Two general practitioners (GPs) with SARS-CoV-2 infection provided in-person patient care to patients of their joint medical practice before and after symptom onset, up until SARS-CoV-2 laboratory confirmation. Through active contact tracing, the local public health authorities recruited the cohort of patients that had contact with either GP in their putative infectious period. In this cohort of patient contacts, we assess the frequency and determinants of SARS-CoV-2-transmission from GPs to patients. We calculated incidence rate ratios (IRR) to explore the type of contact as an explanatory variable for COVID-19 cases. Among the cohort of 83 patient contacts, we identified 22 (27%) COVID-19 cases including 17 (21%) possible, three (4%) probable and two (2%) confirmed cases. All 22 cases had contact with a GP when the GP did not wear a mask, and/or when contact was ≥10 min. Importantly, patients who had contact <10 min with a GP wearing a facemask were at reduced risk (IRR 0.21; 95% CI 0.01–0.99) of COVID-19. This outbreak investigation adds to the body of evidence in supporting current guidelines on measures at preventing the transmission of SARS-CoV-2 in an outpatient setting.

Clinical and genetic risk factors for severe coronavirus disease 2019 (COVID-19) are often considered independently and without knowledge of the magnitudes of their effects on risk. Using severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk = 1.77, 95% confidence interval (CI) 1.64–1.90) and had acceptable discrimination (area under the receiver operating characteristic curve = 0.732, 95% CI 0.708–0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α = −0.08; 95% CI −0.21−0.05) and no evidence or over-or under-dispersion of risk (β = 0.90, 95% CI 0.80–1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.

Coxiella burnetii is a zoonotic agent responsible for human Q fever, a potentially severe disease that can lead to persistent infection. This cross-sectional study aimed to estimate the seroprevalence to C. burnetii antibodies and its association with potential risk factors in the human population of five regions of Québec, Canada. A serum bank comprising sera from 474 dog owners was screened by an enzyme-linked immunosorbent assay followed by confirmation of positive or equivocal sera by an indirect immunofluorescence assay. Observed seroprevalences of 1.2% (95% confidence interval (CI): 0.0–6.6), 2.6% (95% CI: 0.5–7.4) and 5.9% (95% CI: 3.4–9.6) were estimated in the regions of Montréal, Lanaudière and Montérégie, respectively, which all included at least 83 samples. Having lived or worked on a small ruminant farm (prevalence odds ratio (POR) = 5.4; 95% CI: 1.6–17.7) and being a veterinarian or veterinary student (POR = 6.1; 95% CI: 1.6–24.0) were significantly associated with C. burnetii seropositivity. Antibodies against C. burnetii were detected in the human population of Québec. Although seropositivity to this agent was associated with occupational contact with domestic animals, antibodies were also detected in people with no reported professional exposure. No associations with ruminant farm proximity were identified.

An outbreak surveillance system for Salmonella integrating whole genome sequencing (WGS) and epidemiological data was developed in South East and London in 2016–17 to assess local WGS clusters for triage and investigation. Cases genetically linked within a 5 single-nucleotide polymorphism (SNP) single linkage cluster were assessed using a set of locally agreed thresholds based on time, person and place, for reporting to local health protection teams (HPTs). Between September 2016 and September 2017, 230 unique 5-SNP clusters (442 weekly reports) of non-typhoidal Salmonella 5-SNP WGS clusters were identified, of which 208 unique 5-SNP clusters (316 weekly reports) were not reported to the HPTs. In the remaining 22 unique clusters (126 weekly clusters) reported to HPTs, nine were known active outbreak investigations, seven were below locally agreed thresholds and six exceeded local thresholds. A common source or vehicle was identified in four of six clusters that exceeded locally agreed thresholds. This work demonstrates that a threshold-based surveillance system, taking into account time, place and genetic relatedness, is feasible and effective in directing the use of local public health resources for risk assessment and investigation of non-typhoidal Salmonella clusters.

Between 19 May and 12 June 2020, employees of the UZ Brussel were recruited in this study aiming to document the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence, to investigate the potential work-related risk factors for SARS-CoV-2 infection and to estimate the proportion of asymptomatic infections. In total, 2662 participants were included of whom 7.4% had immunoglobulin G antibodies against SARS-CoV-2. Of the participants reporting a positive polymerase chain reaction for SARS-CoV-2, 89% had antibodies at the time of blood sampling. Eleven per cent of the antibody positive participants reported no recent symptoms suggestive of coronavirus disease 2019 (COVID-19). Participants reporting fever, chest pain and/or anosmia/ageusia were significantly more frequently associated with the presence of antibodies against SARS-CoV-2. The presence of antibodies was highest in the group that had had contact with COVID-19-infected individuals outside the hospital with or without using appropriate personnel protective equipment (PPE) (P < 0.001). Inside the hospital, a statistically significant difference was observed for the employees considered as low-risk exposure compared to the intermediate-risk exposure group (P = 0.005) as well as the high-risk exposure group compared to the intermediate exposure risk group (P < 0.001). These findings highlight the importance of using correct PPE.

In the absence of an established gold standard, an understanding of the testing cycle from individual exposure to test outcome report is required to guide the correct interpretation of severe acute respiratory syndrome-coronavirus-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes. Bayesian network models have been used within healthcare to bring clarity to complex problems. We use this modelling approach to construct a comprehensive framework for understanding the real-world predictive value of individual RT-PCR results.

We elicited knowledge from domain experts to describe the test process through a facilitated group workshop. A preliminary model was derived based on the elicited knowledge, then subsequently refined, parameterised and validated with a second workshop and one-on-one discussions.

Causal relationships elicited describe the interactions of pre-testing, specimen collection and laboratory procedures and RT-PCR platform factors, and their impact on the presence and quantity of virus and thus the test result and its interpretation. By setting the input variables as ‘evidence’ for a given subject and preliminary parameterisation, four scenarios were simulated to demonstrate potential uses of the model.

The core value of this model is a deep understanding of the total testing cycle, bridging the gap between a person's true infection status and their test outcome. This model can be adapted to different settings, testing modalities and pathogens, adding much needed nuance to the interpretations of results.

Self-instigated isolation is heavily relied on to curb severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Accounting for uncertainty in the latent and prepatent periods, as well as the proportion of infections that remain asymptomatic, the limits of this intervention at different phases of infection resurgence are estimated. We show that by October 2020, SARS-CoV-2 transmission rates in England had already begun exceeding levels that could be interrupted using this intervention alone, lending support to the second national lockdown on 5th November 2020.

UK universities re-opened in September 2020, amidst the coronavirus epidemic. During the first term, various national social distancing measures were introduced, including banning groups of >6 people and the second lockdown in November; however, outbreaks among university students occurred. We aimed to measure the University of Bristol staff and student contact patterns via an online, longitudinal survey capturing self-reported contacts on the previous day. We investigated the change in contacts associated with COVID-19 guidance periods: post-first lockdown (23/06/2020–03/07/2020), relaxed guidance period (04/07/2020–13/09/2020), ‘rule-of-six’ period (14/09/2020–04/11/2020) and the second lockdown (05/11/2020–25/11/2020). In total, 722 staff (4199 responses) and 738 students (1906 responses) were included in the study. For staff, daily contacts were higher in the relaxed guidance and ‘rule-of-six’ periods than the post-first lockdown and second lockdown. Mean student contacts dropped between the ‘rule-of-six’ and second lockdown periods. For both staff and students, the proportion meeting with groups larger than six dropped between the ‘rule-of-six’ period and the second lockdown period, although was higher for students than for staff. Our results suggest university staff and students responded to national guidance by altering their social contacts. Most contacts during the second lockdown were household contacts. The response in staff and students was similar, suggesting that students can adhere to social distancing guidance while at university. The number of contacts recorded for both staff and students were much lower than those recorded by previous surveys in the UK conducted before the COVID-19 pandemic.

From 24 January 2020 to 18 May 2020, Chaoshan took measures to limit the spread of coronavirus disease 2019 (COVID-19), such as restricting public gatherings, wearing masks and suspending classes. We explored the effects of these measures on the pathogen spectrum of paediatric respiratory tract infections in Chaoshan. Pharyngeal swab samples were collected from 4075 children hospitalised for respiratory tract infection before (May–December 2019) and after (January–August 2020) the COVID-19 outbreak. We used liquid chip technology to analyse 14 respiratory pathogens. The data were used to explore between-group differences, age-related differences and seasonal variations in respiratory pathogens. The number of cases in the outbreak group (1222) was 42.8% of that in the pre-outbreak group (2853). Virus-detection rates were similar in the outbreak (48.3%, 590/1222) and pre-outbreak groups (51.5%, 1468/2853; χ2 = 3.446, P = 0.065), while the bacteria-detection rate was significantly lower in the outbreak group (26.2%, 320/1222) than in the pre-outbreak group (44.1%, 1258/2853; χ2 = 115.621, P < 0.05). With increasing age, the proportions of respiratory syncytial virus (RSV) and cytomegalovirus (CMV) infections decreased, while those of Mycoplasma pneumoniae and adenovirus infections increased. Streptococcus pneumoniae, CMV and rhinovirus infections peaked in autumn and winter, while RSV infections peaked in summer and winter. We found that the proportion of virus-only detection decreased with age, while the proportion of bacteria-only detection increased with age (Table 2). Anti-COVID-19 measures significantly reduced the number of paediatric hospitalisations for respiratory tract infections, significantly altered the pathogen spectrum of such infections and decreased the overall detection rates of 14 common respiratory pathogens. The proportion of bacterial, but not viral, infections decreased.

Monitoring the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) community-wide transmission with a suitable and effective sampling method would be of great support for public health response to the spreading due to asymptomatic subjects in the community.

Here, we describe how using saliva samples for SARS-CoV-2 detection has allowed for a weekly surveillance of a small business company and the early detection of coronavirus disease 2019 cases.

As on 23rd March, two cases were detected and investigated, and control measures were rapidly applied.

Although the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is lasting for more than 1 year, the exposition risks of health-care providers are still unclear. Available evidence is conflicting. We investigated the prevalence of antibodies against SARS-CoV-2 in the staff of a large public hospital with multiple sites in the Antwerp region of Belgium. Risk factors for infection were identified by means of a questionnaire and human resource data. We performed hospital-wide serology tests in the weeks following the first epidemic wave (16 March to the end of May 2020) and combined the results with the answers from an individual questionnaire. Overall seroprevalence was 7.6%. We found higher seroprevalences in nurses [10.0%; 95% confidence interval (CI) 8.9–11.2] than in physicians 6.4% (95% CI 4.6–8.7), paramedical 6.0% (95% CI 4.3–8.0) and administrative staff (2.9%; 95% CI 1.8–4.5). Staff who indicated contact with a confirmed coronavirus disease 2019 (COVID-19) colleague had a higher seroprevalence (12.0%; 95% CI 10.7–13.4) than staff who did not (4.2%; 95% CI 3.5–5.0). The same findings were present for contacts in the private setting. Working in general COVID-19 wards, but not in emergency departments or intensive care units, was also a significant risk factor. Since our analysis points in the direction of active SARS-CoV-2 transmission within hospitals, we argue for implementing a stringent hospital-wide testing and contact-tracing policy with special attention to the health care workers employed in general COVID-19 departments. Additional studies are needed to establish the transmission dynamics.

New Zealand has a strategy of eliminating SARS-CoV-2 that has resulted in a low incidence of reported coronavirus-19 disease (COVID-19). The aim of this study was to describe the spread of SARS-CoV-2 in New Zealand via a nationwide serosurvey of blood donors. Samples (n = 9806) were collected over a month-long period (3 December 2020–6 January 2021) from donors aged 16–88 years. The sample population was geographically spread, covering 16 of 20 district health board regions. A series of Spike-based immunoassays were utilised, and the serological testing algorithm was optimised for specificity given New Zealand is a low prevalence setting. Eighteen samples were seropositive for SARS-CoV-2 antibodies, six of which were retrospectively matched to previously confirmed COVID-19 cases. A further four were from donors that travelled to settings with a high risk of SARS-CoV-2 exposure, suggesting likely infection outside New Zealand. The remaining eight seropositive samples were from seven different district health regions for a true seroprevalence estimate, adjusted for test sensitivity and specificity, of 0.103% (95% confidence interval, 0.09–0.12%). The very low seroprevalence is consistent with limited undetected community transmission and provides robust, serological evidence to support New Zealand's successful elimination strategy for COVID-19.

This study aimed to investigate the characteristics of uncertainty in illness and the coping styles of patients with severe coronavirus 2019 (COVID-19) and to explore their relationship to provide effective guidance for clinical nursing. A cross-sectional survey was used to investigate 56 severe patients with COVID-19 in a designated hospital in Wuhan. A general information questionnaire, the Mishel Uncertainty in Illness Scale for Adults (MUIS-A) and the Medical Coping Modes Questionnaire (MCMQ) were used to collect the data. A statistical analysis was performed. The total score of the MUIS-A was a 66.29 ± 17.25 which was at a low level, while the total score of the MCMQ was 54.16 ± 6.39. The scores of facing and avoiding were significantly higher than those in the norm. The difference in the yielding dimension of patients with different family economic situations was statistically significant. The total score of MUIS-A correlated negatively with the coping style of facing and avoiding and positively correlated with the coping style of yielding. The coping style of patients was one of the factors influencing uncertainty in illness. Nursing staff need to pay close attention to the psychological state of their patients, understand their coping styles and actively correct negative coping styles to reduce the uncertainty in illness and promote physical as well as mental recovery.

Predicting the need for hospitalisation of patients with coronavirus disease 2019 (COVID-19) is important for preventing healthcare disruptions. This observational study aimed to use the COVID-19 Registry Japan (COVIREGI-JP) to develop a simple scoring system to predict respiratory failure due to COVID-19 using only underlying diseases and symptoms. A total of 6873 patients with COVID-19 admitted to Japanese medical institutions between 1 June 2020 and 2 December 2020 were included and divided into derivation and validation cohorts according to the date of admission. We used multivariable logistic regression analysis to create a simple risk score model, with respiratory failure as the outcome for young (18–39 years), middle-aged (40–64 years) and older (≥65 years) groups, using sex, age, body mass index, medical history and symptoms. The models selected for each age group were quite different. Areas under the receiver operating characteristic curves for the simple risk score model were 0.87, 0.79 and 0.80 for young, middle-aged and elderly derivation cohorts, and 0.81, 0.80 and 0.67 in the validation cohorts. Calibration of the model was good. The simple scoring system may be useful in the appropriate allocation of medical resources during the COVID-19 pandemic.

Evidence that more people in some countries and fewer in others are dying because of the pandemic, than is reflected by reported coronavirus disease 2019 (Covid-19) mortality rates, is derived from mortality data. Using publicly available databases, deaths attributed to Covid-19 in 2020 and all deaths for the years 2015–2020 were tabulated for 35 countries together with economic, health, demographic and government response stringency index variables. Residual mortality rates (RMR) in 2020 were calculated as excess mortality minus reported mortality rates due to Covid-19 where excess deaths were observed deaths in 2020 minus the average for 2015–2019. Differences in RMR are differences not attributed to reported Covid-19. For about half the countries, RMR's were negative and for half, positive. The absolute rates in some countries were double those in others. In a regression analysis, population density and proportion of female smokers were positively associated with both Covid-19 and excess mortality while the human development index and proportion of male smokers were negatively associated with both. RMR was not associated with any of the investigated variables. The results show that published data on mortality from Covid-19 cannot be directly comparable across countries. This may be due to differences in Covid-19 death reporting and in addition, the unprecedented public health measures implemented to control the pandemic may have produced either increased or reduced excess deaths due to other diseases. Further data on cause-specific mortality is required to determine the extent to which residual mortality represents non-Covid-19 deaths and to explain differences between countries.

In Germany, Eastern regions had a mild first wave of coronavirus disease 2019 (COVID-19) from March to May 2020, but were badly hit by a second wave later in autumn and winter. It is unknown how the second wave was initiated and developed in Eastern Germany where the number of COVID-19 cases was close to zero in June and July 2020. We used genomic epidemiology to investigate the dynamic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage development across the first and second waves in Eastern Germany. With detailed phylogenetic analyses we could show that SARS-CoV-2 lineages prevalent in the first and second waves in Eastern Germany were different, with several new variants including four predominant lineages in the second wave, having been introduced into Eastern Germany between August and October 2020. The results indicate that the major driving force behind the second wave was the introduction of new variants.

In October 2019, public health surveillance systems in Scotland identified an increase in the number of reported infections of Shiga toxin-producing Escherichia coli (STEC) O26:H11 involving bloody diarrhoea. Ultimately, across the United Kingdom (UK) 32 cases of STEC O26:H11 stx1a were identified, with the median age of 27 years and 64% were male; six cases were hospitalised. Among food exposures there was an association with consuming pre-packed sandwiches purchased at outlets belonging to a national food chain franchise (food outlet A) [odds ratio (OR) = 183.89, P < 0.001]. The common ingredient identified as a component of the majority of the sandwiches sold at food outlet A was a mixed salad of Apollo and Iceberg lettuce and spinach leaves. Microbiological testing of food and environmental samples were negative for STEC O26:H11, although STEC O36:H19 was isolated from a mixed salad sample taken from premises owned by food outlet A. Contamination of fresh produce is often due to a transient event and detection of the aetiological agent in food that has a short-shelf life is challenging. Robust, statistically significant epidemiological analysis should be sufficient evidence to direct timely and targeted on-farm investigations. A shift in focus from testing the microbiological quality of the produce to investigating the processes and practices through the supply chain and sampling the farm environment is recommended.

Pooling of samples in detecting the presence of virus is an effective and efficient strategy in screening carriers in a large population with low infection rate, leading to reduction in cost and time. There are a number of pooling test methods, some being simple and others being complicated. In such pooling tests, the most important parameter to decide is the pool or group size, which can be optimised mathematically. Two pooling methods are relatively simple. The minimum numbers required in these two tests for a population with known infection rate are discussed and compared. Results are useful for identifying asymptomatic carriers in a short time and in implementing health codes systems.

Several studies have demonstrated that higher levels of vitamin D are associated with better prognosis and outcomes in infectious diseases. We aimed to compare the vitamin D levels of paediatric patients with mild/moderate coronavirus disease 2019 (COVID-19) disease and a healthy control group. We retrospectively reviewed the medical records of patients who were hospitalised at our university hospital with the diagnosis of COVID-19 during the period between 25 May 2020 and 24 December 2020. The mean age of the COVID-19 patients was 10.7 ± 5.5 years (range 1–18 years); 43 (57.3%) COVID-19 patients were male. The mean serum vitamin D level was significantly lower in the COVID-19 group than the control group (21.5 ± 10.0 vs. 28.0 ± 11.0 IU, P < 0.001). The proportion of patients with vitamin D deficiency was significantly higher in the COVID-19 group than the control group (44% vs. 17.5%, P < 0.001). Patients with low vitamin D levels were older than the patients with normal vitamin D levels (11.6 ± 4.9 vs. 6.2 ± 1.8 years, P = 0.016). There was a significant male preponderance in the normal vitamin D group compared with the low vitamin D group (91.7% vs. 50.8%, P = 0.03). C-reactive protein level was higher in the low vitamin D group, although the difference did not reach statistical significance (9.6 ± 2.2 vs. 4.5 ± 1.6 mg/l, P = 0.074). Our study provides an insight into the relationship between vitamin D deficiency and COVID-19 for future studies. Empiric intervention with vitamin D can be justified by low serum vitamin D levels.