School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China.
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
PLoS Comput Biol. 2023 Oct 18;19(10):e1011535. doi: 10.1371/journal.pcbi.1011535. eCollection 2023 Oct.
During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi'an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.
在 COVID-19 大流行期间，控制措施，尤其是在及时隔离和隔离后进行大规模接触者追踪，在减轻疾病传播方面发挥着重要作用，量化动态接触率和隔离率并评估其影响仍然具有挑战性。为了准确量化干预措施的强度，我们开发了物理启发神经网络（PINN）的机制，通过将分散的观测数据与深度学习和传染病模型相结合，提出了扩展的传播动力学启发神经网络（TDINN）算法。TDINN 算法不仅可以避免预先假设特定的速率函数，还可以使神经网络在学习过程中遵循传染病系统的规则。我们表明，所提出的算法不仅可以很好地拟合西安、广州和扬州等城市的多源传染病数据，还可以利用不完全报告数据重建海南和新疆的传染病发展趋势。我们推断了接触/隔离率的时间演变模式，从函数族中选择最佳组合，准确模拟 TDINN 算法学习的接触/隔离时间序列，并由此重建传染病过程。基于深度学习推断的时间序列选择的速率函数具有合理的流行病学意义。此外，所提出的 TDINN 算法还通过辽宁省具有多波的 COVID-19 传染病数据进行了验证，并表现出良好的性能。我们发现估计的接触/隔离率存在显著波动，以及干预策略的加强/放松与疫情复发之间的反馈循环。此外，研究结果表明，所考虑地区推断出的接触/隔离率的时间演变曲线形状存在多样性，这表明各地区采用的控制策略强度存在差异。
