bets.covid19 - The BETS Model for Early Epidemic Data
Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.
Last updated 4 years ago
2019-ncov
4.43 score 27 stars 2 scripts 188 downloadsseismic - Predict Information Cascade by Self-Exciting Point Process
An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See http://snap.stanford.edu/seismic/ for more information and datasets.
Last updated 3 years ago
2.00 score 6 scripts 170 downloads