SUMMARYWe propose a Bayesian phase I/II dose-finding trial design that simultaneously accounts for toxicity and efficacy. We model the toxicity and efficacy of investigational doses using a flexible Bayesian dynamic model, which borrows information across doses without imposing stringent parametric assumptions on the shape of the dose-toxicity and dose-efficacy curves. An intuitive utility function that reflects the desirability trade-offs between efficacy and toxicity is used to guide the dose assignment and selection. We also discuss the extension of this design to handle delayed toxicity and efficacy. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. The results show that the proposed design possesses good operating characteristics and is robust to the shape of the dose-toxicity and dose-efficacy curves.

This is the unspecified version of the paper.This version of the publication may differ from the final published version. The metapopulation framework is adopted in a wide array of disciplines to describe systems of well separated yet connected subpopulations. The subgroups/patches are often represented as nodes in a network whose links represent the migration routes among them. The connections has been so far mostly considered as static, but in general evolve in time. Here we address this case by investigating simple contagion processes on time-varying metapopulation networks. We focus on the SIR process, and determine analytically the mobility threshold for the onset of an epidemic spreading in the framework of activity-driven network models. We find profound differences from the case of static networks. The threshold is entirely described by the dynamical parameters defining the average number of instantaneously migrating individuals, and does not depend on the properties of the static network representation. Remarkably, the diffusion and contagion processes are slower in time-varying graphs than in their aggregated static counterparts, the mobility threshold been even two orders of magnitude larger in the first case. The presented results confirm the importance of considering the time-varying nature of complex networks. Many natural and artificial networks evolve in time, with elements that appear, disappear and incessantly reshape their interaction patterns. However, the temporal dimension associated to their units and connections has started to become accessible only very recently [1], thanks to the increasing availability of empirical data [2][3][4][5][6][7][8][9][10][11]. Remarkably, the first analyses of dynamical processes unfolding on these empirical networks have shown that the full consideration of the time-varying patterns is responsible for a specific phenomenology, clearly distinct from the one observed on their time-aggregated counterparts. In particular, the temporal sequence of links and their concurrency play a crucial role when the time scale of the processes, τ DP , is comparable to the time scale of the network, τ G [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. This is the case, for example, of the spreading of sexual transmitted diseases in a population, ideas in scientific communities, and memes in social networks. Permanent repository link

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Background It is widely perceived that COVID-19 has significant influence on higher education and also contribution to development including Sustainable Development Goals (SDGs). However there is insufficient evidence about investigations on such influences, especially at micro level. Design and method A university located in Wuhan, China, was selected for the case study to explore how COVID-19 affects higher education and how universities’ coping strategies of COVID-19 can contribute to SDGs. The method is an analysis of 32 institutional documents published by the university. Results The university in the case study has taken a number of coping strategies of COVID-19, largely in four aspects including medical services, online education, logistic support, and graduate employment promotion. These coping strategies contribute to achieving SDGs, especially SDGs 1, 3, 4, 5, 8, and 10. Conclusions The case study provides micro-level empirical evidence, which supports that appropriate university coping strategies of COVID-19 can contribute to SDGs, even it is widely perceived that the pandemic has brought strong negative impact on higher education and sustainable development. The selection of a university in Wuhan, China can generate more practical implications, as Wuhan is the first city that experienced the unprecedented lockdown, and China is the first country which reopened university campuses after the lockdown.