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Modelling the effects of adult emergence on the surveillance and age distribution of medically important mosquitoes.
Entomological surveillance is an important component of mosquito-borne disease control. Mosquito abundance, infection prevalence and the entomological inoculation rate are the most widely reported entomological metrics, although these data are notoriously noisy and difficult to interpret. For many infections, only older mosquitoes are infectious, which is why, in part, vector control tools that reduce mosquito life expectancy have been so successful. The age structure of wild mosquitoes has been proposed as a metric to assess the effectiveness of interventions that kill adult mosquitoes, and age grading tools are becoming increasingly advanced. Mosquito populations show seasonal dynamics with temporal fluctuations. How seasonal changes in adult mosquito emergence and vector control could affect the mosquito age distribution or other important metrics is unclear. We develop stochastic mathematical models of mosquito population dynamics to show how variability in mosquito emergence causes substantial heterogeneity in the mosquito age distribution, with low frequency, positively autocorrelated changes in emergence being the most important driver of this variability. Fitting a population model to mosquito abundance data collected in experimental hut trials indicates these dynamics are likely to exist in wild Anopheles gambiae populations. Incorporating age structuring into an established compartmental model of mosquito dynamics and vector control, indicates that the use of mosquito age as a metric to assess the efficacy of vector-control tools will require an understanding of underlying variability in mosquito ages, with the mean age and other entomological metrics affected by short-term and seasonal fluctuations in mosquito emergence.
How Accurate Are High Resolution Settlement Maps at Predicting Population Counts in Data Scarce Settings?
ABSTRACTDespite the recent milestone of the world population surpassing 8 billion, disparities in population data reliability persist, with many countries facing outdated or incomplete census data. Such inaccuracies have far‐reaching implications for various sectors, including public health, urban planning, and resource allocation. The study leverages the rich data environment provided by the detailed 2018 Colombian census data and its coverage indicator, which create a high‐quality controlled environment to assess the performance of census‐independent population estimation approaches. Drawing from a diverse range of environmental landscapes in Colombia, we evaluate the effectiveness of satellite imagery‐derived settlement maps in conjunction with various modeling techniques. We explore two estimation approaches based on settlement maps: a data‐driven machine learning approach exemplified by a random forest model and a process‐driven probabilistic approach exemplified by a hierarchical Bayesian model. Our findings underscore the efficacy of Bayesian modeling in addressing data scarcity and bias, providing robust estimates and quantifying model uncertainty. However, the random forest model performs better when data inputs are detailed and unbiased. We further emphasize the importance of considering settlement map characteristics in the modeling process, while recognizing the overall limitations of relying solely on satellite imagery for population counts. Through a rigorous evaluation of different stages of the population modeling pipeline—data input, model selection, and outcome assessment—this study provides key insights into the challenges and requirements of using satellite imagery‐derived settlement maps for population estimation in data‐scarce contexts.
Antimicrobial usage among acutely ill hospitalised children aged 2‒23 months in sub-Saharan Africa and South Asia
Abstract Background Understanding patterns of antimicrobial use is critical to supporting antibiotic stewardship and limiting antimicrobial resistance (AMR). We aimed to describe antimicrobial prescribing in acutely ill hospitalised children aged 2-23 months across a range of rural and urban hospital settings in sub-Saharan Africa and South Asia. Methods The CHAIN cohort collected data daily throughout hospitalisation from children with acute illness aged 2-23 months admitted to nine hospitals from November 2016 to January 2019. We determined proportions of children receiving antimicrobials, inpatient-days receiving antimicrobials, antimicrobial classes, WHO Access, Watch, and Reserve (AWaRe) classifications, and examined factors associated with Watch antimicrobial use. Results Of 3101 admissions, 1422 (46%) received antimicrobials prior to hospitalization. 2816 (91%) children received antimicrobials during 19398/21807 (93%) inpatient child-days. 2477 (76%), 1092 (35%), and 12 (0.3%) children received Access, Watch, and Reserve antimicrobials, mostly <48 hours from admission. 341 (11%) of admissions received an antimicrobial without any indication. Prior admission, chronic illness, diagnoses of sepsis or meningitis, hypoglycemia and duration of admission were associated with receiving Watch antimicrobials, whilst WHO danger signs, severe malnutrition, HIV and receipt of prior antimicrobials were not, despite their known association with mortality and AMR. Conclusions Antimicrobial use was similar across sites with some overuse, and notably limited escalation and de-escalation, likely due to guideline adherence. Guidelines need updating for the absence of relevant antimicrobial sensitivities, to include risk-based antimicrobial prescribing considering mortality risk and prior exposure to antimicrobials and the hospital environment. Hence, clinical trials of risk-differentiated care are needed.
Scalable, open-access and multidisciplinary data integration pipeline for climate-sensitive diseases
Climate-sensitive infectious diseases pose an important challenge for human, animal and environmental health and it has been estimated that over half of known human pathogenic diseases can be aggravated by climate change. While climatic and weather conditions are important drivers of transmission of vector-borne diseases, socio-economic, behavioural, and land-use factors as well as the interactions among them impact transmission dynamics. Analysis of drivers of climate-sensitive diseases require rapid integration of interdisciplinary data to be jointly analysed with epidemiological (including genomic and clinical) data. Current tools for the integration of multiple data sources are often limited to one data type or rely on proprietary data and software. To address this gap, we develop a scalable and open-access pipeline for the integration of multiple spatio-temporal datasets that requires only the declaration of the country and temporal range and resolution of the study. The tool is locally deployable and can easily be integrated into existing climate-disease-modelling applications. We demonstrate the utility of the tool for dengue modelling in Vietnam where epidemiological data are legally required to remain local. We include a pipeline for bias correction of climate data to enhance their quality for downstream modelling tasks. The Dengue Advanced Readiness Tools-Pipeline empowers users by simplifying complex download, correction, and aggregation steps, fostering data-driven discovery of relationships between infectious diseases and their drivers in space and time, and enhancing reproducibility in research. Additional modules and datasets can be added to the existing ones to make the pipeline extendable to use cases other than the ones presented here.
Safety and immunogenicity of the ChAdOx1 nCoV-19 (AZD1222) vaccine in children aged 6-17 years: Final results of a phase 2, single-blind, randomised controlled trial (COV006).
Paediatric COVID-19 vaccination programmes were initiated in response to the coronavirus pandemic declared by the World Health Organisation (WHO) in 2020. Ten COVID-19 vaccines received WHO Emergency Use Listing, however, only five were approved for use in children. ChAdOx1 nCoV-19 (AZD1222) was approved in adults in a two-dose regimen. We previously reported interim findings of a phase 2 study of ChAdOx1 nCoV-19 in children with immunogenicity, comparable with adults. Final results after 12 month follow-up are reported. Single-blind, randomised controlled trial across four UK centres, recruiting 261 children and adolescents (aged 6-17 years). Participants received either two doses of ChAdOx1 nCoV-19 or Bexsero vaccine (controls). The primary outcome was safety (adverse events for 28 days following vaccination and serious adverse events throughout), and secondary outcome was immunogenicity (measured by SARS-CoV-2 anti-spike enzyme-linked immunosorbent assay (ELISA) and enzyme-linked immunosorbent spot (ELISpot)). Five serious adverse events and four adverse events of special interest were reported. None were related to study vaccinations, and there were no deaths. Geometric mean titres (GMTs) from an anti-spike (Wuhan) ELISA in participants aged 6-11 years were 1 EU/ml (95% CI 1-2) at baseline versus 796 EU (95% CI 161-3948, n =4) at D364. In participants aged 12-17 years, GMTs were 1 EU/ml (95% CI 1-2, n=3) at baseline versus 1432 EU/ml (95% CI 2337-6083; n=6) at D364 (2 dose regimen at 112-day interval), compared to 3 EU/ml (95% CI 0-62) at baseline versus 392 EU/ml (95% CI 24, 6493; n=3) at D364 (2 dose regimen at a 28-day interval). A two-dose regimen of ChAdOx1 nCoV-19 was immunogenic and safe in the trial population. No vaccine-related serious adverse events were reported. Immune responses persisted to 12 months in participants who did not experience breakthrough infection, This trial was registered with ISRCTN, trial number 15638344. The study was funded by the Department of Health and Social Care, through the National Institute for Health Research, and AstraZeneca.
Dosing interval is a major factor determining the quality of T cells induced by SARS-CoV-2 mRNA and adenoviral vector vaccines
Functional T cell responses are crucial for protective immunity induced by COVID-19 vaccination, but factors influencing the quality of these responses are incompletely understood. We used an activation-induced marker (AIM) assay and single-cell transcriptomic sequencing to analyze SARS-CoV-2 spike-responsive T cells after mild SARS-CoV-2 infection or after one or two doses of mRNA–lipid nanoparticle (mRNA-LNP) or adenoviral-vectored COVID-19 vaccines. Our findings revealed broad functional and clonal heterogeneity in T cells generated by vaccination or infection, including multiple distinct effector populations. T cell function was largely conserved between COVID-19 vaccine platforms but was distinct compared with SARS-CoV-2 infection. Notably, the dosing interval greatly influenced the quality of T cells after two vaccine doses, particularly after mRNA-LNP vaccination, where a longer interval led to reduced inflammatory signaling and increased secondary proliferation. These insights enhance our understanding of SARS-CoV-2–specific T cells and inform the optimization of mRNA vaccination regimens.
MAIT and other innate-like T cells integrate adaptive immune responses to modulate interval-dependent reactogenicity to mRNA vaccines
Adenoviral (Ad) vectors and mRNA vaccines exhibit distinct patterns of immune responses and reactogenicity, but underpinning mechanisms remain unclear. We longitudinally compared homologous ChAdOx1 nCoV-19 and BNT162b2 vaccination, focusing on cytokine-responsive innate-like lymphocytes—mucosal-associated invariant T (MAIT) cells and Vδ2 + γδ T cells—which sense and tune innate-adaptive cross-talk. Ad priming elicited robust type I interferon (IFN)–mediated innate-like T cell activation, augmenting T cell responses (innate-to-adaptive signaling), which was dampened at boost by antivector immunity. Conversely, mRNA boosting enhanced innate-like responses, driven by prime-induced spike-specific memory T cell–derived IFN-γ (adaptive-to-innate signaling). Extending the dosing interval dampened inflammation at boost because of waning T cell memory. In a separate vaccine trial, preboost spike-specific T cells predicted severe mRNA reactogenicity regardless of the priming platform or interval. Overall, bidirectional innate-like and adaptive cross-talk, and IFN-γ–licensed innate-like T cells, orchestrate interval-dependent early vaccine responses, suggesting modifiable targets for safer, more effective regimens.
Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks.
Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.
Human tonsil organoids reveal innate pathways modulating humoral and cellular responses to ChAdOx1.
The COVID-19 pandemic response demonstrated the effectiveness of adenovirus vector vaccines in inducing protective cellular and antibody responses. However, we still lack mechanistic understanding of the factors regulating immunity induced by this platform, especially innate pathways. We utilized a human tonsil organoid model to study the regulation of adaptive responses to ChAdOx1 nCoV-19. Innate activation and cytokine release occurred within 24 hours and T and B cell activation and antigen-specific antibody secretion occurred during the ensuing 14-day culture. Among the immune cell populations, plasmacytoid dendritic cells (pDCs) exhibited the highest ChAdOx1 transduction levels. pDC-derived IFN-ɑ was critical for humoral responses, but production of antigen in pDCs was dispensable. Furthermore, IL-6 enhanced humoral responses in both IFN-⍺-dependent and independent manners, indicating intricate signaling interplay. IFN-ɑ and IL-6 also regulated the function of vaccine-activated CD4+ T cells, including TFH. These data provide key insights into innate pathways regulating ChAdOx1-induced immunity and highlights the promise of this model for vaccine platform mechanistic studies.
Heterologous COVID-19 vaccine schedule with protein-based prime (NVX-CoV2373) and mRNA boost (BNT162b2) induces strong humoral responses: Results from COV-BOOST trial.
BackgroundHeterologous schedules of booster vaccines for COVID-19 following initial doses of mRNA or adenoviral vector vaccines have been shown to be safe and immunogenic. There are few data on booster doses following initial doses of protein nanoparticle vaccines.MethodsParticipants of the phase 3 clinical trial of the COVID-19 vaccine NVX-CoV2373 (EudraCT 2020-004123-16) enroled between September 28 and November 28, 2020, who received 2 doses of NVX-CoV2373 administered 21 days apart were invited to receive a third dose booster vaccine of BNT162b2 (wild type mRNA vaccine) as a sub-study of the COV-BOOST clinical trial, and were followed up for assessment of safety, reactogenicity and immunogenicity to day 242 post-booster.ResultsThe BNT162b2 booster following two doses of NVX-COV2373 was well-tolerated. Most adverse events were mild to moderate, with no serious vaccine-related adverse events reported. Immunogenicity analysis showed a significant increase in spike IgG titres and T-cell responses post-third dose booster. Specifically, IgG levels peaked at day 14 with a geometric mean concentration (GMC) of 216,255 ELISA laboratory units (ELU)/mL (95% CI 191,083-244,743). The geometric mean fold increase from baseline to day 28 post-boost was 168.6 (95% CI 117.5-241.8). Spike IgG titres were sustained above baseline levels at day 242 with a GMC of 58,686 ELU/mL (95% CI 48,954-74,652), with significant decay between days 28 and 84 (geometric mean ratio 0.58, 95% CI 0.53-0.63). T-cell responses also demonstrated enhancement post-booster, with a geometric mean fold increase of 5.1 (95% CI 2.9-9.0) at day 14 in fresh samples and 3.0 (95% CI 1.8-4.9) in frozen samples as measured by ELISpot. In an exploratory analysis, participants who received BNT162b2 after two doses of NVX-COV2373 exhibited higher anti-spike IgG at Day 28 than those who received homologous three doses of BNT162b2, with a GMR of 5.02 (95% CI: 3.17-7.94). This trend remained consistent across all time points, indicating a similar decay rate between the two schedules.ConclusionsA BNT162b2 third dose booster dose in individuals primed with two doses of NVX-COV2373 is safe and induces strong and durable immunogenic responses, higher than seen in other comparable studies. These findings support the use and investigation of heterologous booster strategies and early investigation of heterologous vaccine technology schedules should be a priority in the development of vaccines against new pathogens.
Estimates of HIV-1 within-host recombination rates across the whole genome.
Recombination plays a pivotal role in generating within-host diversity and enabling HIV's evolutionary success, particularly in evading the host immune response. Despite this, the variability in recombination rates across different settings and the underlying factors that drive these differences remain poorly understood. In this study, we analysed a large dataset encompassing hundreds of untreated, longitudinally sampled infections using both whole-genome long-read and short-read sequencing datasets. By quantifying recombination rates, we uncover substantial variation across subtypes, viral loads, and stages of infection. We also map recombination hot and cold spots across the genome using a sliding window approach, finding that previously reported inter-subtype regions of high or low recombination are replicated at the within-host level. Importantly, our findings reveal the significant influence of selection on recombination, showing that the presence and success of recombinant genomes is strongly interconnected with the fitness landscape. These results offer valuable insights into the contribution of recombination to evolutionary dynamics and demonstrate the enhanced resolution that long-read sequencing offers for studying viral evolution.
Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing
Instantaneous contact tracing New analyses indicate that severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) is more infectious and less virulent than the earlier SARS-CoV-1, which emerged in China in 2002. Unfortunately, the current virus has greater epidemic potential because it is difficult to trace mild or presymptomatic infections. As no treatment is currently available, the only tools that we can currently deploy to stop the epidemic are contact tracing, social distancing, and quarantine, all of which are slow to implement. However imperfect the data, the current global emergency requires more timely interventions. Ferretti et al. explored the feasibility of protecting the population (that is, achieving transmission below the basic reproduction number) using isolation coupled with classical contact tracing by questionnaires versus algorithmic instantaneous contact tracing assisted by a mobile phone application. For prevention, the crucial information is understanding the relative contributions of different routes of transmission. A phone app could show how finite resources must be divided between different intervention strategies for the most effective control. Science , this issue p. eabb6936
Drivers of epidemic dynamics in real time from daily digital COVID-19 measurements
Understanding the drivers of respiratory pathogen spread is challenging, particularly in a timely manner during an ongoing epidemic. In this work, we present insights that we obtained using daily data from the National Health Service COVID-19 app for England and Wales and that we shared with health authorities in almost real time. Our indicator of the reproduction number R ( t ) was available days earlier than other estimates, with an innovative capability to decompose R ( t ) into contact rates and probabilities of infection. When Omicron arrived, the main epidemic driver switched from contacts to transmissibility. We separated contacts and transmissions by day of exposure and setting and found pronounced variability over days of the week and during Christmas holidays and events. For example, during the Euro football tournament in 2021, days with England matches showed sharp spikes in exposures and transmissibility. Digital contact-tracing technologies can help control epidemics not only by directly preventing transmissions but also by enabling rapid analysis at scale and with unprecedented resolution.
OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing
SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.
Epidemiological impacts of the NHS COVID-19 app in England and Wales throughout its first year
AbstractThe NHS COVID-19 app was launched in England and Wales in September 2020, with a Bluetooth-based contact tracing functionality designed to reduce transmission of SARS-CoV-2. We show that user engagement and the app’s epidemiological impacts varied according to changing social and epidemic characteristics throughout the app’s first year. We describe the interaction and complementarity of manual and digital contact tracing approaches. Results of our statistical analyses of anonymised, aggregated app data include that app users who were recently notified were more likely to test positive than app users who were not recently notified, by a factor that varied considerably over time. We estimate that the app’s contact tracing function alone averted about 1 million cases (sensitivity analysis 450,000–1,400,000) during its first year, corresponding to 44,000 hospital cases (SA 20,000–60,000) and 9,600 deaths (SA 4600–13,000).