| 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 |
|
|
10.1101/2<IP_ADDRESS>23606 |
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 |
|
|
10.1101/2<IP_ADDRESS>22186 |
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 |
|
|
10.1101/2<IP_ADDRESS>59055 |
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 |
|
|
10.1101/2<IP_ADDRESS>62893 |
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 |
|
|
10.1101/2<IP_ADDRESS>37689 |
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 |
|
|
10.1101/2<IP_ADDRESS>60822 |
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 |
|
|
10.1101/2<IP_ADDRESS>24901 |
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 |
|
|
10.1101/2<IP_ADDRESS>47019 |
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 |
|
|
10.1101/2<IP_ADDRESS>50369 |
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 |
|
|
10.1101/2<IP_ADDRESS>61002 |
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 |
|
|
10.1101/2<IP_ADDRESS>63271 |
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 |
|
|
10.1101/2<IP_ADDRESS>56697 |
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 |
|
|
10.1101/2<IP_ADDRESS>27169 |
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 |
|
|
10.1101/2<IP_ADDRESS>25262 |
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 |
|
|
10.1101/2<IP_ADDRESS>48942 |
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 |
|
|
10.1101/2<IP_ADDRESS>43067 |
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 |
|
|
10.1101/2<IP_ADDRESS>55426 |
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 |
|
|
10.1101/2<IP_ADDRESS>43612 |
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 |
|
|
10.1101/2<IP_ADDRESS>68775 |
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 |
|
|
10.1101/2<IP_ADDRESS>45104 |
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 |
|
|
10.1101/2<IP_ADDRESS>59881 |
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 |
|
|
10.1101/2<IP_ADDRESS>58123 |
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 |
|
|
10.1101/2<IP_ADDRESS>38950 |
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 |
|
|
10.1101/2<IP_ADDRESS>47894 |
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 |
|
|
10.1101/2<IP_ADDRESS>40139 |
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 |
|
|
10.1101/2<IP_ADDRESS>69278 |
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 |
|
|
10.1101/2<IP_ADDRESS>20461 |
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 |
|
|
10.1101/2<IP_ADDRESS>49486 |
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 |
|
|
10.1101/2<IP_ADDRESS>45898 |
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 |
|
|
10.1101/2<IP_ADDRESS>57463 |
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 |
|
|
10.1101/2<IP_ADDRESS>69179 |
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 |
|
|
10.1101/2<IP_ADDRESS>24570 |
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 |
|
|
10.1101/2<IP_ADDRESS>45005 |
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 |
|
|
10.1101/2<IP_ADDRESS>51466 |
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 |
|
|
10.1101/2<IP_ADDRESS>55244 |
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 |
|
|
10.1101/2<IP_ADDRESS>46532 |
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 |
|
|
10.1101/2<IP_ADDRESS>55327 |
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 |
|
|
10.1101/2<IP_ADDRESS>62588 |
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 |
|
|
10.1101/2<IP_ADDRESS>33498 |
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 |
|
|
10.1101/2<IP_ADDRESS>44289 |
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 |
|
|
10.1101/2<IP_ADDRESS>56937 |
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 |
|
|
10.1101/2<IP_ADDRESS>61051 |
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 |
|
|
10.1101/2<IP_ADDRESS>46250 |
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 |
|
|
10.1101/2<IP_ADDRESS>26070 |
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 |
|
|
10.1101/2<IP_ADDRESS>59329 |
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 |
|
|
10.1101/2<IP_ADDRESS>37770 |
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 |
|
|
10.1101/2<IP_ADDRESS>50310 |
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 |
|
|
10.1101/2<IP_ADDRESS>49734 |
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 |
|
|
10.1101/2<IP_ADDRESS>21931 |
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 |
|
|
10.1101/2<IP_ADDRESS>65607 |
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 |
|
|
10.1101/2<IP_ADDRESS>57851 |
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 |
|
|
10.1101/2<IP_ADDRESS>49452 |
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 |
|
|
10.1101/2<IP_ADDRESS>39388 |
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 |
|
|
10.1101/2<IP_ADDRESS>58933 |
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 |
|
|
10.1101/2<IP_ADDRESS>58636 |
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 |
|
|
10.1101/2<IP_ADDRESS>66712 |
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 |
|
|
10.1101/2<IP_ADDRESS>40915 |
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 |
|
|
10.1101/2<IP_ADDRESS>64220 |
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 |
|
|
10.1101/2<IP_ADDRESS>59824 |
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 |
|
|
10.1101/2<IP_ADDRESS>63792 |
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 |
|
|
10.1101/2<IP_ADDRESS>40956 |
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 |
|
|
10.1101/2<IP_ADDRESS>49825 |
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 |
|
|
10.1101/2<IP_ADDRESS>62257 |
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 |
|
|
10.1101/2<IP_ADDRESS>31872 |
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 |
|
|
10.1101/2<IP_ADDRESS>21519 |
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 |
|
|
10.1101/2<IP_ADDRESS>61838 |
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 |
|
|
10.1101/2<IP_ADDRESS>57943 |
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 |
|
|
10.1101/2<IP_ADDRESS>63008 |
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 |
|
|
10.1101/2<IP_ADDRESS>41095 |
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 |
|
|
10.1101/2<IP_ADDRESS>36202 |
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 |
|
|
10.1101/2<IP_ADDRESS>55863 |
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 |
|
|
10.1101/2<IP_ADDRESS>37192 |
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 |
|
|
10.1101/2<IP_ADDRESS>37671 |
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 |
|
|
10.1101/2<IP_ADDRESS>51680 |
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 |
|
|
10.1101/2<IP_ADDRESS>23655 |
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 |
|
|
10.1101/2<IP_ADDRESS>40444 |
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 |
|
|
10.1101/2<IP_ADDRESS>21071 |
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 |
|
|
10.1101/2<IP_ADDRESS>23465 |
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 |
|
|
10.1101/2<IP_ADDRESS>55830 |
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 |
|
|
10.1101/2<IP_ADDRESS>32847 |
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 |
|
|
10.1101/2<IP_ADDRESS>64824 |
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 |
|
|
10.1101/2<IP_ADDRESS>67306 |
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 |
|
|
10.1101/2<IP_ADDRESS>47118 |
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 |
|
|
10.1101/2<IP_ADDRESS>62927 |
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 |
|
|
10.1101/2<IP_ADDRESS>44578 |
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 |
|
|
10.1101/2<IP_ADDRESS>56945 |
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 |
|
|
10.1101/2<IP_ADDRESS>60319 |
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 |
|
|
10.1101/2<IP_ADDRESS>38141 |
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 |
|
|
10.1101/2<IP_ADDRESS>51441 |
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 |
|
|
10.1101/2<IP_ADDRESS>43026 |
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 |
|
|
10.1101/2<IP_ADDRESS>48009 |
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 |
|
|
10.1101/2<IP_ADDRESS>45625 |
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 |
|
|
10.1101/2<IP_ADDRESS>60426 |
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 |
|
|
10.1101/2<IP_ADDRESS>57489 |
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 |
|
|
10.1101/2<IP_ADDRESS>52340 |
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 |
|
|
10.1101/2<IP_ADDRESS>61952 |
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 |
|
|
10.1101/2<IP_ADDRESS>70292 |
9lw1gb3q |
0.468814 |
|
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>60442 |
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 |
|
|
10.1101/2<IP_ADDRESS>6395 |
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 |
|
|
10.1101/2<IP_ADDRESS>21154 |
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 |
|
|
10.1101/2<IP_ADDRESS>45062 |
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 |
|
|
10.1101/2<IP_ADDRESS>67264 |
6l7igbmx |
0.445129 |
Bocharov_2018 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>1669 |
v4mbry22 |
0.444947 |
|
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 |
|
|
10.1101/2<IP_ADDRESS>59972 |
k5q07y4b |
0.443276 |
|
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 |
|
|
10.1101/2<IP_ADDRESS>34884 |
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 |
|
|
10.1101/2<IP_ADDRESS>60053 |
uza4orb8 |
0.434096 |
|
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 |
|
|
10.1101/2<IP_ADDRESS>42168 |
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 |
|
|
10.1101/2<IP_ADDRESS>54775 |
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 |
|
|
10.1101/2<IP_ADDRESS>37994 |
tg5wbwf9 |
0.425517 |
Li_C_2018, Wu_Q_2014 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>57414 |
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 |
|
|
10.1101/2<IP_ADDRESS>41517 |
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 |
|
|
10.1101/2<IP_ADDRESS>22566 |
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 |
|
|
10.1101/2<IP_ADDRESS>50229 |
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 |
|
|
10.1101/2<IP_ADDRESS>49155 |
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 |
|
|
10.1101/2<IP_ADDRESS>68163 |
87lxnslh |
0.410732 |
|
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>63354 |
nexylnv4 |
0.410514 |
Höhle_2007, Bifolchi_2013, Renna_2020, Bocharov_2018 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>36715 |
2ytec133 |
0.407669 |
|
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>19547 |
rr5qhsam |
0.406449 |
|
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 |
|
|
10.1101/2<IP_ADDRESS>35485 |
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 |
|
|
10.1101/2<IP_ADDRESS>60954 |
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 |
|
|
10.1101/2<IP_ADDRESS>50427 |
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 |
|
|
10.1101/2<IP_ADDRESS>44529 |
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 |
|
|
10.1101/2<IP_ADDRESS>37309 |
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 |
|
|
10.1101/2<IP_ADDRESS>45328 |
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 |
|
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 |
|
|
10.1101/2<IP_ADDRESS>41590 |
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 |
|
|
10.1101/2<IP_ADDRESS>66050 |
0fmeu4h4 |
0.391942 |
Li_C_2018, Wu_Q_2014, Lauro_2020, Kenah_2012 |
|
| 152 |
Lu_J_2020 |
A New, Simple Projection Model for COVID-19 Pandemic |
Jian Lu |
2020 |
2020-03-24 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>39867 |
1y0pl31i |
0.389399 |
|
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 |
|
|
10.1101/2<IP_ADDRESS>3169 |
x443k65a |
0.387601 |
|
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 |
|
10.1002/mma.6345 |
kgrdul35 |
0.384810 |
|
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>29421 |
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 |
|
|
10.1101/2<IP_ADDRESS>27615 |
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 |
|
|
10.1101/2<IP_ADDRESS>47274 |
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 |
|
|
10.1101/2<IP_ADDRESS>62752 |
ztcij4wb |
0.378686 |
Li_C_2018, Wu_Q_2014, Li_K_2011, Lu_J_2020 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>35964 |
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 |
|
|
10.1101/2<IP_ADDRESS>46177 |
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 |
|
|
10.1101/2<IP_ADDRESS>23820 |
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 |
|
|
10.1101/2<IP_ADDRESS>65151 |
mjqbvpw2 |
0.372932 |
Li_C_2018, Kenah_2012, batista_2020, Griette_2020 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>65821 |
iv7dok0v |
0.363424 |
Li_C_2018, Gong_2013, Welch_2011, Sadun_2020 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>49130 |
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 |
|
|
10.1101/2<IP_ADDRESS>24513 |
od8s0zhm |
0.356001 |
Li_C_2018, Wu_Q_2014, Kenah_2012, Lauro_2020 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>44339 |
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 |
|
|
10.1101/2<IP_ADDRESS>47688 |
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 |
|
|
10.1101/2<IP_ADDRESS>48827 |
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 |
|
|
10.1101/2<IP_ADDRESS>55467 |
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 |
|
|
10.1101/2<IP_ADDRESS>54577 |
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 |
|
|
10.1101/2<IP_ADDRESS>50393 |
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 |
|
|
10.1101/2<IP_ADDRESS>50922 |
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 |
|
|
10.1101/2<IP_ADDRESS>65268 |
f36smzln |
0.342158 |
|
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>22806 |
3p2dl8yf |
0.339472 |
Li_C_2018 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>23267 |
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 |
|
|
10.1016/j.scitotenv.2020.138917 |
btng72h7 |
0.335771 |
Li_C_2018, Welch_2011, Bifolchi_2013 |
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>46730 |
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 |
|
|
10.1101/2<IP_ADDRESS>52787 |
zxx7tikz |
0.319482 |
Li_C_2018 |
|
| 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 |
|
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 |
|
|
10.1101/2<IP_ADDRESS>38794 |
ckay4ufw |
0.316729 |
|
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 |
|
|
10.1101/2<IP_ADDRESS>73304 |
ab71pz6v |
0.313423 |
Zhao_2013, Bifolchi_2013, Welch_2011, Li_C_2018 |
|
| 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 |
|
10.1007/s11434-010-3180-x |
0fav1esn |
0.313083 |
|
|
| 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 |
|
|
10.1101/2<IP_ADDRESS>4728 |
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 |
|
|
10.1101/2<IP_ADDRESS>65318 |
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 |
|
|
10.1101/2<IP_ADDRESS>66308 |
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 |
|
|
10.1101/2<IP_ADDRESS>45237 |
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 |
|
|
10.1101/2<IP_ADDRESS>52506 |
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 |
|
|
10.1101/2<IP_ADDRESS>60830 |
y3l6k0qu |
0.300870 |
Li_C_2018 |
Shi_P_2020, Li_S_2020 |


About
Show Stats Restart Achievements Report Bug Settings
This game requires JavaScript; please enable JavaScript and refresh this page.


-
-
Your privacy is important to us. Any personal information you provide to us will be dealt with in accordance with this privacy policy.
All of the MeteoGroup of companies* is committed to protecting your privacy. This privacy policy explains our data processing practices and your options regarding the ways in which your personal data is used. If you have any requests concerning your personal information or any queries with regard to our processing please contact us by sending an e-mail or write to us at: The Data Protection Officer, Legal Department, PA Group, 292 Vauxhall Bridge Road, London SW1V 1AE.
Whenever you supply personal information to any MeteoGroup company we are obliged to deal with that information in a lawful way in accordance with the Data Protection Act 1998.
We may request information from you in order to process your transaction, fulfil your order or provide you with the services and products you expect. This information may include, but is not limited to, details such as your name, your address, your e-mail address, telephone number and bank account details. If you subscribe to any MeteoGroup service or product we will use your personal information to provide you with the service or product you select (e.g. sending you password reminders or notices about the relevant service or product being suspended for emergency or routine maintenance). In addition, we may collect information from you, for example, in order to reply to any enquiry you make of any MeteoGroup company. We may use the information you provide for our own internal statistical purposes.
We will not disclose any personal information you provide to us to any third party organisation outside the MeteoGroup of companies, unless we need to do so to provide you with the products or services you select or we are obliged or permitted by law to disclose it.
Whenever you submit personal information to any MeteoGroup company you may opt out of receiving any further promotional material from the MeteoGroup of companies. If you wish to opt out of receiving any promotional material from any MeteoGroup company please contact the MeteoGroup personnel to whom you submitted your personal information or you may send an e-mail. However, if you do choose to have promotional material sent to you, you will be given the option to "unsubscribe" from receiving any further information about that product or service from us.
If you wish to correct or update any of the personal details we hold about you, please contact your usual MeteoGroup contact as soon as possible or write to us at: The Data Protection Officer, Legal Department, PA Group, 292 Vauxhall Bridge Road, London SW1V 1AE.
You have the right to request a copy of the personal information that we hold about you and have any inaccuracies corrected. Please address requests to: The Data Protection Officer, Legal Department, PA Group, 292 Vauxhall Bridge Road, London SW1V 1AE.
When you visit any MeteoGroup company web site we may use "cookies". Cookies are text files that identify your computer to our server. Our web sites only use cookies to support and improve your experience of our websites.
MeteoGroup web sites may contain links to other web sites which are outside our control and are not covered by this Privacy Policy. Please note that, if you access other sites using the links provided, the operators of these sites may collect information from you which will be used by them in accordance with their privacy policy, which may differ from ours.
"MeteoGroup" means MeteoGroup Limited (or any holding company of it) and each subsidiary from time to time of MeteoGroup Limited (or any holding company of it).
MeteoGroup is in the process of updating its Privacy Policy to provide you with easier information about the cookies on its websites. In the meantime, we would like to inform you that we make use of cookies. These are small files that are placed on your computer's hard disc to remember certain things about your use of our sites, for example remembering your stored preferences for our services or your login details. This is so you do not have to re-enter these details each time you use our service. We also make use of third party cookies such as but not limited to, Google Analytics. The cookies are anonymous and will not register any personal data such as your IP-address. You can decide to modify your browser settings so that cookies are not accepted. Further information can be found at www.aboutcookies.org.
-


| as.data.frame.RGtkDataFrame | The RGtkDataFrame model |
| as.flag | Enums and Flags |
| as.struct | Transparent types in RGtk2 |
| assignProp | Custom GObject classes |
| ATK | ATK |
| boundCairoVersion | Bound versions |
| boundGTKVersion | Bound versions |
| boundPangoVersion | Bound versions |
| CAIRO | CAIRO |
| checkArrType | RGtk2 Type Assertion |
| checkCairo | Bound versions |
| checkGTK | Bound versions |
| checkPango | Bound versions |
| checkPtrType | RGtk2 Type Assertion |
| connectSignal | The GSignal API |
| dim.RGtkDataFrame | The RGtkDataFrame model |
| dimnames.RGtkDataFrame | The RGtkDataFrame model |
| dimnames<-.RGtkDataFrame | The RGtkDataFrame model |
| enums-and-flags | Enums and Flags |
| gClass | Custom GObject classes |
| GConnectFlags | The GSignal API |
| GDK | GDK |
| GDK-Pixbuf | GDK-Pixbuf |
| getProp | Custom GObject classes |
| gIdleAdd | The GLib Main Loop |
| GIO | GIO |
| giocon | R connections for GIO streams |
| giocon.GInputStream | R connections for GIO streams |
| giocon.GIOStream | R connections for GIO streams |
| giocon.GOutputStream | R connections for GIO streams |
| GMainLoop | The GLib Main Loop |
| GObject | The GObject system in RGtk2 |
| gObject | The GObject system in RGtk2 |
| gObjectGet | The GObject system in RGtk2 |
| gObjectGetData | The GObject system in RGtk2 |
| gObjectGetPropInfo | The GObject system in RGtk2 |
| gObjectGetSignals | The GObject system in RGtk2 |
| gObjectNew | The GObject system in RGtk2 |
| gObjectParentClass | The GObject system in RGtk2 |
| gObjectSet | The GObject system in RGtk2 |
| gObjectSetData | The GObject system in RGtk2 |
| GQuark | GQuark |
| GSignal | The GSignal API |
| gSignalConnect | The GSignal API |
| gSignalEmit | The GSignal API |
| GSignalFlags | The GSignal API |
| gSignalGetInfo | The GSignal API |
| gSignalHandlerBlock | The GSignal API |
| gSignalHandlerDisconnect | The GSignal API |
| gSignalHandlerUnblock | The GSignal API |
| gSignalStopEmission | The GSignal API |
| gSourceRemove | The GLib Main Loop |
| gTimeoutAdd | The GLib Main Loop |
| GTK | GTK |
| gtkObject | The GObject system in RGtk2 |
| gtkObjectNew | The GObject system in RGtk2 |
| GType | The GType system |
| gTypeFromName | The GType system |
| gTypeGetAncestors | The GType system |
| gTypeGetClass | The GType system |
| gTypeGetInterfaces | The GType system |
| gTypeGetPropInfo | The GObject system in RGtk2 |
| gTypeGetSignals | The GType system |
| implements | RGtk2 Type Assertion |
| interface | The GObject system in RGtk2 |
| names.GObject | The GObject system in RGtk2 |
| Pango | Pango |
| parentHandler | Custom GObject classes |
| print.CallbackID | The GLib Main Loop |
| print.enum | Enums and Flags |
| print.enums | Enums and Flags |
| print.flag | Enums and Flags |
| print.flags | Enums and Flags |
| print.GType | The GType system |
| registerVirtuals | Custom GObject classes |
| RGtk | The RGtk2 package |
| RGtkDataFrame | The RGtkDataFrame model |
| rGtkDataFrame | The RGtkDataFrame model |
| rGtkDataFrameAppendColumns | The RGtkDataFrame model |
| rGtkDataFrameAppendRows | The RGtkDataFrame model |
| rGtkDataFrameNew | The RGtkDataFrame model |
| rGtkDataFrameSetFrame | The RGtkDataFrame model |
| RGtkObject | The base object of RGtk2 |
| transparent-type | Transparent types in RGtk2 |
| !.flag | Enums and Flags |
| $.GObject | The GObject system in RGtk2 |
| $.RGtkObject | The base object of RGtk2 |
| $<-.GObject | The GObject system in RGtk2 |
| &.flag | Enums and Flags |
| ==.enum | Enums and Flags |
| ==.RGtkObject | The base object of RGtk2 |
| [.enums | Enums and Flags |
| [.flags | Enums and Flags |
| [.GObject | The GObject system in RGtk2 |
| [.RGtkDataFrame | The RGtkDataFrame model |
| [<-.GObject | The GObject system in RGtk2 |
| [<-.RGtkDataFrame | The RGtkDataFrame model |
| [[.GObject | The GObject system in RGtk2 |
| [[.RGtkObject | The base object of RGtk2 |
| [[<-.GObject | The GObject system in RGtk2 |
| |.flag | Enums and Flags |


On the Effective Use of TODO Lists
Introduction
Like many countries around the world, Singapore entered a lockdown (a “circuit-breaker” to use the local terminology) on April 4 to stem the rapid spread of COVID-19. Knowing how easy it is to get distracted from work and lose productivity at home, I made a commitment to myself at the start of the lockdown to use the extra time to improve myself. Specifically I wanted to improve my workflows for many of my daily tasks.
A key part of the effort is my renewed commitment to TODO lists. I’ve made several attempts to integrate TODO lists as a habit into my life before, but the habit has never stuck. As a result, I use TODO lists quite loosely - i.e. when things get super hectic and I have no choice but to use them to keep me sane. Otherwise, I simply remember the big picture of what I want to get done. While this has led to me forgetting to do the odd task, I felt that my overall productivity was not significantly impacted. If I really want to remember something, I might stick a Post-It note on my work desk. However, I decided recently that this was not good enough for me for two reasons:
- I want to improve myself as a person.
- I want to take on more responsibilities and work and this is only possible if I improve this part of myself.
So about a week into the lockdown, I decided to use TODO lists to track all my tasks for the next 30 days. If I saw real improvements in these 30 days, I’d keep the system. If not, I’d try to find a better one. I put some weight into the commitment by purchasing for myself a premium account in the todoist app. While the system felt awkward and cumbersome at the start, I began to notice tangible improvements at the two week mark. With a little experimentation, I was able to optimize TODO lists to work for me. Here’s what I learned from the 30 day experiment.
1. Believe in the System
In all my previous attempts, I started with a lot of skepticism about the effectiveness of using TODO lists. This time, I decided that I was going to proceed as though it was going to work. This seemed to make a big difference in how the experiment worked out. I observed straight away that I was more motivated to find ways to make the system work for me. In the past, I’d give up on the method when some small obstacle popped up (the app not having some small feature that I wanted for instance).
2. Use it for Everything
I read that this is similar to David Allen’s advice in his book “Getting Things Done”. It does help to capture in the list absolutely everything that you want to get done. Doing this meant that I did not need to keep a running list of all my tasks in my brain. I didn’t realize that keeping my task list in my head was generating a low background radiation of anxiety in my brain until I stopped doing it. Capture everything in your task list - both personal and work-related.
3. Use a Cross-Platform System
I’ve experimented with systems like the bullet journal before. While I do like the feeling of having a physical notebook to write down tasks in, I found that I became too dependent on the notebook. If I forgot to bring the notebook anywhere (which can happen), I got lost. Using a cross platform task tracking app that works on both the desktop and on the phone is more convenient and ensures that you’ll have your task list with you pretty much wherever you go.
4. Check the List Before Starting Work
Previously, I’d just sit down and start working on whatever floated to the top of my head at that moment. While this works great when you don’t have that many things to do, it may result in small things being forgotten and pushed to the last minute. In addition to making me more intentional about what I was doing, checking my list before starting work also made me more likely to finish off more of the small tasks before they accumulated to annoying levels.
5. There’s a Time and Place for Specificity
There’s a time and place for everything, but not now. -Professor Oak
The most important thing I learned is to choose the right level of specificity when entering tasks into the list. My first instinct was to overdo it. I made full use of the fact that you could nest subtasks pretty deep in todoist (4 levels) and wrote big picture items into the list and started breaking it down into smaller steps. Then I started doing the task and found out that the steps that I wrote down were either too optimistic completely unnecessary. However, when I tried only writing down the big picture tasks, they became too big and scary and I didn’t want to touch them. In the end what worked for me was to write down only big picture things when I first enter the task into the system. When I start working on the task, I click into it and start adding sub-tasks.
Another thing I learned about “sub-tasking” was not to add sub-tasks too far into the future. The further ahead I planned the more my plan deviated from reality and the more I had to delete what I wrote and replan.
6. Choose the Right Level of Specificity
On a related note to point 5, I recommend experimenting a bit to find the right level of task specificity for the subtasks. I experimented with breaking up large tasks into subtasks that took from 1 minute all the way up to a few hours. I found subtasks that took between 5 and 15 minutes to be the most rewarding. Anything smaller added too much overhead to the tasks. Anything longer exponentially decreased the probability that I’d start working on that task.
Choosing a consistent level of specificity also solved the problem of unequal weighting of tasks for me. Checking off tasks is actually quite rewarding on its own. When the reward for checking off one big task that took 5 hours to do was the same as the reward for a two minute task, it gave me less motivation to tackle the big tasks. Once I started choosing a consistent level of specificity for the tasks that I checked off, larger tasks became naturally more rewarding than the smaller tasks and my productivity on larger tasks improved as a result.
7. Consider Gamification
There’s some evidence that gamification of tasks can help improve the motivation for completing them. I don’t think that gamification can work as a base motivation but it may help a little with the day to day motivation. For me, the simple points system on todoist adds a small touch of motivation without adding too much overhead.
8. Attach it to Your Bedtime Routine
Checking the list one last time before turning in is really helpful. It feels good to clear the schedule for the day and have a sense of resolution. I recommend doing this even if you haven’t finished everything on the list. Simply reschedule the unfinished items to the next day but get to zero tasks remaining for the day. Integrating it into my bedtime routine has also helped with my effort to avoid browsing the phone mindlessly in bed before going to sleep. I set a simple rule for myself: Once I clear the day’s list, I can’t use the phone anymore and must go to sleep.
9. Don’t Let Perfect be the Enemy of Good
This is related to me giving up on systems too early. I realized that I tend to try and find the perfect process to do things before even trying. While this is useful in many situations, it has sometimes led to me not doing things in the first place. If you have a proclivity for perfection, don’t let it get in the way of you trying new but imperfect systems. Just remember that you can always optimize later. The more important thing is to get started.


Mural of activist Greta Thunberg going up in San Francisco
by EndPlaySAN FRANCISCO (AP) - Teen climate activist Greta Thunberg is staring down at pedestrians in the heart of San Francisco where an artist is painting a massive mural of the Nobel Peace Prize nominee.
Argentine muralist Andres Iglesias, who signs his art with the pseudonym Cobre, is set to finish the artwork of the Swedish 16-year-old in Union Square by next week, SFGate reported Friday.
Iglesias told SFGate that he's donating his time to complete the work and that he hopes the mural helps people realize "we have to take care of the world."
He had also painted a mural of Robin Williams in downtown San Francisco that has since been demolished.
Cobre said he was searching for a building for a new mural when environmental nonprofit oneatmosphere.org approached him about the project.
Paul Scott, the nonprofit's executive director, said he believed the artist would be perfect to create the first of what his group hopes will be a series of works honoring climate-change activists.
___
Information from: San Francisco Chronicle, http://www.sfgate.com


---
layout: default
---
{% include widget_price.html %}
Sponsors For Free Bitcoins
We need to translate this into Greek. Since the earliest days of Bitcoin there have been Bitcoin faucets where people can get free bitcoins. This has been a way to spread Bitcoin awareness. However, when you give away money for free then you quickly run out of it. To make it sustainable to give away free bitcoins therefore we have sponsors.
Current News
This is a great page to bookmark. Not only do you get free bitcoins but you can also browse the current Bitcoin news headlines.
Where To Get Free Bitcoins


Spark Streaming
-
At the end of the time interval the batch is done growing.
-
The size of the time intervals is determined by a parameter called
the batch interval.
-
The batch interval is typically between 500 milliseconds and
several seconds and is configured by the application developer.
-
Each input batch forms an RDD.
-
Each RDD is passed to Spark for
processing in a job.
-
Thus, the stream is broken into pieces (RDDs) that
are processed individually from other pieces.


Visma pamokos
Failas
New File
Save (Ctrl + S)
Download
Insert template for current language
Python (3.6.0)
Vykdyti programą (F9)
About
API
Other
Become a Patron
View source code on Github
Report an issue
Subscribe to Judge0 news
Join a Discord server
Contact the author
About the author
JUnit Playground
Nim Playground
V Playground
Share on Reddit
Share on Twitter
Share on Facebook
Close (ESC)
Settings
Editor Mode
Normal
Vim
Emacs


My main coding language is JavaScript and I want to talk about some of the really cool features that JavaScript supports.
My main coding language is JavaScript, and I want to talk about some of the really cool features that JavaScript supports.
JavaScript often abbreviated as JS, is a programming language that conforms to the ECMAScript (latest stable release ES2019, preview release ES2020) specification. JavaScript is high-level, often just-in-time compiled, and multi-paradigm. It has curly-bracket syntax, dynamic typing, prototype-based object-orientation, and first-class functions.
Along with HTML and CSS, JavaScript is one of the core technologies of the World Wide Web. JavaScript enables interactive web pages and is an essential part of web applications. The vast majority of websites use it for client-side page behavior, and all major web browsers have a dedicated JavaScript engine to execute it.
As a multi-paradigm language, JavaScript supports event-driven, functional, and imperative programming styles. It has application programming interfaces (APIs) for working with text, dates, regular expressions, standard data structures, and the Document Object Model (DOM). However, the language itself does not include any input/output (I/O), such as networking, storage, or graphics facilities. The host environment (usually a web browser or Node) provides those APIs.
Originally used only in web browsers, JavaScript engines are also now embedded in server-side website deployments and non-browser applications.
Although there are similarities between JavaScript and Java, including language name, syntax, and respective standard libraries, the two languages are distinct and differ greatly in design.
You can read more on the Wikipedia page about JavaScript.
Every year JavaScript shows us new features and tools to solve different problems more easily. So let me introduce some of them which can help you to write less code in a clearer way.
Before the ES6 standard, JavaScript had two types of scope: Function scope and Global scope and one type of variable initializer called var
. Variables declared outside of the function (Globally) have Global scope and variables declared in a function have a Function scope.
We can access global scope variables from everywhere in the code, but we can access function scope variables inside the function in which that variable declared.
Seems logical, yeah? So let’s see some examples.
I numbered the lines so it’s easier to navigate through
We can see that in the example above there are two scopes we are talking about starting from (line 1 global scope and line 5 function scope). The number variable on line 2 is initialized in the global scope, so we can access it from function twoAdder (line 7) and also from outside of it (line 10).
But on line 6, we have one more variable called increasedNumber which adds 2 to a number value and saves it in it without modifying number. So, we can easily access it from twoAdder function online 7, but not from outside of it on line 11. That’s because variables declared in function (function scope) can be accessed only from inside that function.
That’s the simple representation of global and function scopes.
Continuing the main theme, after ES6 there is another scope called Block scope.
Block scope is the scope which is in {curly} brackets and the variables initialized in that scope can be accessed only from that scope. That scope can be created by functions, for loops, if statements, or just by curly braces.
Hmmm, what is that
let
keyword?
The answer is that after ES6, there are another two new variable initializers: let
and const
(from constant), and only using them you can create variables which support block scope.
With let the newSalary is block scoped in if statement
The difference between let
and const
is that variables initialized with const
can’t be mutated. That’s why the const
keyword comes from constant (means fixed).
You can’t assign other value to variable
But you can mutate the arrays or objects assigned to variable:
let
and const
also prevent hoisting (JavaScript’s default behavior of moving declarations to the top).
Yep, that’s long error message
For more details about hoisting you can read here.
Okay why “let"?
Let is a mathematical statement that was adopted by early programming languages like Scheme and Basic. Variables are considered low-level entities not suitable for higher levels of abstraction, thus the desire of many language designers to introduce similar but more powerful concepts like in Clojure, F#, Scala, where “let" might mean a value or a variable that can be assigned, but not changed, which in turn lets the compiler catch more programming errors and optimize code better. The full answer is in this link.
I highly recommend you use mentioned initializers with this priority:
const > let > var
The function overallShoePrice
calculates the overall shoe price after discount (if there is any) and returns it to us.
In the old way (before ES6) you would have to check if there is a discount passed through function parameter or not (undefined) and after that continue calculation and returning a final price. But with the new way, you can assign a default value to function’s parameter (On our example: discount = 0).
After running this function without passing the discount parameter, it will be assigned to zero by default. It’s easier, cleaner, and less chance to get an error. 🙃
Template literals for embedded expressions
Template literals are string literals allowing embedded expressions. You can use your **${variables}**
in your string much easier or even call **${functions()}**
. You can use multi-line strings and string interpolation features with them. They were called “template strings" in prior editions of the ES2015 specification.
Template literals for multi-line strings
With template literals, we can just start to write long text from the new line and there would be no errors. The text between `backticks` would be compiled as one string.
Destructuring has been added to JavaScript for writing cleaner code and make it easier to unpack values from objects and arrays. So we can use this feature with {objects} and [arrays].
Traditionally, we can access particular object keys like this:
With the new method, it’s like this:
We can see that with the new method, we initialize all needed variables in one line and then pass them into the function printMessage
as an argument.
It may look longer, but it will be cleaner and easier to navigate through the needed keys.
A closer look at destructuring, that’s what is going in there. This:
Equals to this:
It’s also possible to go deeper to nested objects:
we have access to nicknames too
Aaaaand we can destructure in a function’s argument field too, like this:
This feature is amazing if you like to write clean and readable code
If we have some invalid JavaScript identifiers, then we can give them valid names and use them as normal.
In this example, big-boss-nickname is an invalid identifier, but you can use for example badassNickname and have access to NoobMaster69 value through it.
Now let’s talk a bit about destructuring in arrays.
We can see that almost like objects, we can destructure arrays in the same way too.
y is the first element in the array and z is the second. We can add 3 more elements and they will correspond to 3, 4, and 5, or we can use the rest parameter and assign the rest elements in the array to one variable as an array.
the name ‘rest’ can be whatever valid identifier as you want
That’s simple, yeah? 🙃
You can learn more about the destructuring feature there.
There are many helper functions for arrays out there, and they make working with arrays incredibly easy. We will talk about 7 of them.
For example, the task is to reverse the array so [1, 2, 3, 4, 5] to be [5, 4, 3, 2, 1]. There are many ways to do that. Like with a stack, with decrementing loop, recursion, etc.
Let’s do that with a decrementing loop.
In this method, we are looping through all elements of arr from backward and pushing them to the reversedArray which will be our new reversed array.
That took from us about 3-5 mins and 7 lines of code. So JavaScript offers the same solution with just 1 line of code and with the speed of light. 🚀
Just think of it so JavaScript is running that “7 lines" of code under the reverse method with some modifications.
The methods listed above are loop methods.
forEach method executes a provided (callback) function once for each array element. The callback function is the function that we pass to the method as an argument.
DON’T return anything because it’ll be simply discarded
In this example, we have workers’ salaries array and we log every workers’ salary to our console. We have worker, index and array arguments in our callback function. Worker argument is representing array’s every element ( {name: ‘Sam’, salary: 700}, {name: ‘Ani’, salary: 700}, … ). The index argument is the position of a particular element ( 0, 1, … ) and array argument is your array on which you are calling this method. Index and array exist in map, filter, reduce callback functions too.
One more example:
for more than one line of code inside the provided arrow function add paranteces
In this example instead of logging salaries of our workers, we are multiplying them by 2 and pushing to the newSalaries array.
With the map method, you can create a new array without modifying the array on which your method is running. We can demonstrate the map method on our previous example:
Instead of pushing every new element to our newSalaries array, with the map method we just assign to it and return new elements. New elements get automatically pushed to the newSalary array after returning.
I think it’s simpler and easier to use this method for creating new arrays on old arrays or donor array compared with the forEach method.
The filter method receives callback function with a filter condition and creates a new array with elements that meet the condition.
So we have more workers with different salaries, but we need the workers with salaries more than 2000 points. We pass the condition and get the newSalaries array with workers with salaries more than 2000 points.
We can create multiple conditions too.
And that was the filter method.
Now let’s look to reduce method. This was the hardest one I learned from these 4 methods. But it’s easy with some practice, don’t panic 🤫
This method’s callback function accepts 4 arguments (accumulator, currentValue, index, array). The last two methods you already know, so let’s look at the accumulator (let’s call it currentSum) and currentValue.
currentWorker fits better instead of currentValue
We have the same list of our workers and at this time we wanna calculate the sum of all worker salaries. You can notice the second argument of our reduce method (0). It’s the initial value of our calculation.
The currentSum argument gives us a summary of our calculation value at the moment of the current loop phase. For example, if we are on Arthur’s salary phase, we already have the sum of Sam’s and Susan’s salaries (0 (initial value)+ 700 + 700 = 1400), so the currentSum will be 1400.
The currentWorker argument is the same as worker argument in previous method examples.
After all phases of the loop, we will get the sum of all workers’ salaries under the sumOfSalaries variable. We can do any calculation we want with this method.
The includes method determines whether an array includes a certain value among its elements, returning true
or false
as appropriate.
The !goalsOf2020.includes(newGoal)
checks wheather goalsOf2020 has newGoal and if not adds the newGoal.
The flat method creates a new array with all sub-array elements concatenated into it recursively up to the specified depth.
The depth argument is optional, by default it’s 1
The depth argument is for telling the method how deep it can go and concat sub-arrays into one array.
The depth is 1 which is initialized by default, so the array has sub-array
If we want to have no sub-arrays guaranteed for all level depth arrays, we can use this trick bellow by passing Infinity as depth argument.
So using this, you will always have 0 depth array 😌


| Vendor Name | Instrux Media Corp |
|---|---|
| Reference Number | 2177-16-14 |
| Contract Date | 3-6-2016 |
| Description of Work | End-to-End Learning Services (Excludes COTS Training) |
| Delivery Date | 19-12-2016 |
| Contract Value | $76,501.00 |
| Comments | This contract was competitively sourced. This contract is a call-up against a Public Services and Procurement Canada procurement tool. |
| Additional Comments | To develop online courses in the learning management system |


Research Area: Network Coding, Network Information Theory, Coding Theory, Algorithm Design for Networks.
More Information...Research Area: Multiuser Information Theory, Combinatorial Optimization, Probability Theory, Algorithms.
More Information...Research Area: Graphical Models, Quantum Information Processing, Error-control Coding, Information Theory, Data Science, Compressed Sensing, Optimization Theory.
More Information...


PWG April
2009
F2F
Meeting
I. When/Where
The PWG F2F meeting will be held on April, 27-29,
2009 at Oki Data in Mt. Laurel NJ.
Address:
Oki Data Americas, Inc.
2000 Bishops Gate Boulevard
Mount Laurel, NJ 08054
Meeting
registration (and meeting preference survey)
II. Detailed
Meeting
Agenda:
All times are local to the venue (EDT)
|
|
|
|
|
Meeting
Slides |
Meeting
Minutes |
|
Mon. 04/27 |
9:00 AM |
11:30 AM |
IPP 2.0/PSX |
Slides |
|
|
11:30 AM |
1:00 PM |
Lunch |
|||
|
1:00 PM |
5:00 PM |
MFD |
Slides | ||
|
|
|
|
|
|
|
| Tues. 04/28 |
9:00 AM |
10:30 AM |
WIMS/CIM |
Slides |
|
|
10:30 AM |
10:45 AM |
Power Mgmt. (BoF/survey) |
|||
|
10:45 AM |
12:00 PM |
Plenary |
Slides | ||
|
12:00 PM |
1:30 PM |
Lunch |
|||
|
1:30 PM |
5:30 PM |
MFD (session2) |
|||
|
Wed. 04/29 |
9:00 AM |
11:30 AM |
IDS |
Slides | |
|
|
11:30 AM |
1:00 PM |
Lunch |
||
|
|
1:00 PM |
~4:00 PM |
IDS |
III. Hotel
information for
the April 2009 PWG meetings:
There are no hotel arrangements for this meeting.
(attendees are on their own)
Oki Data does have a discount rate at the Doubletree Guest Suites in
Mt. Laurel.
Contact Nancy Chen: nchen(at)okidata(dot)com if you need more
information.


Detail : << {{book.title}} >>
Title: {{book.title}}
Author: {{book.author}}
Category: {{book.category}}
Publisher: {{book.publisher}}
Quantity: {{book.quantity}}
Created at: {{book.created_at|date:"Y/m/d H:i"}}
Last updated by: {{book.updated_by}} at {{book.updated_at|date:"Y/m/d H:i"}}
Borrowed times: {{book.total_borrow_times}}
Status Now : {{book.get_status_display}}
{{book.description}}
Location Id: {{book.floor_number}}{{book.bookshelf_number}}
Floor: {{book.get_floor_number_display}} floor
Bookshelf: {{book.bookshelf_number}}
Borrow History
{% if related_records %}| Borrower | Card | Book Name | Start - End | Status | Return at | # | |
|---|---|---|---|---|---|---|---|
| {{record.borrower}} | {{record.borrower_card}} | {{record.book}} | {{record.start_day | date:"Y/m/d"}} - {{record.end_day | date:"Y/m/d"}} | {{record.get_open_or_close_display}} | {{record.closed_at | date:"Y/m/d"}} | {% if record.final_status == "Delayed" %}{{record.final_status}}, {{record.delay_days}} days | {% else %}{{record.final_status}} | {% endif %}
{% else %}
Pleaseloginto reply
{% endif %}
Total {{ comments.count }} comments
{{ comment.user }} published at {{ comment.created_at|date:"Y/m/d H:i" }} :
{{ comment.body|safe }}
{% endfor %}


Teacher's AI Aide
! This web page is under construction !
TAIA is under development (12/25.2017). Version 0.1.0 is scheduled fro release in early January 2017
TAIA is free software for educators. See GitHub Page for Licence Details. GitHub
If you would like to contribute ideas or code, contact the lead software developer: Nate Russell, ntrusse2 [the at symbol] illinois [dot] edu
Introduction
TAIA is meant to make life easier for teachers. At it's core, TAIA helps educators make, grade and review assessments like homework and tests.
The AI in TAIA referes to algorithms capable of reading and grading tests without the use of an expensive scantron.
TAIA also uses machine learning to Help educators review assessments, learning about their students and about how to make assessments more effective and fair.
Download and Install
It is installable on windows 8.1 and higher as well as Max OS X and Ubuntu.
The Software can be downloaded from here
PyInstaller is used to build a single file executable that is easy to use for those that don't want to build from source.
Version 0.1.0 Scheduled Features
- Easy Download and Launch: Download File, click it To Run
- Persistant Mempry: TAIA remembers where you keep your files
- Graphical User Interface for easy navigation and usage
- Assessment Generator: GUI based assessment builder for making multiple choice and free response questions
- Assessment PDF Builder: Converts an assessment into pdf form that can be printed and given to student to take. This form is designed to improve the accuracy of TAIA's Computer Vision Grading
- Computer Vison Grading: Scan all of your tests as a single big using your local Copy Machine. Then let TAIA do the grading For you using it's Computer Vision
- Review Graded Assessments using interactive data mining tools
Looking to The Future
TAIA is both free software for teachers as well as vehicle for research in AI.
- Improved human handwriting recognition
- More flexible / intuitive GUI
- Word Content Analysis: Look for patterns in semantic content of questions and answers
- Text Book Incorporation: Use knowledge from text books to improve automation of grading and topic modeling. Recommendations for review concepts for students


| Name | Kind | Description |
| BaseServiceConnection | operation | |
| BaseServiceConnection | class | |
| batteryData | relation | |
| BatteryData | class | |
| BatteryData | artifact | |
| BatteryDataManager | operation | |
| BatteryDataManager | artifact | |
| BatteryDataManager | class | |
| BatteryLevelReceiver | artifact | |
| BatteryLevelReceiver | class | |
| batteryServiceConnection | relation | |
| binder | attribute | |
| binder | attribute | |
| binder | attribute | |
| binder | attribute | |
| binder | attribute | |
| binder | attribute | |
| binder | attribute | |
| binder | attribute | |
| binder | attribute | |
| Binder | class | |
| binder | attribute | |
| BootCompletedIntentReceiver | artifact | |
| BootCompletedIntentReceiver | class | |
| BroadcastReceiver | class | |
| build | operation | |
| Builder | class | |
| BUILDING_PLACE_TYPES | attribute | |
| ButItsSunnyOutside | operation |


The Vertical menu option (Menu
Window
)
Use this menu option to arrange several open windows side-by-side.