City-Scale Agent-Based Simulators for the Study of Non-pharmaceutical Interventions in the Context of the COVID-19 Epidemic

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

doi:10.1007/s41745-020-00211-3

Cited by 33 publications

(53 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(15 th April to 3 rd May), Lockdown 3 (4 th May to 3 rd May) and Lockdown 4 (18 th May to 31 st May) were imposed. The lockdown further extended till 30 June 2020 at different parts depending upon the situation there while other parts adopted unlock 1.0 [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Impact of lockdown Covid-19 pandemic on himalayan environment

Bahukhandi

Agarwal

Singhal

2020

International Journal of Environmental Analytical Chemistry

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Impact of lockdown Covid-19 pandemic on himalayan environment

Bahukhandi

Agarwal

Singhal

2020

International Journal of Environmental Analytical Chemistry

View full text Add to dashboard Cite

“…One could also consider agent-based models as in [2], but given the complexity of such a model for Bengaluru alone (12.4 million population), scaling it up six times to the level of Karnataka will involve significant (more-than-linear) complexity increase. Parsimonious SEIRV-based district-level age-stratified ODE models incorporating mobility and seroreversion do provide a good tradeoff point between complexity and the ability to gather insights for public health policy.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Strategies to Mitigate COVID-19 Resurgence Assuming Immunity Waning: A Study for Karnataka, India

Adiga

Athreya

Lewis

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

COVID-19 vaccination is being rolled out among the general population in India. Spatial heterogeneities exist in seroprevalence and active infections across India. Using a spatially explicit age-stratified model of Karnataka at the district level, we study three spatial vaccination allocation strategies under different vaccination capacities and a variety of non-pharmaceutical intervention (NPI) scenarios. The models are initialised using on-the-ground datasets that capture reported cases, seroprevalence estimates, seroreversion and vaccine rollout plans. The three vaccination strategies we consider are allocation in proportion to the district populations, allocation in inverse proportion to the seroprevalence estimates, and allocation in proportion to the case-incidence rates during a reference period. The results suggest that the effectiveness of these strategies (in terms of cumulative cases at the end of a four-month horizon) are within 2% of each other, with allocation in proportion to population doing marginally better at the state level. The results suggest that the allocation schemes are robust and thus the focus should be on the easy to implement scheme based on population. Our immunity waning model predicts the possibility of a subsequent resurgence even under relatively strong NPIs. Finally, given a per-day vaccination capacity, our results suggest the level of NPIs needed for the healthcare infrastructure to handle a surge.

show abstract

“…15 days [35] The days required for nucleic acid testing (which depends on the local medical level). Triangular (0.…”

Section: (37)mentioning

confidence: 99%

“…The proposed multimethod ABM is built using Anylogic software [35] ; here, we present the modeling process using the logic and modules of this software. The ABM is usually built with a state chart library.…”

Section: A the Improved Modelmentioning

confidence: 99%

An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions

et al. 2021

View full text Add to dashboard Cite

The traditional agent-based model requires high computing power of the central processing unit. Thus, an improved agent-based model combined with the discrete event simulation method is proposed. The result of the equation-based Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) model with the same parameter combination, which has been demonstrated to be effective, is used to verify the validity of this improved agent-based model. Additionally, an analysis based on simulation results of the Contact Tracing Measure (CTM), Location-Based Checking-Testing Measure (LCTM), Lockdown Measure (LM), Mobile Cabin Isolation and Hospital Measure (MCHM) is presented. The simulation results show that implementing long-term lockdown measures has the best effect on epidemic control. Moreover, according to the simulation results, we inferred that using only nonpharmaceutical epidemic prevention measures may result in a second outbreak of COVID-19 owing to the risk of asymptomatic transmission.

show abstract

City-Scale Agent-Based Simulators for the Study of Non-pharmaceutical Interventions in the Context of the COVID-19 Epidemic

Cited by 33 publications

References 22 publications

Impact of lockdown Covid-19 pandemic on himalayan environment

Impact of lockdown Covid-19 pandemic on himalayan environment

Strategies to Mitigate COVID-19 Resurgence Assuming Immunity Waning: A Study for Karnataka, India

An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions

Contact Info

Product

Resources

About