Rosa Santiago, Pulido Manuel A, Ruiz Juan J, Cocucci Tadeo J
FaMAF, Universidad Nacional de Córdoba, Córdoba, Córdoba, Argentina.
FaCENA, Universidad Nacional del Nordeste, Corrientes, Corrientes, Argentina.
PLoS One. 2025 Apr 28;20(4):e0318426. doi: 10.1371/journal.pone.0318426. eCollection 2025.
The COVID-19 pandemic, with its multiple outbreaks, has posed significant challenges for governments worldwide. Much of the epidemiological modeling relied on pre-pandemic contact information of the population to model the virus transmission between population age groups. However, said interactions underwent drastic changes due to governmental health measures, referred to as non-pharmaceutical interventions. These interventions, from social distancing to complete lockdowns, aimed to reduce transmission of the virus. This work proposes taking into account the impact of non-pharmaceutical measures upon social interactions among different age groups by estimating the time dependence of these interactions in real time based on epidemiological data. This is achieved by using a time-dependent transmission matrix of the disease between different population age groups. This transmission matrix is estimated using an ensemble-based data assimilation system applied to a meta-population model and time series data of age-dependent accumulated cases and deaths. We conducted a set of idealized twin experiments to explore the performance of different ways in which social interactions can be parametrized through the transmission matrix of the meta-population model. These experiments show that, in an age-compartmental model, all the independent parameters of the transmission matrix cannot be unequivocally estimated, i.e., they are not all identifiable. Nevertheless, the time-dependent transmission matrix can be estimated under certain parameterizations. These estimated parameters lead to an increase in forecast accuracy within age-group compartments compared to a single-compartmental model assimilating observations of age-dependent accumulated cases and deaths in Argentina. Furthermore, they give reliable estimations of the effective reproduction number. The age-dependent data assimilation and forecasting of virus transmission are crucial for an accurate prediction and diagnosis of healthcare demand.
新冠疫情多次爆发，给世界各国政府带来了巨大挑战。许多流行病学模型依靠疫情前的人口接触信息来模拟病毒在不同年龄组人群之间的传播。然而，由于政府采取的被称为非药物干预的卫生措施，上述互动发生了巨大变化。这些干预措施，从保持社交距离到全面封锁，旨在减少病毒传播。这项工作建议通过基于流行病学数据实时估计不同年龄组之间社交互动的时间依赖性，来考虑非药物措施对社交互动的影响。这是通过使用不同年龄组人群之间疾病的时间依赖性传播矩阵来实现的。该传播矩阵是使用基于集合的数据同化系统估计的，该系统应用于一个元种群模型以及年龄依赖性累计病例和死亡的时间序列数据。我们进行了一组理想化的孪生实验，以探索通过元种群模型的传播矩阵对社交互动进行参数化的不同方法的性能。这些实验表明，在一个年龄分层模型中，传播矩阵的所有独立参数无法明确估计，即并非所有参数都是可识别的。然而，在某些参数化条件下，可以估计时间依赖性传播矩阵。与同化阿根廷年龄依赖性累计病例和死亡观测数据的单分层模型相比，这些估计参数提高了年龄组分层内的预测准确性。此外，它们给出了有效繁殖数的可靠估计。病毒传播的年龄依赖性数据同化和预测对于准确预测和诊断医疗需求至关重要。
