{
  "abstract": "Background With the acceleration of globalisation and the increasing frequency of international exchanges, the risk of cross-border transmission of emerging respiratory infectious diseases (ERIDs) has significantly increased. Since the year 2002, epidemics of SARS, Middle East respiratory syndrome (MERS) and COVID-19 have exemplified this trend. These epidemics have impacted the prevalence and transmission of traditional respiratory infectious diseases (RIDs), such as influenza, which share similar transmission routes and control measures. To better explore the impact of ERIDs epidemics on influenza, our study quantitatively evaluates the epidemiological changes in influenza during three representative emerging respiratory coronavirus epidemics: SARS, MERS and COVID-19.Methods Using Global Influenza Surveillance and Response System data, we examined influenza trends across different periods and regions affected by the three coronavirus epidemics. The impact of the epidemic on influenza was revealed by comparing and analysing the reported positive cases (RPCs) of influenza during the pre-epidemic and epidemics, and during the three postpandemic periods. Based on the Susceptible-Exposed-Infected-Asymptomatic-Recovered (SEIAR) compartmental model, the time-varying effective reproduction number ( Rt)) over time was calculated, and the Farrington surveillance algorithm was used to calculate the RPCs in the absence of an epidemic to analyse the characteristics of influenza transmissibility during the epidemics of the three respiratory coronavirus changes.Results There was a significant decline in the RPCs of influenza and transmissibility. The suppressive effect of the COVID-19 epidemic on influenza prevalence was the most pronounced. During the COVID-19 epidemic, the RPCs of the three major influenza subtypes showed the largest decrease compared with historical predictions, with reduction rates of −53.30% for A(H1N1), −57.50% for A(H3N2) and −48.56% for influenza B (p<0.01), with A(H3N2) being the most significantly affected, as most countries experienced reductions exceeding 50%. The impact of the SARS epidemic on influenza was secondary, with total RPCs of A(H1N1) and influenza B decreasing by approximately 84.39% and 45.31%, respectively (p>0.05). During the MERS epidemic, the RPCs of A(H1N1) and A(H3N2) decreased by 28.75% and 17.62%, respectively, although influenza B partially rebounded in the later stages, resulting in a relatively smaller overall impact.Conclusions The COVID-19 epidemic demonstrated the most pronounced suppressive effect on influenza prevalence. The impact of SARS was secondary, while MERS had the least effect. Among different influenza subtypes, A(H3N2) and influenza B exhibited greater declines compared with A(H1N1). The decrease in RPCs during coronavirus epidemics highlighted the importance of non-pharmaceutical interventions (NPIs), demonstrating the broad applicability and high efficacy of comprehensive control strategies for RIDs. Furthermore, when NPIs are lifted during the later stages of coronavirus epidemics, attention should be paid to the potential rebound of traditional respiratory diseases such as influenza.",
  "authors": [
    {
      "affiliations": [
        "State Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, Fujian, China"
      ],
      "name": "Huimin Qu"
    },
    {
      "affiliations": [
        "State Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, Fujian, China"
      ],
      "name": "Kangguo Li"
    },
    {
      "affiliations": [
        "State Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, Fujian, China",
        "Université de Montpellier, Montpellier, France",
        "CIRAD, Intertryp, Montpellier, France"
      ],
      "name": "Jia Rui"
    },
    {
      "affiliations": [
        "Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China"
      ],
      "name": "Qi Chen"
    },
    {
      "affiliations": [
        "State Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, Fujian, China"
      ],
      "name": "Tao Li"
    },
    {
      "affiliations": [
        "State Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, Fujian, China"
      ],
      "name": "Xiaohao Guo"
    },
    {
      "affiliations": [
        "Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China"
      ],
      "name": "Xuhua Guan"
    },
    {
      "affiliations": [
        "State Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, Fujian, China"
      ],
      "name": "Tianmu Chen"
    }
  ],
  "title": "Epidemiological impact of three major respiratory coronavirus epidemics on influenza transmission: a multicountry analysis using surveillance data and mathematical modelling",
  "uid": "5cc1b6d8-fd6a-5231-9ab5-037f0f52b597"
}