Optimal interventions in networks during a pandemic

Uncovering new results on the effects of network configuration, network centrality, and health policies on COVID-19 deaths in nursing homes.

  "georg-arthur-pflueger-eO_JhqabBY0-unsplash" by Georg Arthur Pflueger.

Dr Guy Tchuente has co authored this paper with Roland Pongou and Jean‑Baptiste Tondji using unique data on nursing home networks in the USA, to calibrate, model and quantify state-level preference for prioritizing health over wealth during the COVID-19 pandemic.

‘This paper proposed a new model mixing epidemiology and economics.’ Tchuente explains ‘The new modelling approach is validated empirically using US data on nursing homes.’

The abstract reads:

‘We develop a model of optimal lockdown policy for a social planner who balances population health with short-term wealth accumulation. The unique solution depends on tolerable infection incidence and social network structure. We then use unique data on nursing home networks in the United States to calibrate the model and quantify state-level preference for prioritizing health over wealth. We also empirically validate simulation results derived from comparative statics analyses. Our findings suggest that policies that tolerate more virus spread (laissez-faire) increase state GDP growth and COVID-19 deaths in nursing homes. The detrimental effects of laissez-faire policies are more potent for nursing homes that are more peripheral in networks, nursing homes in poorer counties, and nursing homes that operate on a for-profit basis. We also find that U.S. states with Republican governors have a higher tolerable incidence level, but these policies tend to converge with a high death count.’

In terms of impact Tchuente hopes that the paper has produces lessons from Covid.

‘We think that a policy maker could, using the optimal targeting strategy proposed in the paper, better protect the general public in future pandemics like COVID19.’

Find the paper here.