Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission

Slavoljub Stanojevic; Mirza Ponjavic; Slobodan Stanojevic; Aleksandar Stevanovic; Sonja Radojicic

doi:10.1016/j.mran.2021.100161

Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission

Microb Risk Anal. 2021 Aug:18:100161. doi: 10.1016/j.mran.2021.100161. Epub 2021 Mar 11.

Authors

Slavoljub Stanojevic¹, Mirza Ponjavic², Slobodan Stanojevic³, Aleksandar Stevanovic⁴, Sonja Radojicic⁵

Affiliations

¹ Directorate of National Reference Laboratories, Batajnicki drum 10, 11080 Zemun, Serbia.
² International Burch University, Francuske revolucije bb, Ilidza, 71210, Sarajevo, Bosnia and Herzegovina.
³ Veterinary Scientific Institute of Serbia, Janisa Janulisa 14, 11107, Belgrade, Serbia.
⁴ University of Pittsburgh, Department of Civil and Environmental Engineering, 3700 O'Hara Street, Pittsburgh, PA 15261, United States.
⁵ Belgrade University, Faculty of veterinary medicine Department of Infectious Animals Diseases and Diseases of Bees, Bulevar Oslobodenja 18, 11000 Belgrade, Serbia.

Abstract

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, d-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population. The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of R_o=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

Keywords: COVID-19; SEAIHRDS mathematical model; prediction; vaccination.