Long Covid Symptom Clusters, Correlates and Predictors in a Highly Vaccinated Australian Population in 2023.

Background: Limited data exists regarding long Covid burden following Omicron infection in highly vaccinated populations.

Objective: To (1) characterise long Covid prevalence and predictors and (2) identify key symptom clusters and their correlates among long Covid patients, during an Omicron-predominant period in a highly vaccinated population.

Design: Anonymous, online, cross-sectional survey.

Global Epidemiology of Outbreaks of Unknown Cause Identified by Open-Source Intelligence, 2020-2022.

Epidemic surveillance using traditional approaches is dependent on case ascertainment and is delayed. Open-source intelligence (OSINT)-based syndromic surveillance can overcome limitations of delayed surveillance and poor case ascertainment, providing early warnings to guide outbreak response. It can identify outbreaks of unknown cause for which no other global surveillance exists.

Acute hepatitis of unknown aetiology in children: evidence for and against causal relationships with SARS-CoV-2, HAdv and AAV2.

Background: The cause of acute paediatric hepatitis of unknown aetiology (2022) has not been established despite extensive investigation.

Objective: To summarise the evidence for and against a causal role for human adenovirus (HAdv), adeno-associated virus 2 (AAV-2) and SARS-CoV-2 in outbreaks of paediatric hepatitis in 2022.

Methods: We appraised and summarised relevant evidence for each of the Bradford Hill criteria for causality using quantitative (statistical modelling) and qualitative (narrative coherence) approaches.

Transcriptomics and chromatin accessibility in multiple African population samples.

Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs).

Africa-specific human genetic variation near CHD1L associates with HIV-1 load.

HIV-1 remains a global health crisis, highlighting the need to identify new targets for therapies. Here, given the disproportionate HIV-1 burden and marked human genome diversity in Africa, we assessed the genetic determinants of control of set-point viral load in 3,879 people of African ancestries living with HIV-1 participating in the international collaboration for the genomics of HIV. We identify a previously undescribed association signal on chromosome 1 where the peak variant associates with an approximately 0.

Hospital admissions linked to SARS-CoV-2 infection in children and adolescents: cohort study of 3.2 million first ascertained infections in England.

Objective: To describe hospital admissions associated with SARS-CoV-2 infection in children and adolescents.

Design: Cohort study of 3.2 million first ascertained SARS-CoV-2 infections using electronic health care record data.

Early detection of emerging infectious diseases - implications for vaccine development.

Vast quantities of open-source data from news reports, social media and other sources can be harnessed using artificial intelligence and machine learning, and utilised to generate valid early warning signals of emerging epidemics. Early warning signals from open-source data are not a replacement for traditional, validated disease surveillance, but provide a trigger for earlier investigation and diagnostics. This may yield earlier pathogen characterisation and genomic data, which can enable earlier vaccine development or deployment of vaccines.

Artificial intelligence in public health: the potential of epidemic early warning systems.

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level.

Assessment of COVID-19 as the Underlying Cause of Death Among Children and Young People Aged 0 to 19 Years in the US.

Importance: COVID-19 was the underlying cause of death for more than 940 000 individuals in the US, including at least 1289 children and young people (CYP) aged 0 to 19 years, with at least 821 CYP deaths occurring in the 1-year period from August 1, 2021, to July 31, 2022. Because deaths among US CYP are rare, the mortality burden of COVID-19 in CYP is best understood in the context of all other causes of CYP death.

Objective: To determine whether COVID-19 is a leading (top 10) cause of death in CYP in the US.

COVID-19 and children.

There has been substantial research on adult COVID-19 and how to treat it. But how do severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections afflict children? The COVID-19 pandemic has yielded many surprises, not least that children generally develop less severe disease than older adults, which is unusual for a respiratory disease. However, some children can develop serious complications from COVID-19, such as multisystem inflammatory syndrome in children (MIS-C) and Long Covid, even after mild or asymptomatic COVID-19.

Characteristics and impact of Long Covid: Findings from an online survey.

Background: Long Covid is a public health concern that needs defining, quantifying, and describing. We aimed to explore the initial and ongoing symptoms of Long Covid following SARS-CoV-2 infection and describe its impact on daily life.

Methods: We collected self-reported data through an online survey using convenience non-probability sampling.

Vaccinating adolescents against SARS-CoV-2 in England: a risk-benefit analysis.

Objective: To offer a quantitative risk-benefit analysis of two doses of SARS-CoV-2 vaccination among adolescents in England.

Setting: England.

Design: Following the risk-benefit analysis methodology carried out by the US Centers for Disease Control, we calculated historical rates of hospital admission, Intensive Care Unit admission and death for ascertained SARS-CoV-2 cases in children aged 12-17 in England.

Modelling the impact of lockdown-easing measures on cumulative COVID-19 cases and deaths in England.

Objectives: To assess the potential impacts of successive lockdown-easing measures in England, at a point in the COVID-19 pandemic when community transmission levels were relatively high.

Design: We developed a Bayesian model to infer incident cases and reproduction number () in England, from incident death data. We then used this to forecast excess cases and deaths in multiple plausible scenarios in which increases at one or more time points.

Health equality, race and pharmacogenomics.

Pharmacogenomics is increasingly moving into mainstream clinical practice. Careful consideration must be paid to inclusion of diverse populations in research, translation and implementation, in the historical and social context of population stratification, to ensure that this leads to improvements in healthcare for all rather than increased health disparities. This review takes a broad and critical approach to the current role of diversity in pharmacogenomics and addresses potential pitfalls in order to raise awareness for prescribers.