The global pandemic of COVID-19 presents challenges as well as opportunities for statisticians and data scientists to make impacts in the battle. Over the past year, a large body of new statistical models, methods and research results have been developed to better understand COVID-19 regarding its transmission and incubation, and impacts on the human society in the whole world, as well as mitigation strategies and vaccine development. The special Stat Highlight: Recent statistical research and development on COVID-19 session at the virtual 63rd World Statistics Congress highlights three new methods for forecasting and mapping the outbreak, predicting the dynamics, and making policy recommendations.
The published Stat papers are all free to read in the Stat Virtual Issue on Statistical Research on Coronavirus Disease (COVID-19)
Forecasting subnational COVID-19 mortality using a day-of-the-week adjusted Bayesian hierarchical model. Stat. 2021; 10:e328. https://doi.org/10.1002/sta4.328, , .
Count-valued time series models for COVID-19 daily death dynamics. Stat. 2021: 10;e369. https://doi.org/10.1002/sta4.369, , .
Robust inference for non-linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy. Stat. 2020; 9:e309. https://doi.org/10.1002/sta4.309, , , et al.