Abstract
How effective are ‘lockdown’ measures and other policy interventions to curb the spread of Covid-19 in emerging market cities that are characterized by large heterogeneity and high levels of informality? The most commonly used models to predict the spread of Covid-19 are SEIR models which lack the spatial resolution necessary to answer this question. We develop an agent-based model of social interactions in which the distribution of agents across wards, as well as their travel and interactions are calibrated to real data for Cape Town, South Africa. We characterize the elasticity of various policy interventions including increased likelihood to self-isolate, travel restrictions, assembly bans, and behavioural interventions like washing hands or wearing masks. Even in an informal setting, where agents’ ability to self-isolate is compromised, a lockdown remains an effective intervention. In our model, the lockdown enacted in South Africa reduced expected fatalities in Cape Town by 26% and the expected demand for intensive care beds by 46%. However, our best calibration predicts a substantially higher case load, demand for ICU beds, and expected number of deaths than the current best estimate published for Cape Town.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work was supported by the South African Reserve Bank to study contagion in economic and financial systems.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
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Was not necessary for this simulation study that uses public data only.
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
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Footnotes
* We thank Peter Courtney for his excellent research assistance. The source code and replication files for this paper can be found here. All errors are our own.
Data Availability
All data and scripts are available on Github