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Modelling SARS-CoV-2 Disease Progression in Australia and New Zealand: An Agent-Based Approach to Support Public Health Decision-Making

71 Pages Posted: 26 Oct 2020

See all articles by Jason Thompson

Jason Thompson

University of Melbourne - Transport, Health and Urban Design Research Hub

Roderick McClure

University of New England - Faculty of Medicine and Health

Tony Blakely

University of Melbourne - Population Interventions Unit; University of Melbourne

Nick Wilson

University of Otago - Burden of Disease Epidemiology, Equity and Cost-Effectiveness Programme

Michael Baker

University of Otago - Department of Public Health

Jasper Wijnands

University of Melbourne - Transport, Health and Urban Design (THUD) Research Lab

Thiago Herick de Sa

University of Sao Paulo - Center for Epidemiological Research in Nutrition and Health

Kerry Nice

University of Melbourne - Transport, Health and Urban Design Research Hub

Camilo Cruz-Gambardella

University of Melbourne - Transport, Health and Urban Design (THUD) Research Lab

Mark Stevenson

University of Melbourne - Transport, Health and Urban Design Research Hub

More...

Abstract

Background: For countries with strong border control and an epidemic that is not yet advanced, there is an opportunity to eliminate community transmission of SARS-CoV-2. We show how public health policies and their effects can be modelled to estimate the likely progression of SARS-COV-2 under i) strict physical distancing policies implemented in New Zealand and Australia on the 26 th and 28 th March, 2020 respectively, and ii) from September 1st, 2020 in Victoria, Australia as this state planned its exit from a significant second wave of infections.  

Methods: We developed a dynamic, public health policy model using an agent-based framework that simulated key aspects of both country’s populations, disease, spatial and behavioural dynamics, as well as the mechanism and effect of public health policy responses on the transmission of SARS-CoV-2. We demonstrate the model’s development and application through the first wave of infections in New Zealand, and first and second wave of infections experienced in Australia.

Findings: Under maintained strong physical distancing, we originally estimated a median elimination date of July 12th for Australia (95% SI: June 5th to August 28th) and June 3rd for New Zealand (95% SI May 4th to June 27th). We estimated a 90% probability of elimination was achievable in Australia on August 17th (95% SI: August 8th – August 30th) and on June 14th (95% SI: June 17th – June 29th) in New Zealand. However, under our scenario of decaying adherence to physical distancing from implementation to 60 days post restrictions, we predicted a resurgence in SARS-CoV-2 infections (i.e., a second wave) was likely in Australia and possibly in New Zealand. At 100 days, the probability of elimination was reduced to 10% in Australia and 68% in New Zealand. Having demonstrated the utility of our model above, the subsequent adaptation of the model in Victoria, Australia estimated that a threshold of less than 5 new cases per day over 14 days (from a peak of  ~500  in August) could be achieved on or around the 17th of October. This would enable Victoria to move to, and sustain, lower levels of restrictions to movement and trade than had been possible under its strict lockdown policy.

Interpretation: The findings suggest that during Australia and NZ’s first wave of infections, elimination of community transmission of SARS-CoV-2 in both countries through effective implementation and maintenance of public health interventions was possible. Subsequently, the structure and function of the agent-based policy model developed to analyse the effect of policies during wave 1 has been instrumental in supporting decision-making among public health authorities during Australia’s current second wave of infections.

Funding Statement: MS is funded by a NHMRC Fellowship (APP1136250), JT is funded by an ARC DECRA Fellowship (DE180101411).

Declaration of Interests: None to declare.

Keywords: SARS-CoV-2Agent Based ModellingDecision SupportPublic HealthCOVID-19

Suggested Citation

Thompson, Jason and McClure, Roderick and Blakely, Tony and Wilson, Nick and Baker, Michael and Wijnands, Jasper and de Sa, Thaigo Herick and Nice, Kerry and Cruz-Gambardella, Camilo and Stevenson, Mark, Modelling SARS-CoV-2 Disease Progression in Australia and New Zealand: An Agent-Based Approach to Support Public Health Decision-Making. Available at SSRN: https://ssrn.com/abstract=3712947 or http://dx.doi.org/10.2139/ssrn.3712947

Jason Thompson

University of Melbourne - Transport, Health and Urban Design Research Hub ( email )

Carlton, Victoria
Australia

Roderick McClure

University of New England - Faculty of Medicine and Health ( email )

11 Hills Beach Road
Biddeford, ME 04005
United States

Tony Blakely

University of Melbourne - Population Interventions Unit ( email )

Melbourne
Australia

University of Melbourne ( email )

185 Pelham Street
Carlton, Victoria 3053
Australia

Nick Wilson

University of Otago - Burden of Disease Epidemiology, Equity and Cost-Effectiveness Programme ( email )

New Zealand

Michael Baker

University of Otago - Department of Public Health ( email )

P.O. Box 56
Dunedin, Otago 9010
New Zealand

Jasper Wijnands

University of Melbourne - Transport, Health and Urban Design (THUD) Research Lab ( email )

185 Pelham Street
Carlton, Victoria 3053
Australia

Thaigo Herick De Sa

University of Sao Paulo - Center for Epidemiological Research in Nutrition and Health ( email )

Kerry Nice

University of Melbourne - Transport, Health and Urban Design Research Hub

Carlton, Victoria
Australia

Camilo Cruz-Gambardella

University of Melbourne - Transport, Health and Urban Design (THUD) Research Lab ( email )

185 Pelham Street
Carlton, Victoria 3053
Australia

Mark Stevenson (Contact Author)

University of Melbourne - Transport, Health and Urban Design Research Hub ( email )

Carlton, Victoria
Australia

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