From 76ec30a5478b0deb7ac713bfbeafaf75dbc82983 Mon Sep 17 00:00:00 2001 From: Paulo Cesar Ventura <42674294+paulocv@users.noreply.github.com> Date: Mon, 26 Feb 2024 16:14:59 -0500 Subject: [PATCH] Fix typo on metadata --- model-metadata/CEPH-Rtrend_rsv.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/model-metadata/CEPH-Rtrend_rsv.yaml b/model-metadata/CEPH-Rtrend_rsv.yaml index 571b39e9..1a856f53 100644 --- a/model-metadata/CEPH-Rtrend_rsv.yaml +++ b/model-metadata/CEPH-Rtrend_rsv.yaml @@ -24,15 +24,15 @@ model_contributors: [ "email": "alkummer@iu.edu", }, { - "name": "Alessandro Vespignani", - "affiliation": "Northeastern University", - "email": "a.vespignani@northeastern.edu", + "name": "Shreeya Mhade", + "affiliation": "Indiana University Bloomington", + "email": "smhade@iu.edu", }, ] license: "CC-BY-4.0" data_inputs: "Weekly incident RSV hospitalizations from RSV-NET" methods: "A renewal equation method based on Bayesian estimation of Rt from past hospitalization data." -methods_long: "Model forecasts are obtained by using a renewal equation based on the estimated net reproduction number Rt. We use a spline interpolation to obtain daily data from weekly incidence, then apply a lowpass filter to extract the main trend. We then use MCMC Metropolis-Hastings sampling to estimate the posterior distribution of Rt based on the filtered data, considering an informed prior on Rt based on RSV literature. The estimated Rt in the last weeks of available data is used to forecast Rt in the upcoming weeks, with a drift term proportional to the current incidence. Finally, we use the renewal equation with the posterior distribution and trend of the estimated Rt to forecast rSV hospitalization trajectories." +methods_long: "Model forecasts are obtained by using a renewal equation based on the estimated net reproduction number Rt. We use a spline interpolation to obtain daily data from weekly incidence, then apply a lowpass filter to extract the main trend. We then use MCMC Metropolis-Hastings sampling to estimate the posterior distribution of Rt based on the filtered data, considering an informed prior on Rt based on RSV literature. The estimated Rt in the last weeks of available data is used to forecast Rt in the upcoming weeks, with a drift term proportional to the current incidence. Finally, we use the renewal equation with the posterior distribution and trend of the estimated Rt to forecast RSV hospitalization trajectories." ensemble_of_models: false ensemble_of_hub_models: false website_url: https://publichealth.indiana.edu/research/faculty-directory/profile.html?user=majelli