From a2a29493054087a1a8e1fbd669b80d601b0e0f6f Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 9 Mar 2026 19:10:24 +0000 Subject: [PATCH 1/2] Initial plan From 82aec9dfa53c64db0ed2878d835532a2d5a947e4 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 9 Mar 2026 19:25:49 +0000 Subject: [PATCH 2/2] fix: update article section menu HTML in v3.0 XSLT and regenerate fixtures - Fix ` -
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  • Publication in this collection
    18 Aug 2023
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    Publication Dates

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    2023
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History

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History

  • Received
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    History

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This is an open-access article distributed under the terms of the Creative Commons Attribution License
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VEME light flowchart with description of safety protocols
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Description of the activities performed during VEME light
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+Box 1Description of the activities performed during VEME light
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Como citar
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AutoriaSCIMAGO INSTITUTIONS RANKINGS
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Resumo

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Resumo

O relato descrebeu o primeiro curso presencial visando capacitar profissionais de saúde pública na realização de vigilância genômica em tempo real, durante períodos pandêmicos. Relato de experiência sobre um curso teórico-prático com foco em pesquisa e vigilância genômica, incluindo tecnologias de sequenciamento móvel, bioinformática, filogenética e modelagem epidemiológica. O evento contou com 162 participantes e foi o primeiro grande treinamento presencial realizado durante a epidemia de covid-19 no Brasil. Não foi detectada infecção pelo SARS-CoV-2 ao final do evento em nenhum participante, sugerindo a segurança e efetividade de todas as medidas de segurança adotadas. Os resultados do evento sugerem que é possível executar capacitação profissional com segurança durante pandemias, desde que seguidos todos os protocolos de segurança.

Palavras-chave:
Covid-19; Pandemia; Capacitação Profissional; Capacitação de Recursos Humanos em Saúde.

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Contribuições do estudo

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Contribuições do estudo

Principais resultados A transferência tecnológica pode acontecer em grandes eventos, desde que protocolos de segurança sejam executados rigorosamente. É importante disseminar, nesses eventos, os conceitos do movimento Responsible Research and Innovation (RRI).

Implicações para os serviços Treinamentos presenciais são fundamentais para a capacitação de profissionais de saúde pública. A transferência tecnológica entre instituições de pesquisa e serviços de saúde resulta na atualização e melhora do desempenho do sistema de saúde.

Perspectivas A partir do sucesso da transferência tecnológica relatada, será incorporado um novo módulo na próxima edição do VEME (Panamá, 2022), intitulado Virus Evolution to Public Health Policy Makers.

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Abstract

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Abstract

The objective of this report was to describe the first face-to-face course aimed at training public health professionals in performing real-time genomic surveillance during the pandemic period. Experience report on a theoretical-practical course focusing on genomic research and surveillance, including mobile sequencing technologies, bioinformatics, phylogenetics and epidemiological modeling. There were 162 participants in the event and it was the first major face-to-face training course conducted during the COVID-19 epidemic in Brazil. No cases of SARS-CoV-2 infection was detected among the participants at the end of the event, suggesting the safety and effectiveness of all safety measures adopted. The results of this experience suggest that it is possible to conduct professional training safely during pandemics, as long as all safety protocols are followed.

Keywords:
COVID-19; Pandemic; Professional Training; Health Human Resources Development

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Study contributions

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Study contributions

Main results Technology transfer can take place at large events, as long as safety protocols are strictly enforced. It is important to disseminate, at these events, the concepts of the Responsible Research and Innovation (RRI).

Implications for services Face-to-face training course is fundamental for training public health professionals. Technology transfer between research institutions and health services results in updating and improving health system performance.

Perspectives Based on the success of the reported technology transfer, a new module will be incorporated into the next edition of VEME (Panama 2022), entitled Virus Evolution to Public Health Policy Makers.

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Resumen

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Resumen

Este estudio tuvo como objetivo describir el primer curso presencial para capacitar a los profesionales de la salud pública para llevar a cabo la vigilancia genómica en tiempo real durante los períodos de pandemia. Este es un informe de experiencia en un curso teórico-práctico centrado en la investigación y vigilancia genómica, que incluye secuenciación móvil, bioinformática, filogenética y tecnologías de modelado epidemiológico. Este evento contó con la asistencia de 162 participantes y fue la primera gran capacitación presencial realizada durante la epidemia de COVID-19 en Brasil. No se detectó infección por SARS-CoV-2 al final del evento en ningún participante, lo que sugiere la seguridad y efectividad de todas las medidas de seguridad adoptadas. Por lo tanto, los resultados del evento sugieren que es posible realizar entrenamientos profesionales de manera segura durante pandemias, siempre y cuando se sigan todos los protocolos de seguridad.

Palabras-clave:
COVID-19; Pandemia; Capacitación Profesional; Capacitación de Recursos Humanos en Salud

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RESULTADOS

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Figura 1
Fluxograma de realização do VEME light com descrição dos protocolos de segurança
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DISCUSSÃO

O fato de a organização do evento apenas aconselhar os participantes a permanecerem no hotel durante os dias do evento, sem obrigá-los, e tampouco haver controle de entrada e saída dos participantes, apresentam-se como uma limitação do estudo. Entretanto, o espírito de colaboração dos professores e alunos inscritos refletiu-se na permanência de todos no local do treinamento, respeitando-se os protocolos de segurança propostos.

A realização de transferência tecnológica em um curso presencial, durante a epidemia de covid-19 no Brasil, foi de grande relevância para a capacitação dos profissionais de saúde pública envolvidos. Os tópicos abordados durante o evento possibilitaram não apenas a geração de dados genômicos, mas também sua análise rápida. Esse processo de capacitação foi fundamental para o acompanhamento da evolução da epidemia no nível local. Motivado no sucesso dessa experiência, deve ser incorporado um novo módulo à próxima edição do VEME no Panamá, em 2022, intitulado Virus Evolution to Public Health Policy Makers. Essa capacitação de agentes de saúde em análise de dados genômicos representa um marco na vigilância epidemiológica, permitindo ao Brasil não apenas monitorar de forma eficiente as cepas endêmicas, mas também prever novos surtos, por meio de um monitoramento ativo e análise de dados em tempo real.

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Referências bibliográficas

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Referências bibliográficas

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    » http://www.planalto.gov.br/ccivil_03/leis/l8080.htm +5 Brasil. Ministério da Saúde. Lei nº 8.080, de 19 de setembro de 1990. Dispõe sobre as condições para a promoção, proteção e recuperação da saúde, a organização e o funcionamento dos serviços correspondentes e dá outras providências [Internet]. Diário Oficial da União, Brasília (DF), 1990 Set 20 [citado 2022 Mar 15], Seção 1:18055. Disponível em: Disponível em: http://www.planalto.gov.br/ccivil_03/leis/l8080.htm
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    » https://rega.kuleuven.be/cev/veme-workshop/2021/bioinformatics-workshop-2021 +6 Bioinformatics Workshop on Virus Evolution and Molecular Epidemiology - Face-to-face Workshop (VEME Light) [Internet];2021 Sep 5-10. Belo Horizonte: KU Leuven [cited 2022 Mar 15]. Available from: Available from: https://rega.kuleuven.be/cev/veme-workshop/2021/bioinformatics-workshop-2021
    » https://rega.kuleuven.be/cev/veme-workshop/2021/bioinformatics-workshop-2021
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    » https://doi.org/10.2196/11745 +7 Colizzi V, Mezzana D, Ovseiko PV, Caiati G, Colonnello C, Declich A, et al. Structural Transformation to Attain Responsible BIOSciences (STARBIOS2): protocol for a Horizon 2020 Funded European Multicenter Project to Promote Responsible Research and Innovation. JMIR Res Protoc. 2019;8(3):e11745. doi: 10.2196/11745
    » https://doi.org/10.2196/11745
  • -8 Declich A. RRI implementation in bioscience organisations: guidelines from the STARBIOS2 project [Internet]. Uppsala: Uppsala University; 2020 [cited 2020 Aug 12]. Available from: Available from: http://uu.diva-portal.org/smash/get/diva2:1396179/FULLTEXT01.pdf
    » http://uu.diva-portal.org/smash/get/diva2:1396179/FULLTEXT01.pdf +8 Declich A. RRI implementation in bioscience organisations: guidelines from the STARBIOS2 project [Internet]. Uppsala: Uppsala University; 2020 [cited 2020 Aug 12]. Available from: Available from: http://uu.diva-portal.org/smash/get/diva2:1396179/FULLTEXT01.pdf
    » http://uu.diva-portal.org/smash/get/diva2:1396179/FULLTEXT01.pdf
  • -9 Vilsker M, Moosa Y, Nooij S, Fonseca V, Ghysens Y, Dumon K, et al. Genome detective: an automated system for virus identification from high-throughput sequencing data. Bioinformatics. 2019;35(5):871-3. doi: 10.1093/bioinformatics/bty695
    » https://doi.org/10.1093/bioinformatics/bty695 +9 Vilsker M, Moosa Y, Nooij S, Fonseca V, Ghysens Y, Dumon K, et al. Genome detective: an automated system for virus identification from high-throughput sequencing data. Bioinformatics. 2019;35(5):871-3. doi: 10.1093/bioinformatics/bty695
    » https://doi.org/10.1093/bioinformatics/bty695
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    » https://doi.org/10.1093/bib/bbx108
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    » https://doi.org/10.1093/molbev/msu300 +12 Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32(1):268-74. doi: 10.1093/molbev/msu300
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  • -13 Rambaut A, Lam TT, Carvalho LM, Pybus OG. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol. 2016;2(1): vew007. doi: 10.1093/ve/vew007
    » https://doi.org/10.1093/ve/vew007 +13 Rambaut A, Lam TT, Carvalho LM, Pybus OG. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol. 2016;2(1): vew007. doi: 10.1093/ve/vew007
    » https://doi.org/10.1093/ve/vew007
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    » https://doi.org/10.1093/ve/vey016 +14 Suchard MA, Lemey P, Baele G, Ayres DL, Drummond AJ, Rambaut A. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 2018;4(1):vey016. doi: 10.1093/ve/vey016
    » https://doi.org/10.1093/ve/vey016
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    » http://tree.bio.ed.ac.uk/ +15 Rambaut A. Molecular Evolution, Phylogenetics and Epidemiology [Internet]. [Edinburgh]: [Rambaut A]; c2007 [cited 2022 Aug 18]. Available from: Available from: http://tree.bio.ed.ac.uk/
    » http://tree.bio.ed.ac.uk/
  • -16 Chernomor O, von Haeseler A, Minh BQ. Terrace Aware Data Structure for Phylogenomic Inference from Supermatrices. Syst Biol. 2016;65(6):997-1008. doi: 10.1093/sysbio/syw037
    » https://doi.org/10.1093/sysbio/syw037 +16 Chernomor O, von Haeseler A, Minh BQ. Terrace Aware Data Structure for Phylogenomic Inference from Supermatrices. Syst Biol. 2016;65(6):997-1008. doi: 10.1093/sysbio/syw037
    » https://doi.org/10.1093/sysbio/syw037
  • -17 Posit. RStudio: Open source & professional software for data science teams [Internet]. Boston: Posit; c2022 [cited 2022 Aug 18]. Available from: Available from: https://www.rstudio.com/
    » https://www.rstudio.com/ +17 Posit. RStudio: Open source & professional software for data science teams [Internet]. Boston: Posit; c2022 [cited 2022 Aug 18]. Available from: Available from: https://www.rstudio.com/
    » https://www.rstudio.com/
  • -18 Oliveira EC, Fonseca V, Xavier J, Adelino T, Claro IM, Fabri A, et al. Short report: introduction of chikungunya virus ECSA genotype into the Brazilian midwest and its dispersion through the Americas. PLoS Negl Trop Dis. 2021;15(4):e0009290. doi: 10.1371/journal.pntd.0009290
    » https://doi.org/10.1371/journal.pntd.0009290 +18 Oliveira EC, Fonseca V, Xavier J, Adelino T, Claro IM, Fabri A, et al. Short report: introduction of chikungunya virus ECSA genotype into the Brazilian midwest and its dispersion through the Americas. PLoS Negl Trop Dis. 2021;15(4):e0009290. doi: 10.1371/journal.pntd.0009290
    » https://doi.org/10.1371/journal.pntd.0009290
  • -19 Pereira F, Tosta S, Lima MM, Silva LRO, Nardy VB, Gómez MKA, et al. Genomic surveillance activities unveil the introduction of the SARS‐CoV‐2 B.1.525 variant of interest in Brazil: case report. J Med Virol. 2021;93(9):5523-6. doi: 10.1002/jmv.27086
    » https://doi.org/10.1002/jmv.27086 +19 Pereira F, Tosta S, Lima MM, Silva LRO, Nardy VB, Gómez MKA, et al. Genomic surveillance activities unveil the introduction of the SARS‐CoV‐2 B.1.525 variant of interest in Brazil: case report. J Med Virol. 2021;93(9):5523-6. doi: 10.1002/jmv.27086
    » https://doi.org/10.1002/jmv.27086
  • -20 Giovanetti M, Pereira LAP, Santiago GA, Fonseca V, Mendoza MPG, Oliveira C, et al. Emergence of dengue virus serotype 2 cosmopolitan genotype, Brazil. Emerg Infect Dis. 2022;28(8):1725-7. doi: 10.3201/eid2808.220550
    » https://doi.org/10.3201/eid2808.220550 +20 Giovanetti M, Pereira LAP, Santiago GA, Fonseca V, Mendoza MPG, Oliveira C, et al. Emergence of dengue virus serotype 2 cosmopolitan genotype, Brazil. Emerg Infect Dis. 2022;28(8):1725-7. doi: 10.3201/eid2808.220550
    » https://doi.org/10.3201/eid2808.220550
  • -21 Giovanetti M, Alcantara LCJ, Dorea AS, Ferreira QR, Marques WA, Barros JJF, et al. Promoting Responsible Research and Innovation (RRI) during Brazilian activities of genomic and epidemiological surveillance of arboviruses. Front Public Health. 2021;9:693743. doi: 10.3389/fpubh.2021.693743
    » https://doi.org/10.3389/fpubh.2021.693743 +21 Giovanetti M, Alcantara LCJ, Dorea AS, Ferreira QR, Marques WA, Barros JJF, et al. Promoting Responsible Research and Innovation (RRI) during Brazilian activities of genomic and epidemiological surveillance of arboviruses. Front Public Health. 2021;9:693743. doi: 10.3389/fpubh.2021.693743
    » https://doi.org/10.3389/fpubh.2021.693743
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Referências bibliográficas

AGRADECIMENTOS
Os autores agradecem aos responsáveis pelos Laboratórios Centrais em Saúde Publica, pelo esforço no combate à covid-19 no Brasil; a todos os professores e alunos envolvidos no curso Bioinformatics Workshop on Virus Evolution and Molecular Epidemiology (VEME), por sua participação, assim como aos funcionários do Ouro Minas Palace Hotel; e especialmente, à Dra. Anne-Mieke Vandamme, da Catholic University of Leuven, Bélgica.
  • -Editor associado:
    Lúcia Rolim Santana de Freitas - https://orcid.org/0000-0003-0080-2858
    +Editor associado:
    Lúcia Rolim Santana de Freitas - https://orcid.org/0000-0003-0080-2858
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    Datas de Publicação

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    Datas de Publicação

    • Publicação nesta coleção
      18 Ago 2023
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      Datas de Publicação

      Data do Fascículo
      2023
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    Histórico

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    Histórico

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      18 Ago 2022
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      Histórico

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    Creative Common - by 4.0
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    This is an open-access article distributed under the terms of the Creative Commons Attribution License
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    Creative Common - by 4.0
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    This is an open-access article distributed under the terms of the Creative Commons Attribution License
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    10 Kumar A, Hung N, Wu Y, Baek R, Gupta A. Predictive Modeling for Telemedicine Service Demand. Telehealth Med Today. 2020;5(2):1-14.">10) or interest in terms related to pathology symptoms in web search engines (e.g., Google Trends).(11)

    This context highlights the opportunity to develop statistical models to predict the number of patients hospitalized due to COVID-19 and help hospital managers plan for beds, human resources, and other input sizing and availabilities.

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    OBJECTIVE

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    OBJECTIVE

    To develop, implement, and monitor a predictive model to estimate the number of patients hospitalized due to COVID-19, segmenting patients by department-intensive care units and general wards-whenever possible.

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    METHODS

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    METHODS

    This retrospective study was conducted at Hospital Israelita Albert Einstein (HIAE), a private not-for-profit 624-bed hospital in São Paulo, Brazil, which earmarked 300 beds for COVID-19 patients in March 2021, at the peak of the pandemic.

    Two separate models were developed to achieve the study objectives:

    Model 1: Hospital occupancy was estimated by projecting the hospital admissions and discharges of COVID-19 patients separately.

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    METHODS

    15 Hswen Y, Zhang A, Ventelou B. Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis. JMIR Public Health Surveill. 2021;7(5):e18593.">15)

    Performance metrics: performance monitoring of the proposed models employed accuracy metrics of point forecasts-mean error (ME), mean absolute error (MAE), and root mean squared error (RMSE) as a function of the forecast horizon in days. Considering the average daily predicted values for the ICU and general ward across a forecast horizon, R² was also calculated to demonstrate the adherence of the projected values to the observed values.(16)

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    Confidentiality and ethical approval

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    Confidentiality and ethical approval

    This study was approved by the Ethics Committee of Hospital Israelita Albert Einstein (HIAE), CAAE: 51937121.6.0000.0071; # 5.136.309. Patient confidentiality was preserved by anonymizing the medical records. The requirement for informed consent was waived by the institutional review board prior to data collection and analysis.

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    RESULTS

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    RESULTS

    Model 1: between March 2020 and July 2021, 5,414 patients were admitted to HIAE due to COVID-19, considering all types of discharges (home care, external transfers, and deaths, which were excluded from LOS modeling) and patients who were hospitalized for more than 45 days.

    Admissions: by analyzing the behavior of the overall number of new hospitalizations during the specified period, segmentation of the number of new admissions by department (general ward and ICU) was proposed to minimize the error of the expected flow for each projected day, attempting to extract seasonal factors and decrease the order of magnitude of the residues.

    During the analyzed period, the average (± standard deviation) number of daily patient admissions in the general ward was 7.74 (± 4.66), while new hospitalizations in the ICU was 2.85 (± 2.08).

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    RESULTS

    The three time series were plotted: i) Telemedicine: number of callers diagnosed with COVID-19 specific coding (ICD-10-CM U07.1); number of callers with COVID-19-related ICD-10-CM codes (specific coding, symptoms and chapters related to respiratory diseases); and ii) Google Trends: daily interest scores for the search term "sintomas COVID-19" in São Paulo. The time series of hospitalized patients lagged by 1–14 days, and for each combination of series, the Pearson Correlation Index was calculated iteratively. The results are presented in figure 1.

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    Figure 1
    Pearson Correlation Index calculated between all available time series and the lagged series of daily hospitalized patients
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    RESULTS

    The number of telemedicine visits by patients coded with a COVID-19 diagnosis (ICD-10-CM U07.1) was highlighted as the best predictor of hospitalized patients. Once peak values differed by >0.001 for delays of seven and eight days, the delay that had the greatest anticipation power for the proposed model (8 days) was chosen.

    In a scenario in which telemedicine data were unavailable, Google Trends interest scores for the terms related to symptoms appeared as a predictor, resulting in a Pearson Correlation Index of 0.895 when lagging the hospitalized patients’ series by 9 days. The final model consisted of a time series linear regression comprising the predictors to estimate the number of hospitalized patients based on the telemedicine series.(17)

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    Performance monitoring

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    Performance monitoring

    The results of the two models were followed for 365 days (between May 20, 2021, and May 20, 2022), considering that the first model has predictions for hospitalized patients segmented by department (general ward and ICU) over a 14-day interval and that the second model using the number of telemedicine visits predicts the overall number of hospitalized patients over an 8-day interval.

    When comparing models, the evaluation of their performance indicators is limited by the lowest forecast horizon of 8 days. Computing the average error values, including different time windows, could benefit the model with the smallest forecast horizon, as larger residuals tend to be observed as we move away from the projection date.

    A comparison between the averages of the predicted and observed values by department is illustrated in figures 2, 3, and 4.

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    Figure 2
    Comparison between the daily number of hospitalized patients and the mean values predicted by the described models
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    Performance monitoring

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    Figure 3
    Comparison between the daily number of hospitalized patients in the general ward and the mean values predicted by Model 1
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    Performance monitoring

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    Figure 4
    Comparison between the daily number of hospitalized patients in the intensive care unit and the mean values predicted by Model 1
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    The adherence of the values predicted by the proposed models to the numbers observed during these 365 days can be reinforced by the performance indicators detailed in the Methods section, which also allowed the identification of the strengths and opportunities in both models. Figures 5, 6, and 7 present the ME, MAE, and RMSE values as the forecast horizon increases (the illustrated indicators are accessible in table 1STable 1SPerformance metrics as a function of the forecast horizonMean ErrorMean absolute errorRoot mean squared errorForecast horizon (in days)Model 1: General ward predictionModel 1: Intensive care unit predictionModel 1: Overall predictionModel 2: Overall predictionModel 1: General ward predictionModel 1: Intensive care unit predictionModel 1: Overall predictionModel 2: Overall predictionModel 1: General ward predictionModel 1: Intensive care unit predictionModel 1: Overall predictionModel 2: Overall prediction10,7190,8651,584-0,3433,7582,7924,9613,8081,9391,6712,2271,95122,0781,9063,9840,1175,6054,0527,2884,7252,3682,0132,7002,17432,3843,3615,7450,5877,6425,7309,3875,7922,7642,3943,0642,40742,0394,9927,0310,9799,5197,35611,2866,6943,0852,7123,3592,58750,7716,8787,6491,39711,3849,22612,7877,2363,3743,0373,5762,6906-0,6528,5647,9121,77712,96910,92714,1258,0263,6013,3063,7582,8337-1,42610,3568,9302,18214,78712,93815,9018,5663,8453,5973,9882,92780,0609,6399,6992,56616,49113,12517,9849,6734,0613,6234,2413,11091,0609,61610,67518,58713,68320,1304,3113,6994,487102,0889,08811,17720,26513,84721,9514,5023,7214,685113,0837,89910,98221,75813,56123,7714,6653,6834,876123,9696,60810,57723,11713,66825,4964,8083,6975,049134,6575,2739,93024,41813,89127,2964,9413,7275,225145,1774,0629,23925,76914,10929,4105,0763,7565,423The results are expressed as the mean error, mean absolute error, and root mean squared error calculated for the predictions. form in Supplementary Material).

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    The adherence of the values predicted by the proposed models to the numbers observed during these 365 days can be reinforced by the performance indicators detailed in the Methods section, which also allowed the identification of the strengths and opportunities in both models. Figures 5, 6, and 7 present the ME, MAE, and RMSE values as the forecast horizon increases (the illustrated indicators are accessible in table 1S form in Supplementary Material).

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    Figure 5
    Prediction performance monitoring. Calculated mean error for the proposed models by forecast horizon (days)
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    Performance monitoring

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    Figure 6
    Prediction performance monitoring. Calculated mean absolute error for the proposed models by forecast horizon (days)
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    Performance monitoring

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    Figure 7
    Prediction performance monitoring. Calculated root mean squared error for the proposed models by forecast horizon (days)
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    Performance monitoring

    The MAE accuracy measure, which is a measure minimized by the median, allows the identification of the models that can most accurately estimate the daily number of hospitalized patients. Despite the ME values tending to zero for the predictions of hospitalizations in the general ward for the first model in 10-day forecast horizons, rejecting the presence of bias in the set of estimations, the result is obtained through predictions with greater residues. As a complementary approach, the RMSE highlights the model that provides the best estimates for the average number of cases in a given period.

    The analysis of all three statistics showed that the model built with telemedicine data outperformed the first model when considering the predictions made for the total number of hospitalized patients, also highlighting its capability of maintaining errors below 10 beds for the integrity of its forecast horizon, as evidenced by the MAE.

    The first model shows significant deterioration over a period >3 days; however, its ability to distinguish between the classifications of occupied beds sets a gain for resource planning, even though higher reliability indices are limited to short-term predictions.

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    DISCUSSION

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    DISCUSSION

    The developed models resulted in reliable estimations of the expected number of COVID-19 hospitalizations at HIAE, which were updated daily and used by HIAE managers to define the allocation of beds to COVID-19 and non-COVID-19 patients, mainly by adjusting the volume of elective surgeries that the hospital would be able to perform in the subsequent 7 days, the hiring of qualified professionals to meet the expected demand, and the proper sizing of its supplies.

    Daily capacity adjustments were performed at HIAE during the worst days of the pandemic based on the proposed telemedicine model and were also used by the hospital to prepare for the Delta and Omicron phases of increase in the observed number of hospitalized patients.

    The development and implementation of a data-driven tool to support the decision-making process in a hospital management environment showed that at an atypical moment of great concern with the evolution of the COVID-19 pandemic, maturity in data management, quality, security, and analysis allows a health institution to benefit from the information generated during day-to-day operations.

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    DISCUSSION

    19 Rostami-Tabar B, Rendon-Sanchez JF. Forecasting COVID-19 daily cases using phone call data. Appl Soft Comput. 2021;100:106932.">19) and Convolutional Neural Networks.

    The isolation of the hospital context and its variables implies a trade-off, given that the variables that can explain the remaining variance of the studied phenomenon may be unavailable as the pandemic context evolves dynamically. Among these caveats are the emergence of new variants, vaccines and vaccine coverage, effective medications, mask usage, isolation rates, and the guidelines adopted in care.

    The validation of predictions of this nature encourages the creation of new models for other pathologies, exploring correlations of events before hospitalizations, and studying patterns of care involving a diagnosed patient and their clinical evolution. It also highlights the importance of joining forces, between different areas of the same institution and between various institutions, sharing complementary data, and improving the use of data for decision-making.

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    CONCLUSION

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    CONCLUSION

    The model that estimates the number of COVID-19 hospitalizations in a private not-for-profit hospital based on telemedicine could accurately anticipate the increase and decrease in the volume of patients with a lag of 8 days, demonstrating its usefulness for the effectivemanagement of beds and general resources for caring for patients with COVID-19. The readiness to provide data regarding the volume of care, hospitalizations, patient characteristics, and clinical interventions can help identify patterns to understand the pathology better and provide more accurate decisions regarding the allocation of resources.

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    ACKNOWLEDGEMENTS

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    ACKNOWLEDGEMENTS

    We state that the only sponsor of the study in question was the Hospital Israelita Albert Einstein, where all authors are employed. Because the coordination and preparation of studies and articles was established as a core activity for the positions occupied by the participants in this study, prospecting for new sponsors and requesting additional funding was not necessary. The publication of this study was not contingent upon sponsor approval.

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    REFERENCES

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    REFERENCES

    • 1 de Oliveira Andrade R. Covid-19 is causing the collapse of Brazil’s national health service. BMJ. 2020;370:m3032.
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      REFERENCES

      19 Rostami-Tabar B, Rendon-Sanchez JF. Forecasting COVID-19 daily cases using phone call data. Appl Soft Comput. 2021;100:106932.
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    -Table 1S Performance metrics as a function of the forecast horizon

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    +Table 1S Performance metrics as a function of the forecast horizon

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    Publication Dates

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    Publication Dates

    • Publication in this collection
      08 Mar 2024
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      Publication Dates

      Date of issue
      2024
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    History

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    History

    • Received
      23 Sept 2022
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      History

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    Creative Common - by 4.0
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    This content is licensed under a Creative Commons Attribution 4.0 International License.
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    Creative Common - by 4.0
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    This content is licensed under a Creative Commons Attribution 4.0 International License.
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