polityka Społeczna, 563(2), 1–10.

Subramanian, S. V., & Kumar, A. (2021). Increases in COVID-19 are unrelated to levels of vaccination across 68 countries and 2947 counties in the United States. European Journal of Epidemiology. 36, 1237–1240.

Watson, O. J., Barnsley, G., Toor, J., Hogan, A. B., Winskill, P., & Ghani, A. C. (2022). Global impact of the first year of COVID-19 vaccination: A mathematical modelling study. The Lancet Infectious Diseases, S1473309922003206.




How to Cite

Jarynowski, A. (2022). Reflection on contextual factors associated with burden of infectious diseases in multi-country modelling approach on the example of Eastern Europe. E-Methodology, 8(8), 99–105.



“With the Internet” – Projects

Most read articles by the same author(s)