| 1 |
Anastassopoulou_2020 |
Data-based analysis, modelling and forecasting of the COVID-19 outbreak |
Anastassopoulou, Cleo; Russo, Lucia; Tsakris, Athanasios; Siettos, Constantinos |
2020 |
2020-03-31 |
COMM-USE |
Y |
PMC7108749 |
32231374.0 |
10.1371/journal.pone.0230405 |
uglsx7se |
0.913581 |
Li_C_2018, Liu_Z_2020 |
Anastassopoulou_2020, Zareie_2020 |
| 2 |
batista_2020 |
Estimation of the final size of the coronavirus epidemic by the logistic model |
milan batista |
2020 |
2020-02-18 |
BioRxiv |
N |
|
|
10.1101/2020.02.16.20023606 |
ndnkhj42 |
0.907598 |
batista_2020, Lloyd_2009, Ma_J_2020 |
batista_2020 |
| 3 |
Anastassopoulou_2020 |
Data-Based Analysis, Modelling and Forecasting of the COVID-19 outbreak |
Cleo Anastassopoulou; Lucia Russo; Athanasios Tsakris; Constantinos Siettos |
2020 |
2020-02-13 |
BioRxiv |
Y |
|
|
10.1101/2020.02.11.20022186 |
ssdx8xwi |
0.902626 |
Li_C_2018, Dutra_2020 |
Anastassopoulou_2020, Zareie_2020, Roosa_2020 |
| 4 |
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 |
0.893322 |
Li_C_2018, Liu_Z_2020, Griette_2020, batista_2020, batista_2020 |
Zhan_2020, Caccavo_2020 |
| 5 |
Aboelkassem_2020 |
COVID-19 pandemic: A Hill type mathematical model predicts the US death number and the reopening date |
Yasser Aboelkassem |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.12.20062893 |
rk09xpc3 |
0.888517 |
Liu_Z_2020 |
|
| 6 |
Russo_2020 |
Tracing DAY-ZERO and Forecasting the Fade out of the COVID-19 Outbreak in Lombardy, Italy: A Compartmental Modelling and Numerical Optimization Approach. |
Lucia Russo; Cleo Anastassopoulou; Athanassios Tsakris; Gennaro Nicola Bifulco; Emilio Fortunato Campana; Gerardo Toraldo; Constantinos Siettos |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.17.20037689 |
fuqtwn5a |
0.876013 |
batista_2020, Liu_Z_2020, Li_C_2018 |
|
| 7 |
Dutra_2020 |
Estimate of the Maximum Limit of Total Cases of Infected Patients COVID-19 |
Carlos Maximiliano Dutra; Carlos Augusto Riella de Melo |
2020 |
2020-04-15 |
BioRxiv |
Y |
|
|
10.1101/2020.04.10.20060822 |
zrgax3qa |
0.874028 |
Li_C_2018, batista_2020, Tao_Y_2020 |
|
| 8 |
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 |
0.872164 |
batista_2020, Li_C_2018 |
batista_2020 |
| 9 |
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 |
0.861585 |
Griette_2020, Li_C_2018, batista_2020, batista_2020 |
Zareie_2020 |
| 10 |
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 |
0.846190 |
batista_2020, Li_C_2018, Mondor_2012 |
|
| 11 |
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 |
0.843734 |
|
Zareie_2020, Ranjan_2020 |
| 12 |
Ranjan_2020 |
Estimating the Final Epidemic Size for COVID-19 Outbreak using Improved Epidemiological Models |
Rajesh Ranjan |
2020 |
2020-04-16 |
BioRxiv |
Y |
|
|
10.1101/2020.04.12.20061002 |
emyuny1a |
0.842815 |
Bifolchi_2013, Kenah_2007, Li_C_2018, Liu_Z_2020 |
|
| 13 |
Bhardwaj_2020 |
A Predictive Model for the Evolution of COVID-19 |
Rajneesh Bhardwaj |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.13.20063271 |
xkenld6e |
0.826136 |
Li_C_2018, batista_2020, batista_2020, Ma_J_2020 |
|
| 14 |
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 |
0.820127 |
Li_C_2018, Wu_Q_2014 |
Yong_2016, Zareie_2020 |
| 15 |
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 |
0.807133 |
batista_2020, Griette_2020 |
|
| 16 |
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 |
0.797367 |
Li_C_2018 |
Zareie_2020 |
| 17 |
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 |
0.796610 |
|
Zareie_2020 |
| 18 |
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 |
0.784978 |
batista_2020, batista_2020, Griette_2020 |
Ciufolini_2020, Smeets_2020 |
| 19 |
Ciufolini_2020 |
Mathematical prediction of the time evolution of the COVID-19 pandemic in Italy by a Gauss error function and Monte Carlo simulations |
Ciufolini, Ignazio; Paolozzi, Antonio |
2020 |
2020-04-15 |
COMM-USE |
Y |
PMC7156796 |
|
10.1140/epjp/s13360-020-00383-y |
2bwlxlfo |
0.775347 |
Li_C_2018, Lloyd_2009 |
|
| 20 |
Yadlowsky_2020 |
Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County |
Steve Yadlowsky; Nigam Shah; Jacob Steinhardt |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.24.20043067 |
6vt60348 |
0.769177 |
Li_C_2018, Lauro_2020 |
|
| 21 |
Akhtar_2020 |
Understanding the CoVID-19 pandemic Curve through statistical approach |
Ibrar ul Hassan Akhtar |
2020 |
2020-04-08 |
BioRxiv |
Y |
|
|
10.1101/2020.04.06.20055426 |
14w3ygss |
0.756438 |
batista_2020, Li_C_2018, Dutra_2020 |
|
| 22 |
DISTANTE_2020 |
Covid-19 Outbreak Progression in Italian Regions: Approaching the Peak by March 29th |
COSIMO DISTANTE; PRISCO PISCITELLI; ALESSANDRO MIANI |
2020 |
2020-04-02 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20043612 |
idauypat |
0.754593 |
batista_2020, Li_C_2018 |
Caccavo_2020, Kretzschmar_2020 |
| 23 |
Lin_F_2020 |
Evaluating the different control policies for COVID-19 between mainland China and European countries by a mathematical model in the confirmed cases |
Feng Lin; Yi Huang; Huifang Zhang; Xu He; Yonghua Yin; Jiaxin Liu |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2020.04.17.20068775 |
lgdsi48m |
0.752135 |
batista_2020 |
|
| 24 |
Ciufolini_2020 |
Prediction of the time evolution of the Covid-19 Pandemic in Italy by a Gauss Error Function and Monte Carlo simulations |
Ignazio Ciufolini; Antonio Paolozzi |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.27.20045104 |
ltcfsrb2 |
0.750360 |
Lloyd_2009, Li_C_2018 |
Fanelli_2020, Weber_2020, Zareie_2020 |
| 25 |
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 |
0.748756 |
Zheng_2020 |
Zareie_2020, D'Arienzo_2020 |
| 26 |
Nesteruk_2020 |
Long-term predictions for COVID-19 pandemic dynamics in Ukraine, Austria and Italy |
Igor Nesteruk |
2020 |
2020-04-11 |
BioRxiv |
N |
|
|
10.1101/2020.04.08.20058123 |
hmem8se3 |
0.745608 |
Zheng_2020, Lloyd_2009 |
Saif_2020, Yong_2016, Hackl_2020 |
| 27 |
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 |
0.742256 |
Mondor_2012, batista_2020, Li_C_2018 |
|
| 28 |
Fenga_2020 |
Forecasting the CoViD19 Diffusion in Italy and the Related Occupancy of Intensive Care Units |
Livio Fenga |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20047894 |
4ffbqpkk |
0.733666 |
|
|
| 29 |
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 |
0.721535 |
Li_C_2018 |
Roosa_2020, Peng_2020, Fanelli_2020 |
| 30 |
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 |
0.717717 |
Li_C_2018, Wu_Q_2014, Gong_2013, Chen_2018 |
Weber_2020, Peng_2020 |
| 31 |
Spencer_2020 |
Coronametrics: The UK turns the corner |
Peter D. Spencer; Adam Golinski |
2020 |
2020-04-22 |
BioRxiv |
N |
|
|
10.1101/2020.04.17.20069278 |
zua849mn |
0.714430 |
Zheng_2020 |
|
| 32 |
Zhang_2020 |
Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries |
Zhang, Xiaolei; Ma, Renjun; Wang, Lin |
2020 |
2020-04-20 |
PMC |
Y |
|
|
10.1016/j.chaos.2020.109829 |
v7hmc9sj |
0.714414 |
Mondor_2012, batista_2020, Li_C_2018 |
|
| 33 |
Hermanowicz_2020 |
Forecasting the Wuhan coronavirus (2019-nCoV) epidemics using a simple (simplistic) model - update (Feb. 8, 2020) |
Slav W Hermanowicz |
2020 |
2020-02-05 |
BioRxiv |
N |
|
|
10.1101/2020.02.04.20020461 |
8kdtpwbv |
0.714357 |
batista_2020 |
Shi_P_2020 |
| 34 |
Hermanowicz_2020 |
Simple model for Covid-19 epidemics - back-casting in China and forecasting in the US |
Slav W Hermanowicz |
2020 |
2020-04-03 |
BioRxiv |
Y |
|
|
10.1101/2020.03.31.20049486 |
t7400ru5 |
0.714343 |
batista_2020, Dutra_2020, Li_C_2018 |
Roosa_2020, Weber_2020 |
| 35 |
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 |
0.710575 |
Zheng_2020 |
Nesteruk_2020, Yong_2016 |
| 36 |
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 |
0.708800 |
|
|
| 37 |
Soubeyrand_2020 |
The current COVID-19 wave will likely be mitigated in the second-line European countries |
Samuel Soubeyrand; Melina Ribaud; Virgile Baudrot; Denis Allard; Denys Pommeret; Lionel Roques |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
|
10.1101/2020.04.17.20069179 |
dh3cgd48 |
0.705873 |
|
|
| 38 |
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 |
0.704044 |
batista_2020, Li_C_2018 |
|
| 39 |
Tomar_2020 |
Prediction for the spread of COVID-19 in India and effectiveness of preventive measures |
Tomar, Anuradha; Gupta, Neeraj |
2020 |
2020-04-20 |
PMC |
Y |
|
|
10.1016/j.scitotenv.2020.138762 |
pvf3afug |
0.702313 |
|
|
| 40 |
Lopez_2020 |
A modified SEIR model to predict the COVID-19 outbreak in Spain: simulating control scenarios and multi-scale epidemics |
Leonardo R Lopez; Xavier Rodo |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.27.20045005 |
m27nyzrw |
0.691659 |
Li_C_2018, Wu_Q_2014, Chen_2018, Mondor_2012 |
|
| 41 |
Ranjan_2020 |
Predictions for COVID-19 outbreak in India using Epidemiological models |
Rajesh Ranjan |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.02.20051466 |
3vntjg8d |
0.683197 |
Li_C_2018, Liu_Z_2020, Wu_Q_2014 |
Zareie_2020, Smeets_2020, Yong_2016 |
| 42 |
Qasim_2020 |
Data model to predict prevalence of COVID-19 in Pakistan |
Muhammad Qasim; Waqas Ahmad; Shenghuan Zhang; Muhammad Yasir; Muhammad Azhar |
2020 |
2020-04-10 |
BioRxiv |
Y |
|
|
10.1101/2020.04.06.20055244 |
h4b28cq0 |
0.682893 |
|
Zareie_2020 |
| 43 |
Kuniya_2020 |
Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020 |
Kuniya, Toshikazu |
2020 |
2020-03-13 |
COMM-USE |
Y |
PMC7141223 |
32183172.0 |
10.3390/jcm9030789 |
5z012sr6 |
0.681552 |
|
|
| 44 |
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 |
0.679579 |
Li_C_2018, Mondor_2012 |
Yong_2016, Zareie_2020 |
| 45 |
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 |
0.679273 |
Li_C_2018, Lloyd_2009, Welch_2011 |
Weber_2020 |
| 46 |
Tang_2020 |
The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemics in the final phase of the current outbreak in China |
Tang, Biao; Xia, Fan; Tang, Sanyi; Bragazzi, Nicola Luigi; Li, Qian; Sun, Xiaodan; Liang, Juhua; Xiao, Yanni; Wu, Jianhong |
2020 |
2020-04-17 |
PMC |
Y |
|
|
10.1016/j.ijid.2020.03.018 |
gdsh8wmv |
0.674493 |
|
|
| 47 |
SUN_P_2020 |
An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK |
Peiliang SUN; Kang Li |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.12.20062588 |
9mdxid0u |
0.672527 |
Li_C_2018, Wu_Q_2014, Sadun_2020 |
|
| 48 |
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 |
0.668749 |
batista_2020, Liu_Z_2020 |
|
| 49 |
Liu_X_2020 |
Modeling the situation of COVID-19 and effects of different containment strategies in China with dynamic differential equations and parameters estimation |
Xiuli Liu; Geoffrey J.D. Hewings; Shouyang Wang; Minghui Qin; Xin Xiang; Shan Zheng; Xuefeng Li |
2020 |
2020-03-13 |
BioRxiv |
N |
|
|
10.1101/2020.03.09.20033498 |
z9jk1n7o |
0.663008 |
Zheng_2020, Li_C_2018 |
Shi_P_2020 |
| 50 |
Zhou_2020 |
Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model |
Xiang Zhou; Na Hong; Yingying Ma; Jie He; Huizhen Jiang; Chun Liu; Guangliang Shan; Longxiang Su; Weiguo Zhu; Yun Long |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.26.20044289 |
52zjm9jt |
0.655843 |
Lu_J_2020, Li_C_2018 |
Zareie_2020 |
| 51 |
Hackl_2020 |
Modeling the COVID-19 pandemic - parameter identification and reliability of predictions |
Klaus Hackl |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.07.20056937 |
osez25uj |
0.655325 |
Zheng_2020, Bocharov_2018 |
Smeets_2020, Zareie_2020, Peng_2020, Ghaffarzadegan_2020 |
| 52 |
Ciufolini_2020 |
A Mathematical prediction of the time evolution of the Covid-19 pandemic in some countries of the European Union using Monte Carlo simulations |
Ignazio Ciufolini; Antonio Paolozzi |
2020 |
2020-04-16 |
BioRxiv |
Y |
|
|
10.1101/2020.04.10.20061051 |
watj188m |
0.654481 |
batista_2020, Li_C_2018 |
|
| 53 |
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 |
0.646919 |
Li_C_2018, Wu_Q_2014, Lauro_2020 |
Yong_2016, Saif_2020 |
| 54 |
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 |
0.644391 |
Li_C_2018, Bocharov_2018, Renna_2020 |
Caccavo_2020 |
| 55 |
Shao_2020 |
CoVID-19 in Japan: What could happen in the future? |
Nian Shao; Hanshuang Pan; Xingjie Li; Weijia Li; Shufen Wang; Yan Xuan; Yue Yan; Yu Jiang; Keji Liu; Yu Chen; Boxi Xu; Xinyue Luo; Christopher Y. Shen; Min Zhong; Xiang Xu; Xu Chen; Shuai Lu; Guanghong Ding; Jin Cheng; Wenbin Chen |
2020 |
2020-02-23 |
BioRxiv |
Y |
|
|
10.1101/2020.02.21.20026070 |
u6b8iwr0 |
0.638432 |
Wu_Q_2014, Li_C_2018, O'Dea_2010 |
|
| 56 |
Shen_2020 |
A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany |
Julia Shen |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.09.20059329 |
129608e4 |
0.638052 |
Bifolchi_2013, Chowell_2017, Renna_2020, Höhle_2007 |
Yong_2016, Peng_2020, Pais_2020 |
| 57 |
Liu_P_2020 |
COVID-19 Progression Timeline and Effectiveness of Response-to-Spread Interventions across the United States |
Pai Liu; Payton Beeler; Rajan K Chakrabarty |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.17.20037770 |
6ymuovl2 |
0.637853 |
Mondor_2012 |
Kretzschmar_2020, Smeets_2020, Roosa_2020 |
| 58 |
Roosa_2020 |
Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020 |
Roosa, K.; Lee, Y.; Luo, R.; Kirpich, A.; Rothenberg, R.; Hyman, J.M.; Yan, P.; Chowell, G. |
2020 |
2020-02-14 |
None |
Y |
PMC7033348 |
32110742.0 |
10.1016/j.idm.2020.02.002 |
0zw3ukpx |
0.635526 |
|
|
| 59 |
Zhang_2020 |
Dynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread |
Hongzhe Zhang; Xiaohang Zhao; Kexin Yin; Yiren Yan; Wei Qian; Bintong Chen; Xiao Fang |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.01.20050310 |
ff4937mj |
0.632144 |
|
|
| 60 |
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 |
0.624012 |
Bocharov_2018, Li_C_2018 |
Caccavo_2020, Lopez_2020, DISTANTE_2020 |
| 61 |
Nesteruk_2020 |
Statistics based predictions of coronavirus 2019-nCoV spreading in mainland China |
Igor Nesteruk |
2020 |
2020-02-13 |
BioRxiv |
Y |
|
|
10.1101/2020.02.12.20021931 |
bhm2un6v |
0.617973 |
Zheng_2020, Bocharov_2018, Li_C_2018, Lloyd_2009 |
|
| 62 |
Xinguang_Chen_2020 |
First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model |
Xinguang Chen, Bin Yu |
2020 |
2020-01-01 |
COMM-USE |
N |
PMC7050133 |
32158961.0 |
10.1186/s41256-020-00137-4 |
36g4zdqa |
0.611503 |
Lu_J_2020, Mondor_2012 |
|
| 63 |
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 |
0.604646 |
Mondor_2012 |
|
| 64 |
Bongolan_2020 |
Age-stratified Infection Probabilities Combined with Quarantine-Modified SEIR Model in the Needs Assessments for COVID-19 |
Vena Pearl Bongolan; Jose Marie Antonio Minoza; Romulo de Castro; Jesus Emmanuel Sevilleja |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.08.20057851 |
o8aexv4p |
0.603274 |
Kenah_2012, Li_C_2018, Griette_2020, Welch_2011 |
Safi_2011, Hilton_2020 |
| 65 |
Lieu_2020 |
A Covid-19 case mortality rate without time delay systematics |
Richard Lieu; Siobhan Quenby; Ally Bi-zhu Jiang |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.03.31.20049452 |
cjcxcsx5 |
0.598346 |
Li_C_2018, Kenah_2012, Tao_Y_2020, Lloyd_2009 |
Smeets_2020, Safi_2011 |
| 66 |
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 |
0.593713 |
Li_C_2018 |
|
| 67 |
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 |
0.590354 |
Li_C_2018, Liu_Z_2020, Weber_2020 |
MONDAL_2020, Distante_2020, Weber_2020 |
| 68 |
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 |
0.589196 |
|
Zareie_2020, Caccavo_2020 |
| 69 |
Georgiou_2020 |
COVID-19 outbreak in Greece has passed its rising inflection point and stepping into its peak |
Harris V Georgiou |
2020 |
2020-04-20 |
BioRxiv |
Y |
|
|
10.1101/2020.04.15.20066712 |
ze2hnddp |
0.577289 |
|
|
| 70 |
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 |
0.576617 |
Li_C_2018, Lloyd_2009, Bocharov_2018, Zhao_2013 |
Safi_2011, Zimmer_2017, Cotta_2020 |
| 71 |
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 |
0.576520 |
Li_C_2018, Bocharov_2018, Li_K_2011 |
Zareie_2020 |
| 72 |
Richterich_2020 |
Severe underestimation of COVID-19 case numbers: effect of epidemic growth rate and test restrictions |
Peter Richterich |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.13.20064220 |
p0rqg7uk |
0.575222 |
Li_C_2018, Lauro_2020 |
|
| 73 |
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 |
0.567062 |
|
|
| 74 |
Pan_H_2020 |
Multi-chain Fudan-CCDC model for COVID-19 -- a revisit to Singapore's case |
Hanshuang Pan; Nian Shao; Yue Yan; Xinyue Luo; Shufen Wang; Ling Ye; Jin Cheng; Wenbin Chen |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.13.20063792 |
etnf4i8v |
0.561013 |
Bifolchi_2013, Bocharov_2018, O'Dea_2010, Lloyd_2009 |
|
| 75 |
Ghaffarzadegan_2020 |
Simulation-based Estimation of the Spread of COVID-19 in Iran |
Navid Ghaffarzadegan; Hazhir Rahmandad |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.22.20040956 |
sflu2was |
0.560605 |
Li_C_2018 |
Zareie_2020, Li_S_2020 |
| 76 |
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 |
0.557714 |
Zheng_2020, Mondor_2012 |
Zareie_2020, Eifan_2017 |
| 77 |
Marchant_2020 |
Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions |
Roman Marchant; Noelle I Samia; Ori Rosen; Martin A Tanner; Sally Cripps |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.11.20062257 |
ijac68gh |
0.555965 |
|
|
| 78 |
Oliveiros_2020 |
Role of temperature and humidity in the modulation of the doubling time of COVID-19 cases |
Barbara Oliveiros; Liliana Caramelo; Nuno C Ferreira; Francisco Caramelo |
2020 |
2020-03-08 |
BioRxiv |
Y |
|
|
10.1101/2020.03.05.20031872 |
qz2joxys |
0.554678 |
Li_C_2018, batista_2020, Mondor_2012, Knipl_2016 |
|
| 79 |
Xiong_2020 |
Simulating the infected population and spread trend of 2019-nCov under different policy by EIR model |
Hao Xiong; Huili Yan |
2020 |
2020-02-12 |
BioRxiv |
Y |
|
|
10.1101/2020.02.10.20021519 |
er3zmcz2 |
0.553160 |
Li_C_2018, Wu_Q_2014, Bifolchi_2013, Lu_J_2020 |
Shi_P_2020 |
| 80 |
Hu_F_2020 |
The Estimated Time-Varying Reproduction Numbers during the Ongoing Epidemic of the Coronavirus Disease 2019 (COVID-19) in China |
Fu-Chang Hu; Fang-Yu Wen |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.11.20061838 |
nlpeyh5e |
0.551506 |
Mondor_2012 |
|
| 81 |
Xu_S_2020 |
Estimating the Growth Rate and Doubling Time for Short-Term Prediction and Monitoring Trend During the COVID-19 Pandemic with a SAS Macro |
Stanley Xu; Christina Clarke; Susan Shetterly; Komal Narwaney |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.08.20057943 |
10mbsqmo |
0.549769 |
|
Ke_R_2020 |
| 82 |
ALLALI_2020 |
Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model |
MERIEM ALLALI; PATRICK PORTECOP; MICHEL CARLES; DOMINIQUE GIBERT |
2020 |
2020-04-16 |
BioRxiv |
Y |
|
|
10.1101/2020.04.12.20063008 |
cm678hn4 |
0.549313 |
Li_C_2018, Sadun_2020, Wu_Q_2014, Lauro_2020 |
|
| 83 |
Moghadami_2020 |
Modeling the Corona Virus Outbreak in IRAN |
Maryam Moghadami; Maryam Moghadami; Mohammad Hassanzadeh; ka wa; Aziz Hedayati; Mila Malekolkalami |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.24.20041095 |
f3qeoyvf |
0.548044 |
Li_C_2018 |
|
| 84 |
Li_L_2020 |
Propagation analysis and prediction of the COVID-19 |
LiXiang Li; ZiHang Yang; ZhongKai Dang; Cui Meng; JingZe Huang; HaoTian Meng; DeYu Wang; GuanHua Chen; JiaXuan Zhang; HaiPeng Peng |
2020 |
2020-03-18 |
BioRxiv |
Y |
|
|
10.1101/2020.03.14.20036202 |
nf51yjmj |
0.547855 |
Bocharov_2018 |
Li_L_2020, Zareie_2020, Peng_2020 |
| 85 |
Peirlinck_2020 |
Outbreak dynamics of COVID-19 in China and the United States |
Mathias Peirlinck; Francisco Sahli Costabal; Kevin Linka; Ellen Kuhl |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.06.20055863 |
r0qzv3en |
0.545979 |
Mondor_2012, Lu_J_2020 |
|
| 86 |
Chatterjee_2020 |
Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model |
Chatterjee, Kaustuv; Chatterjee, Kaushik; Kumar, Arun; Shankar, Subramanian |
2020 |
2020-04-02 |
PMC |
Y |
|
|
10.1016/j.mjafi.2020.03.022 |
qyo4lm8f |
0.539680 |
|
|
| 87 |
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 |
0.532770 |
Zheng_2020, Li_C_2018 |
Zhou_2020 |
| 88 |
Ahmadi_2020 |
Modeling and Forecasting Trend of COVID-19 Epidemic in Iran |
Ali Ahmadi; Majid Shirani; Fereydoon Rahmani |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.17.20037671 |
95ka0p8n |
0.531451 |
batista_2020, batista_2020, Li_C_2018, Griette_2020 |
Zareie_2020 |
| 89 |
Liu_C_2020 |
D(2)EA: Depict the Epidemic Picture of COVID-19 |
Liu, Chenzhengyi; Zhao, Jingwei; Liu, Guohang; Gao, Yuanning; Gao, Xiaofeng |
2020 |
2020-04-07 |
PMC |
Y |
PMC7137902 |
|
10.1007/s12204-020-2170-7 |
63hcyb9e |
0.531427 |
Bocharov_2018, Chowell_2017, Renna_2020 |
Peng_2020, Li_S_2020 |
| 90 |
Verma_2020 |
COVID-19 epidemic: Power law spread and flattening of the curve |
Mahendra K. Verma; Ali Asad; Soumyadeep Chatterjee |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.02.20051680 |
o0e6saez |
0.530652 |
Li_C_2018, batista_2020 |
Weber_2020, Smeets_2020, Fanelli_2020 |
| 91 |
Shi_P_2020 |
SEIR Transmission dynamics model of 2019 nCoV coronavirus with considering the weak infectious ability and changes in latency duration |
Pengpeng Shi; Shengli Cao; Peihua Feng |
2020 |
2020-02-20 |
BioRxiv |
Y |
|
|
10.1101/2020.02.16.20023655 |
c800ynvc |
0.526997 |
Li_C_2018, Wu_Q_2014, Bifolchi_2013, O'Dea_2010, Li_K_2011 |
|
| 92 |
Siwiak_2020 |
From a single host to global spread. The global mobility based modelling of the COVID-19 pandemic implies higher infection and lower detection rates than current estimates. |
Marlena M Siwiak; Pawel Szczesny; Marian P Siwiak |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2020.03.21.20040444 |
udtj0lom |
0.523447 |
Li_C_2018, Wu_Q_2014, Lloyd_2009, Chen_2018 |
Kretzschmar_2020 |
| 93 |
Cao_Z_2020 |
Incorporating Human Movement Data to Improve Epidemiological Estimates for 2019-nCoV |
Zhidong Cao; Qingpeng Zhang; Xin Lu; Dirk Pfeiffer; Lei Wang; Hongbing Song; Tao Pei; Zhongwei Jia; Daniel Dajun Zeng |
2020 |
2020-02-09 |
BioRxiv |
Y |
|
|
10.1101/2020.02.07.20021071 |
q14x0i2c |
0.519851 |
|
|
| 94 |
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 |
0.516508 |
Li_C_2018, Wu_Q_2014, Mondor_2012 |
Shi_P_2020, Zhou_2020 |
| 95 |
Schuttler_2020 |
Covid-19 predictions using a Gauss model, based on data from April 2 |
Janik Schuttler; Reinhard Schlickeiser; Frank Schlickeiser; Martin Kroger |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
|
10.1101/2020.04.06.20055830 |
14x9luqu |
0.513689 |
Lloyd_2009, Li_C_2018, Zhao_2013, Bifolchi_2013 |
Distante_2020, Notari_2020, Ciufolini_2020, Ma_Z_2020 |
| 96 |
Zhan_2020 |
Prediction of COVID-19 Spreading Profiles in South Korea, Italy and Iran by Data-Driven Coding |
Choujun Zhan; Chi K. Tse; Zhikang Lai; Tianyong Hao; Jingjing Su |
2020 |
2020-03-10 |
BioRxiv |
Y |
|
|
10.1101/2020.03.08.20032847 |
mr8z65o5 |
0.512917 |
Li_C_2018, batista_2020, Liu_Z_2020 |
Zareie_2020, Zhan_2020, Weber_2020 |
| 97 |
Saez_2020 |
Effectiveness of the measures to flatten the epidemic curve of COVID-19. The case of Spain |
Saez, Marc; Tobias, Aurelio; Varga, Diego; Barceló, Maria Antònia |
2020 |
2020-07-20 |
PMC |
Y |
|
|
10.1016/j.scitotenv.2020.138761 |
fwgffg6k |
0.511693 |
Li_C_2018, Mondor_2012, Zheng_2020 |
|
| 98 |
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 |
0.508598 |
Zhao_2013, Bifolchi_2013, Chowell_2017, Kenah_2007 |
|
| 99 |
Zhou_2020 |
CIRD-F: Spread and Influence of COVID-19 in China |
Zhou, Lingyun; Wu, Kaiwei; Liu, Hanzhi; Gao, Yuanning; Gao, Xiaofeng |
2020 |
2020-04-07 |
PMC |
Y |
PMC7137851 |
|
10.1007/s12204-020-2168-1 |
gi23un26 |
0.506538 |
|
Caccavo_2020 |
| 100 |
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 |
0.501762 |
Mondor_2012, Lu_J_2020, Li_C_2018 |
|
| 101 |
D'Arienzo_2020 |
Assessment of the SARS-CoV-2 basic reproduction number, R 0, based on the early phase of COVID-19 outbreak in Italy |
D'Arienzo, Marco; Coniglio, Angela |
2020 |
2020-04-02 |
PMC |
Y |
|
|
10.1016/j.bsheal.2020.03.004 |
8fu7znit |
0.497872 |
Li_C_2018, Mondor_2012, Liu_Z_2020 |
Zareie_2020 |
| 102 |
Zhu_H_2020 |
Transmission Dynamics and Control Methodology of COVID-19: a Modeling Study |
Hongjun Zhu |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
|
10.1101/2020.03.29.20047118 |
wqvk9bfj |
0.497403 |
Li_C_2018, Bocharov_2018, Wu_Q_2014, Li_K_2011, Renna_2020 |
Kretzschmar_2020 |
| 103 |
Liang_2020 |
Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS |
Liang, Kaihao |
2020 |
2020-08-31 |
PMC |
Y |
PMC7141629 |
32278147.0 |
10.1016/j.meegid.2020.104306 |
t6rtncg9 |
0.496761 |
Li_C_2018, Welch_2011, Wu_Q_2014, Sadun_2020 |
Yong_2016 |
| 104 |
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 |
0.496670 |
Li_C_2018, Lu_J_2020 |
|
| 105 |
Liu_Z_2020 |
Government Responses Matter: Predicting Covid-19 cases in US under an empirical Bayesian time series framework |
Ziyue Liu; Wensheng Guo |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.28.20044578 |
boa0n9dz |
0.489759 |
Li_C_2018 |
Distante_2020, Lopez_2020, Loberg_2020 |
| 106 |
Webb_2020 |
A model to predict COVID-19 epidemics with applications to South Korea, Italy, and Spain |
Glenn F Webb; Pierre Magal; Zhihua Liu; Ousmane Seydi |
2020 |
2020-04-10 |
BioRxiv |
Y |
|
|
10.1101/2020.04.07.20056945 |
c95lntyp |
0.488103 |
Li_C_2018 |
Yong_2016, Smeets_2020 |
| 107 |
Duffey_2020 |
Analysing recovery from pandemics by Learning Theory: the case of CoVid-19 |
Romney B. Duffey; Enrico Zio |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.10.20060319 |
mh7mzuoe |
0.488103 |
Mondor_2012, Wu_Q_2014, Li_C_2018 |
Distante_2020, Smeets_2020 |
| 108 |
Gjini_2020 |
Modeling Covid-19 dynamics for real-time estimates and projections: an application to Albanian data |
Erida Gjini |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2020.03.20.20038141 |
ela022bo |
0.485562 |
Bocharov_2018, Li_C_2018, Chowell_2017 |
Smeets_2020, Ghaffarzadegan_2020 |
| 109 |
Mangiarotti_2020 |
Chaos theory applied to the outbreak of Covid-19: an ancillary approach to decision-making in pandemic context |
Sylvain Mangiarotti; Marisa Peyre; Yan Zhang; Mireille Huc; Francois Roger; Yann Kerr |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.02.20051441 |
gnk3m0b8 |
0.485084 |
|
Ghaffarzadegan_2020, Loberg_2020, Caccavo_2020 |
| 110 |
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 |
0.482558 |
Li_C_2018, Bocharov_2018 |
Yong_2016, Li_S_2020 |
| 111 |
McBryde_2020 |
Flattening the curve is not enough, we need to squash it. An explainer using a simple model |
Emma S McBryde; Michael T Meehan; James M Trauer |
2020 |
2020-04-02 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20048009 |
zmqb140l |
0.480188 |
Li_C_2018 |
Courtney_2020, Lauro_2020, Bioglio_2016 |
| 112 |
Yang_2020 |
Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China |
Qihui Yang; Chunlin Yi; Aram Vajdi; Lee W Cohnstaedt; Hongyu Wu; Xiaolong Guo; Caterina M Scoglio |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.27.20045625 |
kcb68hue |
0.479251 |
Li_C_2018, Li_K_2011, Bifolchi_2013, Wu_Q_2014, Lu_J_2020 |
Roosa_2020 |
| 113 |
Roosa_2020 |
Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020 |
Roosa, Kimberlyn; Lee, Yiseul; Luo, Ruiyan; Kirpich, Alexander; Rothenberg, Richard; Hyman, James M.; Yan, Ping; Chowell, Gerardo |
2020 |
2020-02-22 |
COMM-USE |
Y |
PMC7073898 |
32098289.0 |
10.3390/jcm9020596 |
5ghslfgt |
0.475644 |
|
Roosa_2020, Distante_2020 |
| 114 |
Moran_2020 |
Estimating required lockdown cycles before immunity to SARS-CoV-2: Model-based analyses of susceptible population sizes, S0, in seven European countries including the UK and Ireland |
Rosalyn J Moran; Erik D Fagerholm; Jean Daunizeau; Maell Cullen; Mark P Richardson; Steven Williams; Federico Turkheimer; Rob Leech; Karl Friston |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.10.20060426 |
qub8sdia |
0.475039 |
Li_C_2018, Griette_2020, Zheng_2009, Zhao_2013 |
Kretzschmar_2020, Hilton_2020, Kissler_2020 |
| 115 |
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 |
0.471938 |
Li_C_2018, Lu_J_2020 |
Yong_2016 |
| 116 |
Dropkin_2020 |
COVID-19 UK Lockdown Forecasts and R0 |
Greg Dropkin |
2020 |
2020-04-10 |
BioRxiv |
Y |
|
|
10.1101/2020.04.07.20052340 |
zc7oxui5 |
0.470741 |
batista_2020, Schlickeiser_2020, Liu_Z_2020 |
Ganyani_2020 |
| 117 |
Aslan_2020 |
Modeling COVID-19: Forecasting and analyzing the dynamics of the outbreak in Hubei and Turkey |
ibrahim Halil Aslan; Mahir Demir; Michael Morgan Wise; Suzanne Lenhart |
2020 |
2020-04-15 |
BioRxiv |
Y |
|
|
10.1101/2020.04.11.20061952 |
fsjze3t2 |
0.470317 |
Li_C_2018, Li_K_2011, Wu_Q_2014, O'Dea_2010 |
Peng_2020, Ghaffarzadegan_2020 |
| 118 |
Pant_2020 |
COVID-19 Epidemic Dynamics and Population Projections from Early Days of Case Reporting in a 40 million population from Southern India |
Rashmi Pant; Lincoln Priyadarshi Choudhry; Jammy Guru Rajesh; Vijay V Yeldandi |
2020 |
2020-04-21 |
BioRxiv |
Y |
|
|
10.1101/2020.04.17.20070292 |
9lw1gb3q |
0.468814 |
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| 119 |
Omori_2020 |
Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate |
Omori, Ryosuke; Mizumoto, Kenji; Chowell, Gerardo |
2020 |
2020-04-19 |
PMC |
Y |
|
|
10.1016/j.ijid.2020.04.021 |
dz1lfwzp |
0.464861 |
Li_C_2018, Wu_Q_2014, Mondor_2012 |
|
| 120 |
MONDAL_2020 |
Possibilities of exponential or Sigmoid growth of Covid19 data in different states of India |
SUPRIYA MONDAL; Sabyasachi Ghosh |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.10.20060442 |
bz13hdvz |
0.458459 |
Li_C_2018, Zheng_2020 |
Mondal_2020, Smeets_2020, Distante_2020, Ma_Z_2020 |
| 121 |
Zhao_2020 |
Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak |
Shi Zhao; Qianyin Lin; Jinjun Ran; Salihu S Musa; Guangpu Yang; Weiming Wang; Yijun Lou; Daozhou Gao; Lin Yang; Daihai He; Maggie H Wang |
2020 |
2020-01-24 |
BioRxiv |
Y |
|
|
10.1101/2020.01.23.916395 |
px70bft2 |
0.458066 |
Li_C_2018, batista_2020, Dutra_2020 |
Zhao_2020 |
| 122 |
Sanche_2020 |
The Novel Coronavirus, 2019-nCoV, is Highly Contagious and More Infectious Than Initially Estimated |
Steven Sanche; Yen Ting Lin; Chonggang Xu; Ethan Romero-Severson; Nick Hengartner; Ruian Ke |
2020 |
2020-02-11 |
BioRxiv |
Y |
|
|
10.1101/2020.02.07.20021154 |
45g12waw |
0.457899 |
Li_C_2018, O'Dea_2010, Wu_Q_2014, Welch_2011 |
|
| 123 |
Zhao_2020 |
Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak |
Zhao, Shi; Lin, Qianyin; Ran, Jinjun; Musa, Salihu S.; Yang, Guangpu; Wang, Weiming; Lou, Yijun; Gao, Daozhou; Yang, Lin; He, Daihai; Wang, Maggie H. |
2020 |
2020-01-01 |
None |
Y |
PMC7110798 |
32007643.0 |
10.1016/j.ijid.2020.01.050 |
drqnrwdl |
0.455139 |
Li_C_2018, batista_2020, Griette_2020 |
Zhao_2020 |
| 124 |
Li_L_2020 |
Propagation analysis and prediction of the COVID-19 |
Li, Lixiang; Yang, Zihang; Dang, Zhongkai; Meng, Cui; Huang, Jingze; Meng, Haotian; Wang, Deyu; Chen, Guanhua; Zhang, Jiaxuan; Peng, Haipeng; Shao, Yiming |
2020 |
2020-12-31 |
PMC |
Y |
|
|
10.1016/j.idm.2020.03.002 |
juxlh8xb |
0.451593 |
Bocharov_2018 |
Li_L_2020, Zareie_2020, Ghaffarzadegan_2020 |
| 125 |
Bastolla_2020 |
How lethal is the novel coronavirus, and how many undetected cases there are? The importance of being tested. |
Ugo Bastolla |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
|
10.1101/2020.03.27.20045062 |
2rc8n3x6 |
0.446481 |
Li_C_2018, Welch_2011, Lloyd_2009 |
|
| 126 |
Lee_F_2020 |
A Heuristic Model for Spreading of COVID 19 in Singapore |
Fook Hou Lee |
2020 |
2020-04-18 |
BioRxiv |
N |
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10.1101/2020.04.15.20067264 |
6l7igbmx |
0.445129 |
Bocharov_2018 |
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| 127 |
Chen_2020 |
A mathematical model for simulating the transmission of Wuhan novel Coronavirus |
Tianmu Chen; Jia Rui; Qiupeng Wang; Zeyu Zhao; Jing-An Cui; Ling Yin |
2020 |
2020-01-19 |
BioRxiv |
Y |
|
|
10.1101/2020.01.19.911669 |
v4mbry22 |
0.444947 |
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Yong_2016 |
| 128 |
Pellis_2020 |
Challenges in control of Covid-19: short doubling time and long delay to effect of interventions |
Lorenzo Pellis; Francesca Scarabel; Helena B Stage; Christopher E Overton; Lauren H K Chappell; Katrina A Lythgoe; Elizabeth Fearon; Emma Bennett; Jacob Curran-Sebastian; Rajenki Das; Martyn Fyles; Hugo Lewkowicz; Xiaoxi Pang; Bindu Vekaria; Luke Webb; Thomas A House; Ian Hall |
2020 |
2020-04-15 |
BioRxiv |
Y |
|
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10.1101/2020.04.12.20059972 |
k5q07y4b |
0.443276 |
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Ke_R_2020, Kretzschmar_2020 |
| 129 |
Traini_2020 |
Modelling the epidemic 2019-nCoV event in Italy: a preliminary note |
Marco Claudio Traini; Carla Caponi; Giuseppe Vittorio De Socio |
2020 |
2020-03-17 |
BioRxiv |
Y |
|
|
10.1101/2020.03.14.20034884 |
scbbps9f |
0.439175 |
batista_2020, Zheng_2009, Labadin_2020, Griette_2020 |
Shi_P_2020, Safi_2011 |
| 130 |
Lyra_2020 |
COVID-19 pandemics modeling with SEIR(+CAQH), social distancing, and age stratification. The effect of vertical confinement and release in Brazil. |
Wladimir Lyra; Jose Dias do Nascimento; Jaber Belkhiria; Leandro de Almeida; Pedro Paulo Chrispim; Ion de Andrade |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.09.20060053 |
uza4orb8 |
0.434096 |
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Chin_2019 |
| 131 |
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 |
0.432966 |
Mondor_2012 |
Ng_T_2003 |
| 132 |
Ghosh_2020 |
Increased Detection coupled with Social Distancing and Health Capacity Planning Reduce the Burden of COVID-19 Cases and Fatalities: A Proof of Concept Study using a Stochastic Computational Simulation Model |
Pramit Ghosh; Salah Basheer; Sandip Paul; Partha Chakrabarti; Jit Sarkar |
2020 |
2020-04-07 |
BioRxiv |
Y |
|
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10.1101/2020.04.05.20054775 |
01f5mvsc |
0.427269 |
Li_C_2018, Wu_Q_2014, Chen_2018 |
Kretzschmar_2020 |
| 133 |
Aguilar_2020 |
Investigating the Impact of Asymptomatic Carriers on COVID-19 Transmission |
Jacob B Aguilar; Juan B. Gutierrez |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.18.20037994 |
tg5wbwf9 |
0.425517 |
Li_C_2018, Wu_Q_2014 |
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| 134 |
Prakash_2020 |
A minimal and adaptive prediction strategy for critical resource planning in a pandemic |
Meher K Prakash; Shaurya Kaushal; Soumyadeep Bhattacharya; Akshay Chandran; Aloke Kumar; Santosh Ansumali |
2020 |
2020-04-10 |
BioRxiv |
N |
|
|
10.1101/2020.04.08.20057414 |
stqj3ue5 |
0.422297 |
Bocharov_2018, Zheng_2020 |
Loberg_2020, Jenny_2020, Ohkusa_2005, Nicolau_2020 |
| 135 |
Gross_2020 |
Spatio-temporal propagation of COVID-19 pandemics |
Bnaya Gross; Zhiguo Zheng; Shiyan Liu; Xiaoqi Chen; Alon Sela; Jianxin Li; Daqing Li; Shlomo Havlin |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.23.20041517 |
x2bi3v3u |
0.421904 |
Li_C_2018, Lauro_2020, Wu_Q_2014 |
|
| 136 |
Danon_2020 |
A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing |
Leon Danon; Ellen Brooks-Pollock; Mick Bailey; Matt J Keeling |
2020 |
2020-02-14 |
BioRxiv |
Y |
|
|
10.1101/2020.02.12.20022566 |
x4qdiln9 |
0.420677 |
Mondor_2012, Li_C_2018, Chen_2018, Wu_Q_2014, Liu_Z_2020 |
Kretzschmar_2020 |
| 137 |
Baerwolff_2020 |
A Contribution to the Mathematical Modeling of the Corona/COVID-19 Pandemic |
Guenter K.F. Baerwolff |
2020 |
2020-04-06 |
BioRxiv |
N |
|
|
10.1101/2020.04.01.20050229 |
69wk591l |
0.417896 |
Zheng_2020, Bocharov_2018 |
Yong_2016, Kim_Y_2016, Nadeau_2014 |
| 138 |
Spousta_2020 |
Parametric analysis of early data on COVID-19 expansion in selected European countries |
Martin Spousta |
2020 |
2020-04-03 |
BioRxiv |
Y |
|
|
10.1101/2020.03.31.20049155 |
3b6n8un0 |
0.412909 |
Bocharov_2018, Li_C_2018, Lloyd_2009, Chowell_2017, Bifolchi_2013 |
Smeets_2020, Ciufolini_2020, Zhigljavsky_2020 |
| 139 |
Woody_2020 |
Projections for first-wave COVID-19 deaths across the US using social-distancing measures derived from mobile phones |
Spencer Woody; Mauricio Garcia Tec; Maytal Dahan; Kelly Gaither; Spencer Fox; Lauren Ancel Meyers; James G Scott |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
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10.1101/2020.04.16.20068163 |
87lxnslh |
0.410732 |
|
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| 140 |
Abdulrahman_2020 |
SimCOVID: An Open-Source Simulink-Based Program for Simulating the COVID-19 Epidemic |
Ismael Khorshed Abdulrahman |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.13.20063354 |
nexylnv4 |
0.410514 |
Höhle_2007, Bifolchi_2013, Renna_2020, Bocharov_2018 |
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| 141 |
Wittkowski_2020 |
The first three months of the COVID-19 epidemic: Epidemiological evidence for two separate strains of SARS-CoV-2 viruses spreading and implications for prevention strategies |
Knut M. Wittkowski |
2020 |
2020-03-31 |
BioRxiv |
Y |
|
|
10.1101/2020.03.28.20036715 |
2ytec133 |
0.407669 |
|
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| 142 |
Jung_2020 |
Real time estimation of the risk of death from novel coronavirus (2019-nCoV) infection: Inference using exported cases |
Sung-mok Jung; Andrei R. Akhmetzhanov; Katsuma Hayashi; Natalie M. Linton; Yichi Yang; Baoyin Yuan; Tetsuro Kobayashi; Ryo Kinoshita; Hiroshi Nishiura |
2020 |
2020-02-02 |
BioRxiv |
Y |
|
|
10.1101/2020.01.29.20019547 |
rr5qhsam |
0.406449 |
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Tang_2020 |
| 143 |
Kochanczyk_2020 |
Impact of the contact and exclusion rates on the spread of COVID-19 pandemic |
Marek Kochanczyk; Frederic Grabowski; Tomasz Lipniacki |
2020 |
2020-03-17 |
BioRxiv |
N |
|
|
10.1101/2020.03.13.20035485 |
s956fh59 |
0.405905 |
Li_C_2018, Wu_Q_2014, Tao_Y_2020, Chen_2018, Gong_2013 |
Kretzschmar_2020 |
| 144 |
Katul_2020 |
Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics |
Gabriel G. Katul; Assaad Mrad; Sara Bonetti; Gabriele Manoli; Anthony J. Parolari |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.10.20060954 |
3iec1te8 |
0.404226 |
Li_C_2018, Wu_Q_2014, Sadun_2020 |
Kretzschmar_2020, Chong_2020, Safi_2011 |
| 145 |
Ke_R_2020 |
Fast spread of COVID-19 in Europe and the US and its implications: even modest public health goals require comprehensive intervention |
Ruian Ke; Steven Sanche; Ethan Romero-Severson; Nicholas Hengartner |
2020 |
2020-04-07 |
BioRxiv |
Y |
|
|
10.1101/2020.04.04.20050427 |
mbdah9ey |
0.402782 |
Li_C_2018, Mondor_2012 |
Kretzschmar_2020 |
| 146 |
Notari_2020 |
Temperature dependence of COVID-19 transmission |
Alessio Notari |
2020 |
2020-03-30 |
BioRxiv |
N |
|
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10.1101/2020.03.26.20044529 |
0oma7hdu |
0.402506 |
Li_C_2018, Gong_2013, Wu_Q_2014, Lauro_2020 |
Weber_2020 |
| 147 |
Magdon-Ismail_2020 |
Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics |
Malik Magdon-Ismail |
2020 |
2020-03-20 |
BioRxiv |
Y |
|
|
10.1101/2020.03.17.20037309 |
vwbpkpxd |
0.401172 |
Bocharov_2018, Wu_Q_2014 |
Duffey_2020 |
| 148 |
Menendez_2020 |
Elementary time-delay dynamics of COVID-19 disease |
Jose Menendez |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.27.20045328 |
3y89lumh |
0.397539 |
Li_C_2018, Bifolchi_2013, Lloyd_2009, Renna_2020, Welch_2011 |
Shi_P_2020, Yong_2016, Smeets_2020 |
| 149 |
Zhang_2005 |
A compartmental model for the analysis of SARS transmission patterns and outbreak control measures in China |
Zhang, Juan; Lou, Jie; Ma, Zhien; Wu, Jianhong |
2005 |
2005-03-15 |
PMC |
N |
PMC7134600 |
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10.1016/j.amc.2003.12.131 |
1uggxinp |
0.393111 |
Li_C_2018, Zheng_2020 |
Safi_2011 |
| 150 |
Alvarez_2020 |
Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks |
Mario Moises Alvarez; Everardo Gonzalez-Gonzalez; Grissel Trujillo-de Santiago |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2020.03.23.20041590 |
rvdkkr6i |
0.392349 |
Zheng_2020, Li_C_2018 |
Kretzschmar_2020, Caccavo_2020, Loberg_2020 |
| 151 |
Britton_2020 |
Basic estimation-prediction techniques for Covid-19, and a prediction for Stockholm |
Tom Britton |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
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10.1101/2020.04.15.20066050 |
0fmeu4h4 |
0.391942 |
Li_C_2018, Wu_Q_2014, Lauro_2020, Kenah_2012 |
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| 152 |
Lu_J_2020 |
A New, Simple Projection Model for COVID-19 Pandemic |
Jian Lu |
2020 |
2020-03-24 |
BioRxiv |
Y |
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10.1101/2020.03.21.20039867 |
1y0pl31i |
0.389399 |
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Yong_2016 |
| 153 |
Shao_2020 |
Beware of asymptomatic transmission: Study on 2019-nCoV prevention and control measures based on extended SEIR model |
Peng Shao; Yingji Shan |
2020 |
2020-01-28 |
BioRxiv |
Y |
|
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10.1101/2020.01.28.923169 |
x443k65a |
0.387601 |
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Shi_P_2020 |
| 154 |
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 |
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10.1002/mma.6345 |
kgrdul35 |
0.384810 |
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| 155 |
Song_2020 |
An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China |
Peter X Song; Lili Wang; Yiwang Zhou; Jie He; Bin Zhu; Fei Wang; Lu Tang; Marisa Eisenberg |
2020 |
2020-03-03 |
BioRxiv |
Y |
|
|
10.1101/2020.02.29.20029421 |
m9icky9z |
0.381923 |
Bocharov_2018, Bifolchi_2013 |
Distante_2020 |
| 156 |
Tang_2020 |
Stochastic discrete epidemic modeling of COVID-19 transmission in the Province of Shaanxi incorporating public health intervention and case importation |
Sanyi Tang; Biao Tang; Nicola Luigi Bragazzi; Fan Xia; Tangjuan Li; Sha He; Pengyu Ren; Xia Wang; Zhihang Peng; Yanni Xiao; Jianhong Wu |
2020 |
2020-02-29 |
BioRxiv |
Y |
|
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10.1101/2020.02.25.20027615 |
aoqyx8mk |
0.379520 |
Li_C_2018, Wu_Q_2014 |
Peng_2020 |
| 157 |
Wodarz_2020 |
Patterns of the COVID19 epidemic spread around the world: exponential vs power laws |
Dominik Wodarz; Natalia L. Komarova |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20047274 |
vz829rsy |
0.378914 |
Li_C_2018 |
Weber_2020, Fanelli_2020, Notari_2020 |
| 158 |
MANOU-ABI_2020 |
Analysis of the COVID-19 epidemic in french overseas department Mayotte based on a modified deterministic and stochastic SEIR model |
Solym MANOU-ABI; Julien BALICCHI |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.15.20062752 |
ztcij4wb |
0.378686 |
Li_C_2018, Wu_Q_2014, Li_K_2011, Lu_J_2020 |
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| 159 |
Kevorkian_2020 |
Tracking the Covid-19 pandemic : Simple visualization of the epidemic states and trajectories of select European countries & assessing the effects of delays in official response. |
Antoine Kevorkian; Thierry Grenet; Hubert Gallee |
2020 |
2020-03-17 |
BioRxiv |
Y |
|
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10.1101/2020.03.14.20035964 |
5u04irwz |
0.377841 |
Li_C_2018 |
Kretzschmar_2020, Ghaffarzadegan_2020 |
| 160 |
Huang_2020 |
A data-driven tool for tracking and predicting the course of COVID-19 epidemic as it evolves |
Norden E Huang; Fangli Qiao; Ka-Kit Tung |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.28.20046177 |
mxen3n0k |
0.377574 |
Li_C_2018, Zheng_2020 |
Ghaffarzadegan_2020, Zhigljavsky_2020 |
| 161 |
Ziff_2020 |
Fractal kinetics of COVID-19 pandemic |
Anna L. Ziff; Robert M. Ziff |
2020 |
2020-02-20 |
BioRxiv |
Y |
|
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10.1101/2020.02.16.20023820 |
jljjqs6m |
0.374976 |
Li_C_2018, Li_K_2011, Welch_2011, Lloyd_2009 |
Weber_2020, Peng_2020, Notari_2020, Smeets_2020 |
| 162 |
Rajendrakumar_2020 |
Epidemic Landscape and Forecasting of SARS-CoV-2 in India |
Aravind Lathika Rajendrakumar; Anand Thakarakkattil Narayanan Nair; Charvi Nangia; Prabal Kumar Chourasia; Mehul Kumar Chourasia; Mohammad Ghouse Syed; Anu Sasidharan Nair; Arun B Nair; Muhammed Shaffi Fazaludeen Koya |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
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10.1101/2020.04.14.20065151 |
mjqbvpw2 |
0.372932 |
Li_C_2018, Kenah_2012, batista_2020, Griette_2020 |
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| 163 |
Chowell_2003 |
SARS outbreaks in Ontario, Hong Kong and Singapore: the role of diagnosis and isolation as a control mechanism |
Chowell, G.; Fenimore, P.W.; Castillo-Garsow, M.A.; Castillo-Chavez, C. |
2003 |
2003-09-07 |
PMC |
N |
PMC7134599 |
12900200.0 |
10.1016/s0022-5193(03)00228-5 |
ean2xrnf |
0.365713 |
Li_C_2018, Liu_Z_2020 |
Ng_T_2003 |
| 164 |
Roy_A_2020 |
Nature of transmission of Covid19 in India |
Anushree Roy; Sayan Kar |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
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10.1101/2020.04.14.20065821 |
iv7dok0v |
0.363424 |
Li_C_2018, Gong_2013, Welch_2011, Sadun_2020 |
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| 165 |
Cotta_2020 |
Modelling the COVID-19 epidemics in Brasil: Parametric identification and public health measures influence |
Renato Machado Cotta; Carolina Palma Naveira-Cotta; pierre magal |
2020 |
2020-04-03 |
BioRxiv |
Y |
|
|
10.1101/2020.03.31.20049130 |
3rmrkzuq |
0.356526 |
Li_C_2018, Wu_Q_2014, Lloyd_2009, O'Dea_2010, Bifolchi_2013 |
Safi_2011, Kretzschmar_2020 |
| 166 |
Diao_2020 |
Estimating the cure rate and case fatality rate of the ongoing epidemic COVID-19 |
Ying Diao; Xiaoyun Liu; Tao Wang; Xiaofei Zeng; Chen Dong; Changlong Zhou; Yuanming Zhang; Xuan She; Dingfu Liu; Zhongli Hu |
2020 |
2020-02-20 |
BioRxiv |
Y |
|
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10.1101/2020.02.18.20024513 |
od8s0zhm |
0.356001 |
Li_C_2018, Wu_Q_2014, Kenah_2012, Lauro_2020 |
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| 167 |
Pongkitivanichkul_2020 |
Estimating the size of COVID-19 epidemic outbreak |
Chakrit Pongkitivanichkul; Daris Samart; Takol Tangphati; Phanit Koomhin; Pimchanok Pimton; Punsiri Dam-O; Apirak Payaka; Phongpichit Channuie |
2020 |
2020-03-31 |
BioRxiv |
N |
|
|
10.1101/2020.03.28.20044339 |
auzioqyz |
0.355402 |
Lloyd_2009, Li_C_2018, Zhao_2013 |
Smeets_2020, Safi_2011, Notari_2020 |
| 168 |
Utsunomiya_2020 |
Growth rate and acceleration analysis of the COVID-19 pandemic reveals the effect of public health measures in real time |
Yuri Tani Utsunomiya; Adam Taiti Harth Utsunomiya; Rafaela Beatriz Pintor Torrecilha; Silvana Cassia Paulan; Marco Milanesi; Jose Fernando Garcia |
2020 |
2020-04-02 |
BioRxiv |
Y |
|
|
10.1101/2020.03.30.20047688 |
39ywzw6a |
0.349421 |
Li_C_2018, Welch_2011, Wu_Q_2014, Liu_Z_2020 |
Smeets_2020, Oliveiros_2020 |
| 169 |
Merrin_2020 |
Differences in power-law growth over time and indicators of COVID-19 pandemic progression worldwide |
Jack Merrin |
2020 |
2020-04-02 |
BioRxiv |
Y |
|
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10.1101/2020.03.31.20048827 |
rc88vn6e |
0.348132 |
Li_C_2018 |
Utsunomiya_2020, Wodarz_2020, Weber_2020 |
| 170 |
Courtney_2020 |
COVID-19: Tracking the Pandemic with A Simple Curve Approximation Tool (SCAT) |
Jane Courtney |
2020 |
2020-04-11 |
BioRxiv |
Y |
|
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10.1101/2020.04.06.20055467 |
6kl6uso6 |
0.346235 |
Zheng_2020, Bocharov_2018 |
Zhu_H_2020, Ghaffarzadegan_2020, Safi_2011 |
| 171 |
Bliznashki_2020 |
A Bayesian Logistic Growth Model for the Spread of COVID-19 in New York |
Svetoslav Bliznashki |
2020 |
2020-04-07 |
BioRxiv |
N |
|
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10.1101/2020.04.05.20054577 |
lhv83zac |
0.345589 |
Bifolchi_2013, Chowell_2017, Zhao_2013, Lloyd_2009 |
Chong_2020, Smeets_2020, Black_2013, Safi_2011 |
| 172 |
Rodriguez_2020 |
Predicting Whom to Test is More Important Than More Tests - Modeling the Impact of Testing on the Spread of COVID-19 Virus By True Positive Rate Estimation |
Paul F Rodriguez |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.01.20050393 |
06vc2y9y |
0.344029 |
Li_C_2018 |
Eberhardt_2020 |
| 173 |
Dehning_2020 |
Inferring COVID-19 spreading rates and potential change points for case number forecasts |
Jonas Dehning; Johannes Zierenberg; Frank Paul Spitzner; Michael Wibral; Joao Pinheiro Neto; Michael Wilczek; Viola Priesemann |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2020.04.02.20050922 |
c8zfz8qt |
0.343664 |
Bocharov_2018 |
Smeets_2020, Loberg_2020, Zhigljavsky_2020 |
| 174 |
Tang_2020 |
An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov) |
Tang, Biao; Bragazzi, Nicola Luigi; Li, Qian; Tang, Sanyi; Xiao, Yanni; Wu, Jianhong |
2020 |
2020-02-11 |
None |
Y |
PMC7029158 |
32099934.0 |
10.1016/j.idm.2020.02.001 |
thu861hj |
0.342535 |
Wu_Q_2014, Li_C_2018 |
Kucharski_2015 |
| 175 |
Hochberg_2020 |
Countries should aim to lower the reproduction number R close to 1.0 for the short-term mitigation of COVID-19 outbreaks |
Michael E. Hochberg |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
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10.1101/2020.04.14.20065268 |
f36smzln |
0.342158 |
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| 176 |
Neher_2020 |
Potential impact of seasonal forcing on a SARS-CoV-2 pandemic |
Richard A Neher; Robert Dyrdak; Valentin Druelle; Emma B Hodcroft; Jan Albert |
2020 |
2020-02-17 |
BioRxiv |
Y |
|
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10.1101/2020.02.13.20022806 |
3p2dl8yf |
0.339472 |
Li_C_2018 |
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| 177 |
Hsieh_2006 |
Real-time Forecast of Multiphase Outbreak |
Hsieh, Ying-Hen; Cheng, Yuan-Sen |
2006 |
2006-01-23 |
PMC |
N |
PMC3293463 |
16494728.0 |
10.3201/eid1201.050396 |
h6cfru7u |
0.338863 |
batista_2020, Schlickeiser_2020 |
Roosa_2020 |
| 178 |
Chong_2020 |
A Novel Method for the Estimation of a Dynamic Effective Reproduction Number (Dynamic-R) in the CoViD-19 Outbreak |
Yi Chen Chong |
2020 |
2020-02-25 |
BioRxiv |
Y |
|
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10.1101/2020.02.22.20023267 |
o3hytzwu |
0.336833 |
Zheng_2020, Bocharov_2018 |
Kucharski_2015, Tang_2020 |
| 179 |
Vega_2020 |
Lockdown, one, two, none, or smart. Modeling containing covid-19 infection. A conceptual model |
Vega, Danny Ibarra |
2020 |
2020-04-22 |
PMC |
Y |
|
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10.1016/j.scitotenv.2020.138917 |
btng72h7 |
0.335771 |
Li_C_2018, Welch_2011, Bifolchi_2013 |
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| 180 |
Smeets_2020 |
Scaling analysis of COVID-19 spreading based on Belgian hospitalization data |
Bart Smeets; Rodrigo Watte; Herman Ramon |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
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10.1101/2020.03.29.20046730 |
nc5rtwtd |
0.320462 |
Li_C_2018, Lloyd_2009, Wu_Q_2014, O'Dea_2010, Welch_2011 |
Shi_P_2020, Yong_2016 |
| 181 |
Baker_2020 |
Susceptible supply limits the role of climate in the COVID-19 pandemic |
Rachel E. Baker; Wenchang Yang; Gabriel A. Vecchi; C. Jessica E. Metcalf; Bryan T Grenfell |
2020 |
2020-04-07 |
BioRxiv |
Y |
|
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10.1101/2020.04.03.20052787 |
zxx7tikz |
0.319482 |
Li_C_2018 |
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| 182 |
Lopez_2016 |
Modeling Importations and Exportations of Infectious Diseases via Travelers |
Lopez, Luis Fernandez; Amaku, Marcos; Coutinho, Francisco Antonio Bezerra; Quam, Mikkel; Burattini, Marcelo Nascimento; Struchiner, Claudio José; Wilder-Smith, Annelies; Massad, Eduardo |
2016 |
2016-01-13 |
PMC |
N |
PMC7089300 |
26763222.0 |
10.1007/s11538-015-0135-z |
b7pl62p2 |
0.319199 |
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Denphedtnong_2013, Safi_2011 |
| 183 |
Victor_2020 |
MATHEMATICAL PREDICTIONS FOR COVID-19 AS A GLOBAL PANDEMIC |
Alexander Okhuese Victor |
2020 |
2020-03-24 |
BioRxiv |
Y |
|
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10.1101/2020.03.19.20038794 |
ckay4ufw |
0.316729 |
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Victor_2020 |
| 184 |
Bogacz_2020 |
Estimating the probability of New Zealand regions being free from COVID-19 using a stochastic SEIR model |
Rafal Bogacz |
2020 |
2020-04-21 |
BioRxiv |
Y |
|
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10.1101/2020.04.20.20073304 |
ab71pz6v |
0.313423 |
Zhao_2013, Bifolchi_2013, Welch_2011, Li_C_2018 |
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| 185 |
Chang_2010 |
The novel H1N1 Influenza A global airline transmission and early warning without travel containments |
Chang, ChaoYi; Cao, ChunXiang; Wang, Qiao; Chen, Yu; Cao, ZhiDong; Zhang, Hao; Dong, Lei; Zhao, Jian; Xu, Min; Gao, MengXu; Zhong, ShaoBo; He, QiSheng; Wang, JinFeng; Li, XiaoWen |
2010 |
2010-09-24 |
PMC |
N |
PMC7088564 |
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10.1007/s11434-010-3180-x |
0fav1esn |
0.313083 |
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| 186 |
Zhang_2020 |
A Generalized Discrete Dynamic Model for Human Epidemics |
Wenjun Zhang; Zeliang Chen; Yi Lu; Zhongmin Guo; Yanhong Qi; Guoling Wang; Jiahai Lu |
2020 |
2020-02-12 |
BioRxiv |
N |
|
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10.1101/2020.02.11.944728 |
cv36vc8i |
0.312992 |
Li_C_2018, Galvani_2003, Chen_2018 |
Safi_2011, Denphedtnong_2013, Elazzouzi_2019, Raja_Sekhara_Rao_2015 |
| 187 |
Benvenuto_2020 |
Application of the ARIMA model on the COVID-2019 epidemic dataset |
Benvenuto, Domenico; Giovanetti, Marta; Vassallo, Lazzaro; Angeletti, Silvia; Ciccozzi, Massimo |
2020 |
2020-02-26 |
None |
N |
PMC7063124 |
32181302.0 |
10.1016/j.dib.2020.105340 |
okvu49y3 |
0.312325 |
Bocharov_2018, Chowell_2017, Duan_2015, Bauer_2009, Grassly_2008 |
Yong_2016, Smeets_2020, Ding_2020, Zareie_2020 |
| 188 |
White_2020 |
State-level variation of initial COVID-19 dynamics in the United States: The role of local government interventions |
Easton R White; Laurent R Hébert-Dufresne |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2020.04.14.20065318 |
zqwelqoe |
0.310742 |
Li_C_2018, Mondor_2012 |
|
| 189 |
Kassa_2020 |
Analysis of the mitigation strategies for COVID-19: from mathematical modelling perspective |
Semu Kassa; Hatson John Boscoh Njagarah; Yibeltal Adane Terefe |
2020 |
2020-04-18 |
BioRxiv |
Y |
|
|
10.1101/2020.04.15.20066308 |
celjbnt3 |
0.307763 |
Li_C_2018, Wu_Q_2014, Welch_2011, O'Dea_2010 |
|
| 190 |
Jenny_2020 |
Dynamic Modeling to Identify Mitigation Strategies for Covid-19 Pandemic |
Patrick Jenny; David F Jenny; Hossein Gorji; Markus Arnoldini; Wolf-Dietrich Hardt |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2020.03.27.20045237 |
ngsstnpr |
0.303317 |
Zheng_2020 |
|
| 191 |
Salomon_2020 |
Defining high-value information for COVID-19 decision-making |
Joshua A Salomon |
2020 |
2020-04-08 |
BioRxiv |
Y |
|
|
10.1101/2020.04.06.20052506 |
iymhykq8 |
0.302960 |
|
Nicolau_2020 |
| 192 |
Biswas_2020 |
Risk Assessment of nCOVID-19 Pandemic In India: A Mathematical Model And Simulation |
Swarnava Biswas; Moumita Mukherjee |
2020 |
2020-04-14 |
BioRxiv |
Y |
|
|
10.1101/2020.04.10.20060830 |
y3l6k0qu |
0.300870 |
Li_C_2018 |
Shi_P_2020, Li_S_2020 |