| |
original article |
Date |
Title |
Authors All Authors |
| 1 |
[GO] |
2024―Feb―10 |
Impact of dynamic self-protection intensity on the COVID-19 pandemic: a case study in Shenzhen based on medical resources |
Chao Liu, Haonan Long, Guanpeng Li, Pengzhen Chen, Zhen Zhang, Jie Huang, et al. (+5) Bin Zhu, Xinxin Han, Yanqing Hu, Jian Qing Shi, Dongfeng Gu |
| 2 |
[GO] |
2023―Mar―27 |
Modeling time to event for epicenters of the pandemic COVID-19 |
Babak Jamshidi, Mansour Rezaei, Shahriar Jamshidi Zargaran, Farid Najafi |
| 3 |
[GO] |
2022―Nov―16 |
Modeling the spatial distribution of COVID-19 infections in Europe reveals no similarities between countries during the first year of the pandemic |
M. Jakimowicz, T. Suchocki, J. Liu, J. Szyda |
| 4 |
[GO] |
2022―Jun―01 |
A quadratic trend-based time series method to analyze the early incidence pattern of COVID-19 |
Soudeep Deb, Manidipa Majumdar |
| 5 |
[GO] |
2022―Apr―29 |
Estimation of highly heterogeneous multinomial probabilities observed at the beginning of COVID-19 |
Toru Ogura, Takemi Yanagimoto |
| 6 |
[GO] |
2021―Dec―31 |
Estimation of missing total number of trials in binomial time series analysis by a BDLM process with an illustration of the COVID-19 pandemic data |
Massoud Nakhkoob |
| 7 |
[GO] |
2021―Oct―06 |
A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data |
Davide Sisti, Ettore Rocchi, Sara Peluso, Stefano Amatori, Margherita Carletti |
| 8 |
[GO] |
2021―Aug―10 |
Reproduction number, discrete forecasting model, and chaos analytics for Coronavirus Disease 2019 outbreak in India, Bangladesh, and Myanmar |
Rapin Sunthornwat, Yupaporn Areepong |
| 9 |
[GO] |
2021―Jul―31 |
The analysis and forecasting COVID-19 cases in the United States using Bayesian structural time series models |
Liming Xie |
| 10 |
[GO] |
2021―May―19 |
Comparative analysis of epidemiological models for COVID-19 pandemic predictions |
Rajan Gupta, Gaurav Pandey, Saibal K. Pal |
| 11 |
[GO] |
2021―Apr―25 |
Case fatality risk estimated from routinely collected disease surveillance data: application to COVID-19 |
Ian C. Marschner |
