Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis

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Abstract

Background

Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored.

Objective

We aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends.

Methods

We retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases.

Results

With the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above –0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic.

Conclusions

Influenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns.

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Most cited references 41

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Long Short-Term Memory

Jürgen Schmidhuber, Jürgen Schmidhuber (2002)

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

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Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

Seth Flaxman, Swapnil Mishra, Axel Gandy … (2020)

Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that-for all of the countries we consider here-current interventions have been sufficient to drive Rt below 1 (probability Rt < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions-and lockdowns in particular-have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.

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Effect of non-pharmaceutical interventions to contain COVID-19 in China

Shengjie Lai, Nick Ruktanonchai, Liangcai Zhou … (2020)

Summary On March 11, 2020, the World Health Organization declared COVID-19 a pandemic 1 . The outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective 2 , but quantitative research is still needed to assess the efficacy of NPIs and their timings 3 . Using epidemiological and anonymised human movement data 4,5 , here we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimated that there were a total of 114,325 COVID-19 cases (interquartile range 76,776 -164,576) in mainland China as of February 29, 2020. Without NPIs, the COVID-19 cases would likely have shown a 67-fold increase (interquartile range 44 - 94) by February 29, 2020, with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to have prevented more infections than travel restrictions and contact reductions, but combined NPIs achieved the strongest and most rapid effect. The lifting of travel restrictions since February 17, 2020 does not appear to lead to an increase in cases across China if the social distancing interventions can be maintained, even at a limited level of 25% reduction on average through late April. Our findings contribute to an improved understanding of NPIs on COVID-19 and to inform response efforts across the World.

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Author and article information

Contributors

Luzhao Feng:

ORCID: https://orcid.org/0000-0002-5206-5995

School of Population Medicine and Public HealthChinese Academy of Medical Sciences & Peking Union Medical CollegeNo 9, Dongdan SantiaoDongcheng DistrictBeijing, 100730China86 10 65120716fengluzhao@cams.cn

Journal

Journal ID (nlm-ta): JMIR Public Health Surveill

Journal ID (iso-abbrev): JMIR Public Health Surveill

Journal ID (publisher-id): JPH

Title: JMIR Public Health and Surveillance

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2369-2960

Publication date Collection: 2023

Publication date (Electronic): 12 June 2023

Publication date PMC-release: 12 June 2023

Volume: 9

Electronic Location Identifier: e44970

Affiliations

[1 ] School of Population Medicine and Public Health Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China

[2 ] Department of Statistics Yunnan University Kunming China

[3 ] Division of Infectious Diseases Chinese Center for Disease Control and Prevention Beijing China

Author notes

Corresponding Author: Luzhao Feng fengluzhao@ 123456cams.cn

Author information

Qing Wang https://orcid.org/0000-0002-9855-4348

Mengmeng Jia https://orcid.org/0000-0002-8175-7181

Mingyue Jiang https://orcid.org/0009-0000-3176-6066

Wei Liu https://orcid.org/0009-0001-5770-5357

Jin Yang https://orcid.org/0000-0002-0359-7020

Peixi Dai https://orcid.org/0000-0001-5555-8026

Yanxia Sun https://orcid.org/0000-0002-2122-4009

Jie Qian https://orcid.org/0000-0002-7527-9005

Weizhong Yang https://orcid.org/0000-0002-6599-825X

Luzhao Feng https://orcid.org/0000-0002-5206-5995

Article

Publisher ID: v9i1e44970

DOI: 10.2196/44970

PMC ID: 10263104

PubMed ID: 37191650

SO-VID: 1b044345-4460-4637-8028-0ef6bce9712d

Copyright © ©Qing Wang, Mengmeng Jia, Mingyue Jiang, Wei Liu, Jin Yang, Peixi Dai, Yanxia Sun, Jie Qian, Weizhong Yang, Luzhao Feng. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 12.06.2023.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

History

Date received : 11 December 2022

Date revision requested : 23 March 2023

Date revision received : 15 April 2023

Date accepted : 16 May 2023

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