Prati
César Leonardo Clemente López
César Leonardo Clemente López
PhD in Computer Science
Potvrđena adresa e-pošte na itesm.mx
Naslov
Citirano
Citirano
Godina
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models
D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, JT Davis, A Vespignani, ...
arXiv preprint arXiv:2004.04019, 2020
1492020
An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time
NE Kogan, L Clemente, P Liautaud, J Kaashoek, NB Link, AT Nguyen, ...
Science advances 7 (10), eabd6989, 2021
1482021
Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches
FS Lu, MW Hattab, CL Clemente, M Biggerstaff, M Santillana
Nature communications 10 (1), 147, 2019
1052019
Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models
D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, J Davis, A Vespignani, ...
Journal of medical Internet research 22 (8), e20285, 2020
562020
Improved real-time influenza surveillance: using internet search data in eight Latin American countries
L Clemente, F Lu, M Santillana
JMIR public health and surveillance 5 (2), e12214, 2019
382019
A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
SF McGough, L Clemente, JN Kutz, M Santillana
Journal of The Royal Society Interface 18 (179), 20201006, 2021
282021
Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States
LM Stolerman, L Clemente, C Poirier, KV Parag, A Majumder, S Masyn, ...
Science Advances 9 (3), eabq0199, 2023
112023
Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil
G Koplewitz, F Lu, L Clemente, C Buckee, M Santillana
PLoS Neglected Tropical Diseases 16 (1), e0010071, 2022
62022
& Santillana, M.(2020)
D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, JT Davis
A machine learning methodology for real-time forecasting of the 2019-2020 …, 2004
62004
An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time (preprint)
NE Kogan, L Clemente, P Liautaud, J Kaashoek, NB Link, AT Nguyen, ...
52020
Correction: real-time forecasting of the COVID-19 outbreak in chinese provinces: machine learning approach using novel digital data and estimates from mechanistic models
D Liu, L Clemente, C Poirier, X Ding, M Chinazzi, J Davis, A Vespignani, ...
J Med Internet Res 22 (9), e23996, 2020
22020
Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet
FS Lu, MW Hattab, L Clemente, M Santillana
bioRxiv, 344580, 2018
22018
Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations
SM Mathis, AE Webber, A Basu, JM Drake, LA White, EL Murray, M Sun, ...
medRxiv, 2023.12. 08.23299726, 2023
12023
Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach
SF McGough, CL Clemente, JN Kutz, M Santillana
bioRxiv, 666628, 2019
12019
Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States
TH Song, L Clemente, X Pan, J Jang, M Santillana, K Lee
medRxiv, 2024
2024
School of Engineering and Sciences
CLC Lopez
Google, 2019
2019
Predicting Influenza in Latin America: Using Voting Ensembles to Combine Google Search Activity and Geo-spatial Synchronicities from Historical Flu Activity
CL Clemente Lopez
Instituto Tecnológico y de Estudios Superiores de Monterrey, 2019
2019
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