The breadth of the neutralizing antibody response to original SARS-CoV-2 infection is linked to the presence of Long COVID symptoms.

The associations between longitudinal dynamics and the breadth of SARS-CoV-2 neutralizing antibody (nAb) response with various Long COVID phenotypes before vaccination are not known. The capacity of antibodies to cross-neutralize a variety of viral variants may be associated with ongoing pathology and persistent symptoms. We measured longitudinal neutralizing and cross-neutralizing antibody responses to pre- and post-SARS-CoV-2 Omicron variants in participants infected early in the COVID-19 pandemic, before widespread rollout of SARS-CoV-2 vaccines. Cross-sectional regression models adjusted for clinical covariates and longitudinal mixed-effects models were used to determine the impact of the breadth and rate of decay of neutralizing responses on the development of Long COVID symptoms, as well as Long COVID phenotypes. We identified several novel relationships between SARS-CoV-2 antibody neutralization and the presence of Long COVID symptoms. Specifically, we show that, although nAb responses to the original, infecting strain of SARS-CoV-2 were not associated with Long COVID in cross-sectional analyses, cross-neutralization ID50 levels to the Omicron BA.5 variant approximately 4 months following acute infection was independently and significantly associated with greater odds of Long COVID and with persistent gastrointestinal and neurological symptoms. Longitudinal modeling demonstrated significant associations in the overall levels and rates of decay of neutralization capacity with Long COVID phenotypes. A higher proportion of participants had antibodies capable of neutralizing Omicron BA.5 compared with BA.1 or XBB.1.5 variants. Our findings suggest that relationships between various immune responses and Long COVID are likely complex but may involve the breadth of antibody neutralization responses.

serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes.

serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.

Autoantigen profiling reveals a shared post-COVID signature in fully recovered and long COVID patients.

Some individuals do not return to baseline health following SARS-CoV-2 infection, leading to a condition known as long COVID. The underlying pathophysiology of long COVID remains unknown. Given that autoantibodies have been found to play a role in severity of SARS-CoV-2 infection and certain other post-COVID sequelae, their potential role in long COVID is important to investigate. Here, we apply a well-established, unbiased, proteome-wide autoantibody detection technology (T7 phage-display assay with immunoprecipitation and next-generation sequencing, PhIP-Seq) to a robustly phenotyped cohort of 121 individuals with long COVID, 64 individuals with prior COVID-19 who reported full recovery, and 57 pre-COVID controls. While a distinct autoreactive signature was detected that separated individuals with prior SARS-CoV-2 infection from those never exposed to SARS-CoV-2, we did not detect patterns of autoreactivity that separated individuals with long COVID from individuals fully recovered from COVID-19. These data suggest that there are robust alterations in autoreactive antibody profiles due to infection; however, no association of autoreactive antibodies and long COVID was apparent by this assay.

Metagenomic next-generation sequencing to characterize potential etiologies of non-malarial fever in a cohort living in a high malaria burden area of Uganda.

Causes of non-malarial fevers in sub-Saharan Africa remain understudied. We hypothesized that metagenomic next-generation sequencing (mNGS), which allows for broad genomic-level detection of infectious agents in a biological sample, can systematically identify potential causes of non-malarial fevers. The 212 participants in this study were of all ages and were enrolled in a longitudinal malaria cohort in eastern Uganda. Between December 2020 and August 2021, respiratory swabs and plasma samples were collected at 313 study visits where participants presented with fever and were negative for malaria by microscopy. Samples were analyzed using CZ ID, a web-based platform for microbial detection in mNGS data. Overall, viral pathogens were detected at 123 of 313 visits (39%). SARS-CoV-2 was detected at 11 visits, from which full viral genomes were recovered from nine. Other prevalent viruses included Influenza A (14 visits), RSV (12 visits), and three of the four strains of seasonal coronaviruses (6 visits). Notably, 11 influenza cases occurred between May and July 2021, coinciding with when the Delta variant of SARS-CoV-2 was circulating in this population. The primary limitation of this study is that we were unable to estimate the contribution of bacterial microbes to non-malarial fevers, due to the difficulty of distinguishing bacterial microbes that were pathogenic from those that were commensal or contaminants. These results revealed the co-circulation of multiple viral pathogens likely associated with fever in the cohort during this time period. This study illustrates the utility of mNGS in elucidating the multiple potential causes of non-malarial febrile illness. A better understanding of the pathogen landscape in different settings and age groups could aid in informing diagnostics, case management, and public health surveillance systems.

An evaluation of the early impact of the COVID-19 pandemic on Zambia's routine immunization program.

Implications of the COVID-19 pandemic for both populations and healthcare systems are vast. In addition to morbidity and mortality from COVID-19, the pandemic also disrupted local health systems, including reductions or delays in routine vaccination services and catch-up vaccination campaigns. These disruptions could lead to outbreaks of other infectious diseases that result in an additional burden of disease and strain on the healthcare system. We evaluated the impact of the COVID-19 pandemic on Zambia's routine childhood immunization program in 2020 using multiple sources of data. We relied on administrative vaccination data and Zambia's 2018 Demographic and Health Survey to project national disruptions to district-specific routine childhood vaccination coverage within the pandemic year 2020. Next, we leveraged a 2016 population-based serological survey to predict age-specific measles seroprevalence and assessed the impact of changes in vaccination coverage on measles outbreak risk in each district. We found minor disruptions to routine administration of measles-rubella and pentavalent vaccines in 2020. This was in part due to Zambia's Child Health Week held in June of 2020 which helped to reach children missed during the first six months of the year. We estimated that the two-month delay in a measles-rubella vaccination campaign, originally planned for September of 2020 but conducted in November of 2020 as a result of the pandemic, had little impact on modeled district-specific measles outbreak risks. This study estimated minimal increases in the number of children missed by vaccination services in Zambia during 2020. However, the ongoing SARS-CoV-2 transmission since our analysis concluded means efforts to maintain routine immunization services and minimize the risk of measles outbreaks will continue to be critical. The methodological framework developed in this analysis relied on routinely collected data to estimate disruptions of the COVID-19 pandemic to national routine vaccination program performance and its impact on children missed at the subnational level can be deployed in other countries or for other vaccines.

The Breadth of the Neutralizing Antibody Response to Original SARS-CoV-2 Infection is Linked to the Presence of Long COVID Symptoms.

Background: The associations between longitudinal dynamics and the breadth of SARS-CoV-2 neutralizing antibody response with various Long COVID (LC) phenotypes prior to vaccination are not known. The capacity of antibodies to cross neutralize a variety of viral variants may be associated with ongoing pathology and persistent symptoms. Methods: We measured longitudinal neutralizing and cross-neutralizing antibody responses to pre- and post-SARS-CoV-2 Omicron variants in participants infected during the early waves of the COVID-19 pandemic, prior to wide-spread rollout of SARS-CoV-2 vaccines. Cross sectional regression models adjusted for various clinical covariates and longitudinal mixed effects models were used to determine the impact of the breadth and rate of decay of neutralizing responses on the development of Long COVID symptoms in general, as well as LC phenotypes. Results: We identified several novel relationships between SARS-CoV-2 antibody neutralization and the presence of LC symptoms. Specifically, we show that, although neutralizing antibody responses to the original, infecting strain of SARS-CoV-2 were not associated with LC in cross-sectional analyses, cross-neutralization ID50 levels to the Omicron BA.5 variant approximately 4 months following acute infection was independently and significantly associated with greater odds of LC and with persistent gastrointestinal and neurological symptoms. Longitudinal modeling demonstrated significant associations in the overall levels and rates of decay of neutralization capacity with LC phenotypes. A higher proportion of participants had antibodies capable of neutralizing Omicron BA.5 compared with BA.1 or XBB.1.5 variants. Conclusions: Our findings suggest that relationships between various immune responses and LC are likely complex but may involve the breadth of antibody neutralization responses.

SARS-CoV-2 Serology Across Scales: A Framework for Unbiased Estimation of Cumulative Incidence Incorporating Antibody Kinetics and Epidemic Recency.

Serosurveys are a key resource for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) population exposure. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce estimates of cumulative incidence from raw seroprevalence survey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a postinfection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce estimates of cumulative incidence from 5 large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identified substantial differences between raw seroprevalence and cumulative incidence of over 2-fold in the results of some surveys, and we provide a tool for practitioners to generate cumulative incidence estimates with preset or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

Population Pharmacokinetic Model for Unbound Concentrations of Daptomycin in Patients with MRSA Including Patients Undergoing Hemodialysis.

BACKGROUND AND OBJECTIVE: Unbound daptomycin concentrations are responsible for pharmacologically beneficial and adverse effects, although most previous reports have been limited to the use of total concentrations. We developed a population pharmacokinetic model to predict both total and unbound daptomycin concentrations. METHODS: Clinical data were collected from 58 patients with methicillin-resistant Staphylococcus aureus including patients undergoing hemodialysis. A total of 339 serum total and 329 unbound daptomycin concentrations were used for model construction. RESULTS: Total and unbound daptomycin concentration was explained by a model that assumed first-order distribution with two compartments, and first-order elimination. Normal fat body mass was identified as covariates. Renal function was incorporated as a linear function of renal clearance and independent non-renal clearance. The unbound fraction was estimated to be 0.066 with a standard albumin of 45 g/L and standard creatinine clearance of 100 mL/min. Simulated unbound daptomycin concentration was compared with minimum inhibitory concentration as a measure of clinical effectiveness and exposure-level-related induction of creatine phosphokinase elevation. The recommended doses were 4 mg/kg for patients with severe renal function [creatinine clearance (CLcr) ≤ 30 mL/min] and 6 mg/kg for patients with mild to moderate renal function (CLcr > 30 and ≤ 60 mL/min). A simulation indicated that dose adjusted by body weight and renal function improved target attainment. CONCLUSIONS: This population pharmacokinetics model for unbound daptomycin could help clinicians to select the appropriate dose regimen for patients undergoing daptomycin treatment and reduce associated adverse effects.

Association of activities of daily living and cognitive function with thickness of the upper extremity muscles in children and adults with cerebral palsy.

PURPOSE: We examined the association of activities of daily living (ADL) and cognitive function with the upper extremity muscle thickness and upper extremity range of motion (ROM) and spasticity in children and adults with cerebral palsy (CP). METHODS: The subjects were 20 children and adults with CP. The ADL performed using the upper extremities and cognitive function were assessed using the self-care domain of the Pediatric Evaluation of Disability Inventory (PEDI) and the full-scale intelligence quotient (FSIQ) of the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV), respectively. The WISC-IV was assessed in only seven of 20 subjects able to undergo evaluation. The thickness of the upper extremity muscles was measured using an ultrasound imaging device. Moreover, ROM and spasticity of the upper extremities were assessed using the Modified Ashworth Scale (MAS). Manual manipulation ability was also assessed using the Manual Ability Classification System (MACS). RESULTS: Stepwise regression analysis revealed that the extensor digitorum muscle thickness and MACS level were significant and independent factors of self-care in the PEDI. Partial correlation analysis with MACS level and age as control variables showed that the FSIQ of the WISC-IV was significantly associated with the thickness of the anterior fibers of the deltoid and flexor digitorum superficialis muscles. CONCLUSION: Reduced ADL performed using the upper extremities is associated with decreased extensor digitorum muscle thickness rather than ROM and spasticity of the upper extremities in children and adults with CP.

Antibodies to repeat-containing antigens in Plasmodium falciparum are exposure-dependent and short-lived in children in natural malaria infections.

Protection against Plasmodium falciparum, which is primarily antibody-mediated, requires recurrent exposure to develop. The study of both naturally acquired limited immunity and vaccine induced protection against malaria remains critical for ongoing eradication efforts. Towards this goal, we deployed a customized P. falciparum PhIP-seq T7 phage display library containing 238,068 tiled 62-amino acid peptides, covering all known coding regions, including antigenic variants, to systematically profile antibody targets in 198 Ugandan children and adults from high and moderate transmission settings. Repeat elements - short amino acid sequences repeated within a protein - were significantly enriched in antibody targets. While breadth of responses to repeat-containing peptides was twofold higher in children living in the high versus moderate exposure setting, no such differences were observed for peptides without repeats, suggesting that antibody responses to repeat-containing regions may be more exposure dependent and/or less durable in children than responses to regions without repeats. Additionally, short motifs associated with seroreactivity were extensively shared among hundreds of antigens, potentially representing cross-reactive epitopes. PfEMP1 shared motifs with the greatest number of other antigens, partly driven by the diversity of PfEMP1 sequences. These data suggest that the large number of repeat elements and potential cross-reactive epitopes found within antigenic regions of P. falciparum could contribute to the inefficient nature of malaria immunity.

Autoantigen profiling reveals a shared post-COVID signature in fully recovered and Long COVID patients.

Some individuals do not return to baseline health following SARS-CoV-2 infection, leading to a condition known as Long COVID. The underlying pathophysiology of Long COVID remains unknown. Given that autoantibodies have been found to play a role in severity of COVID infection and certain other post-COVID sequelae, their potential role in Long COVID is important to investigate. Here we apply a well-established, unbiased, proteome-wide autoantibody detection technology (PhIP-Seq) to a robustly phenotyped cohort of 121 individuals with Long COVID, 64 individuals with prior COVID-19 who reported full recovery, and 57 pre-COVID controls. While a distinct autoreactive signature was detected which separates individuals with prior COVID infection from those never exposed to COVID, we did not detect patterns of autoreactivity that separate individuals with Long COVID relative to individuals fully recovered from SARS-CoV-2 infection. These data suggest that there are robust alterations in autoreactive antibody profiles due to infection; however, no association of autoreactive antibodies and Long COVID was apparent by this assay.

Seroprevalence of Antibodies to SARS-CoV-2 in Rural Households in Eastern Uganda, 2020-2022.

Importance: Estimating the true burden of SARS-CoV-2 infection has been difficult in sub-Saharan Africa owing to asymptomatic infections and inadequate testing capacity. Antibody responses from serologic surveys can provide an estimate of SARS-CoV-2 exposure at the population level. Objective: To estimate SARS-CoV-2 seroprevalence, attack rates, and reinfection in eastern Uganda using serologic surveillance from 2020 to early 2022. Design, Setting, and Participants: This cohort study was conducted in the Tororo and Busia districts of eastern Uganda. Plasma samples from participants in the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria in Uganda Border Cohort were obtained at 4 sampling intervals: October to November 2020, March to April 2021, August to September 2021, and February to March 2022. Each participant contributed up to 4 time points for SARS-CoV-2 serology, with almost half of all participants contributing at all 4 time points, and almost 90% contributing at 3 or 4 time points. Information on SARS-CoV-2 vaccination status was collected from participants, with the earliest reported vaccinations in the cohort occurring in May 2021. Main Outcomes and Measures: The main outcomes of this study were antibody responses to the SARS-CoV-2 spike protein as measured with a bead-based serologic assay. Individual-level outcomes were aggregated to population-level SARS-CoV-2 seroprevalence, attack rates, and boosting rates. Estimates were weighted by the local age distribution according to census data. Results: A total of 1483 samples from 441 participants living in 76 households were tested. Of the 441 participants, 245 (55.6%) were female, and their mean (SD) age was 16.04 (16.04) years. By the end of the Delta wave and before widespread vaccination, adjusted SARS-CoV-2 seroprevalence was 67.7% (95% credible interval [CrI], 62.5%-72.6%) in the study population. During the subsequent Omicron wave, 84.8% (95% CrI, 67.9%-93.7%) of unvaccinated, previously seronegative individuals were infected for the first time, and 50.8% (95% CrI, 40.6%-59.7%) of unvaccinated, already seropositive individuals were likely reinfected, leading to an overall seropositivity of 96.0% (95% CrI, 93.4%-97.9%) in this population. These results suggest a lower probability of reinfection in individuals with higher preexisting antibody levels. There was evidence of household clustering of SARS-CoV-2 seroconversion. No significant associations were found between SARS-CoV-2 seroconversion and gender, household size, or recent Plasmodium falciparum malaria exposure. Conclusions and Relevance: In this cohort study in a rural population in eastern Uganda, there was evidence of very high SARS-CoV-2 infection rates throughout the pandemic inconsistent with national level case data and high reinfection rates during the Omicron wave.

Malaria-driven expansion of adaptive-like functional CD56-negative NK cells correlates with clinical immunity to malaria.

Natural killer (NK) cells likely play an important role in immunity to malaria, but the effect of repeated malaria on NK cell responses remains unclear. Here, we comprehensively profiled the NK cell response in a cohort of 264 Ugandan children. Repeated malaria exposure was associated with expansion of an atypical, CD56neg population of NK cells that differed transcriptionally, epigenetically, and phenotypically from CD56dim NK cells, including decreased expression of PLZF and the Fc receptor γ-chain, increased histone methylation, and increased protein expression of LAG-3, KIR, and LILRB1. CD56neg NK cells were highly functional and displayed greater antibody-dependent cellular cytotoxicity than CD56dim NK cells. Higher frequencies of CD56neg NK cells were associated with protection against symptomatic malaria and high parasite densities. After marked reductions in malaria transmission, frequencies of these cells rapidly declined, suggesting that continuous exposure to Plasmodium falciparum is required to maintain this modified, adaptive-like NK cell subset.

Low Prevalence of Interferon α Autoantibodies in People Experiencing Symptoms of Post-Coronavirus Disease 2019 (COVID-19) Conditions, or Long COVID.

Interferon (IFN)-specific autoantibodies have been implicated in severe coronavirus disease 2019 (COVID-19) and have been proposed as a potential driver of the persistent symptoms characterizing "long COVID," a type of postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We report that only 2 of 215 participants with convalescent SARS-CoV-2 infection tested over 394 time points, including 121 people experiencing long COVID symptoms, had detectable IFN-α2 antibodies. Both had been hospitalized during the acute phase of the infection. These data suggest that persistent anti-IFN antibodies, although a potential driver of severe COVID-19, are unlikely to contribute to long COVID symptoms in the postacute phase of the infection.

Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19: a multi-omics study.

Background: The great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients. Methods: Leveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial. Results: We identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2-7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset. Conclusions: Early immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models. Funding: Support for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford's Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.

Reconstructing the SARS-CoV-2 epidemic in eastern Uganda through longitudinal serosurveillance in a malaria cohort.

Importance: Estimating the true burden of SARS-CoV-2 infection has been difficult in sub-Saharan Africa due to asymptomatic infections and inadequate testing capacity. Antibody responses from serologic surveys can provide an estimate of SARS-CoV-2 exposure at the population level. Objective: To estimate SARS-CoV-2 seroprevalence, attack rates, and re-infection in eastern Uganda using serologic surveillance from 2020 to early 2022. Design: Plasma samples from participants in the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria in Uganda (PRISM) Border Cohort were obtained at four sampling intervals: October-November 2020; March-April 2021; August-September 2021; and February-March 2022. Setting: Tororo and Busia districts, Uganda. Participants: 1,483 samples from 441 participants living in 76 households were tested. Each participant contributed up to 4 time points for SARS-CoV-2 serology, with almost half of all participants contributing at all 4 time points, and almost 90% contributing at 3 or 4 time points. Information on SARS-CoV-2 vaccination status was collected from participants, with the earliest reported vaccinations in the cohort occurring in May 2021. Main Outcomes and Measures: The main outcomes of this study were antibody responses to the SARS-CoV-2 spike protein as measured with a bead-based serologic assay. Individual-level outcomes were aggregated to population-level SARS-CoV-2 seroprevalence, attack rates, and boosting rates. Estimates were weighted by the local age distribution based on census data. Results: By the end of the Delta wave and before widespread vaccination, nearly 70% of the study population had experienced SARS-CoV-2 infection. During the subsequent Omicron wave, 85% of unvaccinated, previously seronegative individuals were infected for the first time, and ~50% or more of unvaccinated, already seropositive individuals were likely re-infected, leading to an overall 96% seropositivity in this population. Our results suggest a lower probability of re-infection in individuals with higher pre-existing antibody levels. We found evidence of household clustering of SARS-CoV-2 seroconversion. We found no significant associations between SARS-CoV-2 seroconversion and gender, household size, or recent Plasmodium falciparum malaria exposure. Conclusions and Relevance: Findings from this study are consistent with very high infection rates and re-infection rates for SARS-CoV-2 in a rural population from eastern Uganda throughout the pandemic.

Assessing the role of multiple mechanisms increasing the age of dengue cases in Thailand.

The mean age of dengue hemorrhagic fever (DHF) cases increased considerably in Thailand from 8.1 to 24.3 y between 1981 and 2017 (mean annual increase of 0.45 y). Alternative proposed explanations for this trend, such as changes in surveillance practices, reduced mosquito­human contact, and shifts in population demographics, have different implications for global dengue epidemiology. To evaluate the contribution of each of these hypothesized mechanisms to the observed data, we developed 20 nested epidemiological models of dengue virus infection, allowing for variation over time in population demographics, infection hazards, and reporting rates. We also quantified the effect of removing or retaining each source of variation in simulations of the age trajectory. Shifts in the age structure of susceptibility explained 58% of the observed change in age. Adding heterogeneous reporting by age and reductions in per-serotype infection hazard to models with shifts in susceptibility explained an additional 42%. Reductions in infection hazards were mostly driven by changes in the number of infectious individuals at any time (another consequence of shifting age demographics) rather than changes in the transmissibility of individual infections. We conclude that the demographic transition drives the overwhelming majority of the observed change as it changes both the age structure of susceptibility and the number of infectious individuals. With the projected Thai population age structure, our results suggest a continuing increase in age of DHF cases, shifting the burden toward individuals with more comorbidity. These insights into dengue epidemiology may be relevant to many regions of the globe currently undergoing comparable changes in population demographics.

Using sero-epidemiology to monitor disparities in vaccination and infection with SARS-CoV-2.

As SARS-CoV-2 continues to spread and vaccines are rolled-out, the "double burden" of disparities in exposure and vaccination intersect to determine patterns of infection, immunity, and mortality. Serology provides a unique opportunity to measure prior infection and vaccination simultaneously. Leveraging algorithmically-selected residual sera from two hospital networks in the city of San Francisco, cross-sectional samples from 1,014 individuals from February 4-17, 2021 were each tested on two assays (Ortho Clinical Diagnostics VITROS Anti-SARS-CoV-2 and Roche Elecsys Anti-SARS-CoV-2), capturing the first year of the epidemic and early roll-out of vaccination. We estimated, using Bayesian estimation of infection and vaccination, that infection risk of Hispanic/Latinx residents was five times greater than of White residents aged 18-64 (95% Credible Interval (CrI): 3.2-10.3), and that White residents over 65 were twice as likely to be vaccinated as Black/African American residents (95% CrI: 1.1-4.6). We found that socioeconomically-deprived zipcodes had higher infection probabilities and lower vaccination coverage than wealthier zipcodes. While vaccination has created a 'light at the end of the tunnel' for this pandemic, ongoing challenges in achieving and maintaining equity must also be considered.

TNF-α+ CD4+ T cells dominate the SARS-CoV-2 specific T cell response in COVID-19 outpatients and are associated with durable antibodies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific CD4+ T cells are likely important in immunity against coronavirus 2019 (COVID-19), but our understanding of CD4+ longitudinal dynamics following infection and of specific features that correlate with the maintenance of neutralizing antibodies remains limited. Here, we characterize SARS-CoV-2-specific CD4+ T cells in a longitudinal cohort of 109 COVID-19 outpatients enrolled during acute infection. The quality of the SARS-CoV-2-specific CD4+ response shifts from cells producing interferon gamma (IFNγ) to tumor necrosis factor alpha (TNF-α) from 5 days to 4 months post-enrollment, with IFNγ-IL-21-TNF-α+ CD4+ T cells the predominant population detected at later time points. Greater percentages of IFNγ-IL-21-TNF-α+ CD4+ T cells on day 28 correlate with SARS-CoV-2-neutralizing antibodies measured 7 months post-infection (⍴ = 0.4, p = 0.01). mRNA vaccination following SARS-CoV-2 infection boosts both IFNγ- and TNF-α-producing, spike-protein-specific CD4+ T cells. These data suggest that SARS-CoV-2-specific, TNF-α-producing CD4+ T cells may play an important role in antibody maintenance following COVID-19.

Early immune responses have long-term associations with clinical, virologic, and immunologic outcomes in patients with COVID-19.

The great majority of SARS-CoV-2 infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunologic outcomes in SARS-CoV-2-infected patients. Leveraging longitudinal samples and data from a clinical trial in SARS-CoV-2 infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients within the first 2 weeks of symptom onset. We identify early immune signatures, including plasma RIG-I levels, early interferon signaling, and related cytokines (CXCL10, MCP1, MCP-2 and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2 specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine learning models using 7-10 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset.