The interactions of SARS-CoV-2 with co-circulating pathogens: Epidemiological implications and current knowledge gaps (preprint)

Despite the availability of effective vaccines, the persistence of SARS-CoV-2 suggests that co-circulation with other pathogens and resulting multi-epidemics -- such as twindemics of COVID-19 and influenza -- will become increasingly frequent. To better forecast and control the risk of such multi-epidemics, it is essential to elucidate the potential interactions of SARS-CoV- 2 with other pathogens; these interactions, however, remain poorly defined. Here, we aimed to review the current body of evidence about SARS-CoV-2 interactions. To study pathogen interactions in a systematic way, we first developed a general framework to capture their major components - namely, sign, strength, symmetry, duration, and mechanism. We then reviewed the experimental evidence from animal models about SARS-CoV-2 interactions. The studies identified demonstrated that SARS-CoV-2 and influenza A virus co-infection increased disease severity compared with mono-infection. By contrast, the effect of previous or co-infection on viral load of either virus was inconsistent across studies. Next, we reviewed the epidemiological evidence about SARS-CoV-2 interactions in human populations. Although numerous studies were identified, only few were specifically designed to infer interaction and many were prone to bias and confounding. Nevertheless, their results suggested that influenza and pneumococcal conjugate vaccinations were associated with reduced risk, and earlier influenza infection with increased risk, of SARS-CoV-2 infection and severe COVID-19. Finally, we formulated simple transmission models of SARS-CoV-2 co-circulation with a virus or a bacterium, showing how they can naturally incorporate the proposed framework. More generally, we propose that such models, when designed with an integrative and multidisciplinary perspective, will be invaluable tools in studying SARS-CoV-2 interactions with other pathogens.

The pitfalls of inferring virus-virus interactions from co-detection prevalence data: Application to influenza and SARS-CoV-2 (preprint)

There is growing experimental evidence that many respiratory viruses—including influenza and SARS-CoV-2—can interact, such that their epidemiological dynamics may not be independent. To assess these interactions, standard statistical tests of independence suggest that the prevalence ratio—defined as the ratio of co-infection prevalence to the product of single-infection prevalences—should equal unity for non-interacting pathogens. As a result, earlier epidemiological studies aimed to estimate the prevalence ratio from co-detection prevalence data, under the assumption that deviations from unity implied interaction. To examine the validity of this assumption, we designed a simulation study that built on a broadly applicable epidemiological model of co-circulation of two respiratory viruses causing seasonal epidemics. By focusing on the pair influenza–SARS-CoV-2, we first demonstrate that the prevalence ratio systematically under-estimates the strength of interaction, and can even misclassify antagonistic or synergistic interactions that persist after clearance of infection. In a global sensitivity analysis, we further identify properties of viral infection—such as a high reproduction number or a short infectious period—that blur the interaction inferred from the prevalence ratio. Altogether, our results suggest that epidemiological studies based on co-detection prevalence data provide a poor guide to assess interactions among respiratory viruses.

The impact of co-circulating pathogens on SARS-CoV-2/COVID-19 surveillance: How concurrent epidemics may decrease true SARS-CoV-2 percent positivity (preprint)

Background Circulation of non-SARS-CoV-2 respiratory viruses during the COVID-19 pandemic may alter quality of COVID-19 surveillance, with possible consequences for real-time analysis and delay in implementation of control measures. Here, we assess the impact of an increased circulation of other respiratory viruses on the monitoring of positivity rates of SARS-CoV-2 and interpretation of surveillance data. Methods Using a multi-pathogen Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model formalizing co-circulation of SARS-CoV-2 and another respiratory we assess how an outbreak of secondary virus may inflate the number of SARS-CoV-2 tests and affect the interpretation of COVID-19 surveillance data. Using simulation, we assess to what extent the use of multiplex PCR tests on a subsample of symptomatic individuals can support correction of the observed SARS-CoV-2 percent positive during other virus outbreaks and improve surveillance quality. Results Model simulations demonstrated that a non-SARS-CoV-2 epidemic creates an artificial decrease in the observed percent positivity of SARS-CoV-2, with stronger effect during the growth phase, until the peak is reached. We estimate that performing one multiplex test for every 1,000 COVID-19 tests on symptomatic individuals could be sufficient to maintain surveillance of other respiratory viruses in the population and correct the observed SARS-CoV-2 percent positive. Conclusions This study highlights that co-circulating respiratory viruses can disrupt SARS-CoV-2 surveillance. Correction of the positivity rate can be achieved by using multiplex PCR, and a low number of samples is sufficient to avoid bias in SARS-CoV-2 surveillance. Summary COVID-19 surveillance indicators may be impacted by increased co-circulation of other respiratory viruses delaying control measure implementation. Continued surveillance through multiplex PCR testing in a subsample of the symptomatic population may play a role in fixing this problem.

Immunological heterogeneity informs estimation of the durability of COVID-19 vaccine protection (preprint)

Deciphering the properties of vaccines against coronavirus disease 2019 (COVID-19) is essential to predict the future course of the pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, current uncertainties about COVID-19 vaccine immunity raise the question of how much time will be needed to estimate these properties, in particular the durability of vaccine protection. Here we designed a simulation study, based on empirically validated epidemiological models of SARS-CoV-2 transmission, to predict the impact of a breadth of vaccines with different mean duration (range: 2–5 years) and heterogeneity (coefficient of variation range: 50–100%) of protection against infection. We then assessed how confidently the duration of protection could be estimated under a range of epidemiological scenarios in the year following the start of mass immunization. We found that lower population mean and higher inter-individual variability facilitated estimation of the duration of vaccine protection. Across the vaccines tested, high waning and high heterogeneity permitted complete identification of the duration of protection; in contrast, low waning and low heterogeneity allowed only estimation of the fraction of vaccinees with rapid loss of immunity. These findings suggest that key aspects of COVID-19 vaccine immunity can be estimated with limited epidemiological data. More generally, they highlight that immunological heterogeneity can sensitively determine the impact of COVID-19 vaccines and, it is likely, of other vaccines.

Influenza may facilitate the spread of SARS-CoV-2 (preprint)

As in past pandemics, co-circulating pathogens may play a role in the epidemiology of coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here we hypothesized that influenza interacted with SARS-CoV-2 during the early 2020 epidemic of COVID-19 in Europe. We developed a population-based model of SARS-CoV-2 transmission, combined with mortality incidence data in four European countries, to test a range of assumptions about the impact of influenza. We found consistent evidence for a 2-2.5-fold population-level increase in SARS-CoV-2 transmission associated with influenza during the period of co-circulation. These results suggest the need to increase vaccination against influenza, not only to reduce the burden due to influenza viruses, but also to counteract their facilitatory impact on SARS-CoV-2.