Quantification of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland
https://www.sciencedirect.com/science/article/pii/S1755436521000335?via%3Dihub
2.2. Statistical inference
We fit a logistic model to the frequency of B.1.1.7 samples per day to estimate the logistic growth rate a and the sigmoid’s midpoint t0. From that, we derive an estimate of the transmission fitness advantage of B.1.1.7 under a continuous (fc) and a discrete (fd) model. Each model could plausibly describe the actual dynamics, so we present results from both for comparison. Further, we estimate the reproductive number R for the B.1.1.7 and non-B.1.1.7 infections. The mathematical derivations are described in the supplementary materials in the sections A.3 and A.4. Finally, we show the projected number of confirmed infections in the future under the continuous model. We initialize the model on 01 January 2021 with the estimated number of B.1.1.7 and non-B.1.1.7 confirmed infections on that day. We assume a reproductive number for the non-B.1.1.7 infections as estimated on the national level for 01 January-17 January 2021. Further, we assume that the expected generation time is 4.8 days and the fitness advantage is the estimated fc for the region and dataset of interest (Table 1).
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