| 1 |
Ahmar_2020 |
SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain |
Ahmar, Ansari Saleh; del Val, Eva Boj |
2020 |
2020-04-22 |
PMC |
Y |
|
|
10.1016/j.scitotenv.2020.138883 |
qsitv9yn |
0.989120 |
Ma_X_2020 |
|
| 2 |
Wu_Z_2020 |
Application of COVID-19 pneumonia diffusion data to predict epidemic situation |
Zhenguo Wu |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.11.20061432 |
fizhdrgu |
0.785812 |
|
Zareie_2020, Li_Y_2020 |
| 3 |
Salim_2020 |
COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics |
Naomie Salim; Weng Howe Chan; Shuhaimi Mansor; Nor Erne Nazira Bazin; Safiya Amaran; Ahmad Athif Mohd Faudzi; Anazida Zainal; Sharin Hazlin Huspi; Eric Jiun Hooi Khoo; Shaekh Mohammad Shithil |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.08.20057463 |
652vzlq6 |
0.769943 |
|
Hou_J_2020 |
| 4 |
Piccolomiini_2020 |
Monitoring Italian COVID-19 spread by an adaptive SEIRD model |
Elena Loli Piccolomiini; Fabiana Zama |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.03.20049734 |
vf5gftxb |
0.751099 |
|
Caccavo_2020 |
| 5 |
Zahiri_2020 |
Prediction of Peak and Termination of Novel Coronavirus Covid-19 Epidemic in Iran |
AmirPouyan Zahiri; Sepehr RafieeNasab; Ehsan Roohi |
2020 |
2020-03-31 |
BioRxiv |
Y |
|
|
10.1101/2020.03.29.20046532 |
3bmcglan |
0.734454 |
|
Zareie_2020 |
| 6 |
Li_Y_2020 |
A Note on COVID-19 Diagnosis Number Prediction Model in China |
Yi Li; Xianhong Yin; Meng Liang; Xiaoyu Liu; Meng Hao; Yi Wang |
2020 |
2020-02-23 |
BioRxiv |
Y |
|
|
10.1101/2020.02.19.20025262 |
goqgv2cc |
0.707822 |
|
Zareie_2020, Huang_2020 |
| 7 |
Nesteruk_2020 |
Stabilization of the coronavirus pandemic in Italy and global prospects |
Igor Nesteruk |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.28.20045898 |
vqke48ur |
0.697855 |
|
Nesteruk_2020 |
| 8 |
KUMAR_2020 |
Predication of Pandemic COVID-19 situation in Maharashtra, India |
SUNNY KUMAR |
2020 |
2020-04-11 |
BioRxiv |
N |
|
|
10.1101/2020.04.10.20056697 |
kvh9qt65 |
0.666619 |
|
Bhola_2020, Zareie_2020 |
| 9 |
Wang_2020 |
Use Crow-AMSAA Method to predict the cases of the Coronavirus 19 in Michigan and U.S.A |
Yanshuo Wang |
2020 |
2020-04-08 |
BioRxiv |
N |
|
|
10.1101/2020.04.03.20052845 |
8zidekxt |
0.666577 |
|
Yeo_Y_2020 |
| 10 |
Yeo_Y_2020 |
A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts |
Yao Yu Yeo; Yao-Rui Yeo; Wan-Jin Yeo |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.24.20043026 |
8g64u3ux |
0.610057 |
|
Li_S_2020 |
| 11 |
Peng_2020 |
Epidemic analysis of COVID-19 in China by dynamical modeling |
Liangrong Peng; Wuyue Yang; Dongyan Zhang; Changjing Zhuge; Liu Hong |
2020 |
2020-02-18 |
BioRxiv |
Y |
|
|
10.1101/2020.02.16.20023465 |
m87tapjp |
0.601042 |
|
|
| 12 |
Ding_2020 |
Brief Analysis of the ARIMA model on the COVID-19 in Italy |
Guorong Ding; Xinru Li; Yang Shen; Jiao Fan |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.08.20058636 |
ilwsrir6 |
0.589502 |
|
Zareie_2020, Caccavo_2020, He_J_2020 |
| 13 |
Shao_2020 |
Dynamic models for Coronavirus Disease 2019 and data analysis |
Shao, Nian; Zhong, Min; Yan, Yue; Pan, HanShuang; Cheng, Jin; Chen, Wenbin |
2020 |
2020-03-24 |
PMC |
Y |
PMC7168448 |
|
10.1002/mma.6345 |
kgrdul35 |
0.587727 |
|
|
| 14 |
Griette_2020 |
Estimating the last day for COVID-19 outbreak in mainland China |
Quentin Griette; Zhihua Liu; pierre magal |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.14.20064824 |
z5pg8nij |
0.576454 |
|
|
| 15 |
Cruz-Pacheco_2020 |
Dispersion of a new coronavirus SARS-CoV-2 by airlines in 2020: Temporal estimates of the outbreak in Mexico. |
Gustavo Cruz-Pacheco; Fernando J Bustamante-Castaneda; Jean Guy Caputo; Maria Eugenia Jimenez-Corona; Samuel Ponce-de-Leon |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.24.20042168 |
p2t2wd4t |
0.571570 |
|
|
| 16 |
Ji_X_2020 |
Analysis of Epidemic Situation of New Coronavirus Infection at Home and Abroad Based on Rescaled Range (R/S) Method |
Xiaofeng Ji; Zhou Tang; Kejian Wang; Xianbin Li; Houqiang Li |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.15.20036756 |
xqs4baou |
0.551981 |
Tomie_2020 |
He_J_2020, Wang_2020 |
| 17 |
Wang_2020 |
Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 |
Huiwen Wang; Yanwen Zhang; Shan Lu; Shanshan Wang |
2020 |
2020-03-24 |
BioRxiv |
Y |
|
|
10.1101/2020.03.21.20040139 |
fyh8gjjl |
0.542637 |
Tomie_2020 |
Roosa_2020, Peng_2020, Fanelli_2020 |
| 18 |
Mahmud_2020 |
Applying the SEIR Model in Forecasting The COVID-19 Trend in Malaysia: A Preliminary Study |
Aidalina Mahmud; Poh Ying Lim |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.14.20065607 |
u4veyaat |
0.527091 |
|
|
| 19 |
Koczkodaj_2020 |
1,000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic |
Koczkodaj, W.W.; Mansournia, M.A.; Pedrycz, W.; Wolny-Dominiak, A.; Zabrodskii, P.F.; Strzaška, D.; Armstrong, T.; Zolfaghari, A.H.; Debski, M.; Mazurek, J. |
2020 |
2020-03-23 |
PMC |
Y |
|
|
10.1016/j.gloepi.2020.100023 |
ssa5rzd5 |
0.511067 |
Griette_2020 |
Liu_Q_2020, Chintalapudi_2020, Li_M_2020 |
| 20 |
Li_Y_2020 |
COVID-19 Epidemic Outside China: 34 Founders and Exponential Growth |
Yi Li; Meng Liang; Xianhong Yin; Xiaoyu Liu; Meng Hao; Zixin Hu; Yi Wang; Li Jin |
2020 |
2020-03-03 |
BioRxiv |
Y |
|
|
10.1101/2020.03.01.20029819 |
7reqkx3h |
0.510201 |
Tomie_2020, Griette_2020 |
Wang_2020, He_J_2020 |
| 21 |
Scheuerl_2020 |
The proportion of deaths cases in confirmed patients of COVID-19 is still increasing for cumulative cases reported up to 14 April 2020 |
Thomas Scheuerl |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2020.04.17.20068908 |
23jb5wkh |
0.505261 |
|
|
| 22 |
Li_S_2020 |
Preliminary Assessment of the COVID-19 Outbreak Using 3-Staged Model e-ISHR |
Li, Sijia; Song, Kun; Yang, Boran; Gao, Yucen; Gao, Xiaofeng |
2020 |
2020-04-07 |
PMC |
Y |
PMC7137856 |
|
10.1007/s12204-020-2169-0 |
a1sk6mka |
0.503458 |
|
He_J_2020, Zareie_2020, Huang_2020 |
| 23 |
Li_R_2020 |
Prediction of the Epidemic of COVID-19 Based on Quarantined Surveillance in China |
Rui Li; Wenliang Lu; Xifei Yang; Peihua Feng; Ozarina Muqimova; Xiaoping Chen; Gang Wei |
2020 |
2020-02-29 |
BioRxiv |
Y |
|
|
10.1101/2020.02.27.20027169 |
jadzias9 |
0.502530 |
Lin_Q_2020 |
Zareie_2020 |
| 24 |
Distante_2020 |
Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data |
Cosimo Distante; Igor Gadelha Pereira; Luiz Marcos Garcia Goncalves; Prisco Piscitelli; Alessandro Miani |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.09.20059055 |
azpz6e7q |
0.491702 |
Griette_2020, batista_2020 |
Li_M_2020, Zhan_2020, Caccavo_2020 |
| 25 |
Gupta_2020 |
SEIR and Regression Model based COVID-19 outbreak predictions in India |
Rajan Gupta; Gaurav Pandey; Poonam Chaudhary; Saibal Kumar Pal |
2020 |
2020-04-03 |
BioRxiv |
Y |
|
|
10.1101/2020.04.01.20049825 |
hf0jtfmx |
0.470430 |
|
Zareie_2020 |
| 26 |
Huang_2020 |
Prediction of COVID-19 Outbreak in China and Optimal Return Date for University Students Based on Propagation Dynamics |
Huang, Ganyu; Pan, Qiaoyi; Zhao, Shuangying; Gao, Yucen; Gao, Xiaofeng |
2020 |
2020-04-07 |
PMC |
Y |
PMC7137853 |
|
10.1007/s12204-020-2167-2 |
8b1esll4 |
0.467539 |
|
He_J_2020, Li_S_2020, Zareie_2020, Wang_2020 |
| 27 |
Fanelli_2020 |
Analysis and forecast of COVID-19 spreading in China, Italy and France |
Fanelli, Duccio; Piazza, Francesco |
2020 |
2020-05-31 |
PMC |
Y |
PMC7156225 |
|
10.1016/j.chaos.2020.109761 |
m6479wyv |
0.463148 |
|
Weber_2020, Peng_2020, He_J_2020 |
| 28 |
Yuan_2020 |
The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model |
George X Yuan; Lan Di; Yudi Gu; Guoqi Qian; Xiaosong Qian |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.05.20054346 |
px4b6ykg |
0.447754 |
|
Peng_2020, Roosa_2020, Li_M_2020 |
| 29 |
Ceylan_2020 |
Estimation of COVID-19 prevalence in Italy, Spain, and France |
Ceylan, Zeynep |
2020 |
2020-04-22 |
PMC |
Y |
|
|
10.1016/j.scitotenv.2020.138817 |
axio34pi |
0.443961 |
batista_2020 |
|
| 30 |
Zhan_2020 |
Modeling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data |
Choujun Zhan; Chi K. Tse; Yuxia Fu; Zhikang Lai; Haijun Zhang |
2020 |
2020-02-20 |
BioRxiv |
Y |
|
|
10.1101/2020.02.18.20024570 |
tc8eru1w |
0.435714 |
batista_2020, Lin_Q_2020, Tomie_2020 |
Wang_2020 |
| 31 |
Tomie_2020 |
Understanding the present status and forecasting of COVID―19 in Wuhan |
Toshihisa Tomie |
2020 |
2020-02-14 |
BioRxiv |
N |
|
|
10.1101/2020.02.13.20022251 |
lmoekp1m |
0.430793 |
Jin_G_2020, Lin_Q_2020, Ma_X_2020 |
Liu_Q_2020, Xu_L_2020, Huang_2020, Wang_2020 |
| 32 |
Tang_2020 |
Prediction of New Coronavirus Infection Based on a Modified SEIR Model |
Zhou Tang; Xianbin Li; Houqiang Li |
2020 |
2020-03-06 |
BioRxiv |
N |
|
|
10.1101/2020.03.03.20030858 |
1mu1z4xd |
0.429001 |
|
|
| 33 |
Zareie_2020 |
A model for COVID-19 prediction in Iran based on China parameters |
Bushra Zareie; Amin Roshani; Mohammad Ali Mansournia; Mohammad Aziz Rasouli; Ghobad Moradi |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2020.03.19.20038950 |
p42cgpf0 |
0.424887 |
batista_2020, Tomie_2020 |
He_J_2020, Tian_2020 |
| 34 |
Caccavo_2020 |
Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model |
Diego Caccavo |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2020.03.19.20039388 |
9032hh5c |
0.419649 |
|
Teles_2020 |
| 35 |
Roda_2020 |
Why is it difficult to accurately predict the COVID-19 epidemic? |
Roda, Weston C.; Varughese, Marie B.; Han, Donglin; Li, Michael Y. |
2020 |
2020-12-31 |
PMC |
Y |
PMC7104073 |
|
10.1016/j.idm.2020.03.001 |
vx2t7jgu |
0.415854 |
|
Li_M_2020, Caccavo_2020 |
| 36 |
Liu_Q_2020 |
Assessing the Tendency of 2019-nCoV (COVID-19) Outbreak in China |
Qinghe Liu; Zhicheng Liu; Deqiang Li; Zefei Gao; Junkai Zhu; Junyan Yang; Qiao Wang |
2020 |
2020-02-11 |
BioRxiv |
Y |
|
|
10.1101/2020.02.09.20021444 |
5dd89gnm |
0.408954 |
Muniz-Rodriguez_2020, Tomie_2020, Du_Z_2020 |
He_J_2020, Zareie_2020 |
| 37 |
Vasconcelos_2020 |
Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies |
Giovani L. Vasconcelos; Antônio M. S. Macêdo; Raydonal Ospina; Francisco A. G. Almeida; Gerson C. Duarte-Filho; Inês C. L. Souza |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.02.20051557 |
35b3efom |
0.406979 |
batista_2020 |
Zareie_2020, Hou_J_2020, Fanelli_2020 |
| 38 |
batista_2020 |
Estimation of the final size of the second phase of the coronavirus epidemic by the logistic model |
milan batista |
2020 |
2020-03-16 |
BioRxiv |
N |
|
|
10.1101/2020.03.11.20024901 |
h5zfmhqj |
0.392952 |
|
|
| 39 |
Saif_2020 |
Signature of State measures on the COVID-19 Pandemic in China, Italy, and USA |
Farhan Saif |
2020 |
2020-04-10 |
BioRxiv |
Y |
|
|
10.1101/2020.04.08.20057489 |
qc3pldce |
0.391153 |
|
He_J_2020, Nesteruk_2020, Teles_2020 |
| 40 |
Xiaoxuan_2020 |
Can Search Query Forecast successfully in China's 2019-nCov pneumonia? |
Li Xiaoxuan; Wu Qi; Lv Benfu |
2020 |
2020-02-18 |
BioRxiv |
Y |
|
|
10.1101/2020.02.12.20022400 |
vv1wjen0 |
0.384864 |
|
He_Y_2020, Wang_2020, Li_M_2020 |
| 41 |
Akay_2020 |
MARKOVIAN RANDOM WALK MODELING AND VISUALIZATION OF THE EPIDEMIC SPREAD OF COVID-19 |
Haluk Akay; George Barbastathis |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.12.20062927 |
fetbio7q |
0.383794 |
|
|
| 42 |
Garcia-Iglesias_2020 |
Early behavior of Madrid Covid-19 disease outbreak: A mathematical model |
Daniel Garcia-Iglesias; Francisco Javier de Cos Juez |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20047019 |
q3xoodui |
0.372193 |
Griette_2020, batista_2020, Tomie_2020 |
Zareie_2020 |
| 43 |
Huang_2020 |
Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China |
Chiou-Jye Huang; Yung-Hsiang Chen; Yuxuan Ma; Ping-Huan Kuo |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.23.20041608 |
yw81pkrq |
0.369392 |
|
Li_M_2020 |
| 44 |
Bayyurt_2020 |
Forecasting of COVID-19 Cases and Deaths Using ARIMA Models |
Lutfi Bayyurt; Burcu Bayyurt |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2020.04.17.20069237 |
s7fzex7v |
0.361620 |
|
|
| 45 |
Raheem_2020 |
Estimating Spot Prevalence of COVID-19 from Daily Death Data in Italy |
Ali Raheem |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.17.20037697 |
ao93c31w |
0.360031 |
Scheuerl_2020 |
Marchese-Ragona_2020 |
| 46 |
Khan_2020 |
Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative |
Khan, Muhammad Altaf; Atangana, Abdon |
2020 |
2020-03-14 |
PMC |
Y |
|
|
10.1016/j.aej.2020.02.033 |
ujyukdqu |
0.346236 |
|
Yin_T_2020 |
| 47 |
Chintalapudi_2020 |
COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach |
Chintalapudi, Nalini; Battineni, Gopi; Amenta, Francesco |
2020 |
2020-04-13 |
PMC |
Y |
|
|
10.1016/j.jmii.2020.04.004 |
mapfi8f5 |
0.346218 |
|
Gupta_2020, Roosa_2020, Teles_2020 |
| 48 |
Li_X_2020 |
The lockdown of Hubei Province causing different transmission dynamics of the novel coronavirus (2019-nCoV) in Wuhan and Beijing |
Xinhai Li; Xumao Zhao; Yuehua Sun |
2020 |
2020-02-11 |
BioRxiv |
Y |
|
|
10.1101/2020.02.09.20021477 |
uqgrjjwp |
0.342656 |
Lin_Q_2020 |
Wang_2020, Wang_2020, He_J_2020 |
| 49 |
Zhu_X_2020 |
Spatially Explicit Modeling of 2019-nCoV Epidemic Trend based on Mobile Phone Data in Mainland China |
Xiaolin Zhu; Aiyin Zhang; Shuai Xu; Pengfei Jia; Xiaoyue Tan; Jiaqi Tian; Tao Wei; Zhenxian Quan; Jiali Yu |
2020 |
2020-02-11 |
BioRxiv |
Y |
|
|
10.1101/2020.02.09.20021360 |
6u9q0ox9 |
0.341557 |
|
Zhan_2020, Chen_2020, Ai_S_2020, Jin_G_2020 |
| 50 |
Yuan_2020 |
The Prediction for the Outbreak of COVID-19 for 15 States in USA by Using Turning Phase Concepts as of April 10, 2020 |
George X Yuan; Lan Di; Yudi Gu; Guoqi Qian; Xiaosong Qian |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.13.20064048 |
wotoczu4 |
0.332770 |
|
|
| 51 |
Nicolau_2020 |
Recovery Ratios Reliably Anticipate COVID-19 Pandemic Progression |
Dan Valeriu Nicolau; Alexander Hasson; Mona Bafadhel |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.09.20059824 |
c6bc08kw |
0.329047 |
|
|
| 52 |
Li_M_2020 |
Predicting the epidemic trend of COVID-19 in China and across the world using the machine learning approach |
Mengyuan Li; Zhilan Zhang; Shanmei Jiang; Qian Liu; Canping Chen; Yue Zhang; Xiaosheng Wang |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.18.20038117 |
qpt5dj8p |
0.328119 |
|
Distante_2020, Zareie_2020, Zheng_2020 |
| 53 |
Dehesh_2020 |
Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models |
Tania Dehesh; H.A. Mardani-Fard; Paria Dehesh |
2020 |
2020-03-18 |
BioRxiv |
Y |
|
|
10.1101/2020.03.13.20035345 |
a0pf12jo |
0.325583 |
|
Zareie_2020 |
| 54 |
Gupta_2020 |
Trend Analysis and Forecasting of COVID-19 outbreak in India |
Rajan Gupta; Saibal Kumar Pal |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.26.20044511 |
w2uqaz8p |
0.324539 |
|
Chintalapudi_2020, Gupta_2020, Zareie_2020 |
| 55 |
Al-Awadhi_2020 |
Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns |
Al-Awadhi, Abdullah M.; Al-Saifi, Khaled; Al-Awadhi, Ahmad; Alhamadi, Salah |
2020 |
2020-04-08 |
PMC |
Y |
|
|
10.1016/j.jbef.2020.100326 |
7vm6heh1 |
0.324092 |
|
|
| 56 |
Zhao_2020 |
Modeling the Epidemic Dynamics and Control of COVID-19 Outbreak in China |
Shilei Zhao; Hua Chen |
2020 |
2020-02-29 |
BioRxiv |
Y |
|
|
10.1101/2020.02.27.20028639 |
ry3m6x8c |
0.321512 |
Lin_Q_2020, Ma_X_2020, Tomie_2020 |
He_J_2020 |
| 57 |
qiu_t_2020 |
Revealing the influence of national public health policies for the outbreak of the SARS-CoV-2 epidemic in Wuhan, China through status dynamic modeling |
tianyi qiu; Han Xiao |
2020 |
2020-03-12 |
BioRxiv |
Y |
|
|
10.1101/2020.03.10.20032995 |
9zs68dnn |
0.317428 |
|
Hou_J_2020, He_J_2020 |
| 58 |
Nesteruk_2020 |
Comparison of the coronavirus pandemic dynamics in Europe, USA and South Korea |
Igor Nesteruk |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.18.20038133 |
rfni76sv |
0.315242 |
|
|
| 59 |
MONLEON-GETINO_2020 |
Next weeks of SARS-CoV-2: Projection model to predict time evolution scenarios of accumulated cases in Spain |
TONI MONLEON-GETINO; Jaume Canela-Soler |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.09.20059881 |
9h4pq7up |
0.310051 |
|
Zareie_2020 |
| 60 |
Al-qaness_2020 |
Optimization Method for Forecasting Confirmed Cases of COVID-19 in China |
Al-qaness, Mohammed A. A.; Ewees, Ahmed A.; Fan, Hong; Abd El Aziz, Mohamed |
2020 |
2020-03-02 |
None |
Y |
PMC7141184 |
32131537.0 |
10.3390/jcm9030674 |
x1zq2i9h |
0.307671 |
|
Liu_Q_2020, Zareie_2020, Huang_2020, youbin_2020 |
| 61 |
Roques_2020 |
Mechanistic-statistical SIR modelling for early estimation of the actual number of cases and mortality rate from COVID-19 |
Lionel Roques; Etienne Klein; Julien Papaix; Samuel Soubeyrand |
2020 |
2020-03-24 |
BioRxiv |
Y |
|
|
10.1101/2020.03.22.20040915 |
dqg8fkca |
0.307663 |
|
|
| 62 |
Hu_Z_2020 |
Evaluation and prediction of the COVID-19 variations at different input population and quarantine strategies, a case study in Guangdong province, China |
Hu, Zengyun; Cui, Qianqian; Han, Junmei; Wang, Xia; Sha, Wei E.I.; Teng, Zhidong |
2020 |
2020-04-22 |
PMC |
N |
|
|
10.1016/j.ijid.2020.04.010 |
b59uexhr |
0.306324 |
Tomie_2020 |
|
| 63 |
Rovetta_2020 |
Modelling the epidemiological trend and behavior of COVID-19 in Italy |
Alessandro Rovetta; Akshaya Srikanth Bhagavathula |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2020.03.19.20038968 |
05m50voc |
0.306173 |
Tay_K_2020 |
Saif_2020, Riccardo_2020, Weber_2020 |