| 1 |
Scarabel_2020 |
Canada needs to rapidly escalate public health interventions for its COVID-19 mitigation strategies |
Scarabel, Francesca; Pellis, Lorenzo; Bragazzi, Nicola Luigi; Wu, Jianhong |
2020 |
2020-03-31 |
PMC |
Y |
|
|
10.1016/j.idm.2020.03.004 |
vwwt70mo |
0.925321 |
|
|
| 2 |
Virk_2020 |
Recent update on COVID-19 in India: Is locking down the country enough? |
Jitender Singh Virk; Syed Azmal Ali; Gurjeet Kaur |
2020 |
2020-04-10 |
BioRxiv |
Y |
|
|
10.1101/2020.04.06.20053124 |
r1gpag26 |
0.822989 |
|
He_J_2020, Memish_2020, Remuzzi_2020 |
| 3 |
Sousa-Pinto_2020 |
Is scaling-up COVID-19 testing cost-saving? |
Bernardo Sousa-Pinto; Joao Almeida Fonseca; Altamiro Costa-Pereira; Francisco Nuno Rocha-Goncalves |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.22.20041137 |
s4kfza3o |
0.803218 |
|
|
| 4 |
Weber_2020 |
Trend analysis of the COVID-19 pandemic in China and the rest of the world |
Albertine Weber; Flavio Iannelli; Sebastian Goncalves |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2020.03.19.20037192 |
vsqaxqy4 |
0.751086 |
|
He_J_2020, Teles_2020, Wang_2020 |
| 5 |
Yang_2020 |
Feasibility of Controlling COVID-19 Outbreaks in the UK by Rolling Interventions |
Po Yang; Jun Qi; Shuhao Zhang; Gaoshan Bi; Xulong Wang; Yun Yang; Bin Sheng; Xuxin Mao |
2020 |
2020-04-07 |
BioRxiv |
Y |
|
|
10.1101/2020.04.05.20054429 |
b6r6j1ek |
0.728186 |
|
|
| 6 |
Abdeljaoued-Tej_2020 |
Estimation of Tunisia COVID-19 infected cases based on mortality rate |
Ines Abdeljaoued-Tej; Marc Dhenain |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.15.20065532 |
g44xo465 |
0.659430 |
Scheuerl_2020 |
|
| 7 |
Kumar_2020 |
Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020 through ARIMA Model with Machine Learning Approach |
Pavan Kumar; Himangshu Kalita; Shashikanta Patairiya; Yagya Datt Sharma; Chintan Nanda; Meenu Rani; Jamal Rahmai; Akshaya Srikanth Bhagavathula |
2020 |
2020-03-31 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20046227 |
lxakf79k |
0.646234 |
|
Distante_2020, Li_M_2020, Huang_2020, Roosa_2020 |
| 8 |
Patil_2020 |
Current State and Predicting Future Scenario of Highly Infected Nations for COVID-19 Pandemic |
Nandan L Patil |
2020 |
2020-03-31 |
BioRxiv |
Y |
|
|
10.1101/2020.03.28.20046235 |
pil4uudg |
0.642698 |
Scheuerl_2020 |
Kim_J_2020, He_J_2020 |
| 9 |
Muhammad_2020 |
COVID-19 pandemic and environmental pollution: A blessing in disguise? |
Muhammad, Sulaman; Long, Xingle; Salman, Muhammad |
2020 |
2020-04-20 |
PMC |
Y |
|
|
10.1016/j.scitotenv.2020.138820 |
inkm2q2w |
0.593975 |
|
|
| 10 |
Teles_2020 |
PREDICTING THE EVOLUTION OF COVID-19 IN PORTUGAL USING AN ADAPTED SIR MODEL PREVIOUSLY USED IN SOUTH KOREA FOR THE MERS OUTBREAK |
Pedro Teles |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.18.20038612 |
yrq58n4k |
0.589712 |
|
Memish_2020, Bassetti_2020, He_J_2020 |
| 11 |
Hu_Z_2020 |
Evaluating the effect of public health intervention on the global-wide spread trajectory of Covid-19 |
Zixin Hu; Qiyang Ge; Shudi Li; Li Jin; Momiao Xiong |
2020 |
2020-03-16 |
BioRxiv |
Y |
|
|
10.1101/2020.03.11.20033639 |
44zduv27 |
0.576070 |
|
Scarabel_2020 |
| 12 |
magal_2020 |
Predicting the number of reported and unreported cases for the COVID-19 epidemic in South Korea, Italy, France and Germany |
pierre magal; Glenn Webb |
2020 |
2020-03-24 |
BioRxiv |
Y |
|
|
10.1101/2020.03.21.20040154 |
9zeqigqa |
0.575563 |
|
Chen_2020, Liu_Z_2020 |
| 13 |
Liao_2020 |
A Tempo-geographic Analysis of Global COVID-19 Epidemic Outside of China |
Huipeng Liao; Gifty Marley; Yafei Si; Zaisheng Wang; Yewei Xie; Cheng Wang; Weiming Tang |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2020.03.20.20039602 |
fb0nwixw |
0.575498 |
|
Hou_J_2020, He_J_2020, Weber_2020 |
| 14 |
Gonzalez_2020 |
Is a COVID19 Quarantine Justified in Chile or USA Right Now? |
Rafael Gonzalez Gonzalez; Francisco Munoz; Pablo S Moya; Miguel Kiwi |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.23.20042002 |
7eokrk78 |
0.571942 |
|
Remuzzi_2020, Teixeira_da_Silva_2020, See_K_2020 |
| 15 |
Supino_2020 |
World governments should protect their population from COVID-19 pandemic using Italy and Lombardy as precursor |
Mariano Supino; Alberto d'Onofrio; Federico Luongo; Giovanni Occhipinti; Alma Dal Co |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.25.20042713 |
5war06j2 |
0.571942 |
|
Remuzzi_2020 |
| 16 |
Pais_2020 |
Predicting the evolution and control of COVID-19 pandemic in Portugal. |
Ricardo Jorge Pais; Nuno Taveira |
2020 |
2020-03-31 |
BioRxiv |
Y |
|
|
10.1101/2020.03.28.20046250 |
a8er8wbg |
0.561726 |
|
Saif_2020 |
| 17 |
Gupta_2020 |
A Comprehensive Analysis of COVID-19 Outbreak situation in India |
Rajan Gupta; Saibal Kumar Pal; Gaurav Pandey |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.08.20058347 |
hquc2v2c |
0.552647 |
|
He_J_2020, Zareie_2020 |
| 18 |
Rai_B_2020 |
COVID-19 in India: Predictions, Reproduction Number and Public Health Preparedness |
Balram Rai; Anandi Shukla; Laxmi Kant Dwivedi |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.09.20059261 |
i2mafyzi |
0.552471 |
|
Zareie_2020 |
| 19 |
Brouwer_2020 |
Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries |
Edward De Brouwer; Daniele Raimondi; Yves Moreau |
2020 |
2020-04-04 |
BioRxiv |
Y |
|
|
10.1101/2020.04.02.20046375 |
brurrmi4 |
0.536192 |
|
|
| 20 |
Tradigo_2020 |
On the assessment of more reliable COVID-19 infected number: the italian case. |
Giuseppe Tradigo; Pietro Hiram Guzzi; Pierangelo Veltri |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.25.20043562 |
7hqn997j |
0.523642 |
|
Remuzzi_2020, Marchese-Ragona_2020 |
| 21 |
Jha_P_2020 |
What does simple power law kinetics tell about our response to coronavirus pandemic? |
Prateek Kumar Jha |
2020 |
2020-04-06 |
BioRxiv |
N |
|
|
10.1101/2020.04.03.20051797 |
y1727pi6 |
0.517679 |
|
Devaux_2020 |
| 22 |
TIWARI_2020 |
Modelling and analysis of COVID-19 epidemic in India. |
ALOK TIWARI |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.12.20062794 |
rsjx62i4 |
0.517220 |
|
|
| 23 |
Park_2020 |
Spatial Visualization of Cluster-Specific COVID-19 Transmission Network in South Korea During the Early Epidemic Phase |
James Yeongjun Park |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.18.20038638 |
901m6zw0 |
0.513069 |
Ma_X_2020 |
He_J_2020 |
| 24 |
Mishra_2020 |
A deductive approach to modeling the spread of COVID-19 |
Pranav Kumar Mishra; Shekhar Mishra |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.26.20044651 |
yg5posts |
0.510562 |
|
|
| 25 |
Kinross_2020 |
Rapidly increasing cumulative incidence of coronavirus disease (COVID-19) in the European Union/European Economic Area and the United Kingdom, 1 January to 15 March 2020 |
Kinross, Pete; Suetens, Carl; Gomes Dias, Joana; Alexakis, Leonidas; Wijermans, Ariana; Colzani, Edoardo; Monnet, Dominique L. |
2020 |
2020-03-19 |
COMM-USE |
Y |
PMC7096777 |
32186277.0 |
10.2807/1560-7917.es.2020.25.11.2000285 |
r74u7oyo |
0.495959 |
|
|
| 26 |
Remuzzi_2020 |
COVID-19 and Italy: what next? |
Remuzzi, Andrea; Remuzzi, Giuseppe |
2020 |
2020-04-17 |
PMC |
Y |
|
|
10.1016/s0140-6736(20)30627-9 |
6sgeraws |
0.495582 |
|
|
| 27 |
Hasan_2020 |
Predict the next moves of COVID-19: reveal the temperate and tropical countries scenario |
Neaz A. Hasan; Mohammad Mahfujul Haque |
2020 |
2020-04-07 |
BioRxiv |
Y |
|
|
10.1101/2020.04.04.20052928 |
xcacty89 |
0.495083 |
|
He_J_2020, Weber_2020 |
| 28 |
Barbarossa_2020 |
A first study on the impact of current and future control measures on the spread of COVID-19 in Germany |
Maria Vittoria Barbarossa; Jan Fuhrmann; Julian Heidecke; Hridya Vinod Varma; Noemi Castelletti; Jan H Meinke; Stefan Krieg; Thomas Lippert |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.08.20056630 |
0xymzkzn |
0.483541 |
|
Devaux_2020 |
| 29 |
Metcalfe_2020 |
Mesenchymal stem cells and management of COVID-19 pneumonia |
Metcalfe, Su M. |
2020 |
2020-03-31 |
PMC |
Y |
PMC7147223 |
|
10.1016/j.medidd.2020.100019 |
6gxfop4o |
0.481801 |
|
|
| 30 |
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 |
0.477296 |
Tay_K_2020 |
Saif_2020, Riccardo_2020, Weber_2020 |
| 31 |
Pejman_2020 |
Coronavirus epidemic: prediction and controlling measures |
Mohammad Mehdi Pejman; armaghan fereidooni |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.11.20062125 |
ivb8tm4c |
0.472091 |
|
|
| 32 |
Mondal_2020 |
Fear of exponential growth in Covid19 data of India and future sketching |
Supriya Mondal; Sabyasachi Ghosh |
2020 |
2020-04-11 |
BioRxiv |
N |
|
|
10.1101/2020.04.09.20058933 |
xmx35h3z |
0.457212 |
Weber_2020 |
Distante_2020, Weber_2020 |
| 33 |
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 |
0.453195 |
|
Gupta_2020, Roosa_2020, Teles_2020 |
| 34 |
Weon_2020 |
Doubling time tells how effective Covid-19 prevention works |
Byung Mook Weon |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.26.20044644 |
d950qa2w |
0.452973 |
|
Chen_2020 |
| 35 |
Dehkordi_2020 |
Understanding Epidemic Data and Statistics: A case study of COVID-19 |
Amirhoshang Hoseinpour Dehkordi; Majid Alizadeh; Pegah Derakhshan; Peyman Babazadeh; Arash Jahandideh |
2020 |
2020-03-18 |
BioRxiv |
Y |
|
|
10.1101/2020.03.15.20036418 |
u1i5onqr |
0.450947 |
Bassetti_2020 |
Tian_2020 |
| 36 |
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 |
0.440097 |
|
He_J_2020, Nesteruk_2020, Teles_2020 |
| 37 |
Sahafizadeh_2020 |
Estimating the reproduction number of COVID-19 in Iran using epidemic modeling |
Ebrahim Sahafizadeh; Samaneh Sartoli |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2020.03.20.20038422 |
f3ikyl9d |
0.433204 |
batista_2020 |
Zareie_2020, He_J_2020 |
| 38 |
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 |
0.429268 |
|
Chintalapudi_2020, Gupta_2020, Zareie_2020 |
| 39 |
Syed_2020 |
Estimation of the Final Size of the COVID-19 Epidemic in Pakistan |
Faiza Syed; Syed Sibgatullah |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.01.20050369 |
b7p92sb1 |
0.423894 |
|
Zareie_2020 |
| 40 |
He_J_2020 |
Comparative Analysis of COVID-19 Transmission Patterns in Three Chinese Regions vs. South Korea,Italy and Iran |
Junyu He; Guangwei Chen; Yutong Jiang; Runjie Jin; Mingjun He; Ashton Shortridge; Jiaping Wu; George Christakos |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.09.20053223 |
d9y31nlj |
0.421914 |
Tomie_2020, Ma_X_2020 |
Wang_2020, Ji_X_2020, Ping_2020 |
| 41 |
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 |
0.421486 |
|
Zareie_2020 |
| 42 |
Shim_2020 |
Transmission potential of COVID-19 in South Korea |
Eunha Shim; Amna Tariq; Wongyeong Choi; Yiseul Lee; Gerardo Chowell |
2020 |
2020-02-29 |
BioRxiv |
Y |
|
|
10.1101/2020.02.27.20028829 |
z7a3g6e8 |
0.415698 |
Muniz-Rodriguez_2020 |
Li_P_2020, Wang_2020, Chen_2020 |
| 43 |
Nandagopal_2020 |
COVID-19: An Update on the Epidemiological, Genomic Origin, Phylogenetic study, India centric to Worldwide current status |
Murugan Nandagopal; Sagaya Jansi R |
2020 |
2020-04-21 |
BioRxiv |
Y |
|
|
10.1101/2020.04.17.20070284 |
zdv0ilti |
0.410405 |
|
|
| 44 |
UNKNOWN_2020 |
Coronavirus Disease-19: The First 7,755 Cases in the Republic of Korea |
None |
2020 |
2020-04-23 |
NONCOMM |
Y |
PMC7104685 |
|
10.24171/j.phrp.2020.11.2.05 |
8747t6nj |
0.409177 |
|
Choe_2020 |
| 45 |
Qasim_2020 |
Analysis of the Worldwide Corona Virus (COVID-19) Pandemic Trend;A Modelling Study to Predict Its Spread |
Muhammad Qasim; Waqas Ahmad; Minami Yoshida; Maree Gould; Muhammad Yasir |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20048215 |
8lku99jc |
0.406205 |
Scheuerl_2020 |
Teles_2020 |
| 46 |
Schlickeiser_2020 |
A Gaussian model for the time development of the Sars-Cov-2 corona pandemic disease. Predictions for Germany made on March 30, 2020 |
Reinhard Schlickeiser; Frank Schlickeiser |
2020 |
2020-04-02 |
BioRxiv |
N |
|
|
10.1101/2020.03.31.20048942 |
0sny9dit |
0.401026 |
batista_2020, Griette_2020 |
|
| 47 |
Krishnakumar_2020 |
COVID 19 in INDIA: Strategies to combat from combination threat of life and livelihood |
Krishnakumar, Balaji; Rana, Sravendra |
2020 |
2020-03-28 |
PMC |
Y |
|
|
10.1016/j.jmii.2020.03.024 |
44inap6t |
0.398537 |
|
See_K_2020 |
| 48 |
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 |
0.398226 |
|
|
| 49 |
Kumar_2020 |
Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models |
Pavan Kumar; Ram Kumar Singh; Chintan Nanda; Himangshu Kalita; Shashikanta Patairiya; Yagya Datt Sharma; Meenu Rani; Akshaya Srikanth Bhagavathula |
2020 |
2020-04-02 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20047803 |
b9p5tqhl |
0.393940 |
|
Hou_J_2020 |
| 50 |
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 |
0.385857 |
batista_2020 |
Zareie_2020, Hou_J_2020, Fanelli_2020 |
| 51 |
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 |
0.384959 |
|
Teles_2020 |
| 52 |
Tay_K_2020 |
COVID-19 in Singapore and Malaysia: Rising to the Challenges of Orthopaedic Practice in an Evolving Pandemic |
Tay, K; Kamarul, T; Lok, WY; Mansor, M; Li, X; Wong, J; Saw, A |
2020 |
2020-04-07 |
COMM-USE |
Y |
PMC7169474 |
|
|
qz8pffei |
0.375325 |
|
|
| 53 |
Liu_Z_2020 |
Predicting the number of reported and unreported cases for the COVID-19 epidemics in China, South Korea, Italy, France, Germany and United Kingdom |
Zhihua Liu; pierre magal; Glenn F Webb |
2020 |
2020-04-10 |
BioRxiv |
Y |
|
|
10.1101/2020.04.09.20058974 |
qvaakk1b |
0.373338 |
|
Memish_2020, He_J_2020 |
| 54 |
Megna_2020 |
First month of the epidemic caused by COVID-19 in Italy: current status and real-time outbreak development forecast |
Rosario Megna |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.26.20044628 |
cxmw7bfu |
0.362994 |
|
Teles_2020 |
| 55 |
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 |
0.359038 |
|
|
| 56 |
Zhang_2020 |
The impact of social distancing and epicenter lockdown on the COVID-19 epidemic in mainland China: A data-driven SEIQR model study |
Yuzhen Zhang; Bin Jiang; Jiamin Yuan; Yanyun Tao |
2020 |
2020-03-06 |
BioRxiv |
Y |
|
|
10.1101/2020.03.04.20031187 |
utlponna |
0.358849 |
|
Hou_J_2020 |
| 57 |
Wu_T_2020 |
Open-source analytics tools for studying the COVID-19 coronavirus outbreak |
Tianzhi Wu; Xijin Ge; Guangchuang Yu; Erqiang Hu |
2020 |
2020-02-27 |
BioRxiv |
N |
|
|
10.1101/2020.02.25.20027433 |
qtrquuvv |
0.356708 |
|
|
| 58 |
Wang_2020 |
Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies |
Qinxia Wang; Shanghong Xie; Yuanjia Wang; Donglin Zeng |
2020 |
2020-04-21 |
BioRxiv |
Y |
|
|
10.1101/2020.04.16.20067306 |
tqeyx7yn |
0.355078 |
|
|
| 59 |
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 |
0.348522 |
|
|
| 60 |
Leo_S_2020 |
Analysing and comparing the COVID-19 data: The closed cases of Hubei and South Korea, the dark March in Europe, the beginning of the outbreak in South America |
Stefano De Leo; Gabriel Gulak Maia; Leonardo Solidoro |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.06.20055327 |
9j2ngvlb |
0.347402 |
|
Teles_2020, He_J_2020, Weber_2020 |
| 61 |
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 |
0.345669 |
|
|
| 62 |
Zhang_2020 |
Exponential damping key to successful containment of COVID-19 outbreak |
Feng Zhang; Jinmei Zhang; Menglan Cao; Yong Zhang; Cang Hui |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.22.20041111 |
fqu54j4h |
0.343565 |
|
|
| 63 |
Desjardins_2020 |
Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters |
Desjardins, M.R.; Hohl, A.; Delmelle, E.M. |
2020 |
2020-05-31 |
PMC |
Y |
|
|
10.1016/j.apgeog.2020.102202 |
y691hark |
0.343220 |
|
Ai_J_2020, Hsih_2020 |
| 64 |
Choe_2020 |
Coronavirus disease-19: The First 7,755 Cases in the Republic of Korea |
Young June Choe |
2020 |
2020-03-18 |
BioRxiv |
Y |
|
|
10.1101/2020.03.15.20036368 |
grx4gx95 |
0.337839 |
|
UNKNOWN_2020 |
| 65 |
Tang_2020 |
Lessons drawn from China and South Korea for managing COVID-19 epidemic: insights from a comparative modeling study |
Biao Tang; Fan Xia; Nicola Luigi Bragazzi; Xia Wang; Sha He; Xiaodan Sun; Sanyi Tang; Yanni Xiao; Jianhong Wu |
2020 |
2020-03-13 |
BioRxiv |
Y |
|
|
10.1101/2020.03.09.20033464 |
z7r45291 |
0.332864 |
|
He_J_2020, Wang_2020, Ji_X_2020, Zareie_2020 |
| 66 |
Teixeira_da_Silva_2020 |
Convalescent plasma: A possible treatment of COVID-19 in India |
Teixeira da Silva, Jaime A. |
2020 |
2020-04-15 |
PMC |
Y |
|
|
10.1016/j.mjafi.2020.04.006 |
nv70mlae |
0.329251 |
|
|
| 67 |
Tuite_2020 |
Estimation of COVID-2019 burden and potential for international dissemination of infection from Iran |
Ashleigh R. Tuite; Isaac Bogoch; Ryan Sherbo; Alexander Watts; David N. Fisman; Kamran Khan |
2020 |
2020-02-25 |
BioRxiv |
Y |
|
|
10.1101/2020.02.24.20027375 |
of9wlhga |
0.324556 |
|
He_J_2020, Memish_2020 |
| 68 |
Khachfe_2020 |
An Epidemiological Study on COVID-19: A Rapidly Spreading Disease |
Khachfe, Hussein H; Chahrour, Mohamad; Sammouri, Julie; Salhab, Hamza; Makki, Bassel Eldeen; Fares, Mohamad |
2020 |
2020-03-18 |
COMM-USE |
Y |
PMC7164711 |
|
10.7759/cureus.7313 |
52c7myio |
0.323291 |
Scheuerl_2020 |
|
| 69 |
Bhola_2020 |
Corona Epidemic in Indian context: Predictive Mathematical Modelling |
Jyoti Bhola; Vandana Revathi Venkateswaran; Monika Koul |
2020 |
2020-04-07 |
BioRxiv |
Y |
|
|
10.1101/2020.04.03.20047175 |
tohbzenc |
0.322546 |
|
|
| 70 |
Velavan_2020 |
The COVID‐19 epidemic |
Velavan, Thirumalaisamy P.; Meyer, Christian G. |
2020 |
2020-02-16 |
PMC |
Y |
PMC7169770 |
32052514.0 |
10.1111/tmi.13383 |
e4hmo4yc |
0.315631 |
Ma_X_2020 |
Memish_2020, Arshad_Ali_2020, He_J_2020 |
| 71 |
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 |
0.313378 |
Griette_2020 |
Liu_Q_2020, Chintalapudi_2020, Li_M_2020 |
| 72 |
Mbabazi_2020 |
Projection of COVID-19 Pandemic in Uganda |
Fulgensia Kamugisha Mbabazi |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.02.20051086 |
qgodwdq8 |
0.310886 |
Ma_X_2020, Bassetti_2020 |
Nuwagira_2020, Memish_2020 |
| 73 |
Moradzadeh_2020 |
The challenges and considerations of community-based preparedness at the onset of COVID-19 outbreak in Iran, 2020 |
Moradzadeh, Rahmatollah |
2020 |
2020-04-03 |
COMM-USE |
Y |
PMC7167485 |
32242790.0 |
10.1017/s0950268820000783 |
i49m4y0e |
0.308005 |
|
|