The outbreak of novel coronavirus pneumonia (coronavirus disease 2019 (COVID-19)), declared as a ‘global pandemic’ by the World Health Organization (WHO), is a public health emergency of international concern (PHEIC). The outbreak in multiple locations shows a trend of accelerating spread around the world. China has taken a series of powerful measures to contain the spread of the novel coronavirus. In response to the COVID-19 pandemic, in addition to actively finding effective treatment drugs and developing vaccines, it is more important to identify the source of infection at the community level as soon as possible to block the transmission path of the virus to prevent the spread of the pandemic. The implementation of grid management in the community and the adoption of precise management and control measures to reduce unnecessary personnel movement can effectively reduce the risk of pandemic spread. This paper mainly describes that the grid management mode can promote the refinement and comprehensiveness of community management. As a management system with potential to improve the governance ability of community affairs, it may be helpful to strengthen the prevention and control of the epidemic in the community.

We give a fully polynomial-time randomized approximation scheme (FPRAS) for the number of bases in bicircular matroids. This is a natural class of matroids for which counting bases exactly is #P-hard and yet approximate counting can be done efficiently.

Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population.

In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) – this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020.

In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%.

Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.

Inhibitory control can be divided into motor and cognitive inhibition. The current research is the first study exploring the impact of brief mindfulness training on motor inhibition, measured by a stop signal task in participants without any meditation experience. Motor inhibition performance was compared before and immediately after three different conditions; a brief mindfulness induction, a resting state and an active control session in which participants listened to their favorite music. Post-test learning effect on go-reaction times was seen for the resting and mindfulness conditions, but was absent in the music session, possibly due to emotional arousal might have led slower responses. Brief mindfulness training did not significantly alter inhibitory control, although marginal improvement in stop signal reaction time following the mindfulness induction was observed. Motor inhibition appears unresponsive to either short-term or long-term mindfulness practice. Future mindfulness studies should explore a broad spectrum of cognitive functions and populations.

Despite considerable efforts to control tuberculosis (TB) among Ethiopian immigrants in Israel, an outbreak of TB among second-generation Ethiopian immigrants that involved native Israelis occurred between January 2011 and December 2019. The aim of this article is to report on this outbreak and discuss the patient and health system barriers that led to its propagation. Overall, 13 culture-positive TB patients were diagnosed in this outbreak. An additional 36 cases with identical mycobacterium tuberculosis genotypes were identified through cross-checking with the National TB Laboratory Registry. Among the 32 close contacts of the index case, 18 (56.3%) reported for screening and treatment of latent TB infection (LTBI) was recommended for 11 (61.1%) of them. However, none completed treatment and eight eventually developed TB. Of the 385 close contacts identified in this outbreak, 286 (74.3%) underwent contact investigation, 154 (53.8%) were recommended LTBI treatment, but only 26 (16.9%) completed the treatment. Routine contact investigation and treatment practice measures failed to contain the cascade of infection and disease, leading to the spread of the infecting strain of TB. This report highlights the challenges to identify the high-risk group and address barriers to care among such a vulnerable population.

Outbreaks of cyclosporiasis, a food-borne illness caused by the coccidian parasite Cyclospora cayetanensis have increased in the USA in recent years, with approximately 2300 laboratory-confirmed cases reported in 2018. Genotyping tools are needed to inform epidemiological investigations, yet genotyping Cyclospora has proven challenging due to its sexual reproductive cycle which produces complex infections characterized by high genetic heterogeneity. We used targeted amplicon deep sequencing and a recently described ensemble-based distance statistic that accommodates heterogeneous (mixed) genotypes and specimens with partial genotyping data, to genotype and cluster 648 C. cayetanensis samples submitted to CDC in 2018. The performance of the ensemble was assessed by comparing ensemble-identified genetic clusters to analogous clusters identified independently based on common food exposures. Using these epidemiologic clusters as a gold standard, the ensemble facilitated genetic clustering with 93.8% sensitivity and 99.7% specificity. Hence, we anticipate that this procedure will greatly complement epidemiologic investigations of cyclosporiasis.

In this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database show particularly low and high mortality rates, respectively. To provide corrected tables for the three countries of interest here (France, Italy and West Germany), we use the approach developed by Boumezoued for countries for which the method applies (France and Italy) and provide an extension of the method for West Germany as monthly fertility histories are not sufficient to cover the generations of interest. These mortality tables are crucial inputs to stochastic mortality models forecasting future scenarios, from which the extreme 0.5% longevity improvement can be extracted, allowing for the calculation of the solvency capital requirement. More precisely, to assess the impact of such anomalies in the Solvency II framework, we use a simplified internal model based on three usual stochastic models to project mortality rates in the future combined with a closure table methodology for older ages. Correcting this bias obviously improves the data quality of the mortality inputs, which is of paramount importance today, and slightly decreases the capital requirement. Overall, the longevity risk assessment remains stable, as well as the selection of the stochastic mortality model. As a collateral gain of this data quality improvement, the more regular estimated parameters allow for new insights and a refined assessment regarding longevity risk.

Flood and drought events cause significant freshwater inflow fluctuations in estuaries, potentially leading to physiological stress and altered abundances of pathogens such as Vibrio vulnificus and Perkinsus marinus in oysters. To assess the effects of freshwater pulses to oyster reefs in subtropical estuaries in Texas, this study accomplished two goals: 1) reconstructed a reef-specific history of freshwater pulses through shell stable isotope analysis, 2) quantified the abundance of V. vulnificus and P. marinus through culture-dependent and culture-independent microbiology analyses. Oysters from a low-relief and high-relief reef experienced similar fluctuations in shell isotopes, indicating similar ranges of past environmental conditions. V. vulnificus and P. marinus were detected throughout the study but the abundance of these microorganisms was not correlated with environmental parameters or one another. Importantly, the P. marinus infection intensity was always lower at the high-relief reef, which suggests that high-relief reefs may experience lower infection frequencies.

This paper deals with design of an alternative secure Blockchain network framework to prevent damages from an attacker. The alliance concept from the strategic management perspectives is applied on the top of a general stochastic game framework. This new enhanced hybrid theoretical model is designed to find the best strategies toward preparation for preventing a network malfunction from an attacker through strategic alliances with other genuine nodes and it is developed based on the combination of a strategic management framework and a conventional stochastic model based on the Blockchain Governance Game. Analytically, tractable results for decision-making parameters are fully obtained to predict of the moment for operations and also to provide the optimal number of allegiance nodes to protect a Blockchain network. This research helps those whom are considering initial coin offering or launching new Blockchain-based services by enhancing security features through strategic alliances in a decentralized network.

Increased population movements and increased mobility made it possible for severe acute respiratory syndrome coronavirus 2, which is mainly spread by respiratory droplets, to spread faster and more easily. This study tracked and analysed the development of the coronavirus 2019 (COVID-19) outbreak in the top 100 cities that were destinations for people who left Wuhan before the city entered lockdown. Data were collected from the top 100 destination cities for people who travelled from Wuhan before the lockdown, the proportion of people travelling into each city, the intensity of intracity travel and the daily reports of COVID-19. The proportion of the population that travelled from Wuhan to each city from 10 January 2020 to 24 January 2020, was positively correlated with and had a significant linear relationship with the cumulative number of confirmed cases of COVID-19 in each city after 24 January (all P < 0.01). After the State Council launched a multidepartment joint prevention and control effort on 22 January 2020 and compared with data collected on 18 February, the average intracity travel intensity of the aforementioned 100 cities decreased by 60−70% (all P < 0.001). The average intensity of intracity travel on the nth day in these cities during the development of the outbreak was positively related to the growth rate of the number of confirmed COVID-19 cases on the n + 5th day in these cities and had a significant linear relationship (P < 0.01). Higher intensities of population movement were associated with a higher incidence of COVID-19 during the pandemic. Restrictions on population movement can effectively curb the development of an outbreak.