Determining the optimal COVID-19 policy response using agent-based modelling linked to health and cost modelling: Case study for Victoria, Australia
Authors: Blakely Tony , Thompson Jason , Bablani Laxman , Andersen Patrick , Ouakrim Driss Ait , Carvalho Natalie , Abraham Patrick , Boujaoude Marie-Anne , Katar Ameera , Akpan Edifofon , Wilson Nick , Stevenson Mark 
Published: January 2021

AbstractImportanceDetermining the best policy on social restrictions and lockdowns for the COVID-19 pandemic is challenging.ObjectiveTo determine the optimal policy response ranging from aggressive and moderate elimination, tight suppression (aiming for 1 to 5 cases per million per day) and loose suppression (5 to 25 cases per million per day).DesignTwo simulation models in series: an agent-based model to estimate daily SARS-CoV-2 infection rates and time in four stages of social restrictions; a proportional multistate lifetable model to estimate long-run health impacts (health adjusted life years (HALYs) arising from SARS-CoV-2) and costs (health systems, and health system plus GDP).The net monetary benefit (NMB) of each policy option at varying willingness to pay (WTP) per HALY was calculated: NMB = HALYs × WTP – cost. The optimal policy response was that with the highest NMB.Setting and participantsThe State of Victoria, Australia, using simulation modeling of all residents.Main Outcome and MeasuresSARS-CoV-2 infection rates, time under various stages of restrictions, HALYs, health expenditure and GDP losses.ResultsAggressive elimination resulted in the highest percentage of days with the lowest level of restrictions (median 31.7%, 90% simulation interval 6.6% to 64.4%). However, days in hard lockdown were similar across all four strategies (medians 27.5% to 36.1%).HALY losses (compared to a no-COVID-19 scenario) were similar for aggressive elimination (286, 219 to 389) and moderate elimination (314, 228 to 413), and nearly eight and 40-times higher for tight and loose suppression. The median GDP loss was least for moderate elimination ($US41.7 billion, $29.0 to $63.6 billion), but there was substantial overlap in simulation intervals between the four strategies.From a health system perspective aggressive elimination was optimal in 64% of simulations above a willingness to pay of $15,000 per HALY, followed by moderate elimination in 35% of simulations.Moderate elimination was optimal from a partial societal perspective in half the simulations followed by aggressive elimination in a quarter.Shortening the pandemic duration to 6 months saw loose suppression become preferable under a partial societal perspective.Conclusions and RelevanceElimination strategies were preferable over a 1-year pandemic duration.FundingAnonymous philanthropic donation to the University of Melbourne.Key pointsQuestionTo determine the optimal of four policy responses to COVID-19 in the State of Victoria, Australia (aggressive and moderate elimination, tight suppression (aiming for 1 to 5 cases per million per day) and loose suppression (5 to 25 cases per million per day), based on estimated future health loss and costs from both a health system and partial societal perspective.FindingsFrom a health system perspective aggressive elimination was optimal in 64% of simulations above a willingness to pay of $15,000 per HALY, followed by moderate elimination in 35% of simulations. Moderate elimination was optimal from a partial societal perspective (i.e., including GDP losses) in half the simulations followed by aggressive elimination in a quarter.MeaningWhilst there is considerable uncertainty in outcomes for all the four policy options, the two elimination options are usually optimal from both a health system and a partial societal (health expenditure plus GDP cost) perspective.