Wellcome Open Research

BackgroundRepeated measurements of cross-sectional prevalence of Polymerase Chain Reaction (PCR) positivity or seropositivity provide rich insight into the dynamics of an infection. The UK Office for National Statistics (ONS) Community Infection Survey publishes such measurements for SARS-CoV-2 on a weekly basis based on testing enrolled households, contributing to situational awareness in the country. Here we present estimates of time-varying and static epidemiological quantities that were derived from the estimates published by ONS. MethodsWe used a gaussian process to model incidence of infections and then estimated observed PCR prevalence by convolving our modelled incidence estimates with a previously published PCR detection curve describing the probability of a positive test as a function of the time since infection. We refined our incidence estimates using time-varying estimates of antibody prevalence combined with a model of antibody positivity and waning that moved individuals between compartments with or without antibodies based on estimates of new infections, vaccination, probability of seroconversion and waning. ResultsWe produced incidence curves of infection describing the UK epidemic from late April 2020 until early 2022. We used these estimates of incidence to estimate the time-varying growth rate of infections, and combined them with estimates of the generation interval to estimate time-varying reproduction numbers. Biological parameters describing seroconversion and waning, while based on a simple model, were broadly in line with plausible ranges from individual-level studies. ConclusionsBeyond informing situational awareness and allowing for estimates using individual-level data, repeated cross-sectional studies make it possible to estimate epidemiological parameters from population-level models. Studies or public health surveillance methods based on similar designs offer opportunities for further improving our understanding of the dynamics of SARS-CoV-2 or other pathogens and their interaction with population-level immunity.

ObjectiveNucleos/tide analogue (NA) drugs are used for the long-term treatment of chronic hepatitis B virus (HBV) infection. In a landscape of changing clinical recommendations, we set out to quantify the prescription of NA drugs to date, and to determine the impact of relaxing treatment eligibility criteria in a unique large real-world dataset. DesignWe assimilated longitudinal data from adults with chronic HBV infection from six centres in England through the UK National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) viral hepatitis framework. We describe factors currently associated with receipt of NA treatment, and determine the proportion of the population who would become treatment eligible as thresholds change. ResultsWe reviewed data for 7558 adults, with mean follow up of 4.0 years (SD 3.9 years). NA treatment was prescribed in 2014/7558 (26.6%), and in line with existing guidelines was associated with HBeAg positivity and ALT above the upper limit of normal (ULN). Treatment was significantly more likely in males, older adults, in Asian and Other ethnicities (as compared to White), and significantly less likely in socioeconomically deprived individuals. The proportion of individuals who were treatment eligible was 32.3% based on 2 records of ALT>ULN over 6-12 months; 41.7% based on ALT>ULN and VL > 2000 IU/ml; and 95.1% based on detectable VL and either ALT>ULN or age>30 years. ConclusionWe quantify the proportion of the population living with HBV who may become treatment eligible as guidelines change, providing insights to support the implementation of clinical services. KEY MESSAGES (3-5 sentences required by Gut) * What is already known on this topicTo date, only a minority of adults living with chronic hepatitis B (CHB) infection have been eligible for treatment with nucleos/tide analogue (NA) therapy. However, worldwide guidelines are changing, with recommendations for treatment of an increasing proportion of the population. There is a need for evidence to inform the design of services to meet the needs of people living with CHB as more of the population becomes treatment eligible. * What this study addsWe have determined the proportion of the UK population of people living with HBV infection who are currently treated, and determined the increasing proportion who would become eligible as treatment criteria change, with this proportion reaching 95% based on the least stringent treatment thresholds. * How this study might affect research, practice or policyOur study provides crucial real-world evidence that can inform planning of service delivery and resource allocation for people living with HBV, in a landscape of changing clinical guidelines.

BackgroundAccurate and representative data is vital for precisely reporting the impact of influenza in healthcare systems. Northern hemisphere winter 2022/23 experienced the most substantial influenza wave since the COVID-19 pandemic began in 2020. Simultaneously, new data streams become available within health services because of the pandemic. Comparing these data, surveillance and administrative, supports the accurate monitoring of population level disease trends. MethodsWe analysed admissions rates per capita from four different collection mechanisms covering National Health Service hospital Trusts in England over the winter 2022/23 wave. We adjust for difference in reporting and extracted key epidemic characteristics including the maximum admission rate, peak timing, cumulative season admissions and growth rates by fitting generalised additive models at national and regional levels. ResultsBy modelling the admission rates per capita across surveillance and administrative data systems we show that different data measuring the epidemic produce different estimates of key quantities. Nationally and in most regions the data correspond well for the maximum admission rate, date of peak and growth rate, however, in subnational analysis discrepancies in estimates arose, particularly for the cumulative admission rate. InterpretationThis research shows that the choice of data used to measure seasonal influenza epidemics can influence analysis substantially at sub-national levels. For the admission rate per capita there is comparability in the sentinel surveillance approach (which has other important functions), rapid situational reports, operational databases and time lagged administrative data giving assurance in their combined value. Utilising multiple sources of data aids understanding of the impact of seasonal influenza epidemics in the population.

IntroductionSince the start of the pandemic SARS-CoV-2 infection has most commonly been confirmed using reverse transcriptase polymerase chain reaction (RT-PCR), with results translated into a binary positive/negative outcomes. Previous studies have found that there is additional useful information in the level of the Cycle threshold (Ct value) of positive cases. Here we characterise variation in Ct values as a proxy for viral loads in more than 3 million test-positive COVID-19 cases in England with the aim of better quantifying the utility of such data. MethodsWe used individual N gene Ct values from symptomatic PCR positive (with Ct value less than 30) Pillar 2 cases in England who self-reported the date of symptom onset, and for whom age, reinfection status, variant status, and the number of vaccines received was available. Those with a positive test result more than 6 days after their reported symptom onset were excluded to mitigate the potential impact of recall bias. We used a generalised additive model, to estimate Ct values empirical mean Ct values for each strata of interest independently as well as to predict Ct values using a model that adjusted for a range of demographic and epidemiological covariates jointly. We present empirical Ct values and compare them to predicted mean Ct values. ResultsWe found that mean Ct values varied by vaccine status, and reinfection status with the number of vaccine doses having little apparent effect. Modelling Ct values as a smooth function of time since onset and other variables struggled to reproduce the individual variation in the data but did match the population-level variation over time relatively well with this being apparently dominated by large differences between variants. Other variation over time was also captured to some degree though their remained several periods where the model could not capture the empirical means with a potential explanation being epidemic phase bias. ConclusionsAnalysing a large dataset of routine Ct values from symptomatic COVID-19 cases in England we found variation based on time since symptom onset, vaccine status, age, and variant. Ct values were highest 1-3 days after symptom onset and differed most due to variant status. We found no clear correlation between previously estimated differences in intrinsic transmissibility and Ct values indicating that this is potentially mediated at least partly by factors other than viral load as estimated using Ct values. We found evidence that a model adjusting for a range of covariates could explain some of the population-level variation over time but systematically underestimated Ct values when incidence was increasing, and overestimated them when incidence was decreasing. This indicates the utility of Ct values from this data source as a tool for surveillance, potentially avoiding some of the biases of aggregated positive counts.

Autosomal dominant polycystic liver disease (ADPLD) commonly results from a pathogenic variant in one of 6 genes (GANAB, ALG8, LRP5, PRKCSH, SEC61B, SEC63). Pathogenic variants in these genes are also associated with kidney cysts, which rarely cause kidney failure, but the genes are included in cystic kidney panels. This study determined the population frequency of predicted pathogenic variants in the ADPLD genes in the general population. Variants for each gene were downloaded from gnomAD and annotated with ANNOVAR. The population frequencies were calculated from the number of people with "predicted pathogenic" variants in gnomAD v.2.1.1:loss-of-function structural and copy number; null; and rare, computationally-damaging missense changes that affected a conserved residue. Frequencies were also estimated from the number of gnomADv.4.1 variants assessed as Pathogenic or Likely pathogenic in ClinVar. Predicted pathogenic variants affected one in 95 people using our strategy and gnomAD v.2.1.1, and one in 151 with ClinVar assessments of gnomAD v.4.1 variants. LRP5 and ALG8 which are associated with a milder clinical phenotype, were the commonest affected genes with both strategies. Predicted pathogenic variants in ADPLD appear more frequent in admixed American (one in 100), Finnish (one in 107) and African/African American (one in 130) people (p all <0.0001 compared with Europeans (one in 197).Predicted pathogenic variants for ADPLD may be even more common because of additional unidentified causative genes. However not all ADPLD variants result in liver cysts, nor indeed cystic kidneys, because of incomplete penetrance and variable expressivity.

BackgroundAlpha-1 antitrypsin deficiency (A1ATD) is a hereditary recessive disorder caused by mutations in the SERPINA1 gene. It is a clinically under-recognised disease characterised by low circulating A1AT levels and intracellular accumulation of misfolded A1AT in hepatocytes. Deposition of excessive abnormal A1AT in the liver leads to liver failure, yet no specific treatments are available due to the lack of physiologically relevant disease modelling platforms. MethodsWe have hypothesised that human induced pluripotent stem cell (iPSC)-derived hepatocytes can provide an efficient platform to study A1ATD. Using CRISPR/Cas9, we have generated wild-type and A1ATD iPSC-derived hepatocytes (Opti-HEP) from healthy and A1ATD donors and developed a bioassay that mimics the accumulation of misfolded A1AT in the liver. Responses to the reference drug carbamazepine (CBZ), known to reduce intracellular misfolded A1AT levels, and RNA-based therapeutics were subsequently investigated. ResultsAll lines successfully differentiated into hepatocytes as measured by comparable key hepatic and disease markers to those seen in primary human hepatocytes. The diseased lines displayed increased intracellular accumulation of misfolded A1AT compared to isogenic controls. Diseased cell lines showed significant decreases in intracellular accumulation of polymeric A1AT following transfection with RNA-based therapeutics, but a differential response upon treatment with CBZ. ConclusionWe have developed a specific and robust in vitro model of A1ATD that recapitulates disease pathophysiology and responds to small molecule-based treatments and advanced therapeutic strategies. These data demonstrate the suitability of this model for large-scale efficacy screening studies for the treatment of A1ATD and help pave the way towards the development of novel therapies.

BackgroundWe set out to characterise chronic Hepatitis B (CHB) in the primary care population in England and investigate risk factors for progression to hepatocellular carcinoma (HCC). MethodsWe identified 8039 individuals with CHB in individuals aged [&ge;]18 years between 1999-2019 in the English primary care database QResearch. HCC risk factors were investigated using Cox proportional hazards modelling. FindingsMost of those living with CHB were males (60%) of non-White ethnicity (>70%), and a high proportion were in the most deprived Townsend deprivation quintile (44%). Among 7029 individuals with longitudinal data, 161 HCC cases occurred. Increased HCC hazards significantly associated with male sex (adjusted hazards ratio (aHR) 3.44, 95% Confidence Interval (95CI) 2.07-5.73), older age (for age groups 56-55 and [&ge;]66 years of age, compared to 26-35 years, aHRs 7.52 (95CI 4.14-13.67) and11.89 (95CI 6.26-22.60) respectively), socioeconomic deprivation (aHR for fifth Townsend deprivation quintile 1.69, 95CI 1.01-2.84, compared to third), Caribbean ethnicity (aHR 3.32, 95CI 1.43-7.71, compared to White ethnicity), ascites (aHR 1.85, 95CI 1.02-3.36), cirrhosis (aHR 6.52, 95CI 4.54-9.37) and peptic ulcer disease (aHR 2.20, 95CI 1.39-3.49). Reduced HCC hazards were associated with statin use (aHR 0.47, 95CI 0.22-0.99). InterpretationTargeting resources at vulnerable groups, and addressing modifiable risk factors is essential to improve CHB outcomes, and to support progress towards international goals for the elimination of hepatitis infection as a public health threat. FundingWellcome (grant ref 110110/Z/15/Z), UCLH NIHR Biomedical Research Centre, Nuffield Department of Medicine, University of Oxford, GlaxoSmithKline, NIHR Health Informatics Collaborative, Cancer Research UK. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSTHE CHB population in England has not been well described. Hepatitis B virus (HBV) reports from the UK Health Security Agency (UHKSA) have not previously reported chronic HBV (CHB) prevalence stratified by relevant subgroups, including ethnicity and socioeconomic status. The burdens of comorbid diseases in this population have also not been characterised. Furthermore, risk factors for the progression of CHB to hepatocellular carcinoma (HCC) have previously been identified largely in homogenous patient samples which may not be widely generalisable. Therefore, risk factors identified in previously published studies require validation in diverse multi-ethnic cohorts. Characterisation of CHB and investigation of novel risk factors for HCC is warranted in a large data source which contains parameters for a large percentage of the population which are collected in a systematic and wide-scale manner in order to improve generalisation of findings. Added value of this studyWe have characterised the largest cohort of CHB individuals in the UK to date, using the QResearch primary care electronic health record database, and describing the demographics and burdens of comorbid disease in the population. This is novel and has not previously been done in a large socioeconomically and ethnically diverse patient sample. We have also analysed risk factors for HCC in the cohort, both validating previously reported factors and investigating novel factors. Implications of all the available evidenceThe findings of this study have important implications for CHB prevention, clinical management, and resource planning. Our detailed description of the demographics and disease profile of the CHB population in the UK may facilitate the targeting of health and prevention resources. Findings concerning HCC risk factors have implications for the clinical management of CHB in order to reduce the risk of progression to HCC.

Background and hypothesis Autosomal dominant polycystic kidney disease (ADPKD) affects over 12 million people worldwide including an estimated 30,000-70,000 in the United Kingdom (UK). Tolvaptan is the only disease-modifying therapy approved for rapidly progressing disease. Despite national guidance, prescribing rates were hypothesised to vary by kidney centre. Treatment may not always align with guidelines: some patients eligible for tolvaptan may not be initiated, while other patients initiated on tolvaptan may not meet eligibility criteria. This may have important consequences for healthcare costs and health-related quality of life. Methods The National Registry of Rare Kidney Diseases (RaDaR) collects longitudinal data from UK NHS kidney centres. This retrospective cohort study used routinely collected data (2016-2023) to examine tolvaptan prescribing across kidney centres. Kidney centre-level initiation patterns were described, assessed using mixed-effects logistic regression and visualised with funnel plots. Cost-effectiveness analyses combined observed prescribing practices under likely negotiated commercial discounts to estimate costs and quality-adjusted life year (QALY) consequences of prescribing at the national level. Results Our study included 3,609 people with ADPKD from 72 kidney centres. Patients eligible for tolvaptan who were not initiated accounted for 34.8% (292/839). Across centres, five (6.9%) initiated tolvaptan significantly more than expected among eligible participants, while one centre (1.4%) initiated significantly less. Nationally, this could result in up to {pound}53.7 million in lost savings (assuming a 60% medication price reduction) and result in up to 1,245 lost QALYs. Patients initiated on tolvaptan who were not eligible accounted for 26.1% (103/395). Only one centre had significantly fewer eligible patients than expected among initiated patients. Nationally, this could cost up to {pound}15.9 million (assuming a 60% medication price reduction). Conclusions There is evidence of variation in tolvaptan prescribing in the UK. A substantial proportion of patients eligible for tolvaptan were not initiated at the cohort-level, with evidence of variation between centres suggesting differences in treatment decision-making. A substantial proportion of patients initiated on tolvaptan were not eligible at the cohort-level, but there was limited evidence of variation between centres. Together, these findings raise questions regarding the potential consistency of clinical decision-making, equitable access to a sole disease-modifying therapy in a rare disease, alignment with national guidance, and effective use of healthcare resources.

IntroductionExtreme early-onset hypertension (EEHTN) defines a cohort of patients with a persistent blood pressure above 160/100mmHg under 30 years of age. This cohort is at heightened risk of complications and often undergo a diagnostic odyssey. We used Whole Genome Sequencing (WGS) data provided by the 100,000 Genomes Project (100KGP), to quantify the genetic contributors to EEHTN and to ascertain the diagnostic utility of WGS. MethodsWe performed sequencing-based genome-wide association studies (GWAS) in 901 unrelated EEHTN cases and 20852 ancestry matched unaffected controls. The analysis was inclusive of individuals with diverse genetic ancestry. Enrichment of common, low-frequency minor allele frequency (MAF) > 0.1% and rare (MAF < 0.1%) single-nucleotide variant (SNV), insertion/deletion variants (indels) and rare structural variant (SV) alleles on a genome-wide and per-gene basis was sought using a generalised linear mixed model approach to account for population structure. A validated polygenic risk score (PRS) for hypertension (HTN) was applied to the EEHTN cohort and a primary HTN cohort. ResultsAnalysis of rare SNVs and indels revealed PKD1 (P=2.70 x 10-13) as significantly associated with EEHTN. This signal was lost when we excluded those with known renal disease. 81.5% of the individuals harbouring qualifying PKD1 variants had known cystic kidney disease (CyKD), this is replicated in the UK Biobank (UKBB). There were no common SNVs or rare SV associations with EEHTN. PRS discriminated between cases and controls (P = 2.2x10-16) but not between patients with PHTN or secondary HTN. ConclusionsThese findings represent a thorough examination of the genetic architecture of a national EEHTN cohort using well-controlled statistical methodology. The low diagnostic yield of WGS in this group brings into question its utility as a population level clinical tool but provides insights into EEHTN biology. The PRS findings suggest shared genetic contributors to early-onset extreme hypertension and primary hypertension ascertained at any age.

BackgroundEarly estimates from South Africa indicated that the Omicron COVID-19 variant may be both more transmissible and have greater immune escape than the previously dominant Delta variant. The rapid turnover of the latest epidemic wave in South Africa as well as initial evidence from contact tracing and household infection studies has prompted speculation that the generation time of the Omicron variant may be shorter in comparable settings than the generation time of the Delta variant. MethodsWe estimated daily growth rates for the Omicron and Delta variants in each UKHSA region from the 23rd of November to the 23rd of December 2021 using surveillance case counts by date of specimen and S-gene target failure status with an autoregressive model that allowed for time-varying differences in the transmission advantage of the Delta variant where the evidence supported this. By assuming a gamma distributed generation distribution we then estimated the generation time distribution and transmission advantage of the Omicron variant that would be required to explain this time varying advantage. We repeated this estimation process using two different prior estimates for the generation time of the Delta variant first based on household transmission and then based on its intrinsic generation time. ResultsVisualising our growth rate estimates provided initial evidence for a difference in generation time distributions. Assuming a generation time distribution for Delta with a mean of 2.5-4 days (90% credible interval) and a standard deviation of 1.9-3 days we estimated a shorter generation time distribution for Omicron with a mean of 1.5-3.2 days and a standard deviation of 1.3-4.6 days. This implied a transmission advantage for Omicron in this setting of 160%-210% compared to Delta. We found similar relative results using an estimate of the intrinsic generation time for Delta though all estimates increased in magnitude due to the longer assumed generation time. ConclusionsWe found that a reduction in the generation time of Omicron compared to Delta was able to explain the observed variation over time in the transmission advantage of the Omicron variant. However, this analysis cannot rule out the role of other factors such as differences in the populations the variants were mixing in, differences in immune escape between variants or bias due to using the test to test distribution as a proxy for the generation time distribution.

IntroductionIn November 2020, a new SARS-COV-2 variant or the Kent variant emerged in the UK, and became the dominant UK SARS-COV-2 variant, demonstrating faster transmission than the original variant, which rapidly died out. However, it is unknown if this altered the overall course of the pandemic as genomic analysis was not common place at the outset and other factors such as the climate could alter the viral transmission rate over time. We aimed to test the hypothesis that the overall observed viral transmission was not altered by the emergence of the new variant, by testing a model generated earlier in the pandemic based on lockdown stringency, temperature and humidity. MethodsFrom 1/1/20 to 4/2/21, the daily incidence of SARS-COV-2 deaths and the overall stringency of National Lockdown policy on each day was extracted from the Oxford University Government response tracker. The daily average temperature and humidity for London was extracted from Wunderground.com. The viral reproductive rate was calculated on a daily basis from the daily mortality data for each day. The correlation between log10 of viral reproductive rate and lockdown stringency and weather parameters were compared by Pearson correlation to determine the time lag associated with the greatest correlation. A multivariate model for the log10 of viral reproductive rate was constructed using lockdown stringency, temperature and humidity for the period 1/1/20 to 30/9/20. This model was extrapolated forward from 1/10/20 to 4/2/21 and the predicted viral reproductive rate, daily mortality and cumulative mortality were compared with official data. ResultsOn multivariate linear regression, the optimal model had and R2 0f 0.833 for prediction of log10 viral reproductive rate 13 days later in the model construction period, with (coefficient, probability) lockdown stringency (-0.0109, p=0.0000), humidity (0.0038, p=0.0041) and temperature (-0.0035, p=0.0008). When extrapolated to the validation period (1/10/20 to 4/2/21), the model was highly correlated with daily (Pearson coefficient 0.88, p=0.0000) and cumulated SARS-COV-2 mortality (Pearson coefficient 0.99, p=0.0000). ConclusionThe course of the SARS-COV-2 pandemic in the UK seems highly predicted by an earlier model based on the lockdown stringency, humidity and temperature and unaltered by the emergence of a newer viral genotype.

Background & AimsLiver disease is a rising cause of mortality worldwide. Primary human hepatocytes (PHH) and hepatocellular cancer cells are currently used in drug development, however, they come with limitations, including limited supply, rapid loss of function, and tumorigenic origin. In addition, current iPSC differentiation protocols lead to the generation of hepatocyte-like cells with compromised liver-related features. We hypothesised that optimisation of iPSC differentiation protocols can lead to the generation of hepatocyte-like cells with improved metabolic functionality for disease modelling and toxicity screening studies. MethodsHealthy human iPSCs were differentiated to hepatocyte-like cells (Opti-HEP) using a novel 3-step differentiation protocol. Hepatocyte functionality was assessed, including liver maturity marker expression, urea synthesis, de novo gluconeogenesis, and CYP450 expression, activity, and induction. Suitability of Opti-HEP to predict drug-induced liver injury (DILI) was evaluated by cell viability assays. CRISPR/Cas9 gene editing was employed to generate in vitro inherited metabolic disease models. ResultsOpti-HEP expressed similar liver maturity marker levels to those seen in primary human hepatocytes (PHH), in addition to functional urea and gluconeogenesis pathways. CYP450 expression and activity were comparable between Opti-HEP and PHH, with both cell types showing similar levels of CYP3A4 induction upon 1,25-hydroxy-vitamin D3 treatment. Opti-HEP accurately predicted DILI, following treatment with 7 drugs of known DILI liability. CRISPR-derived Opti-HEP harbouring mutations for inherited metabolic disorders (Ornithine Transcarbamylase Deficiency, Progressive Familial Intrahepatic Cholestasis Type 2, Citrullinemia Type 1) recapitulated key pathophysiological disease features, including reduced protein expression, impaired urea secretion, and bile acid transport. ConclusionsWe demonstrate the generation of optimised iPSC-derived hepatocytes with enhanced liver functionality that is comparable to PHH. These data alongside the expansion capacity and amenability of these cells highlight the opportunities this model can offer in the space of disease modelling and large-scale drug efficacy and hepatotoxicity screening.

The COVID-19 pandemic has led to unprecedented strain on intensive care unit (ICU) admission in parts of the world. Strategies to create surge ICU capacity require complex local and national service reconfiguration and reduction or cancellation of elective activity. These measures have an inevitable lag-time before additional capacity comes on-line. An accurate short-range forecast would be helpful in guiding such difficult, costly, and ethically challenging decisions. At the time this work began, cases in England were starting to increase. If this represents a true spread in disease then ICU demand could increase rapidly. Here we present a short-range forecast based on published real-time COVID-19 case data from the seven National Health Service (NHS) commissioning regions in England (East of England, London, Midlands, North East and Yorkshire, North West, South East and South West). We use a Monte Carlo approach to model the likely impact of current diagnoses on regional ICU capacity over a 14-day horizon under the assumption that the increase in cases represents the start of an exponential growth in infections. Our model is designed to be parsimonious and based on available epidemiological data from the literature at the moment. On the basis of the modelling assumptions made, ICU occupancy is likely to increase dramatically in the days following the time of modelling. If the current exponential growth continues, case numbers will be comparable to current ICU bed numbers within weeks. Despite variable growth in absolute patients, all commissioning regions are forecast to be heavily burdened under the assumptions used. Whilst, like any forecast model, there remain uncertainties both in terms of model specification and robust epidemiological data in this early prospective phase, it would seem that surge capacity will be required in the very near future. Our findings should be interpreted with caution, but we hope that our model will help policy decision makers with their preparations. The uncertainties in the data highlight the urgent need for ongoing real-time surveillance to allow forecasts to be constantly updated using high quality local patient-facing data as it emerges.

The use of network analysis to support livestock disease control in low middle-income countries (LMICs) has historically been hampered by the cost of generating empirical data in the absence of animal movement recording schemes. To fill this gap, methods which exploit freely available demographic and archived molecular data can be used to generate livestock networks based on gravity and phylogeographic modelling techniques, respectively. However, questions remain on the performance of these methods in capturing the topology of empirical networks. Here, we compare output from these network methodologies to a network constructed from either empirical data or randomly generated data. To facilitate this comparison, the spread of infectious diseases was simulated, it is this evaluation that demonstrates their potential utility to inform robust livestock disease control strategies. The molecular network was the closest approximation to the empirical network, both in relation to topological and epidemic characteristics, whereas size of epidemics in the gravity network tended to be larger, better agreement across all three networks was observed when; a) total nodes infected, b) percentage infection take off were compared. These methods consistently identified the same important animal movement and trade hotspots as the empirical networks. We therefore consider this proof-of-concept that demographic data such as censuses and archived molecular data could be repurposed to inform livestock disease management in LMICs. Author summaryLive animal movements in Africa represent a significant risk of transmission and spread of infectious diseases in livestock populations, and therefore, have direct implications on the food security of the continent. Here we explore the potential utility of available data to support control strategies, by comparing movement networks inferred from such data i.e. census and pathogen molecular data using gravity modelling and phylogeography respectively. Their utility is evaluated by comparing their topology and disease spread characteristics to empirical live animal movement. Based on our results, we posit that archived data can be repurposed to support infectious disease control on the African continent.

BackgroundThe World Health Organization (WHO) 2024 Hepatitis B virus (HBV) treatment guidelines expand eligibility for nucleos(t)ide analogue treatment in individuals with chronic HBV infection. For countries to implement these guidelines, there is a critical need to understand the population who are treatment eligible. While HBV drug resistance (HBVDR) is uncommon, monitoring for any potential resistance is a relevant public health consideration. MethodsWe studied a population in rural Uganda to describe the proportion of individuals eligible for treatment based on the 2024 WHO treatment guidelines. We determined how this proportion varies according to the eligibility criteria used, comparing the performance of different assessment tools. We calculated Aspartate Aminotransferase-to-Platelet Ratio Index; APRI, Gamma-Glutamyl Transpeptidase-to-Platelet Ratio; GPR and transient elastography; TE and performed HBV sequencing using Oxford Nanopore Technology to determine the prevalence of HBVDR in treatment naive and treatment experienced individuals. ResultsIn this cohort, 24/63 (38%) individuals with CHB were eligible for treatment. This fell to 14/63 (22%) in a hypothetical scenario where TE was not available for the assessment of liver fibrosis. We demonstrate a lack of concordance between non-invasive tests (NIT) of liver disease in treatment-naive HBV mono-infected individuals. An APRI cut-off of 0.5 had a sensitivity of 23.0% for predicting a TE score of >7 kPa (F2 fibrosis). Sensitivity for detecting F2 fibrosis was increased to 38.5% using an APRI cut off of 0.36, and to 46.2% using the GPR. We did not identify any HBVDR in the HBV mono-infected treatment-naive population (n=58). 24/210 individuals were living with HIV/HBV coinfection; HBV was sequenced in 5 of these of whom 2 had genomic evidence of nucleos(t)ide analogue resistance (rt180M/204V). ConclusionsWhile the WHO 2024 treatment criteria offer an opportunity to expand access to care, there is a need to determine how assessment tools differ in determination of eligibility in different settings. HBVDR remains uncommon but more research is needed to understand its prevalence and clinical impact in African populations.

BackgroundHypertension is a major contributor to global morbidity and mortality. In South Africa, the government has employed a whole systems approach to address the growing burden of non-communicable diseases. We used a novel incident care cascade approach to measure changes in the South African health systems ability to manage hypertension between 2008 and 2017. MethodsWe used data from Waves 1-5 of the National Income Dynamics Study (NIDS) to estimate trends in the hypertension care cascade and unmet treatment need across four successive cohorts with incident hypertension. We used a negative binomial regression to identify factors that may predict higher rates of hypertension control, controlling for socio-demographic and healthcare factors. The largest cascade attrition occurred prior to diagnosis. ResultsIn 2011, 19{middle dot}6% (95%CI 14{middle dot}2, 26{middle dot}2) of individuals with incident hypertension were diagnosed, 15{middle dot}4% (95%CI 10{middle dot}8, 21{middle dot}4) were on treatment and 7.1% had controlled blood pressure. By 2017, the proportion of individuals with diagnosed incident hypertension had increased to 24{middle dot}4% (95%CI 15{middle dot}9, 35{middle dot}4) with increases in treatment (23{middle dot}3%, 95%CI 15{middle dot}0, 34{middle dot}3) and control (22{middle dot}1%, 95%CI 14{middle dot}1, 33{middle dot}.0) were also observed, translating to a decrease in unmet need from 92{middle dot}9% in 2011 to 77{middle dot}9% in 2017. Multivariable regression showed that participants with incident hypertension in 2017 were 3{middle dot}01 (95%CI 1{middle dot}77, 5{middle dot}13) times more likely to have a controlled blood pressure compared to those in 2011. ConclusionsThe proportion of people with incident hypertension who successfully progressed to controlled blood pressure tripled between 2011 and 2017 in South Africa. Despite these improvements, a low absolute proportion of the population were able to control their blood pressure and a high burden of unmet need remains. Summary BoxO_ST_ABSWhat is already knownC_ST_ABSO_LIPrevalent cascades provide insight to where losses in care cascades occur. C_LIO_LIWhile mostly used in the management of HIV, recently they have also been adopted in studying the management of non-communicable diseases on a population level. C_LIO_LIPrevalent hypertension cascades in South Africa showed a high burden of unmet need, with the biggest losses where lost between disease development and diagnosis. C_LI What are the new findingsO_LIIncident hypertension cascades improved from 2008 to 2017 in South Africa. C_LI What do the new findings implyO_LIIncident cascades provide an improved means to measure changes in management cascades as this allows us to distinguish between historical and current health system performance. C_LIO_LIOur data show that while substantial improvements in the care cascade occurred between 2008 and 2017, a large burden of unmet need remains. C_LI

Since 2020, the UK and Europe, have experienced annual epizootics of high pathogenicity avian influenza virus (HPAIV). The first during autumn/winter 2020/21 involved the detected with six H5Nx subtypes although H5N8 HPAIV dominated in the UK. Whilst genetic assessment of the H5N8 HPAIVs within the UK demonstrated relative homogeneity, there was a background of other genotypes circulating at a lower degree with different neuraminidase and internal genes. Following a small number of summer detections of H5N1 in wild birds over the summer of 2021, autumn/winter 2021/22 saw another European H5 HPAIV epizootic, that has dwarfed the prior epizootic. This second epizootic was dominated almost exclusively by H5N1 HPAIV, although six distinct genotypes were defined. We have used genetic analysis to evaluate the emergence of different genotypes and proposed reassortment events that have been observed. The existing data suggests that the H5N1 circulating in Europe during late 2020, continued to circulate in wild birds throughout 2021, with minimal adaptation, but has then gone on to reassort with AIVs in the wild bird population. We have undertaken an in-depth genetic assessment of H5 HPAIVs detected in the UK, over the last two winter seasons and demonstrate the utility of in-depth genetic analyses in defining the diversity of H5 HPAIVs circulating in avian species, the potential for zoonotic risk and whether incidents of lateral spread can be defined over independent incursion of infection from wild birds. Key supporting data for mitigation activities. ImportanceHigh pathogenicity avian influenza virus (HPAIV) outbreaks devastate avian species across all sectors having both economic and ecological impacts through mortalities in poultry and wild birds, respectively. These viruses can also represent a significant zoonotic risk. Since 2020, the UK has experienced two successive outbreaks of H5 HPAIV. Whilst H5N8 HPAIV was predominant during the 2020/21 outbreak, other H5 subtypes were also detected. The following year there was a shift in subtype dominance to H5N1 HPAIV, but multiple H5N1 genotypes were detected. Through thorough utilisation of whole-genome sequencing, it was possible to track and characterise the genetic evolution of these H5 HPAIVs in UK poultry and wild birds. This has enabled us to assess the risk posed by these viruses at the poultry:wild bird and the avian:human interface and to investigate potential lateral spread between infected premises, a key factor in understanding threat to the commercial sector.

BackgroundHepatitis E virus (HEV) genotype 1 is a major cause of acute jaundice in Bangladesh, yet the transmission dynamics and genetic diversity of this virus remains inadequately characterized. This study aims to elucidate the genetic landscape and transmission patterns of HEV infection in Bangladesh through phylogenetic analysis of viral sequences obtained from a nation-wide surveillance program. Methodology/Principal FindingsWe analyzed 104 partial HEV open reading frame 1 (ORF-1) sequences collected from acute jaundice patients admitted to six tertiary hospitals across Bangladesh during December 2014- September 2017. Phylogenetic trees were constructed using maximum likelihood methods, and Bayesian clustering was employed to assess genetic diversity and transmission patterns. All sequences were identified as HEV genotype 1 (HEV-1), with 10 sequences predominantly collected in 2017 classified as subtype 1g, forming a distinct cluster. A lack of geographic clustering across the sequences suggests widespread transmission across the country rather than geographically distinct transmission networks. Of the 104 sequenced cases, 5 (5%) were associated with fatal outcomes, although these sequences did not cluster phylogenetically. Conclusions/SignificanceThis phylogenetic analysis provides evidence of widespread transmission of HEV-1 across Bangladesh, with a reduction in genetic diversity in 2017 suggesting the potential emergence of a dominant viral cluster around that time. Given the paucity of clinical surveillance of HEV, genomics may provide new insights into unobserved aspects of the transmission of the virus locally and globally. Author summaryHepatitis E virus genotype 1 (HEV-1) is a major cause of acute jaundice in Bangladesh, but many cases go unreported, and the viruss transmission patterns are not well understood. In the absence of reliable clinical surveillance data, analyzing the genetic diversity amongst viral samples isolated from infected people can help us infer transmission patterns. To better understand how HEV-1 circulates in Bangladesh we studied 104 HEV-1 genetic sequences isolated from acute jaundice patients at 6 hospitals across the country between 2014-17 and inferred how the sequences most likely relate to each other based on their genetic similarities. We found that the viral sequences did not cluster by region in which the patient lived but rather genetically similar viruses were isolated in geographically distant regions suggesting widespread transmission across the country. Our results captured a reduction in genetic diversity in 2017 with many sequences isolated that year forming a distinct genetically similar group suggesting a potential shift in the viral population or a significant outbreak event around that time. Given the scarcity of clinical surveillance of HEV-1, such insights into unobserved aspects of transmission have the potential to improve our understanding of HEV-1 epidemiology.

In the UK the epidemic of COVID-19 continues to pose a significant threat to public health. On the 14th October the English government introduced a tier system for control of the epidemic but just 3 weeks later a National lockdown across all areas of England was implemented. When English areas emerged from Lockdown many were placed in different tiers (most typically moved up at least one tier). However, the effectiveness of the tier system has been challenged by the emergence of a new variant of SARS-CoV-2 which appears to be much more infectious. In addition, from early November a trial mass testing service was being run in Liverpool. We used publicly available data of daily cases by local authority (local government areas) and estimated the reproductive rate (R value) of the epidemic based on 7-day case numbers compared with the previous 7-day period. There was a clear surge in infections from a few days before to several days after the lockdown was implemented. But this surge was almost exclusively associated with Tier 1 and Tier 2 authorities. In Tier 3 authorities where hospitality venues were only allowed to operate as restaurants there was no such surge. After this initial surge, cases declined in all three tiers with the R value dropping to a mean of about 0.7 independent of tier. London, The South East and East of England Regions saw rising infection rates in the last week or so of lockdown primarily in children of secondary school age. We could find no obvious benefit of the trial mass screening programme in Liverpool city. We conclude that in Tiers 1 and 2 much of the beneficial impact of the national lockdown was lost probably because of the leak of its likely implementation five days early leading to increased socialising in these areas before the start of lockdown. We further conclude that given that the new variant is estimated to have an R value of between 0.39 and 0.93 greater than previous variants, any lockdown as strict as the November one would be insufficient to reverse the increase in infections by itself. The value of city-wide mass testing to control the epidemic remains uncertain.

Background Social contact surveys, which measure who-contacts-whom, are widely used to inform infectious disease transmission models and estimate the reproduction number (R), a key metric for assessing epidemic risk. Despite their widespread use, sample size calculations are not routinely performed. Aims To assess the impact of sample size on estimates of R and determine a practical target sample size for social contact surveys used in epidemic modelling. Methods We conducted a review of social contact surveys (2008-2025) to characterise current practice. We characterised the impact of survey size on epidemic metrics using two social contact surveys, the UK Social Contact Survey and POLYMOD (Europe) and two methods. For each dataset and approach, we generated repeated subsamples and calculated the resulting reproduction numbers, characterised their distributions and measured uncertainty. Results We identified 107 unique social contact surveys from 57 studies. Sample sizes ranged from 30 to more than 10,000 participants, with a median of 1,438. One quarter of surveys contained fewer than 1,000 participants. From our simulations, we find that sample sizes below 200 individuals can result in highly variability reproduction numbers. Increasing sample size increases precision, and the most meaningful gains are up to 1,300 individuals. Increasing sample sizes over 3,000 individuals leads to smaller gains. Conclusions A minimum sample size of approximately 1,200-1,300 participants appears sufficient for general-purpose use. These findings support the inclusion of sample size considerations in the design, reporting and interpretation of social contact surveys used for epidemic intelligence and public health decision-making.