Dynamics of measles immunity from birth and following vaccination.

Measles remains a major threat to human health despite widespread vaccination. While we know that maternal antibodies can impair vaccine-induced immunity, the relative contributions of pre-existing immunity levels, maternal and infant characteristics on vaccine responses remain unclear, hampering evidence-based vaccination policy development. Here we combine serological data from 1,505 individuals (aged 0-12 years) in a mother-infant cohort and in a child cohort with empirical models to reconstruct antibody trajectories from birth. We show that while highly heterogeneous across a population, measles antibody evolution is strongly predictive from birth at the individual level, including following vaccination. Further, we find that caesarean section births were linked with 2.56 (95% confidence interval: 1.06-6.37) increased odds of primary vaccine failure, highlighting the long-term immunological consequences of birth route. Finally, we use our new understanding of antibody evolution to critically assess the population-level consequences of different vaccination schedules, the results of which will allow country-level evaluations of vaccine policy.

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.

Viral genetic clustering and transmission dynamics of the 2022 mpox outbreak in Portugal.

Pathogen genome sequencing during epidemics enhances our ability to identify and understand suspected clusters and investigate their relationships. Here, we combine genomic and epidemiological data of the 2022 mpox outbreak to better understand early viral spread, diversification and transmission dynamics. By sequencing 52% of the confirmed cases in Portugal, we identified the mpox virus sublineages with the highest impact on case numbers and fitted them into a global context, finding evidence that several international sublineages probably emerged or spread early in Portugal. We estimated a 62% infection reporting rate and that 1.3% of the population of men who have sex with men in Portugal were infected. We infer the critical role played by sexual networks and superspreader gatherings, such as sauna attendance, in the dissemination of mpox virus. Overall, our findings highlight genomic epidemiology as a tool for the real-time monitoring and control of mpox epidemics, and can guide future vaccine policy in a highly susceptible population.

Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models.

BACKGROUND: Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI. METHODS: We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194). RESULTS: The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models. CONCLUSIONS: Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.

Comparing the age and sex trajectories of SARS-CoV-2 morbidity and mortality with other respiratory pathogens.

Comparing age and sex differences in SARS-CoV-2 hospitalization and mortality with MERS-CoV, seasonal coronaviruses, influenza and other health outcomes opens the way to generating hypotheses as to underlying mechanisms driving disease risk. Using 60-year-olds as a reference age group, we find that relative rates of hospitalization and mortality associated with the emergent coronaviruses are lower during childhood and start to increase earlier (around puberty) as compared with influenza and seasonal coronaviruses. The changing distribution of disease risk by age for emerging pathogens appears to broadly track the gradual deterioration of the immune system (immunosenescence), which starts around puberty. By contrast, differences in severe disease risk by age from endemic pathogens are more decoupled from the immune ageing process. Intriguingly, age-specific sex differences in hospitalizations are largely similar across endemic and emerging infections. We discuss potential mechanisms that may be associated with these patterns.

Refining Reproduction Number Estimates to Account for Unobserved Generations of Infection in Emerging Epidemics.

BACKGROUND: Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R0) and effective (Rt) reproduction numbers during the initial phases of an epidemic. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase. METHODS: We propose a debiasing procedure that utilizes a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data reported in Italy, Sweden, the United Kingdom, and the United States. RESULTS: In all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias, and the quantification of uncertainty is more precise, as better coverage of the true R0 values is achieved with tighter credible intervals. When applied to real-world data, the proposed adjustment produces basic reproduction number estimates that closely match the estimates obtained in other studies while making use of a minimal amount of data. CONCLUSIONS: The proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications.

Arthralgia resolution rate following chikungunya virus infection.

BACKGROUND: Arthralgia, persistent pain or stiffness of the joints, is the hallmark symptom of chronic chikungunya virus (CHIKV) disease. Associated with significant disability and reduced quality of life, arthralgia can persist for many months following CHIKV infection. Understanding the expected duration of arthralgia persistence is important for managing clinical expectations at the individual-level as well as for estimating long-term burdens on population health following a CHIKV epidemic. METHODS: A review of cohort studies reporting the prevalence of arthralgia post-CHIKV infection over multiple time points was conducted. Generalized linear models were used to estimate the average rate of arthralgia resolution following CHIKV infection. RESULTS: Sixteen cohort studies matching the inclusion criteria were identified and included in the analysis. An average rate of arthralgia resolution of 10.85% (95% confidence interval (CI) 9.05-12.66%) per month was estimated across studies, corresponding to an expected median time to arthralgia resolution of 6.39 months (95% CI 5.48-7.66 months) and an expected arthralgia prevalence of 72.21% (95% CI 68.40-76.23%) at 3 months post-CHIKV infection. CONCLUSIONS: Estimates of the average rate of arthralgia resolution and the expected prevalence of arthralgia over time post-CHIKV infection were derived. These can help inform expectations regarding the long-term public health burdens associated with CHIKV epidemics.

Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa.

INTRODUCTION: HIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small-area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five-year age groups. METHODS: Small-area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district-level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016-2018. RESULTS: Adult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty-eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city. CONCLUSIONS: The Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data.

A Comparative Analysis of Statistical Methods to Estimate the Reproduction Number in Emerging Epidemics, With Implications for the Current Coronavirus Disease 2019 (COVID-19) Pandemic.

BACKGROUND: As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues its rapid global spread, quantification of local transmission patterns has been, and will continue to be, critical for guiding the pandemic response. Understanding the accuracy and limitations of statistical methods to estimate the basic reproduction number, R0, in the context of emerging epidemics is therefore vital to ensure appropriate interpretation of results and the subsequent implications for control efforts. METHODS: Using simulated epidemic data, we assess the performance of 7 commonly used statistical methods to estimate R0 as they would be applied in a real-time outbreak analysis scenario: fitting to an increasing number of data points over time and with varying levels of random noise in the data. Method comparison was also conducted on empirical outbreak data, using Zika surveillance data from the 2015-2016 epidemic in Latin America and the Caribbean. RESULTS: We find that most methods considered here frequently overestimate R0 in the early stages of epidemic growth on simulated data, the magnitude of which decreases when fitted to an increasing number of time points. This trend of decreasing bias over time can easily lead to incorrect conclusions about the course of the epidemic or the need for control efforts. CONCLUSIONS: We show that true changes in pathogen transmissibility can be difficult to disentangle from changes in methodological accuracy and precision in the early stages of epidemic growth, particularly for data with significant over-dispersion. As localized epidemics of SARS-CoV-2 take hold around the globe, awareness of this trend will be important for appropriately cautious interpretation of results and subsequent guidance for control efforts.

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.

Spatiotemporal variability in dengue transmission intensity in Jakarta, Indonesia.

BACKGROUND: Approximately 70% of the global burden of dengue disease occurs on the Asian continent, where many large urban centres provide optimal environments for sustained endemic transmission and periodic epidemic cycles. Jakarta, the capital of Indonesia, is a densely populated megacity with hyperendemic dengue transmission. Characterization of the spatiotemporal distribution of dengue transmission intensity is of key importance for optimal implementation of novel control and prevention programmes, including vaccination. In this paper we use mathematical models to provide the first detailed description of spatial and temporal variability in dengue transmission intensity in Jakarta. METHODOLOGY/PRINCIPAL FINDINGS: We applied catalytic models in a Bayesian framework to age-stratified dengue case notification data to estimate dengue force of infection and reporting probabilities in 42 subdistricts of Jakarta. The model was fitted to yearly and average annual data covering a 10-year period between 2008 and 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129-0.131) per year in Jakarta province, ranging from 0.090 (95%CrI: 0.077-0.103) to 0.164 (95%CrI: 0.153-0.174) across subdistricts. Annual average transmission intensity in Jakarta province during the 10-year period ranged from 0.012 (95%CrI: 0.011-0.013) in 2017 to 0.124 (95%CrI: 0.121-0.128) in 2016. CONCLUSIONS/SIGNIFICANCE: While the absolute number of dengue case notifications cannot be relied upon as a measure of endemicity, the age-distribution of reported dengue cases provides valuable insights into the underlying nature of transmission. Our estimates from yearly and average annual case notification data represent the first detailed estimates of dengue transmission intensity in Jakarta's subdistricts. These will be important to consider when assessing the population-level impact and cost-effectiveness of potential control and prevention programmes in Jakarta province, such as the controlled release of Wolbachia-carrying mosquitoes and vaccination.

Geographic variation in dengue seroprevalence and force of infection in the urban paediatric population of Indonesia.

Understanding the heterogeneous nature of dengue transmission is important for prioritizing and guiding the implementation of prevention strategies. However, passive surveillance data in endemic countries are rarely adequately informative. We analyzed data from a cluster-sample, cross-sectional seroprevalence study in 1-18 year-olds to investigate geographic differences in dengue seroprevalence and force of infection in Indonesia. We used catalytic models to estimate the force of infection in each of the 30 randomly selected sub-districts. Based on these estimates, we determined the proportion of sub-districts expected to reach seroprevalence levels of 50%, 70% and 90% by year of age. We used population averaged generalized estimating equation models to investigate individual- and cluster-level determinants of dengue seropositivity. Dengue force of infection varied substantially across Indonesia, ranging from 4.3% to 30.0% between sub-districts. By age nine, 60% of sub-districts are expected to have a seroprevalence ≥70%, rising to 83% by age 11. Higher odds of seropositivity were associated with higher population density (OR = 1.54 per 10-fold rise in population density, 95% CI: 1.03-2.32) and with City (relative to Regency) administrative status (OR = 1.92, 95% CI: 1.32-2.79). Our findings highlight the substantial variation in dengue endemicity within Indonesia and the importance of understanding spatial heterogeneity in dengue transmission intensity for optimal dengue prevention strategies including future implementation of dengue vaccination programmes.

Intrapartum Antibiotic Chemoprophylaxis Policies for the Prevention of Group B Streptococcal Disease Worldwide: Systematic Review.

BACKGROUND: Intrapartum antibiotic chemoprophylaxis (IAP) prevents most early-onset group B streptococcal (GBS) disease. However, there is no description of how IAP is used around the world. This article is the sixth in a series estimating the burden of GBS disease. Here we aimed to review GBS screening policies and IAP implementation worldwide. METHODS: We identified data through (1) systematic literature reviews (PubMed/Medline, Embase, Literature in the Health Sciences in Latin America and the Caribbean [LILACS], World Health Organization library database [WHOLIS], and Scopus) and unpublished data from professional societies and (2) an online survey and searches of policies from medical societies and professionals. We included data on whether an IAP policy was in use, and if so whether it was based on microbiological or clinical risk factors and how these were applied, as well as the estimated coverage (percentage of women receiving IAP where indicated). RESULTS: We received policy information from 95 of 195 (49%) countries. Of these, 60 of 95 (63%) had an IAP policy; 35 of 60 (58%) used microbiological screening, 25 of 60 (42%) used clinical risk factors. Two of 15 (13%) low-income, 4 of 16 (25%) lower-middle-income, 14 of 20 (70%) upper-middle-income, and 40 of 44 (91%) high-income countries had any IAP policy. The remaining 35 of 95 (37%) had no national policy (25/33 from low-income and lower-middle-income countries). Coverage varied considerably; for microbiological screening, median coverage was 80% (range, 20%-95%); for clinical risk factor-based screening, coverage was 29% (range, 10%-50%). Although there were differences in the microbiological screening methods employed, the individual clinical risk factors used were similar. CONCLUSIONS: There is considerable heterogeneity in IAP screening policies and coverage worldwide. Alternative global strategies, such as maternal vaccination, are needed to enhance the scope of global prevention of GBS disease.

Maternal Colonization With Group B Streptococcus and Serotype Distribution Worldwide: Systematic Review and Meta-analyses.

BACKGROUND: Maternal rectovaginal colonization with group B Streptococcus (GBS) is the most common pathway for GBS disease in mother, fetus, and newborn. This article, the second in a series estimating the burden of GBS, aims to determine the prevalence and serotype distribution of GBS colonizing pregnant women worldwide. METHODS: We conducted systematic literature reviews (PubMed/Medline, Embase, Latin American and Caribbean Health Sciences Literature [LILACS], World Health Organization Library Information System [WHOLIS], and Scopus), organized Chinese language searches, and sought unpublished data from investigator groups. We applied broad inclusion criteria to maximize data inputs, particularly from low- and middle-income contexts, and then applied new meta-analyses to adjust for studies with less-sensitive sampling and laboratory techniques. We undertook meta-analyses to derive pooled estimates of maternal GBS colonization prevalence at national and regional levels. RESULTS: The dataset regarding colonization included 390 articles, 85 countries, and a total of 299924 pregnant women. Our adjusted estimate for maternal GBS colonization worldwide was 18% (95% confidence interval [CI], 17%-19%), with regional variation (11%-35%), and lower prevalence in Southern Asia (12.5% [95% CI, 10%-15%]) and Eastern Asia (11% [95% CI, 10%-12%]). Bacterial serotypes I-V account for 98% of identified colonizing GBS isolates worldwide. Serotype III, associated with invasive disease, accounts for 25% (95% CI, 23%-28%), but is less frequent in some South American and Asian countries. Serotypes VI-IX are more common in Asia. CONCLUSIONS: GBS colonizes pregnant women worldwide, but prevalence and serotype distribution vary, even after adjusting for laboratory methods. Lower GBS maternal colonization prevalence, with less serotype III, may help to explain lower GBS disease incidence in regions such as Asia. High prevalence worldwide, and more serotype data, are relevant to prevention efforts.

Laparoscopic bariatric surgery can be performed safely in the community hospital setting.

BACKGROUND: Bariatric surgery is being performed commonly in various practice settings. To evaluate safety and efficacy, we reviewed the results of our first 516 laparoscopic bariatric operations performed in a community hospital setting. METHODS: We reviewed retrospectively the results of our first 516 consecutive laparoscopic bariatric procedures. RESULTS: Between December 2001 and December 2004, we performed 516 bariatric surgical procedures. Ninety-nine percent of these were accomplished laparoscopically. Thirty-day mortality in our series of 516 patients is 0%. Of these patients, 431 had laparoscopic gastric bypass. The mean BMI in these patients was 51. Mean percent excess weight loss in the laparoscopic gastric bypass patients was 70% at 1 year, 79% at 2 years and 84% at 3 years. Complications in the laparoscopic bypass group requiring reoperation included 11 bowel obstructions (2.5%), 5 episodes of bleeding (1.1%), and 2 laparoscopies for benign reasons. There were 8 anastomotic leaks (1.9%)-7 requiring reoperation, 1 managed nonoperatively. Eighty-five patients had adjustable gastric banding. Mean BMI was 45. Mean percent excess weight loss in the adjustable gastric banding patients was 39% at 1 year and 57% at 2 years. Complications in the adjustable gastric band patients requiring reoperation included 3 access port malfunctions (3.5%), 3 prolapsed bands (3.5%), 1 punctured band requiring replacement (1.2%) and 1 band causing complete obstruction requiring removal (1.2%). CONCLUSION: Laparoscopic bariatric surgery can be performed safely in the community hospital setting with a very low operative morbidity and mortality. This requires an experienced team of bariatric surgeons leading a multidisciplinary team of other health care professionals. Surgeon experience and super obesity can influence the risks.