pnwscm60.github.io
Realtime estimation of Rt in selected Swiss Cantons
Kevin Systrom published an interesting article about realtime estimation of Rt. To know the actual Rt is of special interest now, as Swiss government has started to gradually ease the restrictions since April 27th.
Systrom based his publication on a paper by Bettencourt & Ribeiro, «Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases». They used Bayesian statistics to estimate the most likely value of Rt and a credible interval for the true value of Rt. We used the method described by Systrom to estimate Rt for Swiss cantons with over 750 cases since the beginning of the epidemic, but we used a serial interval of 5.2 (Althaus CL) instead of 4 and used R instead of Python for calculation. We show the last 35 data points (about 5 weeks) for each of these cantons. Smaller case numbers result as expected in wider density intervals. Data are taken from openZH.
Systrom based his publication on a paper by Bettencourt & Ribeiro, «Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases». They used Bayesian statistics to estimate the most likely value of Rt and a credible interval for the true value of Rt. We used the method described by Systrom to estimate Rt for Swiss cantons with over 750 cases since the beginning of the epidemic, but we used a serial interval of 5.2 (Althaus CL) instead of 4 and used R instead of Python for calculation. We show the last 35 data points (about 5 weeks) for each of these cantons. Smaller case numbers result as expected in wider density intervals. Data are taken from openZH.
CAVE: Data delivery depends on the canton. Data are often corrected later for late deliveries by hospitals and homes. This is an estimate of Rt only.
Estimated Rt for Swiss cantons with over 750 cases as per May 3th. Yellow: Highest density interval (95%). Based on data from OpenZH from the Swiss cantons, downloaded on May 4th 6 pm.