Viral interference cannot be concluded from datasets containing only symptomatic patients
Authors: Rickard Nordén, Magnus Lindh, Staffan Nilsson, Johan Westin
Journal: The Lancet Microbe (2021)

In the study by Anchi Wu and colleagues,1Wu A Mihaylova VT Landry ML Foxman EF Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection study.Lancet Microbe. 2020; 1: e254-e262Summary Full Text Full Text PDF Scopus (37) Google Scholar the authors claim that rhinovirus reduces susceptibility to influenza A virus through a mechanism known as viral interference, basing their conclusions on viral detection rates in clinical samples and in-vitro experiments. We agree that viral interference might contribute to a reduction in the number of co-infections. However, this conclusion cannot be drawn from their data, as calculating the expected number of co-infections in a sample of non-randomly selected patients is not statistically accurate. To exemplify this point, assume that two viruses, X and Y, both with a prevalence of infection of 2%, were independently present in a population of 10 000 individuals, and that all individuals had samples taken on the same day. We then expect to find 10 000 × 0·02 × 0·02=4 individuals infected with both viruses, and 196 individuals each infected with either X or Y viruses. Further assume that, of 800 individuals with symptoms, 404 carry pathogens other than X or Y. Supplementary figure 1A (appendix) shows contingency tables with the expected number of individuals in the whole population and in the symptomatic population infected with X, Y, X and Y, or neither. Calculation of the expected number of individuals co-infected with X and Y from the contingency table of symptomatic individuals gives an expected number of 200 × 200/800=50. Such a calculation leads to the conclusion that the observed number (n=4) is lower than the expected number (n=50); the odds ratio would be 0·042. This approach was applied in the study by Wu and colleagues,1Wu A Mihaylova VT Landry ML Foxman EF Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection study.Lancet Microbe. 2020; 1: e254-e262Summary Full Text Full Text PDF Scopus (37) Google Scholar in which they collected data only from patients with symptoms, and calculated the expected number of individuals co-infected with rhinovirus and influenza A virus as 989 × 922/13 707=67. Based on this calculation, they concluded that the observed number of co-infections (n=12) was significantly lower than the expected number (n=67). As shown in the above example, this conclusion is not justified, and the mistake illustrates that calculating the expected number of co-infections from observations in datasets including only individuals with symptoms is misleading, as pathogens that are truly independent in the population by such a calculation will appear to be negatively associated with each other (ie, occur as a co-infection less often than expected). To provide another example, we compared data from the study by Wu and colleagues1Wu A Mihaylova VT Landry ML Foxman EF Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection study.Lancet Microbe. 2020; 1: e254-e262Summary Full Text Full Text PDF Scopus (37) Google Scholar with that of an ongoing study of 8178 samples referred for testing of respiratory pathogens by a multiplex PCR panel at our centre, over the course of 1 year. As shown in supplementary figure 1B and C (appendix), negative associations were observed when the co-infection frequency was compared with the frequency of infection with each virus alone, not only for rhinovirus and influenza A, but also for several virus pairs. Although Wu and colleagues1Wu A Mihaylova VT Landry ML Foxman EF Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection study.Lancet Microbe. 2020; 1: e254-e262Summary Full Text Full Text PDF Scopus (37) Google Scholar concluded that viral interference was responsible for these negative associations (at least for rhinovirus and influenza A), we consider that these results reflect what is statistically anticipated. Importantly, we notice that these considerations were also missing from another study on the same topic published in 2019.2Nickbakhsh S Mair C Matthews L et al.Virus–virus interactions impact the population dynamics of influenza and the common cold.Proc Natl Acad Sci U S A. 2019; 116: 27142-27150Crossref Scopus (125) Google Scholar Our comment on the interpretation of the clinical data in the study by Wu and colleagues1Wu A Mihaylova VT Landry ML Foxman EF Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection study.Lancet Microbe. 2020; 1: e254-e262Summary Full Text Full Text PDF Scopus (37) Google Scholar does not argue against the existence of viral interference. However, a study published in 2019 showed that influenza A blocked rhinovirus replication, but that rhinovirus did not interfere with influenza A virus replication in vitro.3Essaidi-Laziosi M Geiser J Huang S Constant S Kaiser L Tapparel C Interferon-dependent and respiratory virus-specific interference in dual infections of airway epithelia.Sci Rep. 2020; 1010246Crossref PubMed Scopus (9) Google Scholar These results contradict the in-vitro data presented by Wu and colleagues,1Wu A Mihaylova VT Landry ML Foxman EF Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection study.Lancet Microbe. 2020; 1: e254-e262Summary Full Text Full Text PDF Scopus (37) Google Scholar and these opposing conclusions highlight the difficulties in translating experimental observations to a clinical setting. We declare no competing interests. Download .pdf (.11 MB) Help with pdf files Supplementary appendix Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection studyThese findings show that one respiratory virus can block infection with another through stimulation of antiviral defences in the airway mucosa, supporting the idea that interference from rhinovirus disrupted the 2009 IAV pandemic in Europe. These results indicate that viral interference can potentially affect the course of an epidemic, and this possibility should be considered when designing interventions for seasonal influenza epidemics and the ongoing COVID-19 pandemic. Full-Text PDF Open AccessViral interference cannot be concluded from datasets containing only symptomatic patients – Authors' replyWe appreciate the important point raised by Rickard Nordén and colleagues, that when using co-detection analysis to evaluate viral interference, if the virus detection rates in the study population do not match those in the larger community, then a mathematical bias could be introduced. However, if the virus detection rates in the study population do approximate those in the community, co-detection analysis is a useful approach. Based on the available data, the proportion of individuals testing positive for a virus in our study1 was similar to that in the community. Full-Text PDF Open Access