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.

This paper derives asymptotic risk (expected loss) results for shrinkage estimators with multidimensional regularization in high-dimensional settings. We introduce a class of multidimensional shrinkage estimators (MuSEs), which includes the elastic net, and show that—as the number of parameters to estimate grows—the empirical loss converges to the oracle-optimal risk. This result holds when the regularization parameters are estimated empirically via cross-validation or Stein’s unbiased risk estimate. To help guide applied researchers in their choice of estimator, we compare the empirical Bayes risk of the lasso, ridge, and elastic net in a spike and normal setting. Of the three estimators, we find that the elastic net performs best when the data are moderately sparse and the lasso performs best when the data are highly sparse. Our analysis suggests that applied researchers who are unsure about the level of sparsity in their data might benefit from using MuSEs such as the elastic net. We exploit these insights to propose a new estimator, the cubic net, and demonstrate through simulations that it outperforms the three other estimators for any sparsity level.

A long-standing conjecture of Erdős and Simonovits asserts that for every rational number $r\in (1,2)$ there exists a bipartite graph H such that $\mathrm{ex}(n,H)=\Theta(n^r)$. So far this conjecture is known to be true only for rationals of form $1+1/k$ and $2-1/k$, for integers $k\geq 2$. In this paper, we add a new form of rationals for which the conjecture is true: $2-2/(2k+1)$, for $k\geq 2$. This in turn also gives an affirmative answer to a question of Pinchasi and Sharir on cube-like graphs. Recently, a version of Erdős and Simonovits$^{\prime}$s conjecture, where one replaces a single graph by a finite family, was confirmed by Bukh and Conlon. They proposed a construction of bipartite graphs which should satisfy Erdős and Simonovits$^{\prime}$s conjecture. Our result can also be viewed as a first step towards verifying Bukh and Conlon$^{\prime}$s conjecture. We also prove an upper bound on the Turán number of theta graphs in an asymmetric setting and employ this result to obtain another new rational exponent for Turán exponents: $r=7/5$.

We update our previous insights into COVID-19 vaccine acceptance and hesitancy in Finland. Vaccine acceptance increased from 64% (November/December 2020) to 74% (April 2021). However, there was a group of participants that were preferring to wait to get vaccinated ranging from 6% of over-64-years-olds to 29% of under-30-years-olds. The previously identified enablers convenience (below-50-years-olds), worry about severe disease and protection for oneself (above-50-years-olds) were no longer significantly associated with increased vaccine acceptance. Understanding barriers and enablers behind vaccine acceptance is decisive in ensuring a successful implementation of COVID-19 vaccination programs, which will be key to ending the pandemic.

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.

Estimating the spread of SARS-CoV-2 infection in communities is critical. We surveyed 2244 stratified random sample community members of the Gardena valley, a winter touristic area, amidst the first expansion phase of the COVID-19 pandemic in Europe. We measured agreement between Diasorin and Abbott serum bioassay outputs and the Abbott optimal discriminant threshold of serum neutralisation titres with recursive receiver operating characteristic curve. We analytically adjusted serum antibody tests for unbiased seroprevalence estimate and analysed the determinants of infection with non-response weighted multiple logistic regression. SARS-CoV-2 seroprevalence was 26.9% (95% CI 25.2–28.6) by June 2020. The bioassays had a modest agreement with each other. At a lower threshold than the manufacturer's recommended level, the Abbott assay reflected greater discrimination of serum neutralisation capacity. Seropositivity was associated with place and economic activity, not with sex or age. Symptoms like fever and weakness were age-dependent. SARS-CoV-2 mitigation strategies should account for context in high prevalence areas.

Coronavirus disease 2019 (COVID-19) emerged from a city in China and has now spread as a global pandemic affecting millions of individuals. The causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is being extensively studied in terms of its genetic epidemiology using genomic approaches. Andhra Pradesh is one of the major states of India with the third-largest number of COVID-19 cases with a limited understanding of its genetic epidemiology. In this study, we have sequenced 293 SARS-CoV-2 genome isolates from Andhra Pradesh with a mean coverage of 13324X. We identified 564 high-quality SARS-CoV-2 variants. A total of 18 variants mapped to reverse transcription polymerase chain reaction primer/probe sites, and four variants are known to be associated with an increase in infectivity. Phylogenetic analysis of the genomes revealed the circulating SARS-CoV-2 in Andhra Pradesh majorly clustered under the clade A2a (20A, 20B and 20C) (94%), whereas 6% fall under the I/A3i clade, a clade previously defined to be present in large numbers in India. To the best of our knowledge, this is the most comprehensive genetic epidemiological analysis performed for the state of Andhra Pradesh.

COVID-19 research has been produced at an unprecedented rate and managing what is currently known is in part being accomplished through synthesis research. Here we evaluated how the need to rapidly produce syntheses has impacted the quality of the synthesis research. Thus, we sought to identify, evaluate and map the synthesis research on COVID-19 published up to 10 July 2020. A COVID-19 literature database was created using pre-specified COVID-19 search algorithms carried out in eight databases. We identified 863 citations considered to be synthesis research for evaluation in this project. Four-hundred and thirty-nine reviews were fully assessed with A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) and rated as very low-quality (n = 145), low-quality (n = 80), medium-quality (n = 208) and high-quality (n = 151). The quality of these reviews fell short of what is expected for synthesis research with key domains being left out of the typical methodology. The increase in risk of bias due to non-adherence to systematic review methodology is unknown and prevents the reader from assessing the validity of the review. The responsibility to assure the quality is held by both producers and publishers of synthesis research and our findings indicate there is a need to equip readers with the expertise to evaluate the review conduct before using it for decision-making purposes.

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.

Varicella is a highly infectious contagious disease, and Chongqing is one of the high incidence areas in China. To understand the epidemic regularity and predict the epidemic trend of varicella is of great significance to the risk analysis and health resource allocation in the health sector. First, we used the ‘STL’ function to decompose the incidence of varicella to understand its trend and seasonality. Second, we established SARIMA model for linear fitting, and then took the residual of the SARIMA model as the sample to fit the LS-SVM model, to explain the non-linearity of the residuals. The monthly varicella incidence peaks in April to June and October to December. Mixed model was compared to SARIMA model, the prediction error of the hybrid model was smaller, and the RMSE and MAPE values of the hybrid model were 0.7525 and 0.0647, respectively, the mixed model had a better prediction effect. Based on the study, the incidence of varicella in Chongqing has an obvious seasonal trend, and a hybrid model can also predict the incidence of varicella well. Thus, hybrid model analysis is a feasible and simple method to predict varicella in Chongqing.