pwc-archive/papers-with-abstracts huggingface dataset:
Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-prefecture Study in Japan
Date: 2020-07-21 00:00:00

This study analyzed the spread and decay durations of the COVID-19 pandemic
in different prefectures of Japan. During the pandemic, affordable healthcare
was widely available in Japan and the medical system did not suffer a collapse,
making accurate comparisons between prefectures possible. For the 16
prefectures included in this study that had daily maximum confirmed cases
exceeding ten, the number of daily confirmed cases follow bell-shape or
log-normal distribution in most prefectures. A good correlation was observed
between the spread and decay durations. However, some exceptions were observed
in areas where travelers returned from foreign countries, which were defined as
the origins of infection clusters. Excluding these prefectures, the population
density was shown to be a major factor affecting the spread and decay patterns,
with R2=0.39 (p<0.05) and 0.42 (p<0.05), respectively, approximately
corresponding to social distancing. The maximum absolute humidity was found to
affect the decay duration normalized by the population density (R2>0.36, p
<0.05). Our findings indicate that the estimated pandemic spread duration,
based on the multivariate analysis of maximum absolute humidity, ambient
temperature, and population density (adjusted R2=0.53, p-value<0.05), could
prove useful for intervention planning during potential future pandemics,
including a second COVID-19 outbreak.
A reproduction rate which perfectly fits Covid-19
Date: 2020-05-27 00:00:00
Tasks: Time Series, Time Series Analysis

We present a simple technique to compare the development of the Covid-19
epidemic in different regions, based only on the time series of confirmed
cases. Weekly new infections, taken for every day, are interpreted as infection
potential of Covid-19. We derive a robust time-varying reproduction rate for
the infection potential, including asymptomatic cases, which does not depend on
death rate or testing intensity. It requires few assumptions and shows a more
plausible time course than official reproduction rates in several countries.
Within host dynamics of SARS-CoV-2 in humans: Modeling immune responses and antiviral treatments
Authors: Indrajit Ghosh
Date: 2020-06-03 00:00:00
Tasks: Blocking

In December 2019, a newly discovered SARS-CoV-2 virus was emerged from China and propagated worldwide as a pandemic. In the absence of preventive medicine or a ready to use vaccine, mathematical models can provide useful scientific insights about transmission patterns and targets for drug development. In this study, we propose a within-host mathematical model of SARS-CoV-2 infection considering innate and adaptive immune responses. We analyze the equilibrium points of the proposed model and obtain an expression of the basic reproduction number. We then numerically show the existence of a transcritical bifurcation. The proposed model is calibrated to real viral load data of two COVID-19 patients. Using the estimated parameters, we perform global sensitivity analysis with respect to the peak of viral load. Finally, we study the efficacy of antiviral drugs and vaccination on the dynamics of SARS-CoV-2 infection. Our results suggest that blocking the production of the virus by infected cells decreases the viral load more than reducing the infection rate of healthy cells. Vaccination is also found useful but during the vaccine development phase, blocking virus production from infected cells can be targeted for antiviral drug development.
Dynamics of a cell motility model near the sharp interface limit
Authors: Nicolas Bolle, Matthew S. Mizuhara
Date: 2020-06-04 00:00:00

Phase-field models have recently had great success in describing the dynamic morphologies and motility of eukaryotic cells. In this work we investigate the minimal phase-field model introduced in [Berlyand, Potomkin, Rybalko (2017)]. Rigorous analysis of its sharp interface limit dynamics was completed in [Mizuhara, Berlyand, Rybalko, Zhang (2016); Mizuhara, Zhang (2019)], where it was observed that persistent cell motion was not stable. In this work we numerically study the pre-limiting phase-field model near the sharp interface limit, to better understand this lack of persistent motion. We find that immobile, persistent, and rotating states are all exhibited in this minimal model, and investigate the loss of persistent motion in the sharp interface limit. In addition we study cell speed as a function of biophysical parameters.
SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19
Authors: Giulia Fiscon, Federica Conte, Lorenzo Farina, Paola Paci
Date: 2020-06-04 00:00:00
Tasks: Drug Discovery

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity, comorbidity, or for their association to drugs tentatively repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs, actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies, and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.
Is there a wave excitation in the Thalamus?
Date: 2020-05-20 00:00:00
Tasks: Anatomy, Bayesian Inference

This paper proposes that the thalamus is the site of a wave excitation, whose
function is to represent the locations of things around the animal. Neurons
couple to the wave as transmitters and receivers. The wave acts as an analogue
representation of local space. This has benefits over a purely neural
representation of space. Several lines of evidence support this hypothesis;
both theoretical, concerning efficient Bayesian inference in the brain, and
empirical, concerning the neuro-anatomy of the thalamus. Across all species,
the most basic function of the brain is to coordinate movements in space. To
represent positions in space only by neural firing rates would be complex and
inefficient. It is possible that that the brain represents 3D space in a direct
and natural way, by a 3D wave
Evolutionary Matrix-Game Dynamics Under Imitation in Heterogeneous Populations
Authors: Yiheng Fu, Pouria Ramazi
Date: 2020-06-04 00:00:00
Tasks: Decision Making

Decision-making individuals often imitate their highest-earning fellows rather than optimize their own utilities, due to bounded rationality and incomplete information. Perpetual fluctuations between decisions have been reported as the dominant asymptotic outcome of imitative behaviors, yet little attempt has been made to characterize them, particularly in heterogeneous populations. We study a finite well-mixed heterogeneous population of individuals choosing between the two strategies, cooperation and defection, and earning based on their payoff matrices that can be unique to each individual. At each time step, an arbitrary individual becomes active to update her decision by imitating the highest earner in the population. We show that almost surely the dynamics reach either an equilibrium state or a minimal positively invariant set, a fluctuation set, in the long run. In addition to finding all equilibria, for the first time, we characterize the fluctuation sets, provide necessary and sufficient conditions for their existence, and approximate their basins of attraction. We also find that exclusive populations of individuals playing coordination or prisoner's dilemma games always equilibrate, implying that cycles and non-convergence in imitative populations are due to individuals playing anticoordination games. Moreover, we show that except for the two extreme equilibria where all individuals play the same strategy, almost all other equilibria are unstable as long as the population is heterogeneous. Our results theoretically explain earlier reported simulation results and shed new light on the boundedly rational nature of imitation behaviors.
Capital and Labor Income Pareto Exponents across Time and Space
Authors: Tjeerd de Vries, Alexis Akira Toda
Date: 2020-06-03 00:00:00

We estimate capital and labor income Pareto exponents across 475 country-year observations that span 52 countries over half a century (1967-2018). We document two stylized facts: (i) capital income is more unequally distributed than labor income in the tail; namely, the capital exponent (1-3, median 1.46) is smaller than labor (2-5, median 3.35), and (ii) capital and labor exponents are nearly uncorrelated. To explain these findings, we build an incomplete market model with job ladders and capital income risk that gives rise to a capital income Pareto exponent smaller than but nearly unrelated to the labor exponent. Our results suggest the importance of distinguishing income and wealth inequality.
Solar UV$-$B$/$A radiation is highly effective in inactivating SARS$-$CoV$-$2
Authors: F. Nicastro, G. Sironi, E. Antonello, A. Bianco, M. Biasin, J. R. Brucato, I. Ermolli, G. Pareschi, M. Salvati, P. Tozzi, D. Trabattoni, M. Clerici
Date: 2020-06-03 00:00:00
Tasks: Time Series Analysis

Solar UV$-$C photons do not reach Earth's surface, but are known to be endowed with germicidal properties that are also effective on viruses. The effect of softer UV$-$B and UV$-$A photons, which copiously reach the Earth's surface, on viruses are instead little studied, particularly on single$-$stranded RNA viruses. Here we combine our measurements of the action spectrum of Covid$-$19 in response to UV light, Solar irradiation measurements on Earth during the SARS$-$CoV$-$2 pandemics, worldwide recorded Covid$-$19 mortality data and our 'Solar$-$Pump' diffusive model of epidemics to show that (a) UV$-$B$/$A photons have a powerful virucidal effect on the single$-$stranded RNA virus Covid$-$19 and that (b) the Solar radiation that reaches temperate regions of the Earth at noon during summers, is sufficient to inactivate 63\perc of virions in open$-$space concentrations (1.5 x 103 TCID50$/$mL, higher than typical aerosol) in less than 2 min. We conclude that the characteristic seasonality imprint displayed world$-$wide by the SARS$-$Cov$-$2 mortality time$-$series throughout the diffusion of the outbreak (with temperate regions showing clear seasonal trends and equatorial regions suffering, on average, a systematically lower mortality), might have been efficiently set by the different intensity of UV$-$B$/$A Solar radiation hitting different Earth's locations at different times of the year. Our results suggest that Solar UV$-$B$/$A play an important role in planning strategies of confinement of the epidemics, which should be worked out and set up during spring$/$summer months and fully implemented during low$-$solar$-$irradiation periods.
Linear Quadratic Graphon Field Games
Date: 2020-09-30 00:00:00

Linear quadratic graphon field games (LQ-GFGs) are defined to be LQ games
which involve a large number of agents that are weakly coupled via a weighted
undirected graph on which each node represents an agent. The links of the graph
correspond to couplings between the agents' dynamics, as well as between the
individual cost functions, which each agent attempts to minimize. We formulate
limit LQ-GFG problems based on the assumption that these graphs lie in a
sequence which converges to a limit graphon. First, under a finite-rank
assumption on the limit graphon, the existence and uniqueness of solutions to
the formulated limit LQ-GFG problem is established. Second, based upon the
solutions to the limit LQ-GFG problem, epsilon-Nash equilibria are constructed
for the corresponding game problems with a very large but finite number of
players. This result is then generalized to the case with random initial
conditions. It is to be noted that LQ-GFG problems are distinct from the class
of graphon mean field game (GMFG) problems where a population is hypothesized
to be associated with each node of the graph [Caines and Huang CDC 2018, 2019].
Joint Sensor Node Selection and State Estimation for Nonlinear Networks and Systems
Authors: Aleksandar Haber
Date: 2020-06-08 00:00:00
Tasks: State Estimation

State estimation and sensor selection problems for nonlinear networks and systems are ubiquitous problems that are important for the control, monitoring, analysis, and prediction of a large number of engineered and physical systems. Sensor selection problems are extensively studied for linear networks. However, less attention has been dedicated to networks with nonlinear dynamics. Furthermore, widely used sensor selection methods relying on structural (graph-based) observability approaches might produce far from optimal results when applied to nonlinear network dynamics. In addition, state estimation and sensor selection problems are often treated separately, and this might decrease the overall estimation performance. To address these challenges, we develop a novel methodology for selecting sensor nodes for networks with nonlinear dynamics. Our main idea is to incorporate the sensor selection problem into an initial state estimation problem. The resulting mixed-integer nonlinear optimization problem is approximately solved using three methods. The good numerical performance of our approach is demonstrated by testing the algorithms on prototypical Duffing oscillator, associative memory, and chemical reaction networks. The developed codes are available online.
Assessing the Impact of COVID-19 on the Objective and Analysis of Oncology Clinical Trials -- Application of the Estimand Framework
Authors: Evgeny Degtyarev, Kaspar Rufibach, Yue Shentu, Godwin Yung, Michelle Casey, Stefan Englert, Feng Liu, Yi Liu, Oliver Sailer, Jonathan Siegel, Steven Sun, Rui Tang, Jiangxiu Zhou
Date: 2020-06-08 00:00:00
Tasks: valid

COVID-19 outbreak has rapidly evolved into a global pandemic. The impact of COVID-19 on patient journeys in oncology represents a new risk to interpretation of trial results and its broad applicability for future clinical practice. We identify key intercurrent events that may occur due to COVID-19 in oncology clinical trials with a focus on time-to-event endpoints and discuss considerations pertaining to the other estimand attributes introduced in the ICH E9 addendum. We propose strategies to handle COVID-19 related intercurrent events, depending on their relationship with malignancy and treatment and the interpretability of data after them. We argue that the clinical trial objective from a world without COVID-19 pandemic remains valid. The estimand framework provides a common language to discuss the impact of COVID-19 in a structured and transparent manner. This demonstrates that the applicability of the framework may even go beyond what it was initially intended for.
Dynamic Network Risk
Authors: Jozef Barunik, Michael Ellington
Date: 2020-06-08 00:00:00

This paper examines the pricing of short-term and long-term dynamic network risk in the cross-section of stock returns. Stocks with high sensitivities to dynamic network risk earn lower returns. We rationalize our finding with economic theory that allows the stochastic discount factor to load on network risk through the precautionary savings channel. A one-standard deviation increase in long-term (short-term) network risk loadings associate with a 7.66% (6.71%) drop in annualized expected returns.
Autooscillatory Dynamics in a Mathematical Model of the Metabolic Process in Aerobic Bacteria. Influence of the Krebs Cycle on the Self-Organization of a Biosystem
Authors: V. I. Grytsay, A. G. Medentsev, A. Yu. Arinbasarova
Date: 2020-05-28 00:00:00

We have modeled the metabolic process running in aerobic cells as open nonlinear dissipative systems. The map of metabolic paths and the general scheme of a dissipative system participating in the transformation of steroids are constructed. We have studied the influence of the Krebs cycle on the dynamics of the whole metabolic process and constructed projections of the phase portrait in the strange attractor mode. The total spectra of Lyapunov exponents, divergences, Lyapunov's dimensions of the fractality, Kolmogorov--Sinai entropies, and predictability horizons for the given modes are calculated. We have determined the bifurcation diagram presenting the dependence of the dynamics on a small parameter, which defines system's physical state.
Duality for optimal consumption under no unbounded profit with bounded risk
Authors: Michael Monoyios
Date: 2020-06-08 00:00:00

We give a definitive treatment of duality for optimal consumption over the infinite horizon, in a semimartingale incomplete market satisfying no unbounded profit with bounded risk (NUPBR). Rather than base the dual domain on (local) martingale deflators, we use a class of supermartingale deflators such that deflated wealth plus cumulative deflated consumption is a supermartingale for all admissible consumption plans. This yields a strong duality, because the enlarged dual domain of processes dominated by deflators is naturally closed, without invoking its closure. In this way we automatically reach the bipolar of the set of deflators. We complete this picture by proving that the set of processes dominated by local martingale deflators is dense in our dual domain, confirming that we have identified the natural dual space. In addition to the optimal consumption and deflator, we characterise the optimal wealth process. At the optimum, deflated wealth is a supermartingale and a potential, while deflated wealth plus cumulative deflated consumption is a uniformly integrable martingale. This is the natural generalisation of the corresponding feature in the terminal wealth problem, where deflated wealth at the optimum is a uniformly integrable martingale. We use no constructions involving equivalent local martingale measures. This is natural, given that such measures typically do not exist over the infinite horizon and that we are working under NUPBR, which does not require their existence. The structure of the duality proof reveals an interesting feature compared with the terminal wealth problem. There, the dual domain is $L^{1}$-bounded, but here the primal domain has this property, and hence many steps in the duality proof show a marked reversal of roles for the primal and dual domains, compared with the proofs of Kramkov and Schachermayer.
One-Bit Symbol-Level Precoding for MU-MISO Downlink with Intelligent Reflecting Surface
Authors: Silei Wang, Qiang Li, Mingjie Shao
Date: 2020-06-08 00:00:00

This paper considers symbol-level precoding (SLP) for multiuser multi-input single-output (MISO) downlink transmission with the aid of intelligent reflecting surface (IRS). Specifically, by assuming one-bit transmitted signals at the base station (BS), which arises from the use of low-resolution DACs in the regime of massive transmit antennas, a joint design of one-bit SLP at the BS and the phase shifts at the IRS is proposed with a goal of minimizing the worst-case symbol error probability (SEP) of the users under the PSK modulation. This joint design problem is essentially a mixed integer nonlinear program (MINLP). To tackle it, we alternately optimize the one-bit signal and the phase shifts. For the former, a dual of the relaxed one-bit SLP problem is solved by the mirror descent (MD) method with the maximum block improvement (MBI) heuristics. For the latter, the accelerated projected gradient (APG) method is employed to optimize the phases. Numerical results demonstrate that the proposed joint design can attain better SEP performance than the conventional linear precoding and one-bit SLP.
User Cooperation for IRS-aided Secure SWIPT MIMO Systems
Authors: Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Kok Keong Chai, Kai-Kit Wong
Date: 2020-06-09 00:00:00

In this paper, intelligent reflecting surface (IRS) is proposed to enhance the physical layer security in the Rician fading channel where the angular direction of the eavesdropper is aligned with a legitimate user. In this scenario, we consider a two-phase communication system under the active attacks and passive eavesdropping. Particularly, in the first phase, the base station avoids direct transmission to the attacked user. While, in the second phase, other users cooperate to forward signals to the attacked user with the help of IRS and energy harvesting technology. Under the active attacks, we investigate an outage constrained beamforming design problem under the statistical cascaded channel error model, which is solved by using the Bernstein-type inequality. As for the passive eavesdropping, an average secrecy rate maximization problem is formulated, which is addressed by a low complexity algorithm. Numerical results show that the negative effect of the eavesdropper's channel error is greater than that of the legitimate user.
BCG vaccination in infancy does not protect against COVID-19. Evidence from a natural experiment in Sweden
Date: 2020-06-22 00:00:00

The Bacille Calmette-Gu\'erin (BCG) tuberculosis vaccine has immunity
benefits against respiratory infections. Accordingly, it has been hypothesized
that it may have a protective effect against COVID-19. Recent research found
that countries with universal Bacillus Calmette-Gu\'erin (BCG) childhood
vaccination policies tend to be less affected by the COVID-19 pandemic.
However, such ecological studies are biased by numerous confounders. Instead,
this paper takes advantage of a rare nationwide natural experiment that took
place in Sweden in 1975, where discontinuation of newborns BCG vaccination led
to a dramatic fall of the BCG coverage rate from 92% to 2% , thus allowing us
to estimate the BCG's effect without all the biases associated with
cross-country comparisons. Numbers of COVID-19 cases and hospitalizations were
recorded for birth cohorts born just before and just after that change,
representing 1,026,304 and 1,018,544 individuals, respectively. We used
regression discontinuity to assess the effect of BCG vaccination on Covid-19
related outcomes. This method used on such a large population allows for a high
precision that would be hard to achieve using a classical randomized controlled
trial. The odds ratio for Covid-19 cases and Covid-19 related hospitalizations
were 0.9997 (CI95: [0.8002-1.1992]) and 1.1931 (CI95: [0.7558-1.6304]),
respectively. We can thus reject with 95\% confidence that universal BCG
vaccination reduces the number of cases by more than 20% and the number of
hospitalizations by more than 24%. While the effect of a recent vaccination
must be evaluated, we provide strong evidence that receiving the BCG vaccine at
birth does not have a protective effect against COVID-19.