Evidence map›Paper›PMID 38968319›Full record

ArticlePloS one2024

Monitoring emerging pathogens using negative nucleic acid test results from endemic pathogens in pig populations: Application to porcine enteric coronaviruses.

Ana Paula Serafini Poeta Silva, Guilherme Arruda Cezar, Edison Sousa Magalhães, Kinath Rupasinghe, Srijita Chandra, Gustavo S Silva, Marcelo Almeida, Bret Crim, Eric Burrough, Phillip Gauger and 26 more

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

36 authors.

Ana Paula Serafini Poeta SilvaIowa State University, Ames, Iowa, United States of America.ORCID 0000-0002-6069-5014
Guilherme Arruda CezarIowa State University, Ames, Iowa, United States of America.ORCID 0000-0003-3554-457X
Edison Sousa MagalhãesIowa State University, Ames, Iowa, United States of America.
Kinath RupasingheIowa State University, Ames, Iowa, United States of America.
Srijita ChandraIowa State University, Ames, Iowa, United States of America.
Gustavo S SilvaIowa State University, Ames, Iowa, United States of America.ORCID 0000-0001-5884-8803
Marcelo AlmeidaIowa State University, Ames, Iowa, United States of America.
Bret CrimIowa State University, Ames, Iowa, United States of America.
Eric BurroughIowa State University, Ames, Iowa, United States of America.
Phillip GaugerIowa State University, Ames, Iowa, United States of America.ORCID 0000-0003-2540-8769
Christopher SiepkerIowa State University, Ames, Iowa, United States of America.
Marta MainentiIowa State University, Ames, Iowa, United States of America.
Michael ZellerIowa State University, Ames, Iowa, United States of America.ORCID 0000-0001-5505-6931
Rodger G MainIowa State University, Ames, Iowa, United States of America.
Mary ThurnUniversity of Minnesota, Saint Paul, Minnesota, United States of America.
Paulo FioravanteUniversity of Minnesota, Saint Paul, Minnesota, United States of America.
Cesar CorzoUniversity of Minnesota, Saint Paul, Minnesota, United States of America.
Albert RoviraUniversity of Minnesota, Saint Paul, Minnesota, United States of America.
Hemant NaikareUniversity of Minnesota, Saint Paul, Minnesota, United States of America.
Rob McGaugheyKansas State University, Manhattan, Kansas, United States of America.
Franco Matias FerreyraKansas State University, Manhattan, Kansas, United States of America.
Jamie RetallickKansas State University, Manhattan, Kansas, United States of America.
Jordan GebhardtKansas State University, Manhattan, Kansas, United States of America.
Angela PillatzkiSouth Dakota State University, Brookings, South Dakota, United States of America.
Jon GresethSouth Dakota State University, Brookings, South Dakota, United States of America.
Darren KerseySouth Dakota State University, Brookings, South Dakota, United States of America.
Travis ClementSouth Dakota State University, Brookings, South Dakota, United States of America.
Jane Christopher-HenningsSouth Dakota State University, Brookings, South Dakota, United States of America.
Melanie PraratOhio Animal Disease and Diagnostic Laboratory, Reynoldsburg, Ohio, United States of America.
Ashley JohnsonOhio Animal Disease and Diagnostic Laboratory, Reynoldsburg, Ohio, United States of America.
Dennis SummersOhio Animal Disease and Diagnostic Laboratory, Reynoldsburg, Ohio, United States of America.
Craig BowenPurdue University, West Lafayette, Indiana, United States of America.
Kenitra HendrixPurdue University, West Lafayette, Indiana, United States of America.
Joseph BoylePurdue University, West Lafayette, Indiana, United States of America.
Daniel Correia Lima LinharesIowa State University, Ames, Iowa, United States of America.
Giovani TrevisanIowa State University, Ames, Iowa, United States of America.ORCID 0000-0002-4980-526X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluated the use of endemic enteric coronaviruses polymerase chain reaction (PCR)-negative testing results as an alternative approach to detect the emergence of animal health threats with similar clinical diseases presentation. This retrospective study, conducted in the United States, used PCR-negative testing results from porcine samples tested at six veterinary diagnostic laboratories. As a proof of concept, the database was first searched for transmissible gastroenteritis virus (TGEV) negative submissions between January 1st, 2010, through April 29th, 2013, when the first porcine epidemic diarrhea virus (PEDV) case was diagnosed. Secondly, TGEV- and PEDV-negative submissions were used to detect the porcine delta coronavirus (PDCoV) emergence in 2014. Lastly, encountered best detection algorithms were implemented to prospectively monitor the 2023 enteric coronavirus-negative submissions. Time series (weekly TGEV-negative counts) and Seasonal Autoregressive-Integrated Moving-Average (SARIMA) were used to control for outliers, trends, and seasonality. The SARIMA's fitted and residuals were then subjected to anomaly detection algorithms (EARS, EWMA, CUSUM, Farrington) to identify alarms, defined as weeks of higher TGEV-negativity than what was predicted by models preceding the PEDV emergence. The best-performing detection algorithms had the lowest false alarms (number of alarms detected during the baseline) and highest time to detect (number of weeks between the first alarm and PEDV emergence). The best-performing detection algorithms were CUSUM, EWMA, and Farrington flexible using SARIMA fitted values, having a lower false alarm rate and identified alarms 4 to 17 weeks before PEDV and PDCoV emergences. No alarms were identified in the 2023 enteric negative testing results. The negative-based monitoring system functioned in the case of PEDV propagating epidemic and in the presence of a concurrent propagating epidemic with the PDCoV emergence. It demonstrated its applicability as an additional tool for diagnostic data monitoring of emergent pathogens having similar clinical disease as the monitored endemic pathogens.

Indexed as

Coronavirus InfectionsPorcine epidemic diarrhea virusSwine DiseasesTransmissible gastroenteritis virusAnimalsDeltacoronavirusGastroenteritis, Transmissible, of SwinePolymerase Chain ReactionRetrospective StudiesSwineUnited States

Identifiers

PMID38968319
PMCPMC11226029

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.