Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Predominantly asymptomatic infections, such as those for SARS-CoV-2, require robust surveillance testing to identify people who are unknowingly spreading the virus. The US Air Force Academy returned to in-person classes for more than 4000 cadets aged 18-26 years during the fall 2020 semester to meet graduation and leadership training requirements. To enable this sustained cadet footprint, the institution developed a dynamic SARS-CoV-2 response plan using near-real-time data to inform decisions and trigger policies. A surveillance testing program based on mathematical modeling and a policy-driven campus reset option provided a scaled approach to react to SARS-CoV-2 conditions. This program adequately controlled the spread of the virus for the first 2 months of the academic semester but failed to predict or initially mitigate a significant outbreak in the second half of the semester. Although this approach did not completely eliminate SARS-CoV-2 infections in the population, it served as an early warning system to alert public health authorities to potential issues, which allowed timely responses while containment was still possible.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109537 | PMC |
http://dx.doi.org/10.1177/00333549211065520 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!