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Estimating COVID-19 cases in Puerto Rico using an automated surveillance system

Due to concerns regarding limited testing and accuracy of estimation of COVID-19 cases, we created an automated surveillance system called “Puerto Rico Epidemiological Evaluation and Prevention of COVID-19 and Influenza” (PREPCOVI) to evaluate COVID-19 incidence and time trends across Puerto Rico. A...

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Autores principales: Martinez-Lozano, Marijulie, Gadhavi, Rajendra, Vega, Christian, Martinez, Karen G., Acevedo, Waldo, Joshipura, Kaumudi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388143/
https://www.ncbi.nlm.nih.gov/pubmed/35991066
http://dx.doi.org/10.3389/fpubh.2022.947224
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author Martinez-Lozano, Marijulie
Gadhavi, Rajendra
Vega, Christian
Martinez, Karen G.
Acevedo, Waldo
Joshipura, Kaumudi
author_facet Martinez-Lozano, Marijulie
Gadhavi, Rajendra
Vega, Christian
Martinez, Karen G.
Acevedo, Waldo
Joshipura, Kaumudi
author_sort Martinez-Lozano, Marijulie
collection PubMed
description Due to concerns regarding limited testing and accuracy of estimation of COVID-19 cases, we created an automated surveillance system called “Puerto Rico Epidemiological Evaluation and Prevention of COVID-19 and Influenza” (PREPCOVI) to evaluate COVID-19 incidence and time trends across Puerto Rico. Automated text message invitations were sent to random phone numbers with Puerto Rican area codes. In addition to reported COVID-19 test results, we used a published model to classify cases from specific symptoms (loss of smell and taste, severe persistent cough, severe fatigue, and skipped meals). Between 18 November 2020, and 24 June 2021, we sent 1,427,241 messages, 26.8% were reached, and 6,975 participants answered questions about the last 30 days. Participants were aged 21–93 years and represented 97.4% of the municipalities. PREPCOVI total COVID-19 cases were higher among women and people aged between 21 and 40 years and in the Arecibo and Bayamón regions. COVID-19 was confirmed, and probable cases decreased over the study period. Confirmed COVID-19 cases ranged from 1.6 to 0.2% monthly, although testing rates only ranged from 30 to 42%. Test positivity decreased from 13.2% in November to 6.4% in March, increased in April (11.1%), and decreased in June (1.5%). PREPCOVI total cases (6.5%) were higher than cases reported by the Puerto Rico Department of Health (5.3%) for similar time periods, but time trends were similar. Automated surveillance systems and symptom-based models are useful in estimating COVID-19 cases and time trends, especially when testing is limited.
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spelling pubmed-93881432022-08-19 Estimating COVID-19 cases in Puerto Rico using an automated surveillance system Martinez-Lozano, Marijulie Gadhavi, Rajendra Vega, Christian Martinez, Karen G. Acevedo, Waldo Joshipura, Kaumudi Front Public Health Public Health Due to concerns regarding limited testing and accuracy of estimation of COVID-19 cases, we created an automated surveillance system called “Puerto Rico Epidemiological Evaluation and Prevention of COVID-19 and Influenza” (PREPCOVI) to evaluate COVID-19 incidence and time trends across Puerto Rico. Automated text message invitations were sent to random phone numbers with Puerto Rican area codes. In addition to reported COVID-19 test results, we used a published model to classify cases from specific symptoms (loss of smell and taste, severe persistent cough, severe fatigue, and skipped meals). Between 18 November 2020, and 24 June 2021, we sent 1,427,241 messages, 26.8% were reached, and 6,975 participants answered questions about the last 30 days. Participants were aged 21–93 years and represented 97.4% of the municipalities. PREPCOVI total COVID-19 cases were higher among women and people aged between 21 and 40 years and in the Arecibo and Bayamón regions. COVID-19 was confirmed, and probable cases decreased over the study period. Confirmed COVID-19 cases ranged from 1.6 to 0.2% monthly, although testing rates only ranged from 30 to 42%. Test positivity decreased from 13.2% in November to 6.4% in March, increased in April (11.1%), and decreased in June (1.5%). PREPCOVI total cases (6.5%) were higher than cases reported by the Puerto Rico Department of Health (5.3%) for similar time periods, but time trends were similar. Automated surveillance systems and symptom-based models are useful in estimating COVID-19 cases and time trends, especially when testing is limited. Frontiers Media S.A. 2022-08-04 /pmc/articles/PMC9388143/ /pubmed/35991066 http://dx.doi.org/10.3389/fpubh.2022.947224 Text en Copyright © 2022 Martinez-Lozano, Gadhavi, Vega, Martinez, Acevedo and Joshipura. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Martinez-Lozano, Marijulie
Gadhavi, Rajendra
Vega, Christian
Martinez, Karen G.
Acevedo, Waldo
Joshipura, Kaumudi
Estimating COVID-19 cases in Puerto Rico using an automated surveillance system
title Estimating COVID-19 cases in Puerto Rico using an automated surveillance system
title_full Estimating COVID-19 cases in Puerto Rico using an automated surveillance system
title_fullStr Estimating COVID-19 cases in Puerto Rico using an automated surveillance system
title_full_unstemmed Estimating COVID-19 cases in Puerto Rico using an automated surveillance system
title_short Estimating COVID-19 cases in Puerto Rico using an automated surveillance system
title_sort estimating covid-19 cases in puerto rico using an automated surveillance system
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388143/
https://www.ncbi.nlm.nih.gov/pubmed/35991066
http://dx.doi.org/10.3389/fpubh.2022.947224
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