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Review
. 2020;100(4):809-847.
doi: 10.1007/s41745-020-00211-3. Epub 2020 Nov 12.

City-Scale Agent-Based Simulators for the Study of Non-pharmaceutical Interventions in the Context of the COVID-19 Epidemic: IISc-TIFR COVID-19 City-Scale Simulation Team

Shubhada Agrawal  1 , Siddharth Bhandari  1 , Anirban Bhattacharjee  1 , Anand Deo  1 , Narendra M Dixit  2 , Prahladh Harsha  1 , Sandeep Juneja  1 , Poonam Kesarwani  1 , Aditya Krishna Swamy  2 , Preetam Patil  2 , Nihesh Rathod  2 , Ramprasad Saptharishi  1 , Sharad Shriram  2 , Piyush Srivastava  1 , Rajesh Sundaresan  2 , Nidhin Koshy Vaidhiyan  2 , Sarath Yasodharan  2
Affiliations
Review

City-Scale Agent-Based Simulators for the Study of Non-pharmaceutical Interventions in the Context of the COVID-19 Epidemic: IISc-TIFR COVID-19 City-Scale Simulation Team

Shubhada Agrawal et al. J Indian Inst Sci. 2020.

Abstract

We highlight the usefulness of city-scale agent-based simulators in studying various non-pharmaceutical interventions to manage an evolving pandemic. We ground our studies in the context of the COVID-19 pandemic and demonstrate the power of the simulator via several exploratory case studies in two metropolises, Bengaluru and Mumbai. Such tools may in time become a common-place item in the tool kit of the administrative authorities of large cities.

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Figures

Figure 1:
Figure 1:
Timeline of COVID-19 cases, recoveries, and fatalities in India taken from. See and for detailed information on how COVID-19 progressed in India.
Figure 2:
Figure 2:
Schematic representation of an agent-based model.
Figure 3:
Figure 3:
A timeline of Bengaluru interventions.
Figure 4:
Figure 4:
Wards are contained in a ‘soft’ way. The mobility is gradually decreased based on the signal of number of hospitalised cases in the ward. When 1 in 1000 in the ward is hospitalised, a local lockdown comes into effect.
Figure 5:
Figure 5:
Bengaluru daily positive cases estimation. The red bars are the reported cases. The five shaded regions between 14 March and 01 June represent the durations of the various lockdown phases. The shaded region around 15 July represents a short 1-week lockdown. For cumulative case plots, see Fig. 11.
Figure 6:
Figure 6:
Bengaluru daily fatalities’ estimation. For cumulative fatalities, see Fig. 12 in a later case study.
Figure 7:
Figure 7:
Mumbai daily positive cases’ estimation. The five shaded regions between 16 March and 01 June represent the durations of the various lockdown phases in Mumbai. For cumulative case plots, see Fig. 21.
Figure 8:
Figure 8:
Mumbai daily fatalities’ estimation vs. corrected fatality time series, as corrected by BMC on 18 June 2020. For cumulative fatalities, see Fig. 22 in a later case study.
Figure 9:
Figure 9:
Case study A, Sect. 3.1: Bengaluru daily cases’ estimation. For a magnified view of the lower part of the plot, see Fig. 5. The no-intervention situation would have overwhelmed the healthcare system many times over.
Figure 10:
Figure 10:
Case study A, Sect. 3.1: Bengaluru daily fatalities’ estimation. For a magnified view of the lower part of the part, see Fig. 6.
Figure 11:
Figure 11:
Case study A, Sect. 3.1: Bengaluru cumulative cases’ estimation.
Figure 12:
Figure 12:
Case study A, Sect. 3.1: Bengaluru cumulative fatalities’ estimation.
Figure 13:
Figure 13:
Case study A, Sect. 3.1: Bengaluru hospital beds’ estimation. ‘Hospital Beds’ refers to the number of beds occupied for regular care including possibly oxygen support. ‘ICU Beds’ refers to those that need intensive care or ventilation. The no-intervention scenario would have overwhelmed Bengaluru’s healthcare system.
Figure 14:
Figure 14:
Case study B, Sect. 3.2: Bengaluru daily cases’ estimation.
Figure 15:
Figure 15:
Case study B, Sect. 3.2: Bengaluru daily fatalities’ estimation.
Figure 16:
Figure 16:
Case study B, Sect. 3.2: Bengaluru cumulative cases’ estimation.
Figure 17:
Figure 17:
Case study B, Sect. 3.2: Bengaluru cumulative fatalities’ estimation.
Figure 18:
Figure 18:
Case study B, Sect. 3.2: Bengaluru hospital beds’ estimation.
Figure 19:
Figure 19:
Case study C, Sect. 3.3: Mumbai daily cases’ estimation.
Figure 20:
Figure 20:
Case study C, Sect. 3.3: Mumbai daily fatalities’ estimation.
Figure 21:
Figure 21:
Case study C, Sect. 3.3: Mumbai cumulative cases’ estimation.
Figure 22:
Figure 22:
Case study C, Sect. 3.3: Mumbai cumulative fatalities’ estimation.
Figure 23:
Figure 23:
Case study C, Sect. 3.3: Mumbai hospital beds’ estimation.
Figure 24:
Figure 24:
Case study D, Sect. 3.4: Bengaluru daily cases’ estimation.
Figure 25:
Figure 25:
Case study D, Sect. 3.4: Bengaluru daily fatalities’ estimation.
Figure 26:
Figure 26:
Case study D, Sect. 3.4: Bengaluru cumulative cases’ estimation.
Figure 27:
Figure 27:
Case study D, Sect. 3.4: Bengaluru cumulative fatalities’ estimation.
Figure 28:
Figure 28:
Case study D, Sect. 3.4: Bengaluru hospital beds’ estimation.
Figure 29:
Figure 29:
Case study E, Sect. 3.5: Mumbai daily cases’ estimation.
Figure 30:
Figure 30:
Case study E, Sect. 3.5: Mumbai daily fatalities’ estimation.
Figure 31:
Figure 31:
Case study E, Sect. 3.5: Mumbai cumulative cases’ estimation.
Figure 32:
Figure 32:
Case study E, Sect. 3.5: Mumbai cumulative fatalities’ estimation.
Figure 33:
Figure 33:
Case study E, Sect. 3.5: Mumbai hospital beds’ estimation.
Figure 34:
Figure 34:
Case study F, Sect. 3.6: Bengaluru daily cases’ estimation.
Figure 35:
Figure 35:
Case study F, Sect. 3.6: Bengaluru daily fatalities’ estimation.
Figure 36:
Figure 36:
Case study F, Sect. 3.6: Bengaluru cumulative cases’ estimation.
Figure 37:
Figure 37:
Case study F, Sect. 3.6: Bengaluru cumulative fatalities’ estimation.
Figure 38:
Figure 38:
Case study F, Sect. 3.6: Bengaluru hospital beds’ estimation.
Figure 39:
Figure 39:
Various interaction spaces, solid circles inside homes indicate individuals.
Figure 40:
Figure 40:
Bipartite graph abstraction of interaction spaces.
Figure 41:
Figure 41:
Population density maps of Bengaluru and Mumbai.
Figure 42:
Figure 42:
Validation of our synthetic Bengaluru and Mumbai. a–e Shows the validation plots for Bengaluru and f–j shows the validation plots for Mumbai.
Figure 43:
Figure 43:
A simplified model of COVID-19 progression.
See this image and copyright information in PMC

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