ICT4Life srl, Perugia, Italy.
Department of Engineering, University of Perugia, Perugia, Italy.
BMC Infect Dis. 2022 Apr 29;22(1):415. doi: 10.1186/s12879-022-07395-2.
In Italy, the beginning of 2021 was characterized by the emergence of new variants of SARS-CoV-2 and by the availability of effective vaccines that contributed to the mitigation of non-pharmaceutical interventions and to the avoidance of hospital collapse.
We analyzed the COVID-19 propagation in Italy starting from September 2021 with a Susceptible-Exposed-Infected-Recovered (SEIR) model that takes into account SARS-CoV-2 lineages, intervention measures and efficacious vaccines. The model was calibrated with the Bayesian method Conditional Robust Calibration (CRC) using COVID-19 data from September 2020 to May 2021. Here, we apply the Conditional Robustness Analysis (CRA) algorithm to the calibrated model in order to identify model parameters that most affect the epidemic diffusion in the long-term scenario. We focus our attention on vaccination and intervention parameters, which are the key parameters for long-term solutions for epidemic control.
Our model successfully describes the presence of new variants and the impact of vaccinations and non-pharmaceutical interventions in the Italian scenario. The CRA analysis reveals that vaccine efficacy and waning immunity play a crucial role for pandemic control, together with asymptomatic transmission. Moreover, even though the presence of variants may impair vaccine effectiveness, virus transmission can be kept low with a constant vaccination rate and low restriction levels.
In the long term, a policy of booster vaccinations together with contact tracing and testing will be key strategies for the containment of SARS-CoV-2 spread.
2021 年初，意大利出现了 SARS-CoV-2 的新变种，并且有了有效的疫苗，这有助于减轻非药物干预措施的影响，避免医院崩溃。
我们从 2021 年 9 月开始，使用考虑了 SARS-CoV-2 谱系、干预措施和有效疫苗的易感-暴露-感染-恢复（SEIR）模型来分析意大利的 COVID-19 传播情况。该模型使用贝叶斯条件稳健校准（CRC）方法，利用 2020 年 9 月至 2021 年 5 月的 COVID-19 数据进行校准。在这里，我们将条件稳健性分析（CRA）算法应用于校准模型，以确定在长期情景下对流行病扩散影响最大的模型参数。我们关注的是疫苗接种和干预参数，这些参数是长期控制流行病的关键参数。
我们的模型成功地描述了新变种的出现以及疫苗接种和非药物干预措施在意大利的影响。CRA 分析表明，疫苗效力和免疫力衰减对于控制大流行起着至关重要的作用，与无症状传播一起。此外，即使变种的存在可能会降低疫苗的有效性，但如果保持高疫苗接种率和低限制水平，病毒传播仍可以保持较低水平。
从长远来看，加强疫苗接种、接触者追踪和检测将是控制 SARS-CoV-2 传播的关键策略。
