Skip to main content
Log In
More
Log in via Institution
Log in via OpenAthens
Log in using your username and password
For personal accounts OR managers of institutional accounts
Username
*
Password
*
Forgot your log in details?
Register a new account?
Forgot
your user name or password?
Basket
Search
More
Search for this keyword
Advanced search
Latest content
Archive
Authors
About
Public & Patients
Search for this keyword
Advanced search
Close
More
Main menu
Latest content
Archive
Authors
About
Public & Patients
Log in
More
Log in via Institution
Log in via OpenAthens
Log in using your username and password
For personal accounts OR managers of institutional accounts
Username
*
Password
*
Forgot your log in details?
Register a new account?
Forgot
your user name or password?
BMJ Journals
You are here
Home
Archive
Volume 29, Issue 1
Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm
Email alerts
Article metrics
Article menu
Article
Text
Article
info
Citation
Tools
Share
Rapid Responses
Article
metrics
Alerts
PDF
PDF +
Supplementary
Material
Original research
Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm
Online download statistics by month:
Online download statistics by month: December 2022 to March 2024
Abstract
Full
Pdf
Dec 2022
228
233
49
Jan 2023
173
175
14
Feb 2023
214
218
27
Mar 2023
140
143
21
Apr 2023
87
91
13
May 2023
70
72
10
Jun 2023
89
94
13
Jul 2023
57
58
13
Aug 2023
34
37
9
Sep 2023
19
20
8
Oct 2023
21
21
8
Nov 2023
233
233
7
Dec 2023
51
52
14
Jan 2024
39
40
14
Feb 2024
54
54
23
Mar 2024
55
55
90
Total
1564
1596
333
Read the full text or download the PDF:
Subscribe
Log in
Log in via Institution
Log in via OpenAthens
Log in using your username and password
For personal accounts OR managers of institutional accounts
Username
*
Password
*
Forgot your log in details?
Register a new account?
Forgot
your user name or password?
PDF