A now very common task in COVID-19 responses is to estimate growth rates, stratified by geographic units. Using incidence2 + i2extras it should be very easy to make a short, simple Rmarkdown template which would: read data in, filter to retain only the last xxx days of data (possibly removing the last yyy to account for reporting effects), and then use fit_curve() and growth_rate() to get growth rates by geographic unit.
[impact: This type of analysis needs to be done routinely in many places and by many groups, and often uses ad-hoc methodology and complicated scripts. This would provide a solid basis, using sound methodology, for routine estimation of growth rates of COVID-19.]
[originally proposed by @thibautjombart]
[suggested repo: Do not know]
[additional notes: ]
A now very common task in COVID-19 responses is to estimate growth rates, stratified by geographic units. Using incidence2 + i2extras it should be very easy to make a short, simple Rmarkdown template which would: read data in, filter to retain only the last xxx days of data (possibly removing the last yyy to account for reporting effects), and then use fit_curve() and growth_rate() to get growth rates by geographic unit.
[impact: This type of analysis needs to be done routinely in many places and by many groups, and often uses ad-hoc methodology and complicated scripts. This would provide a solid basis, using sound methodology, for routine estimation of growth rates of COVID-19.] [originally proposed by @thibautjombart] [suggested repo: Do not know] [additional notes: ]