key: cord-0130627-jaqs7cba authors: Wong, Anabelle; Barrero, Laura; Goult, Elizabeth; Briga, Michael; Kramer, Sarah C.; Kovacevic, Aleksandra; Opatowski, Lulla; Celles, Matthieu Domenech de title: The interactions of SARS-CoV-2 with co-circulating pathogens: Epidemiological implications and current knowledge gaps date: 2022-05-16 journal: nan DOI: nan sha: 474364724122266f4c8393cc0ceb3fe40a5d29ac doc_id: 130627 cord_uid: jaqs7cba 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. As of February 28, 2022, the pandemic of coronavirus disease 2019 (COVID-19)-caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)has resulted in at least 440 million cases and 6 million deaths worldwide [1] . Despite the implementation of stringent control measures and the increasing roll-out of effective vaccines in many locations, the persistent circulation of SARS-CoV-2 suggests the infeasibility of elimination and the gradual transition to endemic or seasonal epidemic dynamics [2] . Hence, co-circulation of SARS-CoV-2 with other pathogens may become increasingly frequent and cause multiple simultaneous epidemics, such as "twindemics" of COVID-19 and influenza [3] . Interaction-that is, the ability of one pathogen to alter the risk of infection or disease caused by another pathogen (Fig. 1 )-is an essential aspect to forecast the dynamics of cocirculating infectious diseases. From a public health perspective, interactions may significantly aggravate disease burden, as demonstrated for immunosuppressive viruses like measles [4] and human immunodeficiency virus (HIV) [5] . Another interesting, yet understudied public health implication of interactions is the possibility of indirect effects of vaccines on non-target pathogens, as suggested for influenza vaccines [6, 7] . However, despite their potentially large relevance to SARS-CoV-2 epidemiology and COVID-19 control measures, the interactions of SARS-CoV-2 with other pathogens remain poorly defined. Here, we aimed to review the current body of evidence about the interactions of SARS-CoV-2 with co-circulating pathogens. We first present a general framework to capture the complexities of interactions and study them in a systematic way. Using this framework, we then review the results of published experimental and epidemiological studies. Finally, we formulate simple transmission models incorporating the proposed framework to illustrate the potential population-level impact of SARS-CoV-2 interactions. 4 Pathogen interactions can be complex, because of the multiple elements needed to fully characterize them. To study interactions in a systematic and comprehensive way, we propose a conceptual framework-depicted schematically in Fig. 1 -that incorporates three essential components of interaction, detailed below. negative, asymmetric interactions (as in the case of influenza A virus (IAV) and respiratory 5 syncytial virus (RSV)). Interaction can be caused by different biological mechanisms (C), which determine its positive or negative effects on susceptibility to infection, transmission (transmissibility and duration of infection), or disease severity at the individual level and in turn its impact at the population level. The first dimension of this framework is the sign and strength of interaction. Here, we define the sign of interaction as positive in synergistic interactions (where a first pathogen increases the risk of infection or disease of a second pathogen) and negative in antagonistic interactions (where the risk is decreased), and we refer to strength as the magnitude of effect on a given parameter exerted by one pathogen on another. An example of negative interaction exists between influenza A virus (IAV) and human respiratory syncytial virus (RSV), for which experimental studies have shown that a recent IAV infection inhibits the growth of RSV in ferrets [8] and in mice [9] . By contrast, IAV interacts positively with Streptococcus pneumoniae (Sp) by promoting bacterial growth [10, 11] . This illustrates that interaction is pathogen-specific and cannot be easily extrapolated to other pathogen systems. The second dimension of our proposed framework is time-dependency: both the time between infections and the sequence of infection can affect the sign and strength of an interaction. The third dimension in our framework is the mechanism of interaction: interaction can be caused by different biological mechanisms, which determine its positive or negative effects on susceptibility to infection, characteristics of infection (such as transmissibility and duration), or disease severity at the individual level and in turn its impact at the population level (Fig. 1C ). Examples of biological mechanisms of pathogen interaction include intra-cellular and physiological changes and effects on the immune response, on the respiratory microbiota, and on host behaviors. A pathogen can induce changes on the host cells that are beneficial or detrimental to another pathogen. For example, it has been shown that RSV and human and Sp binding in bronchial epithelial cells [26] . In both cases, changes in cellular expression may lead to a positive interaction. A pathogen can cause changes to the host's immune profile (e.g., depletion of CD4+ T cells by HIV [5] , increased IFN response by IAV [9] ), facilitating or hindering infection with a second pathogen. Moreover, a pathogen can change the physiological environment to potentiate a secondary infection by another pathogen. For instance, the replication of IAV in the respiratory epithelium reduces mucociliary clearance and damages epithelial cells, resulting in enhanced attachment and invasion of Sp [21] . Changes in the upper respiratory tract (URT) microbiota by an infection can lead to the acquisition of a new pathogen, or to overgrowth and invasion of an already present pathogen [27] [28] [29] . Lastly, changes in host behaviors caused by infection with a first pathogen can affect the risk of subsequent infection with another pathogen, even in the absence of within-host 8 interaction between the two. Examples include self-isolation to reduce spread of disease in humans and reduced social contacts in infected animals [30, 31] . The biological mechanisms outlined above may affect population-level dynamics through their effects on different epidemiological parameters: susceptibility to infection, Experimental evidence from animal models Having proposed a framework to study interactions, we now review experimental studies on co-infections with SARS-CoV-2 in animal models. As of February 28th, 2022, eight published studies were identified (Fig. 2 Table S1 ). Similarly, in the second study, reducing the interval between infections (from 14 to 7 days) caused higher weight loss in mice [48], Table S1 ). In contrast to the consistent results on disease severity, the impact of co-infection on viral load-in either the lower or upper respiratory tract-varied enormously across studies Table S1 ). The estimated effects spanned several orders of magnitude, with no 11 clear picture emerging regarding the sign of interaction, irrespective of the sequence of infection. In addition to the sources of heterogeneity outlined above, the studies varied in the technique used to quantify viral load (either RT-qPCR or plaque-based assays) and in the sample type (swabs or tissue) and location (lower or upper respiratory tract). These differences may affect the inferred sign and strength of interaction: for example, the load of viable viruses-which only plaque-based assays can quantify-in the upper respiratory tract or in nasal swabs is likely the best proxy of transmissibility. However, the results remained inconsistent across studies, even after factoring in those differences. Overall, the reviewed experimental studies show that co-infection with SARS-CoV-2 and IAV increases disease severity, but its impact on transmission and susceptibility remains unclear. The considerable heterogeneity across studies prevented a quantitative analysis and a systematic comparison of their results. To assess the true extent of heterogeneity, we recommend that further studies of SARS-CoV-2 interactions should aim to replicate the experimental protocols and verify the robustness of previous studies. Furthermore, we argue that the strength of evidence could be increased by varying, within a study, the infectious dose and the infection timelines and by considering different animal models and assays. Next, we reviewed the literature on SARS-CoV-2 and co-infections in human populations. The identified studies could be classified into three broad categories: (1) studies that were based on co-infection prevalence, (2) studies that examined the association between non-COVID vaccines and COVID-19, and (3) studies that examined the association between prior respiratory infections and COVID-19. 13 Studies based on detection of other pathogens among COVID-19 patients Previous reviews and meta-analyses have summarized the evidence on the prevalence of bacterial co-infections among COVID-19 patients [51] [52] [53] [54] [55] . Such studies are useful in guiding therapy but uninformative about the mechanisms of pathogen interaction, especially when the evidence summarized did not provide information about the co-infection timeline [52, 54] , because the mechanism is likely time-dependent, as discussed above. (Table S3 ). All three studies involving PPSV did not find conclusive evidence for association between PPSV history and SARS-CoV-2 related outcomes [76, 77] . PCV was associated with protection against COVID-19 infection, hospitalization, and mortality among older adults in one cohort study [77] , and against symptoms among SARS-CoV-2-infected children in another cohort study [79] . Although inconclusive, the association estimated in a case-control study [76] was consistent with that in the two cohort studies. Findings from vaccine impact studies must be interpreted with caution when attempting to infer pathogen interactions. First, although numerous studies attempted to estimate the effect of various vaccines on COVID-19 outcomes, few accounted for healthy user bias, a common form of selection bias whereby more active health-seeking behaviors can be a source of confounding [80] . As acknowledged by [81] and [82] , this is often a limitation in observational studies, as influenza vaccination is voluntary [82] [83] [84] [85] . Second, even when epidemiological studies adopting more robust study designs (e.g., prospective cohort) and inference methods (e.g., Cox model with inverse propensity weighting) show that non-SARS-CoV-2 vaccines confer protection against SARS-CoV-2 [77], one cannot distinguish if such protection stems from hindering the positive interaction between two pathogens, or from the direct effect of the vaccine on SARS-CoV-2-for example via nonspecific immune responses such as trained innate immunity [86] . Four observational studies reported the association of prior respiratory infections and COVID-19-related outcomes [87-90] (Table S4) . Prior influenza infection was reported to be associated with increased COVID-19 susceptibility (OR: 3.07, 95% CI: 1.61-5.85 for 1-14 days prior, OR 1.91, 95% CI: 1.54-2.37 for 1-90 days prior) and severity (OR: 3.64, 95% CI: 1.55-9.21 for 1-14 days prior, OR: 3.59, 95% CI: 1.42-9.05 for 1-30 days prior) in a case-control study [87] . This evidence, suggestive of a positive interaction between influenza and SARS-CoV-2, is consistent with the findings from a mathematical modeling study [91] . Although a retrospective cohort study reported that prior infection with endemic human coronaviruses (hCoVs) was associated with protection against COVID-related ICU admission . This discrepancy may be explained by the different URI definitions and time frames for exposure measurement, in addition to different study designs and included confounders. Because these studies provided information about the infection timeline, they offered stronger evidence to infer pathogen interactions than studies based on co-infection prevalence, and also more direct evidence than studies examining the association between non-COVID vaccines and COVID-19. Nevertheless, one should beware of how misclassification of exposure and imperfect control for confounding can limit such study designs in inferring pathogen interactions. In summary, the evidence available from human population health data indicates that co-infection prevalence is largely variable, that influenza vaccines and PCVs may be associated with reduced risk of SARS-CoV-2, and that earlier influenza infection may be associated with higher risk of SARS-CoV-2 infection and disease severity. However, our review also highlighted the limitations in the current epidemiological literature, as many studies were prone to bias and confounding and only very few [87-91] were designed to infer interaction. As outlined above ( Fig. 1) , the multiple components required to characterize pathogen interactions make their study complex. Although experimental studies in animal models can inform some of these components, they are insufficient to predict the public health impact of interaction in human populations, for at least two reasons. First, animal models may not fully recapitulate the biology of infection in humans, as illustrated by the ongoing search for an appropriate animal model representative of severe COVID-19 disease in humans [92] . Second, experimental studies are typically under-powered to provide precise estimates of key epidemiological quantities (for example, relative risk of acquisition or severe disease in co-vs. Hence, epidemiological studies remain indispensable to complement experimental studies and to assess the significance of interaction in human populations. As reviewed above, however, the results of such studies may be difficult to interpret and their scope too limited to identify the underlying mechanisms of interaction. Arguably, more integrated approaches are therefore needed to capture the complexities described above and to determine how individual-level mechanisms of interaction translate into population-level dynamics of infection or disease. Mathematical models of transmission offer a powerful and economical tool to study infectious disease dynamics [93] . To study pathogen interactions, such models can be formulated to incorporate biologically explicit mechanisms of interaction (in addition to the other elements of the framework proposed above) and predict their potentially non-linear effects on transmission dynamics [94] . By design, these models translate between scales, such that the population-level impact of a given individual-level mechanism of interaction can be simulated and predicted. To illustrate the relevance of such models, we formulated two basic models of SARS-CoV-2 interaction (see more details and equations in the Supplement), with either an endemic colonizing bacterium (e.g., Sp) or a respiratory virus causing seasonal epidemics (e.g., influenza). In both cases, we assumed a non-symmetric (i.e., no effect of SARS-CoV-2 on the other pathogen) interaction that caused a 1-5 fold (strength) decrease or increase (sign) of SARS-CoV-2 transmission (mechanism) in co-infected individuals (duration of interaction equal to the infectious period of the other pathogen). As shown in Fig. 4A , we find that even a moderately strong interaction with a commensal bacterium can substantially affect the dynamics of SARS-CoV-2, increasing its peak incidence by 3.5 fold for positive interaction when the prevalence of bacterial colonization reaches 50% of the population (as frequently observed in young children for Sp [95,96]). By contrast, an equal interaction with an epidemic virus is predicted to have a much smaller impact on the dynamics of SARS-CoV-2 ( Fig. 4B ). Of note, the maximal impact is predicted at intermediate levels of transmissibility of the epidemic virus, corresponding to maximal epidemic overlap with SARS-CoV-2 ( Figure 4 , right). This finding emphasizes a major difference between endemic and epidemic pathogens: for the latter, the impact of even strong interactions may remain subtle and manifest itself only after a prolonged period of co-circulation with SARS-CoV-2. Overall, these numerical experiments demonstrate the value of mathematical models to study interactions in a biologically explicit and comprehensive way and to predict their complex (and potentially unexpected) effects at the population level. Although voluntarily over-simplified, these models can be readily extended to add components relevant to SARS-CoV-2 epidemiology, such as age, vaccination, or temporal variations in transmission caused by new variants, seasonality, or changing control measures. In real-world applications, however, model parametrization can be a substantial challenge, as the values of many parameters may be neither directly observable nor fixed from empirical evidence. This problem is particularly salient for parameters characterizing interaction, whose values can be only partially inferred from experimental and epidemiological studies. To As population immunity against COVID-19 accrues in many regions worldwide, it is critical to understand the factors that will affect the future transmission dynamics of SARS-CoV-2 [2] . Here, we proposed that interactions with co-circulating pathogens will be such a key factor. Indeed, such interactions may have notable public health implications, in particular for forecasting and controlling SARS-CoV-2 epidemics and for predicting the indirect impact of vaccines. The scientific implications of interaction are also notable and may lead to considering SARS-CoV-2 as part of polymicrobial systems whose individual components cannot be well studied separately. Despite the relevance of interaction, our review identified only a handful of experimental studies in animal models, with markedly different designs but all focused on SARS-CoV-2 and IAV. A robust finding from our comparative analysis is that previous or Altogether, our review highlights the significant gaps that remain in our knowledge of SARS-CoV-2 interactions. The general framework proposed to dissect interaction may therefore be useful to guide further research in this field. We argue that mathematical models of transmission offer an intrinsically efficient way to incorporate this framework. Hence, we submit that such models-designed with a multi-disciplinary perspective that integrates evidence across scientific fields-will prove to be valuable tools to decipher the interactions of SARS-CoV-2. Table S1 . An overview of the experimental designs and results on disease severity, measured as maximal body mass loss or survival at experiment end, from the reviewed studies assessing the interaction between SARS-CoV-2 and influenza A virus (IAV). Values were taken from tables or text, or when these were not available, extracted from the figures using the program PlotDigitizer [9] . Table Bai et al. [1] 2021 K18-hACE2 mice male H1N1 2nd 2 3 See table S1 Lung Tissue 4 NA NA 6,60 1,00 Fig 2D NA NA NA NA NA NA NA NA gc/GAPDH RT-qPCR Bao et al. [2] 2021 Ferrets male H1N1 2nd 5 4 See table S1 Lung Tissue 5 NA NA 1,20 3,20 Fig 2C Throat Swabs 3 5,80 6,20 5,50 5,40 Fig 2A/B log10 gc/mL RT-qPCR Bao et al. [2] 2021 Ferrets male H1N1 2nd 5 4 See table S1 NA NA NA NA NA NA NA NA Throat Swabs 5 5,40 5,30 4,90 4,90 Fig 2A/B log10 gc/mL RT-qPCR Bao et al. [2] 2021 Ferrets male H1N1 2nd 5 4 See table S1 NA NA NA NA NA NA NA NA Throat Swabs 8 4,80 2,90 0,90 0,00 Fig 2A/B log10 gc/mL RT-qPCR Bao et al. [2] 2021 Ferrets male H1N1 2nd 5 4 See table S1 NA NA NA NA NA NA NA NA Throat Swabs 10 2,00 0,00 0,00 0,00 Fig 2A/B log10 gc/mL RT-qPCR Zhang et al. [3] 2021 Syrian hamster male + female H1N1 simultaneous 0 3 See table S1: High dose Lung Tissue 4 4, 1 and Z the state of pathogen 2 [1] . Bacteria-virus interaction model: The bacteria-virus model was constructed such that pathogen 1 is the bacteria and pathogen 2 is the virus. We assumed the interaction was asymmetric, such that colonization with bacteria impacts transmission of the virus, but infection with the virus has no impact on the bacterial dynamics. The model was defined by 2 x 3 = 6 ordinary differential equations as represented in Figure S1 , where the disease states are {S, C} for bacteria, and {S, I, R} for the virus. As an example we consider the S. pneumoniae -SARS-CoV-2 interacting system, hence assuming the bacteria is S. pneumoniae and the virus is SARS-CoV-2. Parameter values are detailed in Table S5 . The model was run for 365 days and the peak viral incidence was calculated for varying rate of bacterial colonization and varying transmission interaction parameter. The model was implemented in the R [2] packages 'pomp' [3] , and 'tidyverse' [4] . Plots were created with 'ggplot2' [5] , 'patchwork' [6] , 'scico' [7] and 'Microsoft PowerPoint'. All code is available at https://github.com/egoult/pathogen_coinfections . Here, / 0,1 and / 0,2 denote the respective basic reproductive numbers for virus 1 and virus 2. We consider the Influenza A -SARS-CoV-2 interacting system as an example, where virus 1 is Influenza A and virus 2 is SARS-CoV-2, so infection with influenza A affects the dynamics of SARS-CoV-2, but infection with SARS-CoV-2 has no impact on influenza A. Parameter values are detailed in Table S6 . The model was run for 365 days and the peak SARS-Cov-2 incidence was calculated, for varying of influenza A basic reproduction numbers and varying transmission interaction parameter. 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Values were obtained from tables or text, or when these were not available, from figures using PlotDigitizer [9] . We developed deterministic compartment models of the two interacting pathogens. Following Shrestha et al. we used a double index notation, e.g ! !,# where Y gives the state of pathogen