Bryan T. Grenfell

Type I interferons (IFN) help clear a viral infection, but IFN levels can then decline to reset the antiviral state and avoid innate immune dysregulation. Such dysregulation can occur following infection with highly pathogenic and pandemic RNA viruses, such as H5N1 influenza A viruses or SARS-CoV-2. In spite of the strong innate immune response that these viruses induce, pandemic viruses replicate and spread successfully, suggesting that they can circumvent the effects of IFN. Innate immune activation is triggered when host cells detect viral RNA, especially defective viral genomes (DVGs). A strong IFN response (e.g. to DVGs or a highly immunogenic wild-type infection) must then decline through negative feedback involving the IFN receptor. How DVGs modulate this feedback remains poorly understood. To quantify positive and negative IFN regulation, we first performed in vitro experiments combining different doses of viral RNA and exogenous IFN, and measured subsequent IFN expression. We next developed an in vivo Bayesian computational model that captures the impact of influenza virus DVGs on both positive and negative IFN feedback. Competition between DVGs and virus is also considered. Finally, we calibrated this model on published in vitro and in vivo influenza virus infection data. Our model suggests that the cumulative IFN exposure of a host cell, rather than the instantaneous IFN level, shapes further IFN induction; this gives DVGs a dual role: beyond triggering IFN induction, they can also lower it through negative feedback. Our model further predicts that these effects are important locally, shaping the spatial dynamics of the response, allowing suppression of innate immune responses in neighbouring, uninfected cells. Through this model, we can explain two important biological observations: i) IFN levels can decline while virus and DVG levels continue to rise in tissue culture and mouse infections; and ii) pandemic viruses that produce high levels of immunogenic RNA molecules can successfully replicate through local innate immune suppression. Overall, we propose that heterogeneity within viral populations (including DVGs and other noncanonical viral RNAs) can modulate positive and negative feedback loops and shape infection outcomes. More broadly, our work may support studies into the therapeutic use of these viral RNA populations. Author summary: Humans and animals have to fight influenza virus and other infections without inducing immunopathology. After detection of an infection, the innate immune system produces type I interferon (IFN), a signal that starts a protective response. However, high levels of IFN and prolonged IFN expression can cause damage to tissues and worsen disease. Negative feedback is one route by which IFN production can stop, but its activation during viral infection remains poorly understood. A central question is why viruses can continue to replicate and spread despite a strong IFN response. We here investigated why IFN levels can begin to decline while virus levels continue to rise during influenza virus infection. This decline is most pronounced when the virus produces large amounts of defective viral genomes (DVGs). These defective genomes arise naturally during viral replication and are produced by many RNA viruses, including SARS-CoV-2, Ebola virus, and measles virus. Using cell-culture experiments, computational modelling, and fitting of our model to cell-culture and mouse data, we find that defective genomes contribute to both IFN activation and its negative feedback. Both the experiments and model support a mechanism in which IFN accumulation during infection suppresses its own production, and that this suppression can start earlier under strong DVG stimulation. At the local level, this can allow viral spread to continue even while overall IFN levels remain high. Overall, our findings reveal an additional level of interplay between defective viral genomes and the host innate immune system. They also suggest that the potential therapeutic use of defective genomes must be carefully calibrated to achieve an optimal antiviral response.

While excess rainfall is associated with mosquito-borne disease because it supports mosquito breeding, drought may also counterintuitively increase disease transmission by altering mosquito and host behavior. This phenomenon is important to understand because climate change is projected to increase both extreme rainfall and drought. In this study, we investigated the extent to which seasonally driven mosquito and primate behavior drove the first yellow fever virus (YFV) epidemic in an urban area in Brazil in nearly a century, coinciding with an equally rare drought, and assessed the role of interventions in ending the outbreak. We hypothesized that drought-like conditions played a key role in the outbreak dynamics by driving the forest mosquitoes and nonhuman primates toward the city in search of water and that the mosquitoes were biting more frequently to avoid desiccation. A dynamical YFV model supports these hypotheses, showing that both behavioral changes were needed to explain the outbreak timing and incidence. Furthermore, a combination of vector control, conservation measures, and vaccination contributed to ending the outbreak, with the strongest effects from vaccination. Together, these results suggest that drought, likely to become more frequent in this region in the coming decades, can considerably influence mosquito-borne disease transmission and that sustained control will require multiple interventions.

Protective immunity against influenza transmission remains poorly understood due to the high prevalence of undetected asymptomatic infections. To address this, we analyze 1518 individuals and 623 infection episodes from a three-year, rural and urban, South African household cohort study that combines pre-season serum collection with twice-weekly virological testing regardless of symptoms. By developing a subtype/lineage-specific household transmission model and incorporating time-resolved viral shedding kinetics for all observed infections, we disentangle household and community exposure, pre-season immunity, and age effects. After adjusting for exposure intensity, pre-season hemagglutination inhibition (HAI) titers ≥1:40 are associated with a reduced infection risk for A(H1N1)pdm09, A(H3N2), and B/Victoria, but not B/Yamagata. However, children exhibit higher susceptibility and longer viral shedding durations than adults across all subtypes/lineages, even after accounting for pre-season HAI titers. Here, we show persistent age-related effects on influenza susceptibility and viral shedding, underscoring the need to explore immune mechanisms beyond HAI-based correlates of protection.

Background: The COVID-19 pandemic has illustrated how nonpharmaceutical interventions (NPIs), such as mask-wearing, social distancing, and purifying air, can successfully mitigate transmission and reduce infections in the short term. However, the longer-term implications of these interventions on infection levels are less clear. In tandem, recent observational evidence suggests that the relative susceptibility of partially immune individuals to infection may be dose-dependent, i.e., higher exposures are more likely to result in (re)infection in individuals with prior immunity from infection or vaccination. Methods: To examine this question, we use mechanistic immuno-epidemiological models to determine the equilibrium infection levels with NPI-induced reductions in transmission in the presence of this dose-dependency. Results: We find that NPIs can successfully decrease the number of infections at endemicity, even in high transmission scenarios. We also show that this effect is heightened by vaccination, especially if a durable, broadly-protective (i.e., broad antigen specificity) vaccine is deployed. Finally, we find that NPI-induced declines in infection levels are strongly magnified if the characteristics of subsequent infections, such as transmissibility or duration, are also dose-dependent. Conclusions: Overall, our results suggest that the long-term usage of NPIs could successfully reduce infections especially where immunity-exposure trade-offs apply due to dose-dependency, and illustrate the urgent need to characterize the underlying relationships between exposure and host immune responses.

Defective interfering particles (DIPs) are incomplete viral genomes that modulate infection by competing with wild–type viruses and activating the innate immune response. Activation of the immune response leads to the production of cytokines and chemokines, including type I interferon (IFN), which restricts viral growth and may cause cell death. How DIPs interact with type I interferon (IFN) in spatially structured environments remains unclear. Focusing here on influenza A viruses, we developed a spatially explicit, stochastic model of in vitro viral infection that integrates virus and DIP replication, IFN signalling, and alternative dispersal modes. We find that: (1) our model captures the ring–like and patchy plaque morphologies observed experimentally; (2) IFN production peaks at an intermediate DIP ratio, reflecting a trade–off between early immune activation and sufficient co–infection; and (3) even a small fraction of long–range spread by virus and DIPs enables escape from the immune-based containment despite long-range IFN diffusion; this causes stronger antiviral responses but earlier peaks in virus egress at similar levels of cell loss. The model is available as an interactive platform: https://shiny-spatial-infection-app-production.up.railway.app/ .

Many respiratory pathogens co-circulate within human populations. Yet, how pathogen community structure shapes the dynamics of infectious diseases remains poorly understood. At the population level, investigating polymicrobial dynamics, with potential underlying competitive or cooperative interactions, is challenging, because of confounding factors such as differing seasonality. This is particularly true for endemic pathogens which typically exhibit stable periodic dynamics. Their disruption due to the implementation of non-pharmaceutical interventions during the COVID-19 pandemic thus represents a unique large-scale natural experiment that can be leveraged to provide valuable insights into the complex interplay between respiratory pathogens. Here, we focus on the population dynamics of human rhinovirus (common cold) and on the potential viral interference of influenza A virus (flu A), which is hypothesized to account for their asynchronous circulation. Using a Bayesian framework, we first show based on simulations that exogenous perturbations can be a powerful tool to disentangle the contribution of pathogen interaction from other epidemiological factors. We then apply our framework to long surveillance time series from the US and Canada spanning the COVID-19 pandemic. We estimate key parameters of rhinovirus but find no conclusive support for an influence of influenza A virus at the population level.

Titrating the importance of endogenous and exogenous drivers for host-pathogen systems remains an important research frontier towards predicting future outbreaks. In Japan, respiratory syncytial virus (RSV), a major childhood respiratory pathogen, displayed a sudden, dramatic shift in outbreak seasonality (from winter to fall) in 2016. We use mathematical models to identify processes that could lead to this outcome. In line with previous analyses, we identify a robust quadratic relationship between transmission against mean specific humidity and mean temperature, with maximum transmission occurring at low and high humidity as well as low and high temperature. This drives semiannual patterns of seasonal transmission rates that peak in summer and winter. Under this transmission regime, a subtle increase in population-level susceptibility or transmission can cause a sudden shift in seasonality, where the degree of shift is primarily determined by the interval between the two peaks of seasonal transmission rate. We hypothesize that an increase in children attending childcare facilities may have contributed to the increase in the overall RSV transmission through increased contact rates between susceptible and infected hosts. Our analysis underscores the power of studying infectious disease dynamics to titrate the roles of underlying drivers of dynamical transitions in ecology.

Defective interfering particles (DIPs) are incomplete viral genomes that modulate infection by competing with wild–type viruses and activating innate immunity. How DIPs interact with type I interferon (IFN) in spatially structured environments remains unclear. Focusing initially on influenza A, we developed a spatially explicit, stochastic model of in vitro viral infection integrating virus and DIP replication, IFN signalling, and alternative dispersal modes. We find that: (1) our model captures the ring–like and patchy plaque morphologies observed experimentally; (2) IFN production peaks at an intermediate DIP ratio, reflecting a trade–off between early immune activation and sufficient co–infection; and (3) even a small fraction of long–range spread of virus and DIPs escape containment despite longer IFN ranges; this causes stronger antiviral responses but earlier peaks in lysis at similar levels of cell loss. The model is available as an interactive platform: https://shiny-spatial-infection-app-production.up.railway.app/ .

Respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) are closely related pathogens responsible for a significant burden of acute respiratory infections. Interactions between RSV and hMPV have been hypothesized, but the mechanisms of interaction are largely unknown. Here, we use a mathematical model to quantify the likelihood of interactions from population-level surveillance data and investigate whether interactions could lead to increases in hMPV burden under RSV medical interventions, including active and passive immunization. In Scotland, Korea, and three regions of Canada, annual hMPV outbreaks lag RSV outbreaks by up to 18 weeks; two Canadian regions show patterns consistent with out-of-phase biennial outbreaks. Using a two-pathogen transmission model, we show that a negative effect of RSV infection on hMPV transmissibility can explain these dynamics. We use post-pandemic RSV-hMPV rebound dynamics as an out of sample test for our model, and the model with interactions better predicts this period than a model where the pathogens are assumed to be independent. Finally, our model suggests that hMPV peak timing and magnitude may change under RSV interventions. Our analysis provides a foundation for detecting possible RSV-hMPV interactions at the population level, although such a model oversimplifies important complexities about interaction mechanisms.

Extreme weather is common in high malaria burden areas and is likely to increase in severity owing to climate change–related severe weather events. Yet, data on infection rates after these events and the consequences for planning disease control programs remain rare. Data on malaria infection in the wake of major tropical cyclones in Madagascar show that infection is likely to rebound rapidly during the gaps in interventions that occur after extreme events. Relative to other control options, recently available malaria vaccines have a longer duration of protection, with the potential to address interruptions in prevention and treatment deployment. Evaluating the use of vaccination in a climate context, we quantified the reduction in symptomatic infections expected for a range of vaccination scenarios.