Data supporting the article: When can physical distancing be relaxed? A health production function approach for COVID-19 control policy

This data record contains 7 data files in .xlsx file format.

The 6 .xlsx files are: Additional file 3_France_rem1.xlsx, Additional file 4_Germany_rem1.xlsx, Additional file 5_Italy_rem1.xlsx, Additional file 6_UK_rem1.xlsx, Additional file 7_US_rem1.xlsx and Additional file 8_Indonesia_rem1.xlsx.

The Excel files contain data on COVID-19
cumulative- and daily-number of cases in France, Germany, Italy, the UK, the US
and Indonesia from the first day of official recording to June 30th, 2020, and
their daily average products, marginal products, and production elasticities
based on the data's exponential moving average (EMA). The Bayesian
probabilities of meeting a policy target given a production elasticity range,
are also included in the Excel files.

Study aims and methodology:

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 authors established a short-run
health production function, representing the cumulative number of COVID-19
cases, from the standard Susceptible-Infected-Recovered
(SIR) SIR model.

This study employed time as the health
input variable. Because only one variable was used, the authors were dealing with
a short-run health production function. The cumulative number of COVID-19 cases
was employed as the health status output. For more details on the methodology,
please read the related article.