Physical distancing measures to control the COVID-19 pandemic come at a heavy short-term economic cost. But easing the measures too early carries a high risk of transmission re-escalations. To assess if physical distancing can be relaxed, a number of epidemic indicators are used, most notably the reproduction number R. Many developing countries, however, have limited capacities to estimate R accurately. This study aims to demonstrate how health production function can be used to assess the state of COVID-19 transmission and to determine a risk-based physical distancing relaxation policy.
The author establishes a short-run health production function, representing the cumulative number of COVID-19 cases, from the standard SIR model. Three zones defining the state of transmission are shown. The probability of meeting a policy target, given a production elasticity range, is computed. The method is applied to France, Germany, Italy, the UK and the US, and to Indonesia as an example of application in developing countries.
As of June 30, 2020, France, Germany, Italy and the UK have arrived in the “green zone” where relaxation can be considered. The US is still in the “red zone” where physical distancing still needs to be applied. France, Germany and Italy can set a policy target of maximum daily-cases of 500, while the UK has to make do with a target of 1,100 daily-cases. France, Germany, Italy and the UK still exhibit a relatively high risk of their daily-cases failing to meet the policy target or even rising. Indonesia is still in the “red zone”, so it comes as no surprise that the country’s daily-cases rose sharply after relaxation of physical distancing.
Short-run health production function can be used to assess the state of COVID-19 transmission and to determine a risk-based physical distancing relaxation policy. Given its simplicity and minimum data requirement, the approach is very useful for developing countries which are unable to have reliable estimates of the reproduction number R. Follow-up research from this study may include estimating an economically optimal date for relaxing distancing measures and application of this method to other epidemics.