This Arxiv pre-print may be of interest to the list: https://arxiv.org/pdf/2003.10218.pdf

 

Abstract below

 

In this paper we develop an agent-based model for a fine-grained computational simulation of the ongoingCOVID-19 pandemic in Australia. This model is calibrated to reproduce several characteristics of COVID-19 transmission, accounting for its reproductive number, the length of incubation and generation periods, age-dependent attack rates, and the growth rate of cumulative incidence during a sustained and unmitigated local transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We then apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation,nsocial distancing with varying levels of compliance, and school closures. School closures are not found tonbring decisive benefits. We report an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels. This suggests that a compliance of below 70% is unlikely to succeed for any duration of social distancing, while a compliance at the 90% level is likely to control thendisease within 13–14 weeks, when coupled with effective case isolation and international travel restrictions.

 

 

Dr Jason Thompson 

PhD(Medicine), M.Psych, BSc(Hons)

Melbourne School of Design

Transport, Health and Urban Design (THUD) Research Hub

Australian Research Council Discovery Early Career Research Award (DECRA) Fellow

Room 416, Level 4, Melbourne School of Design (Building 133)

The University of Melbourne, Victoria 3010 Australia

T:   +61 (3)8344 7782 M: +61 (0)457502134 E: [log in to unmask]

 

 

 

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