Health Informatics

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach

1. Bradley S. Price
2. Maryam Khodaverdi
3. Adam Halasz
4. Brian Hendricks
5. Wesley Kimble
6. Gordon S. Smith
7. Sally L. Hodder

Reviewed by ScreenIT

Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data

1. Laila Rasmy
2. Masayuki Nigo
3. Bijun Sai Kannadath
4. Ziqian Xie
5. Bingyu Mao
6. Khush Patel
7. Yujia Zhou
8. Wanheng Zhang
9. Angela Ross
10. Hua Xu
11. Degui Zhi

Reviewed by ScreenIT

Phylodynamics of a regional SARS-CoV-2 rapid spreading event in Colorado in late 2020

1. Kristen J. Wade
2. Samantha Tisa
3. Chloe Barrington
4. Jesslyn C. Henriksen
5. Kristy R. Crooks
6. Christopher R. Gignoux
7. Austin T. Almand
8. J. Jordan Steel
9. John C. Sitko
10. Joseph W. Rohrer
11. Douglas P. Wickert
12. Erin A. Almand
13. David D. Pollock
14. Olivia S. Rissland

Reviewed by ScreenIT

Standardized incidence ratio of the COVID-19 pandemic: a case study in a Midwestern state

1. Emma Spors
2. Semhar Michael

Reviewed by ScreenIT

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

1. Ashleigh Myall
2. James R Price
3. Robert L Peach
4. Mohamed Abbas
5. Sid Mookerjee
6. Nina Zhu
7. Isa Ahmad
8. Damien Ming
9. Farzan Ramzan
10. Daniel Teixeira
11. Christophe Graf
12. Andrea Y Weiße
13. Stephan Harbarth
14. Alison Holmes
15. Mauricio Barahona

Reviewed by ScreenIT

Toward Using Twitter Data to Monitor COVID-19 Vaccine Safety in Pregnancy: Proof-of-Concept Study of Cohort Identification

1. Ari Z Klein
2. Karen O'Connor
3. Graciela Gonzalez-Hernandez

Reviewed by ScreenIT

Optimized Post-Vaccination Strategies and Preventative Measures for SARS-CoV-2

1. Rowland Pettit
2. Bo Peng
3. Patrick Yu
4. Peter G. Matos
5. Alexander L. Greninger
6. Julie McCashin
7. Christopher Ian Amos

Reviewed by ScreenIT

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study

1. Riku Klén
2. Disha Purohit
3. Ricardo Gómez-Huelgas
4. José Manuel Casas-Rojo
5. Juan Miguel Antón-Santos
6. Jesús Millán Núñez-Cortés
7. Carlos Lumbreras
8. José Manuel Ramos-Rincón
9. Noelia García Barrio
10. Miguel Pedrera-Jiménez
11. Antonio Lalueza Blanco
12. María Dolores Martin-Escalante
13. Francisco Rivas-Ruiz
14. Maria Ángeles Onieva-García
15. Pablo Young
16. Juan Ignacio Ramirez
17. Estela Edith Titto Omonte
18. Rosmery Gross Artega
19. Magdy Teresa Canales Beltrán
20. Pascual Ruben Valdez
21. Florencia Pugliese
22. Rosa Castagna
23. Ivan A Huespe
24. Bruno Boietti
25. Javier A Pollan
26. Nico Funke
27. Benjamin Leiding
28. David Gómez-Varela

• Curated by eLife

  Evaluation Summary:

  This article is dealing with the unmet need to generate a machine-learning approach for the early and accurate estimation of the risk among COVID-19 admission. The presented data generate confidence on the validity since they have been developed in a vast number of patients and they are validated in cohorts from different geographical regions.

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their name with the authors.)

Reviewed by eLife, ScreenIT

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

1. Mohanad Alkhodari
2. Ahsan H. Khandoker

Reviewed by ScreenIT

Distributed Counterfactual Modeling Approach for Investigating Hospital-Associated Racial Disparities in COVID-19 Mortality

1. Mackenzie Edmondson
2. Chongliang Luo
3. Nazmul Islam
4. David Asch
5. Jiang Bian
6. Yong Chen

Reviewed by ScreenIT