A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 143

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3098
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: Attempt to read property "Count" on bool

Filename: helpers/my_audit_helper.php

Line Number: 3100

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3100
Function: _error_handler

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Wastewater-based effective reproduction number and prediction under the absence of shedding information. | LitMetric

Wastewater-based effective reproduction number and prediction under the absence of shedding information.

Environ Int

Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Avenue, Tucson, AZ 85724, United States. Electronic address:

Published: December 2024

AI Article Synopsis

  • The study introduces a new framework for estimating the effective reproduction number (R) and predicting disease rates using wastewater data, without needing information on how viruses are shed.
  • The framework was tested with simulated and real data on Influenza A and SARS-CoV-2, showing it can accurately estimate R, especially during rapid shedding periods.
  • Results indicate that this method can improve real-time monitoring of infectious diseases, making it especially valuable for areas lacking reliable clinical data.

Article Abstract

Estimating effective reproduction number (R) and predicting disease incidences are essential to formulate effective strategies for disease control. Although recent studies developed models for inferring R from wastewater-based data, they require information on shedding dynamics. Here, we proposed a framework of R estimation and prediction without shedding information. The framework consists of a space-state model for smoothing wastewater-based data and a renewal equation modified for wastewater-based data. The applicability of the framework was tested with simulated data and real-world data on Influenza A virus (IAV) and SARS-CoV-2 concentration in wastewater in 2022/2023 season in the USA. We confirmed the state-space model effectively fits various simulated epidemic curves and real-world data. In simulations, we found wastewater-based R (R) closely aligns with instantaneous clinical R when shedding dynamics are rapid. For more prolonged shedding, R approximates a smoothed R over time. We also observed the necessary sampling frequency to trace dynamics of wastewater concentration and R accurately in the framework varies depending on the precision of detection methods, the epidemic status, the transmissibility of infectious diseases, and shedding dynamics. By applying our framework to real-world data, we found R for SARS-CoV-2 showed similar trend and values to clinically-based R. R for IAV ranged from 0.66 to 1.52 with a clear peak in the winter season, which agrees with previously reported R. We also succeeded in predicting wastewater concentration in a few weeks from available wastewater-based data. These results indicate that our framework potentially enables near real-time monitoring of approximated R and prediction of infectious disease dynamics through wastewater surveillance, which limits the delay between infection and reporting. Our framework is useful especially for regions where reliable clinical surveillance is not available and notifiable surveillance is abolished, and can be expanded to multiple infectious diseases that have been detected from wastewater.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.envint.2024.109128DOI Listing

Publication Analysis

Top Keywords

wastewater-based data
16
shedding dynamics
12
real-world data
12
effective reproduction
8
reproduction number
8
data
8
dynamics wastewater
8
wastewater concentration
8
infectious diseases
8
framework
7

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!