Stanojevic Slavoljub, Ponjavic Mirza, Stanojevic Slobodan, Stevanovic Aleksandar, Radojicic Sonja
Directorate of National Reference Laboratories, Batajnicki drum 10, 11080 Zemun, Serbia.
International Burch University, Francuske revolucije bb, Ilidza, 71210, Sarajevo, Bosnia and Herzegovina.
Microb Risk Anal. 2021 Aug;18:100161. doi: 10.1016/j.mran.2021.100161. Epub 2021 Mar 11.
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=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.
作为对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒引发的大流行的应对措施，2020年3月15日，塞尔维亚共和国出台了全面抗疫措施以遏制新冠疫情。在疫情放缓后，2020年5月6日，监管部门决定放宽已实施的措施。然而，疫情形势很快再次恶化。截至2021年2月7日，塞尔维亚共报告了406352例SARS-CoV-2感染病例，4112人死于新冠疫情。为了更好地了解疫情动态并预测可能的结果，我们开发了一个适应性数学模型SEAIHRDS（S-易感者，E-暴露者，A-无症状感染者，I-感染者，H-住院者，R-康复者，d-死于新冠感染，S-易感者）。该模型可用于模拟已实施干预措施的各种情景，并计算可能的疫情结果，包括所需的医院容量。考虑到在新冠疫苗研发方面取得的有前景的成果，该模型得到扩展以模拟不同人群阶层的疫苗接种情况。各种模拟情景的结果表明，通过实施严格的减少接触措施，有可能控制新冠疫情并减少死亡人数。研究结果还表明，特别需要关注在最易感人群阶层内限制有效接触。然而，研究结果也表明，该疾病有可能在人群中长期存在，可能呈现季节性模式。如果有疗效等于或高于65%的疫苗可用，它可能有助于显著减缓或完全阻止病毒在人群中的传播。疫苗接种的效果主要取决于：1. 现有疫苗的疗效，2. 疫苗接种人群类别的优先级，以及3. 人群的总体疫苗接种覆盖率，前提是疫苗能在接种个体中产生稳固的免疫力。在预期基本繁殖数R=2.46且疫苗疗效为68%的情况下，87%的接种覆盖率足以阻止病毒传播。
