Talks

Abstract Due to noisy data and nonlinear dynamics, even simple stochastic epidemic models such as the Susceptible-Infectious-Removed (SIR) present significant challenges to inference. In particular, computing the marginal likelihood of such stochastic processes conditioned on observed endpoints a notoriously difficult task. As a result, likelihood-based inference is typically considered intractable in missing data settings typical of observational data, and practitioners often resort to intensive simulation methods or approximations. We discuss recent contributions that enable "exact" inference, focusing on a perspective that makes use of latent variables to explore configurations of the missing data within a Markov chain Monte Carlo framework. Motivated both by count data from large outbreaks and high-resolution contact data from mobile health studies, we show how our data-augmented approach successfully learns the interpretable epidemic parameters and scales to handle large realistic data settings efficiently.

Abstract In this talk, we present a modeling framework to study the effects of testing policy on voluntary social distancing and the spread of an infection. Agents decide their social activity level, which determines the social network over which the virus spreads. Testing enables the isolation of infected individuals, slowing down the infection. But greater testing also reduces voluntary social distancing or increases social activity, exacerbating the spread of the virus. We show that the effect of testing on infections is non-monotone. This non-monotonicity also implies that the optimal testing policy may leave some of the testing capacity of society unused. This also implies that testing should be combined with mandatory social distancing measures to avoid these adverse behavioral effects.

We revisit the problem of how interactions emerge in quantum gravity. Namely, we show that bulk scattering of multiple particles in the AdS space requires multipartite entanglement on the boundary. This statement can be proven by two totally different methods, 1) general relativity and 2) quantum cryptographic argument. Furthermore, we argue that interactions among particles in the scattering event emerge from the mechanism of entanglement-assisted quantum error-correcting codes (EAQECCs) which utilize pre-existing multipartite entanglement in CFT. We also propose a concrete protocol to implement a certain class of multi-partite unitary interactions by using transversal logical operators of quantum codes. This talk is based on a (very) recent work with Alex May and Jonathan Sorce.

Zoom Link: https://pitp.zoom.us/j/91349028320?pwd=TGF2Q2ZNdTZtZGxkQ0NiMURLdW5Zdz09

A complex contagion is an infectious process in which individuals may require multiple transmissions before changing state. These are used to model behaviours if an individual only adopts a particular behaviour after perceiving a consensus among others. We may think of individuals as beginning inactive and becoming active once they are contacted by a sufficient number of active partners.  Here we study the dynamics of the Watts threshold model (WTM).  We adapt techniques developed for infectious disease modelling to develop an analyse analytic models for the dynamics of the WTM in configuration model networks and a class of random clustered (triangle-based) networks.  We derive conditions under which cascades happen with an arbitrarily small initial proportion active. We also observe hybrid phase transitions when cascades are not possible for small initial conditions, but occur for large enough initial conditions.

Abstract In the talk I will briefly outline the idea of the so-called dynamical survival analysis (DSA) which uses survival analysis methods to build approximate models of individual level epidemic dynamics by utilizing some well known mean-field approximations. I will show the DSA connection with classical agent based models for epidemics and also some frailty models that have been successfully applied to recent COVID-19 epidemic.  

The phase diagram of a material is of central importance in describing the properties and behaviour of a condensed matter system. Indeed, the study of quantum phase transitions has formed a central part of 20th and 21st Century physics. We examine the complexity and computability of determining the phase diagram of a general Hamiltonian. We show that in the worst case it is uncomputable and in more restricted cases, where the Hamiltonian is “better behaved”, it remains computationally intractable even for a quantum computer. Finally, we take a look at the relations between the Renormalization Group and uncomputable Hamiltonians.

Zoom Link: https://pitp.zoom.us/j/96048987715?pwd=WGtwWk1SUnFsanNIVTZVYjNmbTh3Zz09

Abstract The dynamics of an infectious disease is usually approached at the population scale. However, the event of an epidemic outbreak depends on the existence of an active infection at the level of the  individual. A full study of the interaction between the infection of hosts and its transmission in the population requires the incorporation of many factors such as physiological age, age of infection, risk conditions, contact structures and other variables involving different spatial and temporal scales. Nevertheless, simple models can still give some insight on the intricate mechanisms of interactions necessary for the occurrence of an epidemic outbreak, in particular, one can explore the role that the reproductive numbers at the between-host and within-host levels play. In this talk I will review some results on the epidemiology of between-host, within host interactions.

Abstract We will discuss some adaptations of the standard epidemic models to incorporate various kinds of restrictions on the interaction between susceptible and infected individuals and study the effect of such restrictions on the prognosis of an epidemic. In one case, we study the effect of avoiding known infected neighbors on the persistence of a recurring infection process. In another case, we develop a flexible mathematical framework for pool-testing and badging protocol in the context of controlling contagious epidemics and tackling the far-reaching associated challenges, including understanding and evaluating individual and collective risks of returning prior infected individuals to normal society and other economic and social arrangements and interventions to protect against disease.

Abstract TBA

Zoom link:  https://pitp.zoom.us/j/91804523922?pwd=M0NBa21NVklLUjBiY2pPR1ExdXZxQT09

Abstract The novel coronavirus that emerged in December 2019, COVID-19, is the greatest public health challenge humans have faced since the 1918 influenza pandemic (it has so far caused over 615 million confirmed cases and 6.5 million deaths). In this talk, I will present some mathematical models for assessing the population-level impact of the various intervention strategies (pharmaceutical and non-pharmaceutical) being used to control and mitigate the burden of the pandemic. Continued human interference with the natural ecosystems, such as through anthropogenic climate change, environmental degradation, and land use changes, make us increasingly vulnerable to the emergence, re-emergence and resurgence of infectious diseases (particularly respiratory pathogens with pandemic potential). I will discuss some of the lessons learned from our COVID-19 modeling studies and propose ways to mitigate the next respiratory pandemic.