Estimating the Growth Rate and Doubling Time for Short-Term Prediction and Monitoring Trend During the COVID-19 Pandemic with a SAS Macro

Stanley Xu; Christina Clarke; Susan Shetterly; Komal Narwaney

/************************************************************* * Title: * Programmer: Stanley Xu and Christina Clarke Institute for Health Research * Kaiser Permanente Colorado * * Date Created: 4/3/2020 * Description: This macro is designed to calculate a predicted * growth and doubling time of a disease given observed * data. In particular, these models were based on observed * deaths since the true denominator is often unknown given * testing may not be done on all symptomatic or asymtomatic * individuals. Further, hospitalizations could be used if they * are known. * * Input: indat = input dataset with the number of deaths and date of those deaths during a date range * that is to be modeled. * dateofevent = variable name of te date the deaths occurred from the indat dataset * numevents = variable name that has the number of deaths that occurred on each date of death * from the indat dataset * int_length -number of days in each interval -our * example examined 7 day intervals to create piece-wise growth intervals * * * Output: * * References: * * Changes to the code log : cclarke CH001 remove the state variable option **************************************************************/ %macro Calc_GrowthRates(indat, dateofevent, numevents, int_length); *First, we need to get the start and end dates from the input dataset.; proc sql noprint; select distinct min(&dateofevent) ,max(&dateofevent) into :fup_start ,:fup_end from &indat ; quit; /*For QA -Prints the first and last date found in the input data file which will appear in the log*/ data _null_; start = &fup_start; endloop = &fup_end-&int_length+1;