Oh Jooha, Apio Catherine, Park Taesung
Department of Statistics, Seoul National University, Seoul 08826, Korea.
Interdisciplinary Programs in Bioinformatics, Seoul 08826, Korea.
Genomics Inform. 2022 Jun;20(2):e22. doi: 10.5808/gi.22025. Epub 2022 Jun 22.
The rise of newer coronavirus disease 2019 (COVID-19) variants has brought a challenge to ending the spread of COVID-19. The variants have a different fatality, morbidity, and transmission rates and affect vaccine efficacy differently. Therefore, the impact of each new variant on the spread of COVID-19 is of interest to governments and scientists. Here, we proposed mathematical SEIQRDVP and SEIQRDV3P models to predict the impact of the Omicron variant on the spread of the COVID-19 situation in South Korea. SEIQEDVP considers one vaccine level at a time while SEIQRDV3P considers three vaccination levels (only one dose received, full doses received, and full doses + booster shots received) simultaneously. The omicron variant's effect was contemplated as a weighted sum of the delta and omicron variants' transmission rate and tuned using a hyperparameter k. Our models' performances were compared with common models like SEIR, SEIQR, and SEIQRDVUP using the root mean square error (RMSE). SEIQRDV3P performed better than the SEIQRDVP model. Without consideration of the variant effect, we don't see a rapid rise in COVID-19 cases and high RMSE values. But, with consideration of the omicron variant, we predicted a continuous rapid rise in COVID-19 cases until maybe herd immunity is developed in the population. Also, the RMSE value for the SEIQRDV3P model decreased by 27.4%. Therefore, modeling the impact of any new risen variant is crucial in determining the trajectory of the spread of COVID-19 and determining policies to be implemented.
新型冠状病毒病2019（COVID-19）变种的出现给终结COVID-19的传播带来了挑战。这些变种具有不同的致死率、发病率和传播率，对疫苗效力的影响也各不相同。因此，每个新变种对COVID-19传播的影响受到政府和科学家的关注。在此，我们提出了数学SEIQRDVP和SEIQRDV3P模型，以预测奥密克戎变种对韩国COVID-19疫情传播的影响。SEIQEDVP每次考虑一个疫苗接种水平，而SEIQRDV3P同时考虑三个疫苗接种水平（仅接种一剂、接种完整剂量、接种完整剂量并接种加强针）。奥密克戎变种的影响被视为德尔塔变种和奥密克戎变种传播率的加权和，并使用超参数k进行调整。我们将模型的性能与SEIR、SEIQR和SEIQRDVUP等常用模型进行比较，采用均方根误差（RMSE）。SEIQRDV3P的表现优于SEIQRDVP模型。在不考虑变种影响的情况下，我们看不到COVID-19病例的快速上升以及较高的RMSE值。但是，考虑到奥密克戎变种，我们预测COVID-19病例将持续快速上升，直到人群中可能形成群体免疫。此外，SEIQRDV3P模型的RMSE值下降了27.4%。因此，对任何新出现变种的影响进行建模对于确定COVID-19传播轨迹以及确定要实施的政策至关重要。
