Longitudinal profiling of respiratory and systemic immune responses reveals myeloid cell-driven lung inflammation in severe COVID-19.

Immune response dynamics in coronavirus disease 2019 (COVID-19) and their severe manifestations have largely been studied in circulation. Here, we examined the relationship between immune processes in the respiratory tract and circulation through longitudinal phenotypic, transcriptomic, and cytokine profiling of paired airway and blood samples from patients with severe COVID-19 relative to heathy controls. In COVID-19 airways, T cells exhibited activated, tissue-resident, and protective profiles; higher T cell frequencies correlated with survival and younger age. Myeloid cells in COVID-19 airways featured hyperinflammatory signatures, and higher frequencies of these cells correlated with mortality and older age. In COVID-19 blood, aberrant CD163+ monocytes predominated over conventional monocytes, and were found in corresponding airway samples and in damaged alveoli. High levels of myeloid chemoattractants in airways suggest recruitment of these cells through a CCL2-CCR2 chemokine axis. Our findings provide insights into immune processes driving COVID-19 lung pathology with therapeutic implications for targeting inflammation in the respiratory tract.

Impact of waning immunity against SARS-CoV-2 severity exacerbated by vaccine hesitancy.

The SARS-CoV-2 pandemic has generated a considerable number of infections and associated morbidity and mortality across the world. Recovery from these infections, combined with the onset of large-scale vaccination, have led to rapidly-changing population-level immunological landscapes. In turn, these complexities have highlighted a number of important unknowns related to the breadth and strength of immunity following recovery or vaccination. Using simple mathematical models, we investigate the medium-term impacts of waning immunity against severe disease on immuno-epidemiological dynamics. We find that uncertainties in the duration of severity-blocking immunity (imparted by either infection or vaccination) can lead to a large range of medium-term population-level outcomes (i.e. infection characteristics and immune landscapes). Furthermore, we show that epidemiological dynamics are sensitive to the strength and duration of underlying host immune responses; this implies that determining infection levels from hospitalizations requires accurate estimates of these immune parameters. More durable vaccines both reduce these uncertainties and alleviate the burden of SARS-CoV-2 in pessimistic outcomes. However, heterogeneity in vaccine uptake drastically changes immune landscapes toward larger fractions of individuals with waned severity-blocking immunity. In particular, if hesitancy is substantial, more robust vaccines have almost no effects on population-level immuno-epidemiology, even if vaccination rates are compensatorily high among vaccine-adopters. This pessimistic scenario for vaccination heterogeneity arises because those few individuals that are vaccine-adopters are so readily re-vaccinated that the duration of vaccinal immunity has no appreciable consequences on their immune status. Furthermore, we find that this effect is heightened if vaccine-hesitants have increased transmissibility (e.g. due to riskier behavior). Overall, our results illustrate the necessity to characterize both transmission-blocking and severity-blocking immune time scales. Our findings also underline the importance of developing robust next-generation vaccines with equitable mass vaccine deployment.

Healthy dynamics of CD4 T cells may drive HIV resurgence in perinatally-infected infants on antiretroviral therapy.

In 2019 there were 490,000 children under five living with HIV. Understanding the dynamics of HIV suppression and rebound in this age group is crucial to optimizing treatment strategies and increasing the likelihood of infants achieving and sustaining viral suppression. Here we studied data from a cohort of 122 perinatally-infected infants who initiated antiretroviral treatment (ART) early after birth and were followed for up to four years. These data included longitudinal measurements of viral load (VL) and CD4 T cell numbers, together with information regarding treatment adherence. We previously showed that the dynamics of HIV decline in 53 of these infants who suppressed VL within one year were similar to those in adults. However, in extending our analysis to all 122 infants, we find that a deterministic model of HIV infection in adults cannot explain the full diversity in infant trajectories. We therefore adapt this model to include imperfect ART adherence and natural CD4 T cell decline and reconstitution processes in infants. We find that individual variation in both processes must be included to obtain the best fits. We also find that infants with faster rates of CD4 reconstitution on ART were more likely to experience resurgences in VL. Overall, our findings highlight the importance of combining mathematical modeling with clinical data to disentangle the role of natural immune processes and viral dynamics during HIV infection.

Modeling the impacts of antiviral prophylaxis strategies in mitigating seasonal influenza outbreaks in nursing homes.

BACKGROUND: Antiviral chemoprophylaxis is recommended for use during influenza outbreaks in nursing homes to prevent transmission and severe disease among non-ill residents. Centers for Disease Control and Prevention (CDC) guidance recommends prophylaxis be initiated for all non-ill residents once an influenza outbreak is detected and be continued for at least 14 days and until seven days after the last laboratory-confirmed influenza case is identified. However, not all facilities strictly adhere to this guidance and the impact of such partial adherence is not fully understood. METHODS: We developed a stochastic compartmental framework to model influenza transmission within an average-sized U.S. nursing home. We compared the number of symptomatic illnesses and hospitalizations under varying prophylaxis implementation strategies, in addition to different levels of prophylaxis uptake and adherence by residents and healthcare personnel (HCP). RESULTS: Prophylaxis implemented according to current guidance reduced total symptomatic illnesses and hospitalizations among residents by an average of 12% and 36%, respectively, compared with no prophylaxis. We did not find evidence that alternative implementations of prophylaxis were more effective: compared to full adoption of current guidance, partial adoption resulted in increased symptomatic illnesses and/or hospitalizations, and longer or earlier adoption offered no additional improvements. In addition, increasing uptake and adherence among nursing home residents was effective in reducing resident illnesses and hospitalizations, but increasing HCP uptake had minimal indirect impacts for residents. CONCLUSIONS: The greatest benefits of influenza prophylaxis during nursing home outbreaks will likely be achieved through increasing uptake and adherence among residents and following current CDC guidance.

Evaluating Potential Impacts of a Preferential Vaccine Recommendation for Adults 65 Years of Age and Older on US Influenza Burden.

BACKGROUND: High-dose, adjuvanted, and recombinant influenza vaccines may offer improved effectiveness among older adults compared with standard-dose, unadjuvanted, inactivated vaccines. However, the Advisory Committee on Immunization Practices (ACIP) only recently recommended preferential use of these "higher-dose or adjuvanted" vaccines. One concern was that individuals might delay or decline vaccination if a preferred vaccine is not readily available. METHODS: We mathematically model how a recommendation for preferential use of higher-dose or adjuvanted vaccines in adults ≥65 years might impact influenza burden in the United States during exemplar "high-" and "low-"severity seasons. We assume higher-dose or adjuvanted vaccines are more effective than standard vaccines and that such a recommendation would increase uptake of the former but could cause (i) delays in administration of additional higher-dose or adjuvanted vaccines relative to standard vaccines and/or (ii) reductions in overall coverage if individuals only offered standard vaccines forego vaccination. RESULTS: In a best-case scenario, assuming no delay or coverage reduction, a new recommendation could decrease hospitalizations and deaths in adults ≥65 years by 0%-4% compared with current uptake. However, intermediate and worst-case scenarios, with assumed delays of 3 or 6 weeks and/or 10% or 20% reductions in coverage, included projections in which hospitalizations and deaths increased by over 7%. CONCLUSIONS: We estimate that increased use of higher-dose or adjuvanted vaccines could decrease influenza burden in adults ≥65 in the United States provided there is timely and adequate access to these vaccines, and that standard vaccines are administered when they are unavailable.

Tissue-Resident Memory T Cells in Mice and Humans: Towards a Quantitative Ecology.

In recent years, tissue-resident memory T cells (TRM) have emerged as essential components of immunological memory. Following antigenic challenge, TRM remain in nonlymphoid tissues and defend against re-exposure. Although accumulating evidence suggests important roles for TRM in mediating protective immunity, fundamental aspects of the population biology of TRM remain poorly understood. In this article, we discuss how results from different systems shed light on the ecological dynamics of TRM in mice and humans. We highlight the importance of dissecting processes contributing to TRM maintenance, and how these might vary across phenotypically and spatially heterogeneous subsets. We also discuss how the diversity of TRM communities within specific tissues may evolve under competition and in response to antigenic perturbation. Throughout, we illustrate how mathematical models can clarify inferences obtained from experimental data and help elucidate the homeostatic mechanisms underpinning the ecology of TRM populations.

ushr: Understanding suppression of HIV in R.

BACKGROUND: HIV/AIDS is responsible for the deaths of one million people every year. Although mathematical modeling has provided many insights into the dynamics of HIV infection, there is still a lack of accessible tools for researchers unfamiliar with modeling techniques to apply them to their own clinical data. RESULTS: Here we present ushr, a free and open-source R package that models the decline of HIV during antiretroviral treatment (ART) using a popular mathematical framework. ushr can be applied to longitudinal data of viral load measurements, and provides processing tools to prepare it for computational analysis. By mathematically fitting the data, important biological parameters can then be estimated, including the lifespans of short and long-lived infected cells, and the time to reach viral suppression below a defined detection threshold. The package also provides visualization and summary tools for fast assessment of model results. CONCLUSIONS: ushr enables researchers without a strong mathematical or computational background to model the dynamics of HIV using longitudinal clinical data. Increasing accessibility to such methods may facilitate quantitative analysis across a broader range of independent studies, so that greater insights on HIV infection and treatment dynamics may be gained.

Modeling the measles paradox reveals the importance of cellular immunity in regulating viral clearance.

Measles virus (MV) is a highly contagious member of the Morbillivirus genus that remains a major cause of childhood mortality worldwide. Although infection induces a strong MV-specific immune response that clears viral load and confers lifelong immunity, transient immunosuppression can also occur, leaving the host vulnerable to colonization from secondary pathogens. This apparent contradiction of viral clearance in the face of immunosuppression underlies what is often referred to as the 'measles paradox', and remains poorly understood. To explore the mechanistic basis underlying the measles paradox, and identify key factors driving viral clearance, we return to a previously published dataset of MV infection in rhesus macaques. These data include virological and immunological information that enable us to fit a mathematical model describing how the virus interacts with the host immune system. In particular, our model incorporates target cell depletion through infection of host immune cells-a hallmark of MV pathology that has been neglected from previous models. We find the model captures the data well, and that both target cell depletion and immune activation are required to explain the overall dynamics. Furthermore, by simulating conditions of increased target cell availability and suppressed cellular immunity, we show that the latter causes greater increases in viral load and delays to MV clearance. Overall, this signals a more dominant role for cellular immunity in resolving acute MV infection. Interestingly, we find contrasting dynamics dominated by target cell depletion when viral fitness is increased. This may have wider implications for animal morbilliviruses, such as canine distemper virus (CDV), that cause fatal target cell depletion in their natural hosts. To our knowledge this work represents the first fully calibrated within-host model of MV dynamics and, more broadly, provides a new platform from which to explore the complex mechanisms underlying Morbillivirus infection.

Detection of Novel Influenza Viruses Through Community and Healthcare Testing: Implications for Surveillance Efforts in the United States.

BACKGROUND: Novel influenza viruses pose a potential pandemic risk, and rapid detection of infections in humans is critical to characterizing the virus and facilitating the implementation of public health response measures. METHODS: We use a probabilistic framework to estimate the likelihood that novel influenza virus cases would be detected through testing in different community and healthcare settings (urgent care, emergency department, hospital, and intensive care unit [ICU]) while at low frequencies in the United States. Parameters were informed by data on seasonal influenza virus activity and existing testing practices. RESULTS: In a baseline scenario reflecting the presence of 100 novel virus infections with similar severity to seasonal influenza viruses, the median probability of detecting at least one infection per month was highest in urgent care settings (72%) and when community testing was conducted at random among the general population (77%). However, urgent care testing was over 15 times more efficient (estimated as the number of cases detected per 100,000 tests) due to the larger number of tests required for community testing. In scenarios that assumed increased clinical severity of novel virus infection, median detection probabilities increased across all healthcare settings, particularly in hospitals and ICUs (up to 100%) where testing also became more efficient. CONCLUSIONS: Our results suggest that novel influenza virus circulation is likely to be detected through existing healthcare surveillance, with the most efficient testing setting impacted by the disease severity profile. These analyses can help inform future testing strategies to maximize the likelihood of novel influenza detection.

The role of asymptomatic infections in influenza transmission: what do we really know.

Before the COVID-19 pandemic, the role of asymptomatic influenza virus infections in influenza transmission was uncertain. However, the importance of asymptomatic infection with SARS-CoV-2 for onward transmission of COVID-19 has led experts to question whether the role of asymptomatic influenza virus infections in transmission had been underappreciated. We discuss the existing evidence on the frequency of asymptomatic influenza virus infections, the extent to which they contribute to infection transmission, and remaining knowledge gaps. We propose priority areas for further evaluation, study designs, and case definitions to address existing knowledge gaps.

Influenza virus shedding and symptoms: Dynamics and implications from a multiseason household transmission study.

Isolation of symptomatic infectious persons can reduce influenza transmission. However, virus shedding that occurs without symptoms will be unaffected by such measures. Identifying effective isolation strategies for influenza requires understanding the interplay between individual virus shedding and symptom presentation. From 2017 to 2020, we conducted a case-ascertained household transmission study using influenza real-time RT-qPCR testing of nasal swabs and daily symptom diary reporting for up to 7 days after enrolment (≤14 days after index onset). We assumed real-time RT-qPCR cycle threshold (Ct) values were indicators of quantitative virus shedding and used symptom diaries to create a score that tracked influenza-like illness (ILI) symptoms (fever, cough, or sore throat). We fit phenomenological nonlinear mixed-effects models stratified by age and vaccination status and estimated two quantities influencing isolation effectiveness: shedding before symptom onset and shedding that might occur once isolation ends. We considered different isolation end points (including 24 h after fever resolution or 5 days after symptom onset) and assumptions about the infectiousness of Ct shedding trajectories. Of the 116 household contacts with ≥2 positive tests for longitudinal analyses, 105 (91%) experienced ≥1 ILI symptom. On average, children <5 years experienced greater peak shedding, longer durations of shedding, and elevated ILI symptom scores compared with other age groups. Most individuals (63/105) shed <10% of their total shed virus before symptom onset, and shedding after isolation varied substantially across individuals, isolation end points, and infectiousness assumptions. Our results can inform strategies to reduce transmission from symptomatic individuals infected with influenza.

Estimating the generation time for influenza transmission using household data in the United States.

The generation time, representing the interval between infections in primary and secondary cases, is essential for understanding and predicting the transmission dynamics of seasonal influenza, including the real-time effective reproduction number (Rt). However, comprehensive generation time estimates for seasonal influenza, especially post the 2009 influenza pandemic, are lacking. We estimated the generation time utilizing data from a 7-site case-ascertained household study in the United States over two influenza seasons, 2021/2022 and 2022/2023. More than 200 individuals who tested positive for influenza and their household contacts were enrolled within 7 days of the first illness in the household. All participants were prospectively followed for 10 days completing daily symptom diaries and collecting nasal swabs, which were tested for influenza via RT-PCR. We analyzed these data by modifying a previously published Bayesian data augmentation approach that imputes infection times of cases to obtain both intrinsic (assuming no susceptible depletion) and realized (observed within household) generation times. We assessed the robustness of the generation time estimate by varying the incubation period, and generated estimates of the proportion of transmission before symptomatic onset, infectious period, and latent period. We estimated a mean intrinsic generation time of 3.2 (95% credible interval, CrI: 2.9-3.6) days, with a realized household generation time of 2.8 (95% CrI: 2.7-3.0) days. The generation time exhibited limited sensitivity to incubation period variation. Estimates of the proportion of transmission that occurred before symptom onset, the infectious period, and the latent period were sensitive to variation in incubation periods. Our study contributes to the ongoing efforts to refine estimates of the generation time for influenza. Our estimates, derived from recent data following the COVID-19 pandemic, are consistent with previous pre-pandemic estimates, and will be incorporated into real-time Rt estimation efforts.

Medium-term scenarios of COVID-19 as a function of immune uncertainties and chronic disease.

As the SARS-CoV-2 trajectory continues, the longer-term immuno-epidemiology of COVID-19, the dynamics of Long COVID, and the impact of escape variants are important outstanding questions. We examine these remaining uncertainties with a simple modelling framework that accounts for multiple (antigenic) exposures via infection or vaccination. If immunity (to infection or Long COVID) accumulates rapidly with the valency of exposure, we find that infection levels and the burden of Long COVID are markedly reduced in the medium term. More pessimistic assumptions on host adaptive immune responses illustrate that the longer-term burden of COVID-19 may be elevated for years to come. However, we also find that these outcomes could be mitigated by the eventual introduction of a vaccine eliciting robust (i.e. durable, transmission-blocking and/or 'evolution-proof') immunity. Overall, our work stresses the wide range of future scenarios that still remain, the importance of collecting real-world epidemiological data to identify likely outcomes, and the crucial need for the development of a highly effective transmission-blocking, durable and broadly protective vaccine.

Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes.

Given vaccine dose shortages and logistical challenges, various deployment strategies are being proposed to increase population immunity levels to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Two critical issues arise: How timing of delivery of the second dose will affect infection dynamics and how it will affect prospects for the evolution of viral immune escape via a buildup of partially immune individuals. Both hinge on the robustness of the immune response elicited by a single dose as compared with natural and two-dose immunity. Building on an existing immuno-epidemiological model, we find that in the short term, focusing on one dose generally decreases infections, but that longer-term outcomes depend on this relative immune robustness. We then explore three scenarios of selection and find that a one-dose policy may increase the potential for antigenic evolution under certain conditions of partial population immunity. We highlight the critical need to test viral loads and quantify immune responses after one vaccine dose and to ramp up vaccination efforts globally.

Vaccine nationalism and the dynamics and control of SARS-CoV-2.

Vaccines provide powerful tools to mitigate the enormous public health and economic costs that the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues to exert globally, yet vaccine distribution remains unequal among countries. To examine the potential epidemiological and evolutionary impacts of "vaccine nationalism," we extend previous models to include simple scenarios of stockpiling between two regions. In general, when vaccines are widely available and the immunity they confer is robust, sharing doses minimizes total cases across regions. A number of subtleties arise when the populations and transmission rates in each region differ, depending on evolutionary assumptions and vaccine availability. When the waning of natural immunity contributes most to evolutionary potential, sustained transmission in low-access regions results in an increased potential for antigenic evolution, which may result in the emergence of novel variants that affect epidemiological characteristics globally. Overall, our results stress the importance of rapid, equitable vaccine distribution for global control of the pandemic.

Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes.

As the threat of Covid-19 continues and in the face of vaccine dose shortages and logistical challenges, various deployment strategies are being proposed to increase population immunity levels. How timing of delivery of the second dose affects infection burden but also prospects for the evolution of viral immune escape are critical questions. Both hinge on the strength and duration (i.e. robustness) of the immune response elicited by a single dose, compared to natural and two-dose immunity. Building on an existing immuno-epidemiological model, we find that in the short-term, focusing on one dose generally decreases infections, but longer-term outcomes depend on this relative immune robustness. We then explore three scenarios of selection, evaluating how different second dose delays might drive immune escape via a build-up of partially immune individuals. Under certain scenarios, we find that a one-dose policy may increase the potential for antigenic evolution. We highlight the critical need to test viral loads and quantify immune responses after one vaccine dose, and to ramp up vaccination efforts throughout the world.

Immune life history, vaccination, and the dynamics of SARS-CoV-2 over the next 5 years.

The future trajectory of the coronavirus disease 2019 (COVID-19) pandemic hinges on the dynamics of adaptive immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); however, salient features of the immune response elicited by natural infection or vaccination are still uncertain. We use simple epidemiological models to explore estimates for the magnitude and timing of future COVID-19 cases, given different assumptions regarding the protective efficacy and duration of the adaptive immune response to SARS-CoV-2, as well as its interaction with vaccines and nonpharmaceutical interventions. We find that variations in the immune response to primary SARS-CoV-2 infections and a potential vaccine can lead to markedly different immune landscapes and burdens of critically severe cases, ranging from sustained epidemics to near elimination. Our findings illustrate likely complexities in future COVID-19 dynamics and highlight the importance of immunological characterization beyond the measurement of active infections for adequately projecting the immune landscape generated by SARS-CoV-2 infections.

Quantifying the Dynamics of HIV Decline in Perinatally Infected Neonates on Antiretroviral Therapy.

BACKGROUND: Mathematical modeling has provided important insights into HIV infection dynamics in adults undergoing antiretroviral treatment (ART). However, much less is known about the corresponding dynamics in perinatally infected neonates initiating early ART. SETTING: From 2014 to 2017, HIV viral load (VL) was monitored in 122 perinatally infected infants identified at birth and initiating ART within a median of 2 days. Pretreatment infant and maternal covariates, including CD4 T cell counts and percentages, were also measured. METHODS: From the initial cohort, 53 infants demonstrated consistent decline and suppressed VL below the detection threshold (20 copies mL) within 1 year. For 43 of these infants with sufficient VL data, we fit a mathematical model describing the loss of short-lived and long-lived infected cells during ART. We then estimated the lifespans of infected cells and the time to viral suppression, and tested for correlations with pretreatment covariates. RESULTS: Most parameters governing the kinetics of VL decline were consistent with those obtained previously from adults and other infants. However, our estimates of the lifespan of short-lived infected cells were longer than published values. This difference may reflect sparse sampling during the early stages of VL decline, when the loss of short-lived cells is most apparent. In addition, infants with higher pretreatment CD4 percentage or lower pretreatment VL trended toward more rapid viral suppression. CONCLUSIONS: HIV dynamics in perinatally infected neonates initiating early ART are broadly similar to those observed in other age groups. Accelerated viral suppression is also associated with higher CD4 percentage and lower VL.

Analysis of respiratory and systemic immune responses in COVID-19 reveals mechanisms of disease pathogenesis.

Immune responses to respiratory viruses like SARS-CoV-2 originate and function in the lung, yet assessments of human immunity are often limited to blood. Here, we conducted longitudinal, high-dimensional profiling of paired airway and blood samples from patients with severe COVID-19, revealing immune processes in the respiratory tract linked to disease pathogenesis. Survival from severe disease was associated with increased CD4 + T cells and decreased monocyte/macrophage frequencies in the airway, but not in blood. Airway T cells and macrophages exhibited tissue-resident phenotypes and activation signatures, including high level expression and secretion of monocyte chemoattractants CCL2 and CCL3 by airway macrophages. By contrast, monocytes in blood expressed the CCL2-receptor CCR2 and aberrant CD163 + and immature phenotypes. Extensive accumulation of CD163 + monocyte/macrophages within alveolar spaces in COVID-19 lung autopsies suggested recruitment from circulation. Our findings provide evidence that COVID-19 pathogenesis is driven by respiratory immunity, and rationale for site-specific treatment and prevention strategies.

Analysis of multi-level spatial data reveals strong synchrony in seasonal influenza epidemics across Norway, Sweden, and Denmark.

Population structure, spatial diffusion, and climatic conditions mediate the spatiotemporal spread of seasonal influenza in temperate regions. However, much of our knowledge of these dynamics stems from a few well-studied countries, such as the United States (US), and the extent to which this applies in different demographic and climatic environments is not fully understood. Using novel data from Norway, Sweden, and Denmark, we applied wavelet analysis and non-parametric spatial statistics to explore the spatiotemporal dynamics of influenza transmission at regional and international scales. We found the timing and amplitude of epidemics were highly synchronized both within and between countries, despite the geographical isolation of many areas in our study. Within Norway, this synchrony was most strongly modulated by population size, confirming previous findings that hierarchical spread between larger populations underlies seasonal influenza dynamics at regional levels. However, we found no such association when comparing across countries, suggesting that other factors become important at the international scale. Finally, to frame our results within a wider global context, we compared our findings from Norway to those from the US. After correcting for differences in geographic scale, we unexpectedly found higher levels of synchrony in Norway, despite its smaller population size. We hypothesize that this greater synchrony may be driven by more favorable and spatially uniform climatic conditions, although there are other likely factors we were unable to consider (such as reduced variation in school term times and differences in population movements). Overall, our results highlight the importance of comparing influenza spread at different spatial scales and across diverse geographic regions in order to better understand the complex mechanisms underlying disease dynamics.