Resurgence of influenza during COVID-19 in Chongqing, China: A retrospective analysis.

To better understand the trends of influenza and the impact of public health and social measures (PHSMs) implemented during the coronavirus disease 2019 (COVID-19) period in Chongqing, China. Data from the China Influenza Surveillance Information System from January 2017 to June 2022 were extracted. Epidemiological characteristics (influenza-like illness [ILI] and ILI%) and virological characteristics (influenza positive rate and circulating (sub)types) of influenza were described and compared between the pre-COVID-19 period and the COVID-19 period. Our survey showed that the implementation of PHSMs during the COVID-19 period had a positive impact on reducing influenza transmission. However, influenza activity resurged in 2021-2022 as the PHSMs were eased. Children under 5 years old constituted the highest proportion of ILI cases. The overall influenza positive rate was 23.70%, with a higher rate observed during the pre-COVID-19 period (31.55%) compared to the COVID-19 period (13.68%). Influenza virus subtypes co-circulated and the predominant subtype varied each year, with influenza A subtypes predominated in 2018/2019, while influenza B/Victoria lineage dominated in 2020/2021. PHSMs are effective measures to mitigate the spread of influenza. The findings underscore the need for bolstering monitoring systems, advocating influenza vaccination, and implementing practical PHSMs to strengthen prevention and control measures against influenza.

A Novel Approach on Deep Learning-Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs' Effect over COVID-19 Pandemic.

Following the outbreak of the COVID-19 pandemic, the continued emergence of major variant viruses has caused enormous damage worldwide by generating social and economic ripple effects, and the importance of PHSMs (Public Health and Social Measures) is being highlighted to cope with this severe situation. Accordingly, there has also been an increase in research related to a decision support system based on simulation approaches used as a basis for PHSMs. However, previous studies showed limitations impeding utilization as a decision support system for policy establishment and implementation, such as the failure to reflect changes in the effectiveness of PHSMs and the restriction to short-term forecasts. Therefore, this study proposes an LSTM-Autoencoder-based decision support system for establishing and implementing PHSMs. To overcome the limitations of existing studies, the proposed decision support system used a methodology for predicting the number of daily confirmed cases over multiple periods based on multiple output strategies and a methodology for rapidly identifying varies in policy effects based on anomaly detection. It was confirmed that the proposed decision support system demonstrated excellent performance compared to models used for time series analysis such as statistical models and deep learning models. In addition, we endeavored to increase the usability of the proposed decision support system by suggesting a transfer learning-based methodology that can efficiently reflect variations in policy effects. Finally, the decision support system proposed in this study provides a methodology that provides multi-period forecasts, identifying variations in policy effects, and efficiently reflects the effects of variation policies. It was intended to provide reasonable and realistic information for the establishment and implementation of PHSMs and, through this, to yield information expected to be highly useful, which had not been provided in the decision support systems presented in previous studies.