This study analysed the reported incidence of COVID-19 and associated epidemiological and socio-economic factors in the WHO African region. Data from COVID-19 confirmed cases and SARS-CoV-2 tests reported to the WHO by Member States between 25 February and 31 December 2020 and publicly available health and socio-economic data were analysed using univariate and multivariate binomial regression models. The overall cumulative incidence was 1846 cases per million population. Cape Verde (21 350 per million), South Africa (18 060 per million), Namibia (9840 per million), Eswatini (8151 per million) and Botswana (6044 per million) recorded the highest cumulative incidence, while Benin (260 per million), Democratic Republic of Congo (203 per million), Niger (141 cases per million), Chad (133 per million) and Burundi (62 per million) recorded the lowest. Increasing percentage of urban population (β = −0.011, P = 0.04) was associated with low cumulative incidence, while increasing number of cumulative SARS-CoV-2 tests performed per 10 000 population (β = 0.0006, P = 0.006) and the proportion of population aged 15–64 years (adjusted β = 0.174, P < 0.0001) were associated with high COVID-19 cumulative incidence. With limited testing capacities and overwhelmed health systems, these findings highlight the need for countries to increase and decentralise testing capacities and adjust testing strategies to target most at-risk populations.

Via generalized interval arithmetic, we propose a Generalized Interval Arithmetic Center and Range (GIA-CR) model for random intervals, where parameters in the model satisfy linear inequality constraints. We construct a constrained estimator of the parameter vector and develop asymptotically uniformly valid tests for linear equality constraints on the parameters in the model. We conduct a simulation study to examine the finite sample performance of our estimator and tests. Furthermore, we propose a coefficient of determination for the GIA-CR model. As a separate contribution, we establish the asymptotic distribution of the constrained estimator in Blanco-Fernández (2015, Multiple Set Arithmetic-Based Linear Regression Models for Interval-Valued Variables) in which the parameters satisfy an increasing number of random inequality constraints.

We propose a novel conditional quantile prediction method based on complete subset averaging (CSA) for quantile regressions. All models under consideration are potentially misspecified, and the dimension of regressors goes to infinity as the sample size increases. Since we average over the complete subsets, the number of models is much larger than the usual model averaging method which adopts sophisticated weighting schemes. We propose to use an equal weight but select the proper size of the complete subset based on the leave-one-out cross-validation method. Building upon the theory of Lu and Su (2015, Journal of Econometrics 188, 40–58), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987, Annals of Statistics 15, 958–975) We check the finite sample performance via Monte Carlo simulations and empirical applications.

Radars used to observe meteor trails in the mesosphere deliver information on winds and temperature. Use of these radars is becoming a standard method for determining mesospheric dynamics and temperatures worldwide due to relatively low costs and ease of deployment. However, recent studies have revealed that temperatures may be overestimated in conditions such as high geomagnetic activity. The effect is thought to be most prevalent at high latitude, although this is not yet proven. Here, we demonstrate how temperatures might be corrected for geomagnetic effects; the demonstration is for a particular geographic location (Svalbard, 78°N, 16°E) because it is local geomagnetic disturbances that affects local temperature measurements, therefore requiring co-located instruments. We see that summer temperatures require a correction (reduction) of a few Kelvin, but winter estimates are more accurate.

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.

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.

Adenovirus pneumonia can occur in immunocompetent youths and adults. We conducted a retrospective analysis on five immunocompetent patients (aged ⩾14 years) with adenovirus pneumonia who visited our fever clinic between 1 February 2020 and 29 February 2020. The symptoms at clinical onset were fever, with cough and phlegm production either absent or appearing several days after disease onset. One patient with severe disease exhibited dyspnoea and a rapid development of respiratory failure. A subset of patients had concurrent gastrointestinal symptoms. The results of blood tests revealed normal leukocyte counts, decreased lymphocyte counts and increased C-reactive protein levels. The imaging findings resembled those of bacterial pneumonia, and pleural effusions were present in some cases. Most patients had a good prognosis with symptomatic treatment and supportive care. However, one patient with severe disease and a MuLBSTA score of >12 had a poor prognosis and ultimately died. Immunocompetent youths and adults may develop adenovirus pneumonia, and severe cases are at the risk of death. Since no effective treatments for adenovirus pneumonia are currently known, the early diagnosis and provision of symptomatic treatment and supportive care should be adopted to prevent the development and progression of severe disease.

To investigate temporal trends in coronavirus disease 2019 (COVID-19)-related outcomes and to evaluate whether the impacts of potential risk factors and disparities changed over time, we conducted a retrospective cohort study with 249 075 patients tested or treated for COVID-19 at Michigan Medicine (MM), from 10 March 2020 to 3 May 2021. Among these patients, 26 289 were diagnosed with COVID-19. According to the calendar time in which they first tested positive, the COVID-19-positive cohort were stratified into three-time segments (T1: March–June, 2020; T2: July–December, 2020; T3: January–May, 2021). Potential risk factors that we examined included demographics, residential-level socioeconomic characteristics and preexisting comorbidities. The main outcomes included COVID-19-related hospitalisation and intensive care unit (ICU) admission. The hospitalisation rate for COVID-positive patients decreased from 36.2% in T1 to 14.2% in T3, and the ICU admission rate decreased from 16.9% to 2.9% from T1 to T3. These findings confirm that COVID-19-related hospitalisation and ICU admission rates were decreasing throughout the pandemic from March 2020 to May 2021. Black patients had significantly higher (compared to White patients) hospitalisation rates (19.6% vs. 11.0%) and ICU admission rates (6.3% vs. 2.8%) in the full COVID-19-positive cohort. A time-stratified analysis showed that racial disparities in hospitalisation rates persisted over time and the estimates of the odds ratios (ORs) stayed above unity in both unadjusted [full cohort: OR = 1.98, 95% confidence interval (CI) (1.79, 2.19); T1: OR = 1.70, 95% CI (1.36, 2.12); T2: OR = 1.40, 95% CI (1.17, 1.68); T3: OR = 1.55, 95% CI (1.29, 1.86)] and adjusted analysis, accounting for differences in demographics, socioeconomic status, and preexisting comorbid conditions (full cohort: OR = 1.45, 95% CI (1.25, 1.68); T1: OR = 1.26, 95% CI (0.90, 1.76); T2: OR = 1.29, 95% CI (1.01, 1.64); T3: OR = 1.29, 95% CI (1.00, 1.67)).

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.

Indian Ocean islands are endemic areas for human and animal leptospirosis. Maintenance host species for Leptospira spp. have still not been completely elucidated, and recently the role of cats (Felis catus) has been questioned. This cross-sectional study aims to determine whether cats are part of the maintenance community of different strains of Leptospira spp. in Reunion Island. The prevalence of Leptospira infection in an opportunistic sample of stray and domestic cats (n = 92) from Reunion Island has been studied using serological (microagglutination test) and molecular detection (polymerase chain reaction (PCR)). The results revealed a seroprevalence of 37.0% (34/92) (cut-off 1:40) without a significant difference in the living conditions of animals. The predominant serogroup was Icterohaemorrhagiae, but Ballum, Cynopteri and Australis were also detected. Using PCR, 28.6% (12/42) of stray cats were tested positive. Leptospiral DNA was detected in renal tissue, urine and blood of respectively 14.3% (6/42), 10.3% (4/39) and 11.9% (5/42) of stray cats, but 0% (0/3), 0% (0/50) and 0% (0/36) of domestic cats (P = non-applicable, P = 0,038, P = 0,058 respectively). Partial rrs gene (16S rRNA) sequencing identified Leptospira interrogans in all PCR-positive samples. Our study confirms that renal carriage and urinary shedding are possible, positioning cats, and especially stray cats as potential actors within the maintenance community of L. interrogans in Reunion Island.

Allogenic hematopoietic stem cell transplant (HSCT) recipients are susceptible to any kind of infectious agents including Clostridium difficile. We studied 86 allogenic-HSCT patients who faced diarrhoea while receiving antibiotics. DNA from stool samples were explored for the presence of C. difficile toxin genes (tcdA; tcdB) by multiplex real-time PCR. Results showed nine toxigenic C. difficile amongst which seven were positive for both toxins and two were positive for tcdB. Six of toxigenic C. difficile organisms harbouring both toxin genes were also isolated by toxigenic culture. Clostridium difficile infection was controlled successfully with oral Metronidazole and Vancomycin in the confirmed infected patients.

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.

The COVID-19 global pandemic has had considerable health impact, including sub-Saharan Africa. In Malawi, a resource-limited setting in Africa, gaining access to data to inform the COVID-19 response is challenging. Information on adherence to physical distancing guidelines and reducing contacts are nonexistent, but critical to understanding and communicating risk, as well as allocating scarce resources. We present a case study which leverages aggregated call detail records into a daily data pipeline which summarize population density and mobility in an easy-to-use dashboard for public health officials and emergency operations. From March to April 2021, we have aggregated 6-billion calls and text messages and continue to process 12 million more daily. These data are summarized into reports which describe, quantify, and locate mass gatherings and travel between subdistricts. These reports are accessible via web dashboards for policymakers within the Ministry of Health and Emergency Operations Center to inform COVID-19 response efforts and resource allocation.

This study aimed to evaluate the performance of Cobas human papillomavirus (HPV) test in cervical cancer screening. A total of 3442 women aged ⩾20 years used Cobas HPV and hybrid capture 2 (HC2) tests were included in this study. Women with any positive result were examined by liquid-based cytology (LBC) test. Then subjects with abnormal LBC or positive Cobas HPV16/18 were further checked by colposcopy to observe the visible lesions to perform the pathological examination. Of these 3442 women, 328 cases were Cobas HPV positive, and the positive rate was 9.53% (95% confidence interval (CI) 8.50–10.53). The positive rate of HPV16, HPV18, and other 12 types of high-risk HPV were 1.54% (95% CI 1.12–1.95), 0.55% (95% CI 0.30–0.80), and 7.44% (95% CI 6.56–8.32), respectively. The coincidence rate of Cobas HPV test and HC2 test was 90% (95% CI 89.00–91.00; Kappa = 0.526) in the primary screening. Age had a non-linear relationship with Cobas HPV positive rate (χ2 = 4.240, P = 0.040) and HPV16/18 typing positive rate (χ2 = 6.610, P = 0.010). Compared with the LBC test, the Cobas HPV test had higher sensitivity when detecting patients with high cervical intraepithelial neoplasia (CIN2+ and CIN3+).

The COVID-19 pandemic and associated measures implemented have rapidly changed how people move about and behave in society. Utilizing data on people’s mobility could provide unique and valuable insights to governments and institutions to better manage the crisis. These entities, however, have not traditionally had access to, nor the experience of applying, continuous anonymized and aggregated data on people mobility. This article aims to show how the Public Health Agency in Sweden successfully collaborated with a Nordic Telecoms operator to make use of such data during the COVID-19 pandemic. Specifically, it investigates how the collaboration started, approaches used to go from data to insight, outcomes and impact, and lessons learned on both sides. Telia, the largest telecom operator in the Nordics, had an existing product commercially available that provided anonymized and aggregated insights about people’s movement. Several challenges existed within Telia as it was the first time worldwide a collaboration with a Public Health Agency would take place and social benefits had to be weighed against commercial and reputational risks. The hypothesis at the beginning of the pandemic was that the solution could be adapted to fit the needs of policymakers and the internal challenges could be overcome, while providing a meaningful contribution to the fight against the virus. The results show that it is possible to both form a mutually beneficial collaboration between a telecom operator and a public institution, and to make use of mobility data in evidence-based policymaking without compromising applicable personal data protection laws.

Our population-based study objectives were to describe characteristics and outcomes of Escherichia coli bloodstream infections (BSIs), and to evaluate factors associated with outcomes. We included incident E. coli BSIs from western interior residents (British Columbia, Canada; 04/2010–03/2020). We obtained data including patient demographics, location of onset, infection focus, Charlson comorbidity index (CCI), antimicrobial resistance, 30-day all-cause mortality and length of hospital stay (LOS). Using multivariable logistic regression models fitted with generalised estimating equations, we estimated factors associated with 30-day mortality and long post-infection LOS (>75th percentile). We identified 1080 incident E. coli BSIs in 1009 patients. The crude incidence and 30-day mortality rates were 59.1 BSIs and 6.8 deaths/100 000 person-years, respectively. The 30-day case fatality risk was 11.5%. Compared to community-acquired E. coli BSIs, either healthcare-associated or nosocomial cases had higher odds of 30-day mortality. Older cases, non-urogenital BSI foci and CCI ⩾ 3 had higher odds of 30-day mortality compared to younger cases, urogenital foci and CCI < 3. In patients that survived to discharge, those with extended-spectrum β-lactamase (ESBL)-producing E. coli BSIs, nosocomial BSIs, and CCI ⩾ 3 had higher odds of long post-infection LOS compared to those with non-ESBL-producing, community-acquired and healthcare-associated, and CCI < 3. There is a substantial disease burden from E. coli BSIs.

In a classical chess round-robin tournament, each of $n$ players wins, draws, or loses a game against each of the other $n-1$ players. A win rewards a player with 1 points, a draw with 1/2 point, and a loss with 0 points. We are interested in the distribution of the scores associated with ranks of $n$ players after ${{n \choose 2}}$ games, that is, the distribution of the maximal score, second maximum, and so on. The exact distribution for a general $n$ seems impossible to obtain; we obtain a limit distribution.

In this paper, we consider a history-dependent mixed shock model which is a combination of the history-dependent extreme shock model and the history-dependent $\delta$-shock model. We assume that shocks occur according to the generalized Pólya process that contains the homogeneous Poisson process, the non-homogeneous Poisson process and the Pólya process as the particular cases. For the defined survival model, we derive the corresponding survival function, the mean lifetime and the failure rate. Further, we study the asymptotic and monotonicity properties of the failure rate. Finally, some applications of the proposed model have also been included with relevant numerical examples.

In the context of the Solvency II directive, the operation of an internal risk model is a possible way for risk assessment and for the determination of the solvency capital requirement of an insurance company in the European Union. A Monte Carlo procedure is customary to generate a model output. To be compliant with the directive, validation of the internal risk model is conducted on the basis of the model output. For this purpose, we suggest a new test for checking whether there is a significant change in the modeled solvency capital requirement. Asymptotic properties of the test statistic are investigated and a bootstrap approximation is justified. A simulation study investigates the performance of the test in the finite sample case and confirms the theoretical results. The internal risk model and the application of the test is illustrated in a simplified example. The method has more general usage for inference of a broad class of law-invariant and coherent risk measures on the basis of a paired sample.