监测是传染病防控的关键环节。在全球暴发的新冠疫情暴露了传统监测方法的局限性，但也为探索新的监测方法提供了契机。本研究旨在利用百度搜索指数和流感样病例（ILI）监测数据，估计SARS-CoV-2的变异株奥密克戎BF.7在北京市应急状态下的传播和流行趋势。本研究创新性地提出了一种复合模型[多注意力双向门控循环单元（MABG）-易感-暴露-感染-恢复（SEIR）]，该模型利用深度学习算法（MABG）对ILI历史数据和发烧、发热、咳嗽、咽痛、退烧药、流涕等多种百度指数流感样症状相关关键词进行分析。基于百度指数以及ILI与新冠病毒感染之间的相关性，构建了一个估计SARS-CoV-2传播和流行趋势的传染病传播动力学模型（SEIR）。在新冠病毒感染大流行期间，当常规监测措施暂停时，ILI可以作为评估新冠病毒感染流行病学趋势的重要指标。研究结果显示，北京市自2022年12月17日起累计感染率超过80.25%（95% CI: 77.51%~82.99%），本研究预测疫情高峰时间为2022年12月12日，现存感染者数量的高峰将在该高峰后的三天出现。有效再生数（R_t）代表流行期间某一时间点单个感染者所致平均继发感染人数，该值自2022年12月17日一直低于1。本研究强调，传统的疾病监测系统应辅之以现代监测数据，例如具有先进技术支持的网络信息数据源。现代监测渠道应主要用于监测新发传染病和疾病暴发。应建立对新冠病毒感染的症状监测，以跟踪疫情趋势、疾病严重程度和医疗资源需求。

Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)-susceptible-exposed-infected-removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%-82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (R_t) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical resource demand.