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Certain infectious agents are recognised causes of cancer and other chronic diseases. To understand the pathological mechanisms underlying such relationships, here we design a Multiplex Serology platform to measure quantitative antibody responses against 45 antigens from 20 infectious agents including human herpes, hepatitis, polyoma, papilloma, and retroviruses, as well as Chlamydia trachomatis , Helicobacter pylori and Toxoplasma gondii , then assayed a random subset of 9695 UK Biobank participants. We find seroprevalence estimates consistent with those expected from prior literature and confirm multiple associations of antibody responses with sociodemographic characteristics (e.g., lifetime sexual partners with C. trachomatis ), HLA genetic variants (rs6927022 with Epstein-Barr virus (EBV) EBNA1 antibodies) and disease outcomes (human papillomavirus-16 seropositivity with cervical intraepithelial neoplasia, and EBV responses with multiple sclerosis). Our accessible dataset is one of the largest incorporating diverse infectious agents in a prospective UK cohort offering opportunities to improve our understanding of host-pathogen-disease relationships with significant clinical and public health implications. Here, the authors design a multiplex serology platform to quantitatively measure antibodies against 20 infectious agents in UK Biobank participants and confirm associations of antibody responses with sociodemographic characteristics, HLA genetic variants, and disease outcomes.

Travel is important in the acquisition and dissemination of infection. We aimed to assess European surveillance data for travel-related illness to profile imported infections, track trends, identify risk groups, and assess the usefulness of pre-travel advice. We analysed travel-associated morbidity in ill travellers presenting at EuroTravNet sites during the 5-year period of 2008–12. We calculated proportionate morbidity per 1000 ill travellers and made comparisons over time and between subgroups. We did 5-year trend analyses (2008–12) by testing differences in proportions between subgroups using Pearson's χ2 test. We assessed the effect of the pre-travel consultation on infection acquisition and outcome by use of proportionate morbidity ratios. The top diagnoses in 32 136 patients, ranked by proportionate morbidity, were malaria and acute diarrhoea, both with high proportionate morbidity (>60). Dengue, giardiasis, and insect bites had high proportionate morbidity (>30) as well. 5-year analyses showed increases in vector borne infections with significant peaks in 2010; examples were increased Plasmodium falciparum malaria (χ2=37·57, p<0·001); increased dengue fever (χ2=135·9, p<0·001); and a widening geographic range of acquisition of chikungunya fever. The proportionate morbidity of dengue increased from 22 in 2008 to 36 in 2012. Five dengue cases acquired in Europe contributed to this increase. Dermatological diagnoses increased from 851 in 2008 to 1102 in 2012, especially insect bites and animal-related injuries. Respiratory infection trends were dominated by the influenza H1N1 pandemic in 2009. Illness acquired in Europe accounted for 1794 (6%) of all 32 136 cases—mainly, gastrointestinal (634) and respiratory (357) infections. Migration within Europe was associated with more serious infection such as hepatitis C, tuberculosis, hepatitis B, and HIV/AIDS. Pre-travel consultation was associated with significantly lower proportionate morbidity ratios for P falciparum malaria and also for acute hepatitis and HIV/AIDS. The pattern of travel-related infections presenting in Europe is complex. Trend analyses can inform on emerging infection threats. Pre-travel consultation is associated with reduced malaria proportionate morbidity ratios and less severe illness. These findings support the importance and effectiveness of pre-travel advice on malaria prevention, but cast doubt on the effectiveness of current strategies to prevent travel-related diarrhoea. European Centre for Disease Prevention and Control, University Hospital Institute Méditerranée Infection, US Centers for Disease Control and Prevention, and the International Society of Travel Medicine.

Staphylococcus aureus bacteraemia is one of the most common serious bacterial infections worldwide. In the UK alone, around 12 500 cases each year are reported, with an associated mortality of about 30%, yet the evidence guiding optimum management is poor. To date, fewer than 1500 patients with S aureus bacteraemia have been recruited to 16 controlled trials of antimicrobial therapy. Consequently, clinical practice is driven by the results of observational studies and anecdote. Here, we propose and review ten unanswered clinical questions commonly posed by those managing S aureus bacteraemia. Our findings define the major areas of uncertainty in the management of S aureus bacteraemia and highlight just two key principles. First, all infective foci must be identified and removed as soon as possible. Second, long-term antimicrobial therapy is required for those with persistent bacteraemia or a deep, irremovable focus. Beyond this, the best drugs, dose, mode of delivery, and duration of therapy are uncertain, a situation compounded by emerging S aureus strains that are resistant to old and new antibiotics. We discuss the consequences on clinical practice, and how these findings define the agenda for future clinical research.

Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts. Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, presents a global public health challenge. Hospitals have been at the forefront of this battle, treating large numbers of sick patients over several waves of infection. Finding ways to manage the spread of the virus in hospitals is key to protecting vulnerable patients and workers, while keeping hospitals running, but to generate effective infection control, researchers must understand how SARS-CoV-2 spreads. A range of factors make studying the transmission of SARS-CoV-2 in hospitals tricky. For instance, some people do not present any symptoms, and, amongst those who do, it can be difficult to determine whether they caught the virus in the hospital or somewhere else. However, comparing the genetic information of the SARS-CoV-2 virus from different people in a hospital could allow scientists to understand how it spreads. Samples of the genetic material of SARS-CoV-2 can be obtained by swabbing infected individuals. If the genetic sequences of two samples are very different, it is unlikely that the individuals who provided the samples transmitted the virus to one another. Illingworth, Hamilton et al. used this information, along with other data about how SARS-CoV-2 is transmitted, to develop an algorithm that can determine how the virus spreads from person to person in different hospital wards. To build their algorithm, Illingworth, Hamilton et al. collected SARS-CoV-2 genetic data from patients and staff in a hospital, and combined it with information about how SARS-CoV-2 spreads and how these people moved in the hospital . The algorithm showed that, for the most part, patients were infected by other patients (20 out of 22 cases), while staff were infected equally by patients and staff. By further probing these data, Illingworth, Hamilton et al. revealed that 80% of hospital-acquired infections were caused by a group of just 21% of individuals in the study, identifying a ‘superspreader’ pattern. These findings may help to inform SARS-CoV-2 infection control measures to reduce spread within hospitals, and could potentially be used to improve infection control in other contexts.

The prevalence of, and risk factors for, herpes simplex virus type-1 (HSV-1) infection and reactivation in older individuals are poorly understood. This is a prospective population-based study among community-dwelling individuals aged 40-79 years, followed from 1993, formed as a random subsample of the UK-based EPIC-Norfolk cohort. HSV-1 seropositivity was derived from immunoglobulin G measurements and frequent oro-labial HSV reactivation was self-reported. We carried out two cross-sectional studies using logistic regression to investigate childhood social and environmental conditions as risk factors for HSV-1 seropositivity and comorbidities as risk factors for apparent HSV oro-labial reactivation. Of 9,929 participants, 6310 (63.6%) were HSV-1 IgG positive, and 870 (of 4,934 seropositive participants with reactivation data) experienced frequent oro-labial reactivation. Being born outside the UK/Ireland, contemporaneous urban living and having ≥4 siblings were risk factors for HSV-1 seropositivity. Ever diagnosed with kidney disease, but no other comorbidities, was associated with an increased risk of frequent HSV reactivation (adjOR 1.87, 95%CI: 1.02-3.40). Apparent HSV-1 seropositivity and clinical reactivation are common within an ageing UK population. HSV-1 seropositivity is socially patterned while risk factors for oro-labial HSV reactivation are less clear. Further large studies of risk factors are needed to inform HSV-1 control strategies.

Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant’s vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.

Biological evidence suggests that inflammation might induce type 2 diabetes (T2D), and epidemiological studies have shown an association between higher white blood cell count (WBC) and T2D. However, the association has not been systematically investigated. Studies were identified through computer-based and manual searches. Previously unreported studies were sought through correspondence. 20 studies were identified (8,647 T2D cases and 85,040 non-cases). Estimates of the association of WBC with T2D were combined using random effects meta-analysis; sources of heterogeneity as well as presence of publication bias were explored. The combined relative risk (RR) comparing the top to bottom tertile of the WBC count was 1.61 (95% CI: 1.45; 1.79, p = 1.5*10(-18)). Substantial heterogeneity was present (I(2) = 83%). For granulocytes the RR was 1.38 (95% CI: 1.17; 1.64, p = 1.5*10(-4)), for lymphocytes 1.26 (95% CI: 1.02; 1.56, p = 0.029), and for monocytes 0.93 (95% CI: 0.68; 1.28, p = 0.67) comparing top to bottom tertile. In cross-sectional studies, RR was 1.74 (95% CI: 1.49; 2.02, p = 7.7*10(-13)), while in cohort studies it was 1.48 (95% CI: 1.22; 1.79, p = 7.7*10(-5)). We assessed the impact of confounding in EPIC-Norfolk study and found that the age and sex adjusted HR of 2.19 (95% CI: 1.74; 2.75) was attenuated to 1.82 (95% CI: 1.45; 2.29) after further accounting for smoking, T2D family history, physical activity, education, BMI and waist circumference. A raised WBC is associated with higher risk of T2D. The presence of publication bias and failure to control for all potential confounders in all studies means the observed association is likely an overestimate.