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. 2023 Jun 20;13(3):28-46.
doi: 10.5493/wjem.v13.i3.28.

Role of children in the Bulgarian COVID-19 epidemic: A mathematical model study

Latchezar Tomov  1 , Hristiana Batselova  2 , Snezhina Lazova  3   4 , Borislav Ganev  3 , Iren Tzocheva  5 , Tsvetelina Velikova  6
Affiliations

Role of children in the Bulgarian COVID-19 epidemic: A mathematical model study

Latchezar Tomov et al. World J Exp Med. .

Abstract

Background: The coronavirus disease 2019 (COVID-19) pandemic affects all aspects of our lives, including children. With the advancement of the pandemic, children under five years old are at increased risk of hospitalization relative to other age groups. This makes it paramount that we develop tools to address the two critical aspects of preserving children's health - new treatment protocols and new predictive models. For those purposes, we need to understand better the effects of COVID-19 on children, and we need to be able to predict the number of affected children as a proportion of the number of infected children. This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID, as a part of the general picture of post-COVID among this age group.

Aim: To demonstrate the role of children in the COVID-19 spread in Bulgaria and to test the hypothesis that there are no secondary transmissions in schools and from children to adults.

Methods: Our modeling and data show with high probability that in Bulgaria, with our current measures, vaccination strategy and contact structure, the pandemic is driven by the children and their contacts in school.

Results: This makes it paramount that we develop tools to address the two critical aspects of preserving children's health - new treatment protocols and new predictive models. For those purposes, we need to understand better the effects of COVID-19 on children, and we need to be able to predict the number of affected children as a proportion of the number of infected children. This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID, as a part of the general picture of post-Covid among this age group.

Conclusion: Our modeling rejects that hypothesis, and the epidemiological data supports that. We used epidemiological data to support the validity of our modeling. The first summer wave in 2020 from the listed here school proms endorse the idea of transmissions from students to teachers.

Keywords: ARIMA; COVID-19; Cardiac involvement; Children; Multisystem inflammation in children; Pandemic; Regression model; Time-series modeling.

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Conflict of interest statement

Conflict-of-interest statement: All the authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Relative growth of cases by age groups. An apparent lag between 0-19 and others can be seen.
Figure 2
Figure 2
Sample cross-correlation function between new weekly cases of coronavirus disease 2019 and multisystem inflammation in children cases.MIS-C: Multisystem inflammation in children.
Figure 3
Figure 3
Relative growth of national-level coronavirus disease 2019 cases and multisystem inflammation in children cases in Pirogov Hospital. COVID-19: Coronavirus disease 2019; MIS-C: Multisystem inflammation in children.
Figure 4
Figure 4
Principal component analysis Biplot for age groups on a weekly basis. PCA: Principal component analysis.
Figure 5
Figure 5
Residuals of Model I – Factors contributing to the spread among children. A: Residuals of the model; B: Autocorrelation function of the residuals; C: Distribution of the residuals.
Figure 6
Figure 6
Fitted Model I – Factors contributing to the spread among children. COVID-19: Coronavirus disease 2019.
Figure 7
Figure 7
Residuals of MODEL III –III – Temporal spread across age groups 20-49. A: Residuals of the model; B: Autocorrelation function of the residuals; C: Distribution of the residuals.
Figure 8
Figure 8
Fitted MODEL III – Temporal spread across age groups 20-49. COVID-19: Coronavirus disease 2019.
Figure 9
Figure 9
Residuals of Model II – Temporal spread across age groups 60-89. A: Residuals of the model; B: Autocorrelation function of the residuals; C: Distribution of the residuals.
Figure 10
Figure 10
Fitted Model II – Temporal spread across age groups 60-89. COVID-19: Coronavirus disease 2019.
Figure 11
Figure 11
Probability density distribution of the number of multisystem inflammation in children cases on a weekly basis. Poisson distribution for total cases up to 4.12.2021 is with λ = 0.50∈ [0.355, 0.835]. MIS-C: Multisystem inflammation in children.
Figure 12
Figure 12
Residuals of model for the bi-weekly period. A: Residuals of the model; B: Autocorrelation function of the residuals; C: Distribution of the residuals.
Figure 13
Figure 13
Fitted model for a bi-weekly period. MIS-C: Multisystem inflammation in children.
Figure 14
Figure 14
Share of fully vaccinated individuals per age group up to November 16, 2021. Data for the number of fully vaccinated individuals is taken from[35].
See this image and copyright information in PMC

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