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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.
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Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.
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OBJECTIVE: We investigated whether a zip code's location or demographics are most predictive of changes in daily mobility throughout the course of the COVID-19 pandemic. DESIGN: We used a population-level study to examine the predictability of daily mobility during the COVID-19 pandemic using a two-stage regression approach, where generalised additive models (GAM) predicted mobility trends over time at a large spatial level, then the residuals were used to determine which factors (location, zip code-level features or number of non-pharmaceutical interventions (NPIs) in place) best predict the difference between a zip code's measured mobility and the average trend on a given date. SETTING: We analyse zip code-level mobile phone records from 26 metropolitan areas in the USA on 15 March-31 September 2020, relative to October 2020. RESULTS: While relative mobility had a general trend, a zip code's city-level location significantly helped to predict its daily mobility patterns. This effect was time-dependent, with a city's deviation from general mobility trends differing in both direction and magnitude throughout the course of 2020. The characteristics of a zip code further increased predictive power, with the densest zip codes closest to a city centre tended to have the largest decrease in mobility. However, the effect on mobility change varied by city and became less important over the course of the pandemic. CONCLUSIONS: The location and characteristics of a zip code are important for determining changes in daily mobility patterns throughout the course of the COVID-19 pandemic. These results can determine the efficacy of NPI implementation on multiple spatial scales and inform policy makers on whether certain NPIs should be implemented or lifted during the ongoing COVID-19 pandemic and when preparing for future public health emergencies.
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COVID-19 , COVID-19/epidemiología , Humanos , Estados Unidos/epidemiología , Análisis de Regresión , SARS-CoV-2 , Pandemias , Factores Sociodemográficos , Ciudades/epidemiología , GeografíaRESUMEN
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.
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Background: The emergence of COVID-19 variants with immune scape and the waning of primary vaccine schemes effectiveness have prompted many countries to indicate first and second booster COVID-19 vaccine doses to prevent severe COVID-19. However, current available evidence on second booster dose effectiveness are mostly limited to high-income countries, older adults, and mRNA-based vaccination schemes scenarios. We aimed to investigate the relative vaccine effectiveness (rVE) of the fourth dose compared to three doses for severe COVID-19 outcomes in Brazil; and compare the rVE of a fourth dose with an mRNA vaccine compared to adenovirus-based product in the same settings. Methods: We performed a target emulated trial using a population-based cohort of individuals aged 40 years or older who have received a homologous primary scheme of CoronaVac, ChAdOx1, or BNT162b2, and any third dose product and were eligible for the fourth dose in Brazil. The primary outcome was COVID-19 associated hospitalization or death. We built Cohort A matching individuals vaccinated with a fourth dose to individuals who received three doses to estimate the rVE of the fourth dose. We built Cohort B, a subset of Cohort A, matching mRNA-based (mRNA) to adenovirus-based fourth dose vaccinated individuals to compare their relative hazards for severe COVID-19. Findings: 46,693,484 individuals were included in Cohort A and 6,763,016 in Cohort B. 45% of them were aged between 40 and 60 years old, and 48% between 60 and 79 years old. In Cohort A, the most common previous series was a ChAdOx1 two-dose followed by BNT162b2 (44%), and a CoronaVac two-dose followed by a BNT162b2 (36%). Among those fourth dose vaccinated, 36.9% received ChAdOx1, 32.7% Ad26.COV2.S, 25.8% BNT162b2, and 4.7% CoronaVac. In Cohort B, among those who received an adenovirus fourth dose, 53.7% received ChAdOx1 and 46.3% received Ad26.COV2.S. The estimated rVE for the primary outcome of four doses compared to three doses was 44.1% (95% CI 42.3-46.0), with some waning during follow-up (rVE 7-60 days 46.8% [95% CI 44.4-49.1], rVE after 120 days 33.8% [95% CI 18.0-46.6]). Among fourth dose vaccinated individuals, mRNA-based vaccinated individuals had lower hazards for hospitalization or death compared to adenovirus-vaccinated individuals (HR 0.81, 95% CI 0.75-0.87). After 120 days, no difference in hazards between groups was observed (HR 1.35, 95% CI 0.93-1.97). Similar findings were observed for hospitalization and death separately, except no evidence for differences between fourth dose brands for death in Cohort B. Interpretation: In a heterogeneous scenario of primary and first booster vaccination combinations, a fourth dose provided meaningful and durable protection against severe COVID-19 outcomes. Compared to adenovirus-based booster, a fourth dose wild-type mRNA vaccine was associated with immediate lower hazards of hospitalization or death unsustained after 120 days. Funding: None.
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Many pathogens continuously change their protein structure in response to immune-driven selection, resulting in weakened protection even in previously exposed individuals. In addition, for some pathogens, such as dengue virus, poorly targeted immunity is associated with increased risk of severe disease through a mechanism known as antibody-dependent enhancement. However, it remains unclear whether the antigenic distances between an individual's first infection and subsequent exposures dictate disease risk, explaining the observed large-scale differences in dengue hospitalizations across years. Here, we develop a framework that combines detailed antigenic and genetic characterization of viruses with details on hospitalized cases from 21 years of dengue surveillance in Bangkok, Thailand, to identify the role of the antigenic profile of circulating viruses in determining disease risk. We found that the risk of hospitalization depended on both the specific order of infecting serotypes and the antigenic distance between an individual's primary and secondary infections, with risk maximized at intermediate antigenic distances. These findings suggest that immune imprinting helps determine dengue disease risk and provide a pathway to monitor the changing risk profile of populations and to quantifying risk profiles of candidate vaccines.
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Antígenos Virales , Virus del Dengue , Dengue , Humanos , Dengue/inmunología , Dengue/epidemiología , Dengue/virología , Virus del Dengue/inmunología , Antígenos Virales/inmunología , Tailandia/epidemiología , Factores de Riesgo , HospitalizaciónRESUMEN
Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual's antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology remains unclear. Here, we used a multi-level mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns and to investigate how influenza incidence varies over time, space and age in this population. We estimated median annual influenza infection rates to be approximately 18% from 1968 to 2015, but with substantial variation between years. 88% of individuals were estimated to have been infected at least once during the study period (2009-2015), and 20% were estimated to have three or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual's antibody profile. Finally, we reconstructed each individual's expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long term, epidemiological trends, within-host processes and immunity when analyzed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.
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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.
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Diabetes Mellitus , Preeclampsia , Femenino , Humanos , Recién Nacido , Embarazo , Número de Embarazos , Oxitocina/metabolismo , Placenta/metabolismo , Preeclampsia/metabolismo , Proteómica , Receptores de Oxitocina/metabolismo , Factor A de Crecimiento Endotelial Vascular/metabolismo , Receptor 2 de Factores de Crecimiento Endotelial Vascular/metabolismoRESUMEN
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.
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COVID-19 , Enfermedades Desatendidas , Medicina Tropical , Enfermedades Desatendidas/prevención & control , Humanos , COVID-19/epidemiología , COVID-19/prevención & control , Modelos Teóricos , Organización Mundial de la Salud , SARS-CoV-2 , Toma de Decisiones , Salud GlobalRESUMEN
Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has spread globally. However, the contribution of community versus household transmission to the overall risk of infection remains unclear. Methods: Between November 2021 and March 2022, we conducted an active case-finding study in an urban informal settlement with biweekly visits across 1174 households with 3364 residents. Individuals displaying coronavirus disease 2019 (COVID-19)-related symptoms were identified, interviewed along with household contacts, and defined as index and secondary cases based on reverse-transcription polymerase chain reaction (RT-PCR) and symptom onset. Results: In 61 households, we detected a total of 94 RT-PCR-positive cases. Of 69 sequenced samples, 67 cases (97.1%) were attributed to the Omicron BA.1* variant. Among 35 of their households, the secondary attack rate was 50.0% (95% confidence interval [CI], 37.0%-63.0%). Women (relative risk [RR], 1.6 [95% CI, .9-2.7]), older individuals (median difference, 15 [95% CI, 2-21] years), and those reporting symptoms (RR, 1.73 [95% CI, 1.0-3.0]) had a significantly increased risk for SARS-CoV-2 secondary infection. Genomic analysis revealed substantial acquisition of viruses from the community even among households with other SARS-CoV-2 infections. After excluding community acquisition, we estimated a household secondary attack rate of 24.2% (95% CI, 11.9%-40.9%). Conclusions: These findings underscore the ongoing risk of community acquisition of SARS-CoV-2 among households with current infections. The observed high attack rate necessitates swift booster vaccination, rapid testing availability, and therapeutic options to mitigate the severe outcomes of COVID-19.
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Epidemic data are often difficult to interpret due to inconsistent detection and reporting. As these data are critically relied upon to inform policy and epidemic projections, understanding reporting trends is similarly important. Early reporting of the COVID-19 pandemic in particular is complicated, due to changing diagnostic and testing protocols. An internal audit by the State of Florida, USA found numerous specific examples of irregularities in COVID-19 case and death reports. Using case, hospitalization, and death data from the the first year of the COVID-19 pandemic in Florida, we present approaches that can be used to identify the timing, direction, and magnitude of some reporting changes. Specifically, by establishing a baseline of detection probabilities from the first (spring) wave, we show that transmission trends among all age groups were similar, with the exception of the second summer wave, when younger people became infected earlier than seniors, by approximately 2 weeks. We also found a substantial drop in case-fatality risk (CFR) among all age groups over the three waves during the first year of the pandemic, with the most drastic changes seen in the 0 to 39 age group. The CFR trends provide useful insights into infection detection that would not be possible by relying on the number of tests alone. During the third wave, for which we have reliable hospitalization data, the CFR was remarkably stable across all age groups. In contrast, the hospitalization-to-case ratio varied inversely with cases while the death-to-hospitalization ratio varied proportionally. Although specific trends are likely to vary between locales, the approaches we present here offer a generic way to understand the substantial changes that occurred in the relationships among the key epidemic indicators.
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COVID-19 , Humanos , Recién Nacido , COVID-19/diagnóstico , COVID-19/epidemiología , SARS-CoV-2 , Florida/epidemiología , Pandemias , HospitalizaciónRESUMEN
The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross-validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric significantly improved model performance.
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Virus del Dengue , Dengue , Humanos , Dengue/diagnóstico , Dengue/epidemiología , Modelos Estadísticos , Pronóstico , Clima , FiebreRESUMEN
OBJECTIVES: The SARS-CoV-2 BQ.1* variant rapidly spread globally in late 2022, posing a challenge due to its increased immune evasion. METHODS: We conducted a prevalence survey in Brazil from November 16 to December 22, 2022, as part of a cohort study. We conducted interviews and collected nasal samples for reverse transcription-polymerase chain reaction (RT-PCR) testing and whole-genome sequencing. Cumulative incidence was estimated using RT-PCR positivity, cycle threshold values, and external data on the dynamics of RT-PCR positivity following infection. RESULTS: Among 535 participants, 54% had documented SARS-CoV-2 exposure before this outbreak and 74% had received COVID-19 vaccination. In this study, 14.8% tested positive for SARS-CoV-2, with BQ.1* identified in 90.7% of cases. Using case data and cycle threshold values, cumulative incidence was estimated at 56% (95% confidence interval, 36-88%). Of the 79 positive participants, 48.1% had a symptomatic illness, with a lower proportion fulfilling the World Health Organization COVID-19 case definition compared to prior Omicron waves. No participants required medical attention. CONCLUSIONS: Despite high population-level hybrid immunity, the BQ.1* variant attacked 56% of our population. Lower disease severity was associated with BQ.1* compared to prior Omicron variants. Hybrid immunity may provide protection against future SARS-CoV-2 variants but in this case was not able to prevent widespread transmission.
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Vacunas contra la COVID-19 , COVID-19 , Humanos , Estudios de Cohortes , Prevalencia , SARS-CoV-2/genética , COVID-19/diagnóstico , COVID-19/epidemiología , COVID-19/prevención & control , Inmunidad AdaptativaRESUMEN
Although it is known that household infections drive the transmission of dengue virus (DENV), it is unclear how household composition and the immune status of inhabitants affect the individual risk of infection. Most population-based studies to date have focused on paediatric cohorts because more severe forms of dengue mainly occur in children, and the role of adults in dengue transmission is understudied. Here we analysed data from a multigenerational cohort study of 470 households, comprising 2,860 individuals, in Kamphaeng Phet, Thailand, to evaluate risk factors for DENV infection. Using a gradient-boosted regression model trained on annual haemagglutination inhibition antibody titre inputs, we identified 1,049 infections, 90% of which were subclinical. By analysing imputed infections, we found that individual antibody titres, household composition and antibody titres of other members in the same household affect an individual's risk of DENV infection. Those individuals living in households with high average antibody titres, or households with more adults, had a reduced risk of infection. We propose that herd immunity to dengue acts at the household level and may provide insight into the drivers of the recent change in the shifting age distribution of dengue cases in Thailand.
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Virus del Dengue , Dengue , Adulto , Humanos , Niño , Estudios Prospectivos , Estudios de Cohortes , Estudios Longitudinales , Tailandia/epidemiologíaRESUMEN
Background: Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods: Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results: By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions: The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.
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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.
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Virus del Dengue , Dengue , Dengue Grave , Humanos , Lactante , Recién Nacido , Anticuerpos Antivirales , Anticuerpos Neutralizantes , Acrecentamiento Dependiente de AnticuerpoRESUMEN
The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric, significantly improved model performance.
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Many pathogens continuously change their protein structure in response to immune-driven selection, resulting in weakened protection. In addition, for some pathogens such as dengue virus, poorly targeted immunity is associated with increased risk of severe disease, through a mechanism known as antibody-dependent enhancement. However, it remains a mystery whether the antigenic distance between an individual's first infection and subsequent exposures dictate disease risk, explaining the observed large-scale differences in dengue hospitalisations across years. Here we develop an inferential framework that combines detailed antigenic and genetic characterisation of viruses, and hospitalised cases from 21 years of surveillance in Bangkok, Thailand to identify the role of the antigenic profile of circulating viruses in determining disease risk. We find that the risk of hospitalisation depends on both the specific order of infecting serotypes and the antigenic distance between an individual's primary and secondary infections, with risk maximised at intermediate antigenic distances. These findings suggest immune imprinting helps determine dengue disease risk, and provides a pathway to monitor the changing risk profile of populations and to quantifying risk profiles of candidate vaccines.
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Whether SARS-CoV-2 infection and COVID-19 vaccines confer exposure-dependent ("leaky") protection against infection remains unknown. We examined the effect of prior infection, vaccination, and hybrid immunity on infection risk among residents of Connecticut correctional facilities during periods of predominant Omicron and Delta transmission. Residents with cell, cellblock, and no documented exposure to SARS-CoV-2 infected residents were matched by facility and date. During the Omicron period, prior infection, vaccination, and hybrid immunity reduced the infection risk of residents without a documented exposure (HR: 0.36 [0.25-0.54]; 0.57 [0.42-0.78]; 0.24 [0.15-0.39]; respectively) and with cellblock exposures (0.61 [0.49-0.75]; 0.69 [0.58-0.83]; 0.41 [0.31-0.55]; respectively) but not with cell exposures (0.89 [0.58-1.35]; 0.96 [0.64-1.46]; 0.80 [0.46-1.39]; respectively). Associations were similar during the Delta period and when analyses were restricted to tested residents. Although associations may not have been thoroughly adjusted due to dataset limitations, the findings suggest that prior infection and vaccination may be leaky, highlighting the potential benefits of pairing vaccination with non-pharmaceutical interventions in crowded settings.