In this work we consider three well-studied broadcast protocols: push, pull and push&pull. A key property of all these models, which is also an important reason for their popularity, is that they are presumed to be very robust, since they are simple, randomized and, crucially, do not utilize explicitly the global structure of the underlying graph. While sporadic results exist, there has been no systematic theoretical treatment quantifying the robustness of these models. Here we investigate this question with respect to two orthogonal aspects: (adversarial) modifications of the underlying graph and message transmission failures.

We explore in particular the following notion of local resilience: beginning with a graph, we investigate up to which fraction of the edges an adversary may delete at each vertex, so that the protocols need significantly more rounds to broadcast the information. Our main findings establish a separation among the three models. On one hand, pull is robust with respect to all parameters that we consider. On the other hand, push may slow down significantly, even if the adversary may modify the degrees of the vertices by an arbitrarily small positive fraction only. Finally, push&pull is robust when no message transmission failures are considered, otherwise it may be slowed down.

On the technical side, we develop two novel methods for the analysis of randomized rumour-spreading protocols. First, we exploit the notion of self-bounding functions to facilitate significantly the round-based analysis: we show that for any graph the variance of the growth of informed vertices is bounded by its expectation, so that concentration results follow immediately. Second, in order to control adversarial modifications of the graph we make use of a powerful tool from extremal graph theory, namely Szemerédi’s Regularity Lemma.

Our study aimed to systematically analyse the risk factors of coronavirus disease 2019 (COVID-19) patients with severe disease. An electronic search in eight databases to identify studies describing severe or critically ill COVID-19 patients from 1 January 2020 to 3 April 2020. In the end, we meta-analysed 40 studies involving 5872 COVID-19 patients. The average age was higher in severe COVID-19 patients (weighted mean difference; WMD = 10.69, 95%CI 7.83–13.54). Patients with severe disease showed significantly lower platelet count (WMD = −18.63, 95%CI −30.86 to −6.40) and lymphocyte count (WMD = −0.35, 95%CI −0.41 to −0.30) but higher C-reactive protein (CRP; WMD = 42.7, 95%CI 31.12–54.28), lactate dehydrogenase (LDH; WMD = 137.4, 95%CI 105.5–169.3), white blood cell count（WBC), procalcitonin（PCT）, D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and creatinine（Cr）. Similarly, patients who died showed significantly higher WBC, D-dimer, ALT, AST and Cr but similar platelet count and LDH as patients who survived. These results indicate that older age, low platelet count, lymphopenia, elevated levels of LDH, ALT, AST, PCT, Cr and D-dimer are associated with severity of COVID-19 and thus could be used as early identification or even prediction of disease progression.

The spread of COVID-19 is recent in India, which has within 4 months caused over 190 000 infections, as of 1 June 2020, despite four stringent lockdowns. With the current rate of the disease transmission in India, which is home to over 1.35 billion people, the infection spread is predicted to be worse than the USA in the upcoming months. To date, there is a major lack of understanding of the transmission dynamics and epidemiological characteristics of the disease in India, inhibiting effective measures to control the pandemic. We collected all the available data of the individual patients, cases and a range of parameters such as population distribution, testing and healthcare facilities, and weather, across all Indian states till May 2020. Numerical analysis was conducted to determine the effect of each parameter on the COVID-19 situation in India. A significant amount of local transmission in India initiated with travellers returning from abroad. Maharashtra, Tamil Nadu and Delhi are currently the top three infected states in India with doubling time of 14.5 days. The average recovery rate across Indian states is 42%, with a mortality rate below 3%. The rest 55% are currently active cases. In total, 88% of the patients experienced symptoms of high fever, 68% suffered from dry cough and 7.1% patients were asymptomatic. In total, 66.8% patients were males, 73% were in the age group of 20–59 years and over 83% recovered in 11–25 days. Approximately 3.4 million people were tested between 1 April and 25 May 2020, out of which 4% were detected COVID-19-positive. Given the current doubling time of infections, several states may face a major shortage of public beds and healthcare facilities soon. Weather has minimal effect on the infection spread in most Indian states. The study results will help policymakers to predict the trends of the disease spread in the upcoming months and devise better control measures.

This paper studies asymptotic properties of a posterior probability density and Bayesian estimators of spatial econometric models in the classical statistical framework. We focus on the high-order spatial autoregressive model with spatial autoregressive disturbance terms, due to a computational advantage of Bayesian estimation. We also study the asymptotic properties of Bayesian estimation of the spatial autoregressive Tobit model, as an example of nonlinear spatial models. Simulation studies show that even when the sample size is small or moderate, the posterior distribution of parameters is well approximated by a normal distribution, and Bayesian estimators have satisfactory performance, as classical large sample theory predicts.

A variable annuity (VA) is an equity-linked annuity that provides investment guarantees to its policyholder and its contributions are normally invested in multiple underlying assets (e.g., mutual funds), which exposes VA liability to significant market risks. Hedging the market risks is therefore crucial in risk managing a VA portfolio as the VA guarantees are long-dated liabilities that may span decades. In order to hedge the VA liability, the issuing insurance company would need to construct a hedging portfolio consisting of the underlying assets whose positions are often determined by the liability Greeks such as partial dollar Deltas. Usually, these quantities are calculated via nested simulation approach. For insurance companies that manage large VA portfolios (e.g., 100k+ policies), calculating those quantities is extremely time-consuming or even prohibitive due to the complexity of the guarantee payoffs and the stochastic-on-stochastic nature of the nested simulation algorithm. In this paper, we extend the surrogate model-assisted nest simulation approach in Lin and Yang [(2020) Insurance: Mathematics and Economics, 91, 85–103] to efficiently calculate the total VA liability and the partial dollar Deltas for large VA portfolios with multiple underlying assets. In our proposed algorithm, the nested simulation is run using small sets of selected representative policies and representative outer loops. As a result, the computing time is substantially reduced. The computational advantage of the proposed algorithm and the importance of dynamic hedging are further illustrated through a profit and loss (P&L) analysis for a large synthetic VA portfolio. Moreover, the robustness of the performance of the proposed algorithm is tested with multiple simulation runs. Numerical results show that the proposed algorithm is able to accurately approximate different quantities of interest and the performance is robust with respect to different sets of parameter inputs. Finally, we show how our approach could be extended to potentially incorporate stochastic interest rates and estimate other Greeks such as Rho.

In this paper, we complement joint time-series and cross-section convergence results derived in a companion paper Hahn, Kuersteiner, and Mazzocco (2016, Central Limit Theory for Combined Cross-Section and Time Series) by allowing for serial correlation in the time-series sample. The implications of our analysis are limiting distributions that have a well-known form of long-run variances for the time-series limit. We obtain these results at the cost of imposing strict stationarity for the time-series model and conditional independence between the time-series and cross-section samples. Our results can be applied to estimators that combine time-series and cross-section data in the presence of aggregate uncertainty in models with rationally forward-looking agents.

In predictive regressions with variables of unknown persistence, the use of extended IV (IVX) instruments leads to asymptotically valid inference. Under highly persistent regressors, the standard normal or chi-squared limiting distributions for the usual t and Wald statistics may, however, differ markedly from the actual finite-sample distributions which exhibit in particular noncentrality. Convergence to the limiting distributions is shown to occur at a rate depending on the choice of the IVX tuning parameters and can be very slow in practice. A characterization of the leading higher-order terms of the t statistic is provided for the simple regression case, which motivates finite-sample corrections. Monte Carlo simulations confirm the usefulness of the proposed methods.

Considering the Panigrahi and Chatterjee model (2017) for variable generalised Chaplygin gas, in this paper we found for this kind of exotic matter an analytic expression for the adiabatic compressibility βs. It was analyzed the behaviour of the adiabatic compressibility in the limit of high and low pressure. The derived equation for βs was used to deduce the value of the heat capacity at constant pressure Cp for variable generalised Chaplygin gas.

Coronavirus disease 2019 (COVID-19) is a global health threat. A hospital in Zhuhai adopted several measures in Fever Clinic Management (FCM) to respond to the outbreak of COVID-19. FCM has been proved to be effective in preventing nosocomial cross infection. Faced with the emergency, the hospital undertook creative operational steps in relation to the control and spread of COVID-19, with special focuses on physical and administrative layout of buildings, staff training and preventative procedures. The first operational step was to set up triaging stations at all entrances and then complete a standard and qualified fever clinic, which was isolated from the other buildings within our hospital complex. Secondly, the hospital established its human resource reservation for emergency response and the allocation of human resources to ensure strict and standardised training methods through the hospital for all medical staff and ancillary employees. Thirdly, the hospital divided the fever clinic into partitioned areas and adapted a three-level triaging system. The experiences shared in this paper would be of practical help for the facilities that are encountering or will encounter the challenges of COVID-19, i.e. to prevent nosocomial cross infection among patients and physicians.

A well-known observation of Lovász is that if a hypergraph is not 2-colourable, then at least one pair of its edges intersect at a single vertex. In this short paper we consider the quantitative version of Lovász’s criterion. That is, we ask how many pairs of edges intersecting at a single vertex should belong to a non-2-colourable n-uniform hypergraph. Our main result is an exact answer to this question, which further characterizes all the extremal hypergraphs. The proof combines Bollobás’s two families theorem with Pluhar’s randomized colouring algorithm.

Diagnostic testing for the novel coronavirus is an important tool to fight the coronavirus disease (Covid-19) pandemic. However, testing capacities are limited. A modified testing protocol, whereby a number of probes are ‘pooled’ (i.e. grouped), is known to increase the capacity for testing. Here, we model pooled testing with a double-average model, which we think to be close to reality for Covid-19 testing. The optimal pool size and the effect of test errors are considered. The results show that the best pool size is three to five, under reasonable assumptions. Pool testing even reduces the number of false positives in the absence of dilution effects.

This study examines changes in individual social capital during adult life within a 19-year period. Social capital theory and life course theory are combined, and it is argued that changes in social networks do not necessarily go together with changes in social capital: while personal networks are known to decline in size with age, social capital can be expected to accumulate, in particular for those who had a better starting position and therefore more resources to share. Panel data from the survey of the social networks of the Dutch (SSND) (1999–2018) at four points of measurement are employed to inquire into this argument. Social capital is measured by the position generator instrument, and three indicators, that is, resource extensity, mean prestige access, and resource range are analyzed. Results of fixed effect models show that, on average, people maintain access to social capital, and that men and higher educated gain social capital through their life as opposed to women and lower educated. Implications for the understanding of the reproduction of social inequality are discussed. The paper concludes with a reflection upon the value of ego-centered network analysis in the era of big data and data science.

Community-based physical activity programs, such as the Recreovía, are effective in promoting healthy behaviors in Latin America. To understand Recreovías’ challenges and scalability, we characterized its social network longitudinally while studying its participants’ social cohesion and interactions. First, we constructed the Main network of the program’s Facebook profile in 2013 to determine the main stakeholders and communities of participants. Second, we studied the Temporal network growth of the Facebook profiles of three Recreovía locations from 2008 to 2016. We implemented a Time Windows in Networks algorithm to determine observation periods and a scaling model of cities’ growth to measure social cohesion over time. Our results show physical activity instructors as the main stakeholders (20.84% nodes of the network). As emerging cohesion, we found: (1) incremental growth of Facebook users (43–272 nodes), friendships (55–2565 edges), clustering coefficient (0.19–0.21), and density (0.04–0.07); (2) no preferential attachment behavior; and (3) a social cohesion super-linear growth with 1.73 new friendships per joined user. Our results underscore the physical activity instructors’ influence and the emergent cohesion in innovation periods as a co-benefit of the program. This analysis associates the social and healthy behavior dimensions of a program occurring in natural environments under a systemic approach.