Impact of COVID-19 outbreaks and interventions on influenza in China and the United States

Feng, Luzhao; Zhang, Ting; Wang, Qing; Xie, Yanjun; Peng, Zhibin; Zheng, Jiandong; Qin, Ying; Zhang, Muli; Lai, Shengjie; Wang, Dayan; Feng, Zijian; Li, Zhongjie; Gao, George F.

doi:10.1038/s41467-021-23440-1

Cited by 199 publications

(203 citation statements)

References 31 publications

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“…The current preliminary findings reaffirm the usefulness of the NPI measures recommended by WHO and remind us of the need and importance of educational campaigns and the practical implementation of public health policies. In particular, with a continuous circulation of COVID-19 and use of NPIs in many countries, detailed studies are critically needed to evaluate the causes of decreased respiratory infections [ 89 – 91 ]. Additionally, until viable pharmaceutical options for HRSV become available, it is important to continue to evaluate non-pharmaceutical strategies and their potential benefits.…”

Section: Discussionmentioning

confidence: 99%

Decreased human respiratory syncytial virus activity during the COVID-19 pandemic in Japan: an ecological time-series analysis

et al. 2021

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Background Non-pharmaceutical interventions (NPIs), such as sanitary measures and travel restrictions, aimed at controlling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may affect the transmission dynamics of human respiratory syncytial virus (HRSV). We aimed to quantify the contribution of the sales of hand hygiene products and the number of international and domestic airline passenger arrivals on HRSV epidemic in Japan. Methods The monthly number of HRSV cases per sentinel site (HRSV activity) in 2020 was compared with the average of the corresponding period in the previous 6 years (from January 2014 to December 2020) using a monthly paired t-test. A generalized linear gamma regression model was used to regress the time-series of the monthly HRSV activity against NPI indicators, including sale of hand hygiene products and the number of domestic and international airline passengers, while controlling for meteorological conditions (monthly average temperature and relative humidity) and seasonal variations between years (2014–2020). Results The average number of monthly HRSV case notifications in 2020 decreased by approximately 85% (p < 0.001) compared to those in the preceding 6 years (2014–2019). For every average ¥1 billion (approximately £680,000/$9,000,000) spent on hand hygiene products during the current month and 1 month before there was a 0.29% (p = 0.003) decrease in HRSV infections. An increase of average 1000 domestic and international airline passenger arrivals during the previous 1–2 months was associated with a 3.8 × 10− 4% (p < 0.001) and 1.2 × 10− 3% (p < 0.001) increase in the monthly number of HRSV infections, respectively. Conclusions This study suggests that there is an association between the decrease in the monthly number of HRSV cases and improved hygiene and sanitary measures and travel restrictions for COVID-19 in Japan, indicating that these public health interventions can contribute to the suppression of HRSV activity. These findings may help in public health policy and decision making.

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Section: Discussionmentioning

confidence: 99%

Decreased human respiratory syncytial virus activity during the COVID-19 pandemic in Japan: an ecological time-series analysis

et al. 2021

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“…Thus, in the context of this research, we observed that the main focus of the tweets related mainly to COVID-19 pandemic events (disease, confinement, politician talks, vaccines approval, and vaccination) and increased over time, like the prevalence of the terms “vaccines” and “vaccination,” and this was in contrast with the term “flu,” which disappeared over time from the tweets. This reflects that COVID-19 measures, such as social distancing and mask wearing, significantly reduced the seasonal influenza rates in 2020-2021 [ 73 , 90 , 91 ]. However, a potential major reason and mechanism of these changes in trends and therefore of the folksonomy content may be associated with the diversion of citizens’ attention to annual influenza spread, caused by the disruptive and menacing COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 91%

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Benis¹,

Chatsubi²,

Levner³

et al. 2021

JMIR Infodemiology

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Background Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

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“…Starting from January 2020, people were asked to wear masks in crowded closed places and public places, reduce participation in non-essential gatherings, and maintain a social distance of more than 1 meter. All provinces in mainland China adopted stringent public health measures from January 2020 ( 11 ). Taking Wuhan as an example, on January 14, 2020, the government implemented the entry and exit policy, and established temperature detection points in public places.…”

Section: Introductionmentioning

confidence: 99%

Public Health Measures During the COVID-19 Pandemic Reduce the Spread of Other Respiratory Infectious Diseases

Tang

et al. 2021

Front. Public Health

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Background: Public health measures (such as wearing masks, physical distancing, and isolation) have significantly reduced the spread of the coronavirus disease-2019 (COVID-19), but the impact of public health measures on other respiratory infectious diseases is unclear.Objective: To assess the correlation between public health measures and the incidence of respiratory infectious diseases in China during the COVID-19 pandemic.Methods: We collected the data from the National Health and Construction Commission in China on the number of patients with six respiratory infectious diseases (measles, tuberculosis, pertussis, scarlet fever, influenza, and mumps) from 2017 to 2020 and assessed the correlation between public health measures and the incidence of respiratory infectious diseases. Finally, we used the data of the six respiratory infectious diseases in 2021 to verify our results.Results: We found public health measures significantly reduced the incidence of measles (p = 0.002), tuberculosis (p = 0.002), pertussis (p = 0.004), scarlet fever (p = 0.002), influenza (p = 0.034), and mumps (p = 0.002) in 2020, and prevented seasonal peaks. Moreover, the effects of public health measures were most marked during the peak seasons for these infections. Of the six respiratory infectious diseases considered, tuberculosis was least affected by public health measures.Conclusion: Public health measures were very effective in reducing the incidence of respiratory infectious diseases, especially when the respiratory infectious diseases would normally have been at their peak.

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Impact of COVID-19 outbreaks and interventions on influenza in China and the United States

Cited by 199 publications

References 31 publications

Decreased human respiratory syncytial virus activity during the COVID-19 pandemic in Japan: an ecological time-series analysis

Decreased human respiratory syncytial virus activity during the COVID-19 pandemic in Japan: an ecological time-series analysis

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Public Health Measures During the COVID-19 Pandemic Reduce the Spread of Other Respiratory Infectious Diseases

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