Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review
Abstract Background The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-01-01
|
| Series: | Systematic Reviews |
| Online Access: | https://doi.org/10.1186/s13643-023-02411-1 |
| _version_ | 1826971304579825664 |
|---|---|
| author | Oscar Espinosa Laura Mora Cristian Sanabria Antonio Ramos Duván Rincón Valeria Bejarano Jhonathan Rodríguez Nicolás Barrera Carlos Álvarez-Moreno Jorge Cortés Carlos Saavedra Adriana Robayo Oscar H. Franco |
| author_facet | Oscar Espinosa Laura Mora Cristian Sanabria Antonio Ramos Duván Rincón Valeria Bejarano Jhonathan Rodríguez Nicolás Barrera Carlos Álvarez-Moreno Jorge Cortés Carlos Saavedra Adriana Robayo Oscar H. Franco |
| author_sort | Oscar Espinosa |
| collection | DOAJ |
| description | Abstract Background The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). Methodology PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. Findings In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. Interpretation The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential. |
| first_indexed | 2024-03-08T12:40:46Z |
| format | Article |
| id | doaj.art-d2a0502e989d4e2c8a83dd50db3a642a |
| institution | Directory Open Access Journal |
| issn | 2046-4053 |
| language | English |
| last_indexed | 2025-02-18T05:03:35Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Systematic Reviews |
| spelling | doaj.art-d2a0502e989d4e2c8a83dd50db3a642a2024-11-17T12:12:31ZengBMCSystematic Reviews2046-40532024-01-0113112110.1186/s13643-023-02411-1Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic reviewOscar Espinosa0Laura Mora1Cristian Sanabria2Antonio Ramos3Duván Rincón4Valeria Bejarano5Jhonathan Rodríguez6Nicolás Barrera7Carlos Álvarez-Moreno8Jorge Cortés9Carlos Saavedra10Adriana Robayo11Oscar H. Franco12Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de ColombiaDirectorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS)Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS)Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de ColombiaDirectorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS)Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de ColombiaDirectorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de ColombiaDirectorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS)Faculty of Medicine, Universidad Nacional de ColombiaFaculty of Medicine, Universidad Nacional de ColombiaFaculty of Medicine, Universidad Nacional de ColombiaDirectorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS)University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard UniversityAbstract Background The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). Methodology PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. Findings In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. Interpretation The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.https://doi.org/10.1186/s13643-023-02411-1 |
| spellingShingle | Oscar Espinosa Laura Mora Cristian Sanabria Antonio Ramos Duván Rincón Valeria Bejarano Jhonathan Rodríguez Nicolás Barrera Carlos Álvarez-Moreno Jorge Cortés Carlos Saavedra Adriana Robayo Oscar H. Franco Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review Systematic Reviews |
| title | Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review |
| title_full | Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review |
| title_fullStr | Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review |
| title_full_unstemmed | Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review |
| title_short | Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review |
| title_sort | predictive models for health outcomes due to sars cov 2 including the effect of vaccination a systematic review |
| url | https://doi.org/10.1186/s13643-023-02411-1 |
| work_keys_str_mv | AT oscarespinosa predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT lauramora predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT cristiansanabria predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT antonioramos predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT duvanrincon predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT valeriabejarano predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT jhonathanrodriguez predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT nicolasbarrera predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT carlosalvarezmoreno predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT jorgecortes predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT carlossaavedra predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT adrianarobayo predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview AT oscarhfranco predictivemodelsforhealthoutcomesduetosarscov2includingtheeffectofvaccinationasystematicreview |