Age-specific mortality and immunity patterns of SARS-CoV-2.

Estimating the size of the coronavirus disease 2019 (COVID-19) pandemic and the infection severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is made challenging by inconsistencies in the available data. The number of deaths associated with COVID-19 is often used as a key indicator for the size of the epidemic, but the observed number of deaths represents only a minority of all infections1,2. In addition, the heterogeneous burdens in nursing homes and the variable reporting of deaths of older individuals can hinder direct comparisons of mortality rates and the underlying levels of transmission across countries3. Here we use age-specific COVID-19-associated death data from 45 countries and the results of 22 seroprevalence studies to investigate the consistency of infection and fatality patterns across multiple countries. We find that the age distribution of deaths in younger age groups (less than 65 years of age) is very consistent across different settings and demonstrate how these data can provide robust estimates of the share of the population that has been infected. We estimate that the infection fatality ratio is lowest among 5-9-year-old children, with a log-linear increase by age among individuals older than 30 years. Population age structures and heterogeneous burdens in nursing homes explain some but not all of the heterogeneity between countries in infection fatality ratios. Among the 45 countries included in our analysis, we estimate that approximately 5% of these populations had been infected by 1 September 2020, and that much higher transmission rates have probably occurred in a number of Latin American countries. This simple modelling framework can help countries to assess the progression of the pandemic and can be applied in any scenario for which reliable age-specific death data are available.

Maternally derived antibody titer dynamics and risk of hospitalized infant dengue disease.

Infants less than 1 y of age experience high rates of dengue disease in dengue virus (DENV) endemic countries. This burden is commonly attributed to antibody-dependent enhancement (ADE), whereby concentrations of maternally derived DENV antibodies become subneutralizing, and infection-enhancing. Understanding antibody-related mechanisms of enhanced infant dengue disease risk represents a significant challenge due to the dynamic nature of antibodies and their imperfect measurement processes. Further, key uncertainties exist regarding the impact of long-term shifts in birth rates, population-level infection risks, and maternal ages on the DENV immune landscape of newborns and their subsequent risks of severe dengue disease in infancy. Here, we analyze DENV antibody data from two infant cohorts (N = 142 infants with 605 blood draws) and 40 y of infant dengue hospitalization data from Thailand. We use mathematical models to reconstruct maternally derived antibody dynamics, accounting for discretized measurement processes and limits of assay detection. We then explore possible antibody-related mechanisms of enhanced infant dengue disease risk and their ability to reconstruct the observed age distribution of hospitalized infant dengue cases. We find that ADE mechanisms are best able to reconstruct the observed data. Finally, we describe how the shifting epidemiology of dengue in Thailand, combined with declining birth rates, have decreased the absolute risk of infant dengue disease by 88% over a 40-y period while having minimal impact on the mean age of infant hospitalized dengue disease.

Periodic synchronisation of dengue epidemics in Thailand over the last 5 decades driven by temperature and immunity.

The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, less work has been done to understand how climate variability and change affects dengue in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatiotemporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last 30 years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronisation events. We show that although patterns in synchrony of dengue are similar to those observed in temperature, the relationship between the two is most consistent during synchronous periods, while during asynchronous periods, temperature plays a less prominent role. With simulations from temperature-driven models, we explore how dynamics of immunity interact with temperature to produce the observed patterns in synchrony. The simulations produced patterns in synchrony that were similar to observations, supporting an important role of immunity. We demonstrate that multiannual oscillations produced by immunity can lead to asynchronous dynamics and that synchrony in temperature can then synchronise these dengue dynamics. At higher mean temperatures, immune dynamics can be more predominant, and dengue dynamics more insensitive to multiannual fluctuations in temperature, suggesting that with rising mean temperatures, dengue dynamics may become increasingly asynchronous. These findings can help underpin predictions of disease patterns as global temperatures rise.

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.

Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.

Severity-dependent test-seeking behaviors and test-negative designs: impact on estimated vaccine effectiveness and utility of analytic and design choices.

Test-negative designs are increasingly used to evaluate vaccine effectiveness because of desirable properties like reduced confounding due to healthcare-seeking behaviors and lower cost compared to other study designs. An individual's decision to seek care often depends on their disease severity, with severe disease more likely to be captured than mild disease. As many vaccines likely attenuate disease severity, this phenomenon generally results in an upward-biased estimate of vaccine effectiveness against symptomatic disease. To address the resulting bias, analytic solutions like adjusting for or matching on severity have been suggested. In this paper, we examine the performance of the test-negative design under different vaccine effects on disease severity and the utility of adjusting or matching on severity. We further consider the implications of studies that focus only on milder disease by restricting recruitment to outpatient settings. Through an analytic framework and simulations accompanied by a real-world example, we demonstrate that, when vaccination attenuates disease severity, the magnitude of bias is influenced by the degree of under-ascertainment of mild disease relative to severe disease. When vaccination does not attenuate disease severity, bias is not present. We further show that analytic fixes negligibly impact bias and that outpatient-only studies frequently produce downward-biased estimates.

Beneath the surface: Amino acid variation underlying two decades of dengue virus antigenic dynamics in Bangkok, Thailand.

Neutralizing antibodies are important correlates of protection against dengue. Yet, determinants of variation in neutralization across strains within the four dengue virus serotypes (DENV1-4) is imperfectly understood. Studies focus on structural DENV proteins, especially the envelope (E), the primary target of anti-DENV antibodies. Although changes in immune recognition (antigenicity) are often attributed to variation in epitope residues, viral processes influencing conformation and epitope accessibility also affect neutralizability, suggesting possible modulating roles of nonstructural proteins. We estimated effects of residue changes in all 10 DENV proteins on antigenic distances between 348 DENV collected from individuals living in Bangkok, Thailand (1994-2014). Antigenic distances were derived from response of each virus to a panel of twenty non-human primate antisera. Across 100 estimations, excluding 10% of virus pairs each time, 77 of 295 positions with residue variability in E consistently conferred antigenic effects; 52 were within ±3 sites of known binding sites of neutralizing human monoclonal antibodies, exceeding expectations from random assignments of effects to sites (p = 0.037). Effects were also identified for 16 sites on the stem/anchor of E which were only recently shown to become exposed under physiological conditions. For all proteins, except nonstructural protein 2A (NS2A), root-mean-squared-error (RMSE) in predicting distances between pairs held out in each estimation did not outperform sequences of equal length derived from all proteins or E, suggesting that antigenic signals present were likely through linkage with E. Adjusted for E, we identified 62/219 sites embedding the excess signals in NS2A. Concatenating these sites to E additionally explained 3.4% to 4.0% of observed variance in antigenic distances compared to E alone (50.5% to 50.8%); RMSE outperformed concatenating E with sites from any protein of the virus (ΔRMSE, 95%IQR: 0.01, 0.05). Our results support examining antigenic determinants beyond the DENV surface.

Proteomic studies of VEGFR2 in human placentas reveal protein associations with preeclampsia, diabetes, gravidity, and labor.

VEGFR2 (Vascular endothelial growth factor receptor 2) is a central regulator of placental angiogenesis. The study of the VEGFR2 proteome of chorionic villi at term revealed its partners MDMX (Double minute 4 protein) and PICALM (Phosphatidylinositol-binding clathrin assembly protein). Subsequently, the oxytocin receptor (OT-R) and vasopressin V1aR receptor were detected in MDMX and PICALM immunoprecipitations. Immunogold electron microscopy showed VEGFR2 on endothelial cell (EC) nuclei, mitochondria, and Hofbauer cells (HC), tissue-resident macrophages of the placenta. MDMX, PICALM, and V1aR were located on EC plasma membranes, nuclei, and HC nuclei. Unexpectedly, PICALM and OT-R were detected on EC projections into the fetal lumen and OT-R on 20-150 nm clusters therein, prompting the hypothesis that placental exosomes transport OT-R to the fetus and across the blood-brain barrier. Insights on gestational complications were gained by univariable and multivariable regression analyses associating preeclampsia with lower MDMX protein levels in membrane extracts of chorionic villi, and lower MDMX, PICALM, OT-R, and V1aR with spontaneous vaginal deliveries compared to cesarean deliveries before the onset of labor. We found select associations between higher MDMX, PICALM, OT-R protein levels and either gravidity, diabetes, BMI, maternal age, or neonatal weight, and correlations only between PICALM-OT-R (p < 2.7 × 10-8), PICALM-V1aR (p < 0.006), and OT-R-V1aR (p < 0.001). These results offer for exploration new partnerships in metabolic networks, tissue-resident immunity, and labor, notably for HC that predominantly express MDMX.

Reconstruction of antibody dynamics and infection histories to evaluate dengue risk.

As with many pathogens, most dengue infections are subclinical and therefore unobserved 1 . Coupled with limited understanding of the dynamic behaviour of potential serological markers of infection, this observational problem has wide-ranging implications, including hampering our understanding of individual- and population-level correlates of infection and disease risk and how these change over time, between assay interpretations and with cohort design. Here we develop a framework that simultaneously characterizes antibody dynamics and identifies subclinical infections via Bayesian augmentation from detailed cohort data (3,451 individuals with blood draws every 91 days, 143,548 haemagglutination inhibition assay titre measurements)2,3. We identify 1,149 infections (95% confidence interval, 1,135-1,163) that were not detected by active surveillance and estimate that 65% of infections are subclinical. After infection, individuals develop a stable set point antibody load after one year that places them within or outside a risk window. Individuals with pre-existing titres of ≤1:40 develop haemorrhagic fever 7.4 (95% confidence interval, 2.5-8.2) times more often than naive individuals compared to 0.0 times for individuals with titres >1:40 (95% confidence interval: 0.0-1.3). Plaque reduction neutralization test titres ≤1:100 were similarly associated with severe disease. Across the population, variability in the size of epidemics results in large-scale temporal changes in infection and disease risk that correlate poorly with age.

Optimizing the detection of emerging infections using mobility-based spatial sampling.

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.

Dynamics of Anti-influenza Mucosal IgA Over a Season in a Cohort of Individuals Living or Working in a Long-term Care Facility.

BACKGROUND: Serological surveys are used to ascertain influenza infection and immunity, but evidence for the utility of mucosal immunoglobulin A (IgA) as a correlate of infection or protection is limited. METHODS: We performed influenza-like illness (ILI) surveillance on 220 individuals living or working in a retirement community in Gainesville, Florida from January to May 2018, and took pre- and postseason nasal samples of 11 individuals with polymerase chain reaction (PCR)-confirmed influenza infection and 60 randomly selected controls. Mucosal IgA against 10 strains of influenza was measured from nasal samples. RESULTS: Overall, 28.2% and 11.3% of individuals experienced a 2-fold and 4-fold rise, respectively, in mucosal IgA to at least 1 influenza strain. Individuals with PCR-confirmed influenza A had significantly lower levels of preseason IgA to influenza A. Influenza-associated respiratory illness was associated with a higher rise in mucosal IgA to influenza strains of the same subtype, and H3N2-associated respiratory illness was associated with a higher rise in mucosal IgA to other influenza A strains. CONCLUSIONS: By comparing individuals with and without influenza illness, we demonstrated that mucosal IgA is a correlate of influenza infection. There was evidence for cross-reactivity in mucosal IgA across influenza A subtypes.

Household Transmission Dynamics of Seasonal Human Coronaviruses.

BACKGROUND: Household transmission studies inform how viruses spread among close contacts, but few characterize household transmission of endemic coronaviruses. METHODS: We used data collected from 223 households with school-age children participating in weekly disease surveillance over 2 respiratory virus seasons (December 2015 to May 2017), to describe clinical characteristics of endemic human coronaviruses (HCoV-229E, HcoV-HKU1, HcoV-NL63, HcoV-OC43) infections, and community and household transmission probabilities using a chain-binomial model correcting for missing data from untested households. RESULTS: Among 947 participants in 223 households, we observed 121 infections during the study, most commonly subtype HCoV-OC43. Higher proportions of infected children (<19 years) displayed influenza-like illness symptoms than infected adults (relative risk, 3.0; 95% credible interval [CrI], 1.5-6.9). The estimated weekly household transmission probability was 9% (95% CrI, 6-13) and weekly community acquisition probability was 7% (95% CrI, 5-10). We found no evidence for differences in community or household transmission probabilities by age or symptom status. Simulations suggest that our study was underpowered to detect such differences. CONCLUSIONS: Our study highlights the need for large household studies to inform household transmission, the challenges in estimating household transmission probabilities from asymptomatic individuals, and implications for controlling endemic CoVs.

Use of Whole-Genome Sequencing to Estimate the Contribution of Immune Evasion and Waning Immunity on Decreasing COVID-19 Vaccine Effectiveness.

BACKGROUND: The impact variant-specific immune evasion and waning protection have on declining coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE) remains unclear. Using whole-genome sequencing (WGS), we examined the contribution these factors had on the decline that followed the introduction of the Delta variant. Furthermore, we evaluated calendar-period-based classification as a WGS alternative. METHODS: We conducted a test-negative case-control study among people tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 1 April and 24 August 2021. Variants were classified using WGS and calendar period. RESULTS: We included 2029 cases (positive, sequenced samples) and 343 727 controls (negative tests). VE 14-89 days after second dose was significantly higher against Alpha (84.4%; 95% confidence interval [CI], 75.6%-90.0%) than Delta infection (68.9%; 95% CI, 58.0%-77.1%). The odds of Delta infection were significantly higher 90-149 than 14-89 days after second dose (P value = .003). Calendar-period-classified VE estimates approximated WGS-classified estimates; however, calendar-period-based classification was subject to misclassification (35% Alpha, 4% Delta). CONCLUSIONS: Both waning protection and variant-specific immune evasion contributed to the lower effectiveness. While calendar-period-classified VE estimates mirrored WGS-classified estimates, our analysis highlights the need for WGS when variants are cocirculating and misclassification is likely.

Testing Frequency Matters: An Evaluation of the Diagnostic Performance of a Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Rapid Antigen Test in US Correctional Facilities.

BACKGROUND: The Centers for Disease Control and Prevention recommends serial rapid antigen assay collection within congregate facilities. Although modeling and observational studies from communities and long-term care facilities have shown serial collection provides adequate sensitivity and specificity, the accuracy within correctional facilities remains unknown. METHODS: Using Connecticut Department of Correction data from 21 November 2020 to 15 June 2021, we estimated the accuracy of a rapid assay, BinaxNOW (Abbott), under 3 collection strategies: single test collection and serial collection of 2 and 3 tests separated by 1-4 days. The sensitivity and specificity of the first (including single), second, and third serially collected BinaxNOW tests were estimated relative to RT-PCRs collected ≤1 day of the BinaxNOW test. The accuracy metrics of the testing strategies were then estimated as the sum (sensitivity) and product (specificity) of tests in each strategy. RESULTS: Of the 13 112 residents who contributed ≥1 BinaxNOW test during the study period, 3825 contributed ≥1 RT-PCR paired BinaxNOW test. In relation to RT-PCR, the 3-rapid-antigen-test strategy had a sensitivity of 95.9% (95% CI: 93.6-97.5%) and specificity of 98.3% (95% CI: 96.7-99.1%). The sensitivities of the 2- and 1-rapid-antigen-test strategies were 88.8% and 66.8%, and the specificities were 98.5% and 99.4%, respectively. The sensitivity was higher among symptomatic residents and when RT-PCRs were collected before BinaxNOW tests. CONCLUSIONS: We found serial antigen test collection resulted in high diagnostic accuracy. These findings support serial collection for outbreak investigation, screening, and when rapid detection is required (such as intakes or transfers).

Population-Based Serologic Survey of Vibrio cholerae Antibody Titers before Cholera Outbreak, Haiti, 2022.

A Vibrio cholerae O1 outbreak emerged in Haiti in October 2022 after years of cholera absence. In samples from a 2021 serosurvey, we found lower circulating antibodies against V. cholerae lipopolysaccharide in children <5 years of age and no vibriocidal antibodies, suggesting high susceptibility to cholera, especially among young children.

A Mixture Model for Estimating SARS-CoV-2 Seroprevalence in Chennai, India.

Serological assays used to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often rely on manufacturers' cutoffs established on the basis of severe cases. We conducted a household-based serosurvey of 4,677 individuals in Chennai, India, from January to May 2021. Samples were tested for SARS-CoV-2 immunoglobulin G (IgG) antibodies to the spike (S) and nucleocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cutoffs and using a mixture model based on measured IgG level. Using manufacturer cutoffs, there was a 5-fold difference in seroprevalence estimated by each assay. This difference was largely reconciled using the mixture model, with estimated anti-S and anti-N IgG seroprevalence of 64.9% (95% credible interval (CrI): 63.8, 66.0) and 51.5% (95% CrI: 50.2, 52.9), respectively. Age and socioeconomic factors showed inconsistent relationships with anti-S and anti-N IgG seropositivity using manufacturer cutoffs. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. Estimates of SARS-CoV-2 seroprevalence using alternative targets must consider heterogeneity in seroresponse to ensure that seroprevalence is not underestimated and correlates are not misinterpreted.

The Role of Host Genetic Factors in Coronavirus Susceptibility: Review of Animal and Systematic Review of Human Literature.

The SARS-CoV-2 pandemic raises many scientific and clinical questions. These include how host genetic factors affect disease susceptibility and pathogenesis. New work is emerging related to SARS-CoV-2; previous work has been conducted on other coronaviruses that affect different species. We reviewed the literature on host genetic factors related to coronaviruses, systematically focusing on human studies. We identified 1,832 articles of potential relevance. Seventy-five involved human host genetic factors, 36 of which involved analysis of specific genes or loci; aside from one meta-analysis, all were candidate-driven studies, typically investigating small numbers of research subjects and loci. Three additional case reports were described. Multiple significant loci were identified, including 16 related to susceptibility (seven of which identified protective alleles) and 16 related to outcomes (three of which identified protective alleles). The types of cases and controls used varied considerably; four studies used traditional replication/validation cohorts. Among other studies, 30 involved both human and non-human host genetic factors related to coronavirus, 178 involved study of non-human (animal) host genetic factors related to coronavirus, and 984 involved study of non-genetic host factors related to coronavirus, including involving immunopathogenesis. Previous human studies have been limited by issues that may be less impactful now, including low numbers of eligible participants and limited availability of advanced genomic methods; however, these may raise additional considerations. We outline key genes and loci from animal and human host genetic studies that may bear investigation in the study of COVID-19. We also discuss how previous studies may direct current lines of inquiry.

T-cell engineered with a fully humanized B-cell maturation antigen-specific T-cell antigen coupler receptor effectively target multiple myeloma.

B-cell maturation antigen (BCMA) is a clinically validated target for multiple myeloma. T-cell engineered with chimeric antigen receptors (CARs) directed against BCMA have demonstrated robust therapeutic activity in clinical trials, but toxicities remain a significant concern for a subset of patients, supporting continued investigation of other engineered T-cell platforms that may offer equal efficacy with an improved toxicity profile. The authors recently described a BCMA-specific, T-cell-centric synthetic antigen receptor, the T-cell antigen coupler (TAC) receptor, that can be used to engineer T-cell with robust anti-myeloma activity. Here the authors describe the creation of a fully humanized BCMA-specific TAC receptor. Single-chain variable fragments (scFvs) were developed from BCMA-specific F(ab)s that were identified in a fully human phage display library. Twenty-four configurations of the F(ab)s were evaluated in a medium-throughput screening using primary T-cell, and a single F(ab), TRAC 3625, emerged as the most robust following in vitro and in vivo evaluation. An optimized BCMA-specific TAC receptor was developed through iterations of the BCMA-TAC design that evaluated a next-generation TAC scaffold sequence, different domains connecting the TAC to the 3625 scFv and different orientations of the TRAC 3625 heavy and light variable regions.

Individual, Household, and Community Drivers of Dengue Virus Infection Risk in Kamphaeng Phet Province, Thailand.

BACKGROUND: Dengue virus (DENV) often circulates endemically. In such settings with high levels of transmission, it remains unclear whether there are risk factors that alter individual infection risk. METHODS: We tested blood taken from individuals living in multigenerational households in Kamphaeng Phet province, Thailand for DENV antibodies (N = 2364, mean age 31 years). Seropositivity ranged from 45.4% among those 1-5 years old to 99.5% for those >30 years. Using spatially explicit catalytic models, we estimated that 11.8% of the susceptible population gets infected annually. RESULTS: We found that 37.5% of the variance in seropositivity was explained by unmeasured household-level effects with only 4.2% explained by spatial differences between households. The serostatus of individuals from the same household remained significantly correlated even when separated by up to 15 years in age. CONCLUSIONS: These findings show that despite highly endemic transmission, persistent differences in infection risk exist across households, the reasons for which remain unclear.

Influence of Preseason Antibodies Against Influenza Virus on Risk of Influenza Infection Among Healthcare Personnel.

BACKGROUND: The association of hemagglutination inhibition (HAI) antibodies with protection from influenza among healthcare personnel (HCP) with occupational exposure to influenza viruses has not been well-described. METHODS: The Respiratory Protection Effectiveness Clinical Trial was a cluster-randomized, multisite study that compared medical masks to N95 respirators in preventing viral respiratory infections among HCP in outpatient healthcare settings for 5180 participant-seasons. Serum HAI antibody titers before each influenza season and influenza virus infection confirmed by polymerase chain reaction were studied over 4 study years. RESULTS: In univariate models, the risk of influenza A(H3N2) and B virus infections was associated with HAI titers to each virus, study year, and site. HAI titers were strongly associated with vaccination. Within multivariate models, each log base 2 increase in titer was associated with 15%, 26% and 33%-35% reductions in the hazard of influenza A(H3N2), A(H1N1), and B infections, respectively. Best models included preseason antibody titers and study year, but not other variables. CONCLUSIONS: HAI titers were associated with protection from influenza among HCP with routine exposure to patients with respiratory illness and influenza season contributed to risk. HCP can be reassured about receiving influenza vaccination to stimulate immunity.