An agent based modelling approach to study lockdown efficacy for infectious disease spreads

  1. Anagh Pathak
  2. Varun Madan Mohan
  3. Arpan Banerjee

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Abstract

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 start times and duration. Thus, we propose that ABM approach can be used to assess various lockdown strategies that aim to prevent breakdown of medical infrastructure while accounting for realistic social network configurations specific to a local population.

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    SciScore for 10.1101/2020.06.22.20137828: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    An important caveat of our approach is an analytical solution to optimal lockdown remains out of scope. For example, ODE-based models can also be tweaked by introducing time-dependent infection rates [Rachel, 2020, Hethcote, 2000], to genereate analytically tractable lockdown windows. However, we would like to reemphasize, a network-motivated interventions are not possible within the ODE-framework. Whether carefully selected topological modifications such as dynamic changes in network properties during lockdown period could lead to a widening or deepening of optimal lockdown windows would be an interesting avenue for future mathematical research. In a similar vein, it may be worthwhile to add even greater details to the interaction landscape such as by incorporating precise city-specific details such as population densities, age-distributions and features of transport network etc[Harsha et al., 2020]. Role of topology: An important observation from our analysis was that the small-world networks seems to be worse affected by disease spreads in terms of MFII. However, the effect of lockdown on both the networks is almost identical. The reason for this result can be arrived at by looking at the parameters that affect disease transmission in a network in relation to the lockdown strategy employed. In this study, the lockdown is an intervention which alters the usual course of the epidemic by abruptly reducing the probability of infection spread by restricting the interactions of ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 12, 13, 14, 15 and 16. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.06.22.20137828v1 on medRxiv