key: cord-0738225-l5akcvow
authors: Tran, Leon King; Huang, Dai-Wei; Li, Nien-Kung; Li, Lucy M.; Palacios, Julia A.; Chang, Hsiao-Han
title: The impact of the COVID-19 preventive measures on influenza transmission: molecular and epidemiological evidence
date: 2021-12-10
journal: Int J Infect Dis
DOI: 10.1016/j.ijid.2021.12.323
sha: adcc739d63eeaf0a6b9e07086247b76799a3fc05
doc_id: 738225
cord_uid: l5akcvow

Objective We quantify the impact of COVID-19-related control measures on the spread of human influenza virus H1N1 and H3N2. Methods We analyzed case numbers to estimate the length of the 2019-2020 influenza season and compare its length to the median of the previous nine seasons. In addition, we used influenza molecular data to compare within-region and between-region genetic diversity and effective population size from 2019 to 2020. Finally, we analyzed personal behavior data, and policy stringency data for each region. Results The 2019-2020 influenza season was shorter than the median of the previous nine seasons in all regions. For H1N1 and H3N2, there was an increase in between-region genetic diversity in almost all pairs of regions between 2019 and 2020. For 10 of 11 regions for H1N1 and 9 of 11 regions for H3N2, there was a decrease in within-region genetic diversity. For 10 of 13 regions for H1N1 and 3 of 7 regions for H3N2, there was a decrease in effective population size. Conclusions We found consistent evidence of decrease in influenza incidence after the introduction of preventive measures due to COVID-19 emergence.

The emergence and spread of COVID-19 in 2020 led to a number of large-scale public health measures to limit international travel, reduce gatherings, and increase mask-wearing. While these preventive measures were implemented to curtail the spread of COVID-19, they seem to have also impacted the spread of other respiratory illnesses. There have been several reports on the decrease in case numbers during 2019-2020 influenza season in the northern hemisphere (Kuo et al., 2020) , and the lack of a 2020 influenza season in the southern hemisphere (The Economist Newspaper, 2020) . We quantify the impact of the COVID-19 preventive measures on the spread of influenza in terms of incidence and viral molecular diversity (Bedford et al., 2015) .

We analyzed weekly case count data of influenza available in FluNet (World Health Organization, 2021) from various regions during the 2010-2020 influenza seasons. We defined T S and T E as the weeks during which the estimated number of cases reached 10% and 90% of the total case numbers in each influenza season, respectively. Since the influenza outbreaks for most regions started before the COVID-19 pandemic, we compared T E and durations of influenza seasons pre-onset and post-onset of the COVID-19 pandemic. We defined the duration of an influenza season as the difference between T S and T E , and standardized the duration in the 2019-2020 season by the average and standard deviation of the duration from previous 9 seasons.

We analyzed the HA segment of human influenza A H1N1 and H3N2 sequences available in the GISAID EpiFlu database on November 1 st 2020. The collection dates of the sequences ranged from January 2016 to December 2020. We used BEAST (Drummond and Rambaut, 2007) to estimate the effective population size (N e ) from 2016 to 2020 for each location. The numbers of sequences analyzed are indicated in Table S1 .

For each location and each influenza type, we calculated the within-location genetic diversity, Watterson's θ (Watterson, 1975) , for the first half of 2019 and for the first of 2020.

To examine the indirect impact of COVID-19 on influenza dynamics, we compared the 2019-2020 influenza season with previous 9 seasons in 21 locations across 5 continents (Table 1) (Kuo et al., 2020; The Economist Newspaper, 2020) .

In addition to case count data, we analyzed molecular data to evidence our findings. We found that for 12 out of 14 locations we analyzed, genetic diversity (θ) decreased from 2019 to 2020 for H1N1, and 9 out of 12 locations for H3N2 (Table S2) . On the other hand, we calculated between-region genetic diversity for each pair of regions once in 2019 and again in 2020; 11 out of 14 locations for H1N1 and 9 out of 13 locations for H3N2 had their between-region genetic diversity increase from 2019 to 2020 in at least 50% of pairs for which the location was involved, reflecting reduced travel between regions in 2020 (Table S3) .

While θ measures overall viral diversity, the effective population size (N e ) quantifies genetic diversity over time (Frost and Volz, 2010) . We estimated N e for H1N1 in 13 countries and found a decrease in N e in 10 countries, including Italy and Taiwan (Figure 1; Figure S1 ). For H3N2, we analyzed 7 countries, and found a decrease in N e in South Africa, United Arab Emirates, and Taiwan in 2020 (Figure 1 ; Figure S2 ). This genetic diversity analysis confirms that the observed reduction in influenza case numbers was not just due to under-reporting during the COVID-19 pandemic, and this observed reduction is in line with other studies carried out without genetic analysis (Feng et al., 2021; Akhtar et al., 2021; Stojanovic et al., 2021 , Olsen et al., 2020 .

For personal measures and government policies against the spread of COVID-19, we also noticed that Asian countries tend to act earlier than countries in other continents, especially in wearing masks (Table S4) . Taken together, we observed earlier ends of flu seasons in Asia than in Europe and America, which could be explained by the earlier implementation of nonpharmaceutical interventions.

While using publicly available influenza genetic data restricted our analysis to countries with sufficient data and may suffer from sampling bias, we were able to identify a general trend (with exceptions) with samples across multiple continents. Our combined analyses of case counts, genetic data, and behavioral data support the conclusion that COVID-19 preventive measures led to the reduction of influenza transmission in several countries in 2020. 

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We thank GISAID and sequencing laboratories for making the influenza A viral molecular sequences available Specific laboratories and accession numbers are found at the end of the Supplementary materials.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Approval was not required.

This work was supported by the Ministry of Science and Technology in Taiwan [MOST 110-2636-B-007-009]; and the Yushan Scholar Program.The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. LML is currently a full-time employee at The Public Health Company, who had no input in the design or execution of this study.