Papers by Varun Madan Mohan
medRxiv, 2020
We sought to identify optimal temporal windows for lockdown-based mitigation strategies on infect... more We sought to identify optimal temporal windows for lockdown-based mitigation strategies on infectious disease spreads. An age-structured multi-compartmental Susceptible-Infected-Recovered (SIR) model was used to estimate infection spreads under parametric variation of lockdown intensity and duration from the data of SARS-CoV2 cases in India between January to July, 2020. The resulting parameter values were used to simulate lockdown outcomes for a wide range of start times and durations. Lockdowns were simulated as intervention strategies that modified weights assigned to social contact matrices for work, school and other places. Lockdown efficacy was assessed by the maximum number of infections recorded during a simulation run. Our analysis shows that lockdown outcomes depend sensitively on the timing of imposition and that it is possible to minimize lockdown durations while limiting case loads to numbers below the hospitalization thresholds. Such timing based effects arise naturall...
Network Neuroscience
How communication among neuronal ensembles shapes functional brain dynamics is a question of fund... more How communication among neuronal ensembles shapes functional brain dynamics is a question of fundamental importance to Neuroscience. Communication in the brain can be viewed as a product of the interaction of node activities with the structural network over which these activities flow. The study of these interactions is however restricted by the difficulties in describing the complex dynamics of the brain. There is thus a need to develop methods to study these network-dynamical interactions and how they impact information flow, without having to ascertain dynamics a priori or resort to restrictive analytical approaches. Here, we identify the potential of a recently established network perturbative formalism for such investigations, and adapt it to a neuroscientific setting to study information flow in the brain. For proof-of-concept, we apply the approach on in silico whole-brain models. We expound on the functional implications of the distributions of metrics that capture network-d...
A scale-invariant perturbative approach to study information communication in dynamic brain networks
How communication among neuronal ensembles shapes functional brain dynamics at the large scale is... more How communication among neuronal ensembles shapes functional brain dynamics at the large scale is a question of fundamental importance to Neuroscience. To date, researchers have primarily relied on two alternative ways to address this issue 1) in-silico neurodynamical modelling of functional brain dynamics by choosing biophysically inspired non-linear systems, interacting via a connection topology driven by empirical data; and 2) identifying topological measures to quantify network structure and studying them in tandem with functional metrics of interest, e.g. co-variation of time series in brain regions from fast (EEG/ MEG) and slow (fMRI) timescales. While the modelling approaches are limited in scope to only scales of the nervous system for which dynamical models are well defined, the latter approach does not take into account how the network architecture and intrinsic regional node dynamics contribute together to inter-regional communication in the brain. Thus, developing a gene...
Lockdowns are disease mitigation strategies that aim to contain the spread of an infection by res... more Lockdowns are disease mitigation strategies that aim to contain the spread of an infection by restricting the interactions of its carriers. Lockdowns can thus have a considerable economic cost, which makes the identification of optimal lockdown windows that minimize both infection spread and economic disruption imperative. A well-known feature of complex dynamical systems is their sensitivity to the timing of external inputs. Hence, we hypothesized that the timing and duration of lockdowns should dictate lockdown outcomes. We demonstrate this concept computationally from two perspectives - Firstly, a stochastic "small-scale" Agent Based Model (ABM) of a Susceptible-Infected-Recovered (SIR) disease spread and secondly, a deterministic "large-scale" perspective using a multi-group SIR mass model with parameters determined from the SARS-CoV2 data in India. Lockdowns were implemented as an effective reduction of interaction probabilities in both models. This allowed ...
We sought to simulate lockdown scenarios using an Agent Based Modelling (ABM) strategy, which is ... more We sought to simulate lockdown scenarios using an Agent Based Modelling (ABM) strategy, which is a new modelling paradigm that seeks to simulate the actions and interactions of autonomous agents within an environment. The spread of infectious viral diseases occur over a connected social network. Specifically, the goal was to understand the effect of network topology and lockdown strategies on disease spreading dynamics. To explore the effect of topology we assumed the social network over which the disease spreads to have small-world or scale-free properties characterized by a rewiring probability and degree distribution respectively. Lockdowns were simulated as intervention strategies that modified the spreading dynamics of infection over a given graph structure through changes in properties of agent interaction. Lockdown efficacy was assessed by the maximum number of infections recorded during a simulation run. Thereafter, lockdown efficacy was evaluated as a function of lockdown s...
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Papers by Varun Madan Mohan