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Article ; Online: A Bayesian functional approach to test models of life course epidemiology over continuous time.

Bodelet, Julien / Potente, Cecilia / Blanc, Guillaume / Chumbley, Justin / Imeri, Hira / Hofer, Scott / Harris, Kathleen Mullan / Muniz-Terrera, Graciela / Shanahan, Michael

International journal of epidemiology

2024 Volume 53, Issue 1

Abstract: Background: Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement ... ...

Abstract	Background: Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk. Methods: We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer. Results: Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms. Conclusions: The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.
MeSH term(s)	Humans ; Female ; Life Change Events ; Bayes Theorem ; Inflammation ; Renal Insufficiency, Chronic/epidemiology ; Breast Neoplasms/epidemiology
Language	English
Publishing date	2024-02-12
Publishing country	England
Document type	Journal Article
ZDB-ID	187909-1
ISSN	1464-3685 ; 0300-5771
ISSN (online)	1464-3685
ISSN	0300-5771
DOI	10.1093/ije/dyad190
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: How to update a living systematic review and keep it alive during a pandemic: a practical guide.

Heron, Leonie / Buitrago-Garcia, Diana / Ipekci, Aziz Mert / Baumann, Rico / Imeri, Hira / Salanti, Georgia / Counotte, Michel Jacques / Low, Nicola

Systematic reviews

2023 Volume 12, Issue 1, Page(s) 156

Abstract: Background: The covid-19 pandemic has highlighted the role of living systematic reviews. The speed of evidence generated during the covid-19 pandemic accentuated the challenges of managing high volumes of research literature.: Methods: In this ... ...

Abstract	Background: The covid-19 pandemic has highlighted the role of living systematic reviews. The speed of evidence generated during the covid-19 pandemic accentuated the challenges of managing high volumes of research literature. Methods: In this article, we summarise the characteristics of ongoing living systematic reviews on covid-19, and we follow a life cycle approach to describe key steps in a living systematic review. Results: We identified 97 living systematic reviews on covid-19, published up to 7th November 2022, which focused mostly on the effects of pharmacological interventions (n = 46, 47%) or the prevalence of associated conditions or risk factors (n = 30, 31%). The scopes of several reviews overlapped considerably. Most living systematic reviews included both observational and randomised study designs (n = 45, 46%). Only one-third of the reviews has been updated at least once (n = 34, 35%). We address practical aspects of living systematic reviews including how to judge whether to start a living systematic review, methods for study identification and selection, data extraction and evaluation, and give recommendations at each step, drawing from our own experience. We also discuss when it is time to stop and how to publish updates. Conclusions: Methods to improve the efficiency of searching, study selection, and data extraction using machine learning technologies are being developed, their performance and applicability, particularly for reviews based on observational study designs should improve, and ways of publishing living systematic reviews and their updates will continue to evolve. Finally, knowing when to end a living systematic review is as important as knowing when to start.
MeSH term(s)	Humans ; COVID-19 ; Machine Learning ; Observational Studies as Topic ; Pandemics ; Research Design ; Risk Factors ; Systematic Reviews as Topic
Language	English
Publishing date	2023-09-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2662257-9
ISSN	2046-4053 ; 2046-4053
ISSN (online)	2046-4053
ISSN	2046-4053
DOI	10.1186/s13643-023-02325-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: How to update a living systematic review and keep it alive during a pandemic

Heron, Leonie / Buitrago-Garcia, Diana / Ipekci, Aziz Mert / Baumann, Rico / Imeri, Hira / Salanti, Georgia / Counotte, Michel Jacques / Low, Nicola

Systematic Reviews

: a practical guide

2023 Volume 12, Issue 1

Abstract	Background: The covid-19 pandemic has highlighted the role of living systematic reviews. The speed of evidence generated during the covid-19 pandemic accentuated the challenges of managing high volumes of research literature. Methods: In this article, we summarise the characteristics of ongoing living systematic reviews on covid-19, and we follow a life cycle approach to describe key steps in a living systematic review. Results: We identified 97 living systematic reviews on covid-19, published up to 7th November 2022, which focused mostly on the effects of pharmacological interventions (n = 46, 47%) or the prevalence of associated conditions or risk factors (n = 30, 31%). The scopes of several reviews overlapped considerably. Most living systematic reviews included both observational and randomised study designs (n = 45, 46%). Only one-third of the reviews has been updated at least once (n = 34, 35%). We address practical aspects of living systematic reviews including how to judge whether to start a living systematic review, methods for study identification and selection, data extraction and evaluation, and give recommendations at each step, drawing from our own experience. We also discuss when it is time to stop and how to publish updates. Conclusions: Methods to improve the efficiency of searching, study selection, and data extraction using machine learning technologies are being developed, their performance and applicability, particularly for reviews based on observational study designs should improve, and ways of publishing living systematic reviews and their updates will continue to evolve. Finally, knowing when to end a living systematic review is as important as knowing when to start.
Keywords	Covid-19 ; Epidemiology ; Public health ; Research design
Subject code	720
Language	English
Publishing country	nl
Document type	Article ; Online
ZDB-ID	2662257-9
ISSN	2046-4053
ISSN	2046-4053
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature.

Knafou, Julien / Haas, Quentin / Borissov, Nikolay / Counotte, Michel / Low, Nicola / Imeri, Hira / Ipekci, Aziz Mert / Buitrago-Garcia, Diana / Heron, Leonie / Amini, Poorya / Teodoro, Douglas

Systematic reviews

2023 Volume 12, Issue 1, Page(s) 94

Abstract: Background: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health ... ...

Abstract	Background: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process. Methods: In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6365 publications manually classified into two classes, three subclasses, and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results: The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best-performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion: This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.
MeSH term(s)	Humans ; Deep Learning ; COVID-19 ; Pandemics ; Retrospective Studies ; Language
Language	English
Publishing date	2023-06-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2662257-9
ISSN	2046-4053 ; 2046-4053
ISSN (online)	2046-4053
ISSN	2046-4053
DOI	10.1186/s13643-023-02247-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis.

Buitrago-Garcia, Diana / Egli-Gany, Dianne / Counotte, Michel J / Hossmann, Stefanie / Imeri, Hira / Ipekci, Aziz Mert / Salanti, Georgia / Low, Nicola

PLoS medicine

2020 Volume 17, Issue 9, Page(s) e1003346

Abstract: Background: There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become ...

Abstract	Background: There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) Amongst people with SARS-CoV-2 infection who are asymptomatic when diagnosed, what proportion will develop symptoms later? (3) What proportion of SARS-CoV-2 transmission is accounted for by people who are either asymptomatic throughout infection or presymptomatic? Methods and findings: We searched PubMed, Embase, bioRxiv, and medRxiv using a database of SARS-CoV-2 literature that is updated daily, on 25 March 2020, 20 April 2020, and 10 June 2020. Studies of people with SARS-CoV-2 diagnosed by reverse transcriptase PCR (RT-PCR) that documented follow-up and symptom status at the beginning and end of follow-up or modelling studies were included. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with an adapted checklist for case series, and the relevance and credibility of modelling studies were assessed using a published checklist. We included a total of 94 studies. The overall estimate of the proportion of people who become infected with SARS-CoV-2 and remain asymptomatic throughout infection was 20% (95% confidence interval [CI] 17-25) with a prediction interval of 3%-67% in 79 studies that addressed this review question. There was some evidence that biases in the selection of participants influence the estimate. In seven studies of defined populations screened for SARS-CoV-2 and then followed, 31% (95% CI 26%-37%, prediction interval 24%-38%) remained asymptomatic. The proportion of people that is presymptomatic could not be summarised, owing to heterogeneity. The secondary attack rate was lower in contacts of people with asymptomatic infection than those with symptomatic infection (relative risk 0.35, 95% CI 0.10-1.27). Modelling studies fit to data found a higher proportion of all SARS-CoV-2 infections resulting from transmission from presymptomatic individuals than from asymptomatic individuals. Limitations of the review include that most included studies were not designed to estimate the proportion of asymptomatic SARS-CoV-2 infections and were at risk of selection biases; we did not consider the possible impact of false negative RT-PCR results, which would underestimate the proportion of asymptomatic infections; and the database does not include all sources. Conclusions: The findings of this living systematic review suggest that most people who become infected with SARS-CoV-2 will not remain asymptomatic throughout the course of the infection. The contribution of presymptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention measures, with enhanced hand hygiene, masks, testing tracing, and isolation strategies and social distancing, will continue to be needed.
MeSH term(s)	Asymptomatic Diseases/epidemiology ; Asymptomatic Infections/epidemiology ; Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/physiopathology ; Coronavirus Infections/transmission ; Disease Progression ; Humans ; Mass Screening ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/physiopathology ; Pneumonia, Viral/transmission ; Reverse Transcriptase Polymerase Chain Reaction ; SARS-CoV-2
Keywords	covid19
Language	English
Publishing date	2020-09-22
Publishing country	United States
Document type	Journal Article ; Meta-Analysis ; Research Support, Non-U.S. Gov't ; Systematic Review
ZDB-ID	2185925-5
ISSN	1549-1676 ; 1549-1277
ISSN (online)	1549-1676
ISSN	1549-1277
DOI	10.1371/journal.pmed.1003346
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 6042: Show issues

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Article ; Online: Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature: a retrospective study

Knafou, Julien / Haas, Quentin / Borissov, Nikolay / Counotte, Michel / Low, Nicola / Imeri, Hira / Ipekci, Aziz Mert / Buitrago-Garcia, Diana / Heron, Leonie / Amini, Poorya / Teodoro, Douglas

bioRxiv

Abstract	Background: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19 related publications to help scale-up the epidemiological curation process. Methods: In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6,365 publications manually classified into two classes, three subclasses and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results: The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion: This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.
Keywords	covid19
Language	English
Publishing date	2023-01-19
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2023.01.18.524571
Database	COVID19

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Article ; Online: Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature

Knafou, Julien / Haas, Quetin / Borissov, Nikolay / Counotte, Michel / Low, Nicola / Imeri, Hira / Ipekci, Aziz Mert / Buitrago-Garcia, Diana / Heron, Leonie / Amini, Poorya / Teodoro, Douglas

Systematic Reviews

2023 Volume 12

Abstract: BackgroundThe COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health ... ...

Abstract	BackgroundThe COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process.MethodsIn this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6365 publications manually classified into two classes, three subclasses, and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article.ResultsThe ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best-performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset.ConclusionThis study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support ...
Keywords	Life Science
Subject code	006
Language	English
Publishing country	nl
Document type	Article ; Online
ZDB-ID	2662257-9
ISSN	2046-4053
ISSN	2046-4053
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis

Buitrago-Garcia, Diana / Egli-Gany, Dianne / Counotte, Michel J / Hossmann, Stefanie / Imeri, Hira / Ipekci, Aziz Mert / Salanti, Georgia / Low, Nicola

PLoS Med

Abstract: BACKGROUND: There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become ... ...

Abstract	BACKGROUND: There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) Amongst people with SARS-CoV-2 infection who are asymptomatic when diagnosed, what proportion will develop symptoms later? (3) What proportion of SARS-CoV-2 transmission is accounted for by people who are either asymptomatic throughout infection or presymptomatic? METHODS AND FINDINGS: We searched PubMed, Embase, bioRxiv, and medRxiv using a database of SARS-CoV-2 literature that is updated daily, on 25 March 2020, 20 April 2020, and 10 June 2020. Studies of people with SARS-CoV-2 diagnosed by reverse transcriptase PCR (RT-PCR) that documented follow-up and symptom status at the beginning and end of follow-up or modelling studies were included. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with an adapted checklist for case series, and the relevance and credibility of modelling studies were assessed using a published checklist. We included a total of 94 studies. The overall estimate of the proportion of people who become infected with SARS-CoV-2 and remain asymptomatic throughout infection was 20% (95% confidence interval [CI] 17-25) with a prediction interval of 3%-67% in 79 studies that addressed this review question. There was some evidence that biases in the selection of participants influence the estimate. In seven studies of defined populations screened for SARS-CoV-2 and then followed, 31% (95% CI 26%-37%, prediction interval 24%-38%) remained asymptomatic. The proportion of people that is presymptomatic could not be summarised, owing to heterogeneity. The secondary attack rate was lower in contacts of people with asymptomatic infection than those with symptomatic infection (relative risk 0.35, 95% CI 0.10-1.27). Modelling studies fit to data found a higher proportion of all SARS-CoV-2 infections resulting from transmission from presymptomatic individuals than from asymptomatic individuals. Limitations of the review include that most included studies were not designed to estimate the proportion of asymptomatic SARS-CoV-2 infections and were at risk of selection biases; we did not consider the possible impact of false negative RT-PCR results, which would underestimate the proportion of asymptomatic infections; and the database does not include all sources. CONCLUSIONS: The findings of this living systematic review suggest that most people who become infected with SARS-CoV-2 will not remain asymptomatic throughout the course of the infection. The contribution of presymptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention measures, with enhanced hand hygiene, masks, testing tracing, and isolation strategies and social distancing, will continue to be needed.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #788852
Database	COVID19

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Article ; Online: The role of asymptomatic SARS-CoV-2 infections: rapid living systematic review and meta-analysis

Buitrago-Garcia, Diana C / Egli-Gany, Dianne / Counotte, Michel J / Hossmann, Stefanie / Imeri, Hira / Salanti, Georgia / Low, Nicola

medRxiv

Abstract: Background: There is substantial disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a population. The disagreement results, in part, from the interpretation of studies that report a ... ...

Abstract	Background: There is substantial disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a population. The disagreement results, in part, from the interpretation of studies that report a proportion of asymptomatic people with SARS-CoV-2 detected at a single point. Review questions: 1. Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? 2. Amongst people with SARS-CoV-2 infection who are asymptomatic when diagnosed, what proportion will develop symptoms later? 3. What proportion of SARS-CoV-2 transmission is accounted for by people who are either asymptomatic throughout infection, or pre-symptomatic? Methods: Rapid living systematic review (protocol https://osf.io/9ewys/). We searched Pubmed, Embase, bioRxiv and medRxiv using a living evidence database of SARS-CoV-2 literature on 25.03.2020. We included studies of people with SARS-CoV-2 diagnosed by reverse transcriptase PCR (RT-PCR) that documented follow-up and symptom status at the beginning and end of follow-up and modelling studies. Study selection, data extraction and bias assessment were done by one reviewer and verified by a second, with disagreement resolved by discussion or a third reviewer. We used a common-effect model to synthesise proportions from comparable studies. Results: We screened 89 studies and included 11. We estimated an upper bound for the proportion of asymptomatic SARS-CoV-2 infections of 29% (95% confidence interval 23 to 37%) in eight studies. Selection bias and likely publication bias affected the family case investigation studies. One statistical modelling study estimated the true proportion of asymptomatic infections at 18% (95% credibility interval 16 to 20%). Estimates of the proportions of pre-symptomatic individual in four studies were too heterogeneous to combine. In modelling studies, 40-60% of all SARS-CoV-2 infections are the result of transmission from pre-symptomatic individuals, with a smaller contribution from asymptomatic individuals. Conclusions: An intermediate contribution of pre-symptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention, with enhanced hand and respiratory hygiene, testing tracing and isolation strategies and social distancing, will continue to be needed. The findings of this systematic review of publications early in the pandemic suggests that most SARS-CoV-2 infections are not asymptomatic throughout the course of infection.
Keywords	covid19
Language	English
Publishing date	2020-04-29
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.04.25.20079103
Database	COVID19

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Article ; Online: Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections

Buitrago-Garcia, Diana / Egli-Gany, Dianne / Counotte, Michel J. / Hossmann, Stefanie / Imeri, Hira / Ipekci, Aziz Mert / Salanti, Georgia / Low, Nicola

Buitrago-Garcia, Diana; Egli-Gany, Dianne; Counotte, Michel J.; Hossmann, Stefanie; Imeri, Hira; Ipekci, Aziz Mert; Salanti, Georgia; Low, Nicola (2020). Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis. PLoS medicine, 17(9), e1003346. Public Library of Science 10.1371/journal.pmed.1003346

A living systematic review and meta-analysis.

2020

Abstract: BACKGROUND There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become ... ...

Abstract	BACKGROUND There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) Amongst people with SARS-CoV-2 infection who are asymptomatic when diagnosed, what proportion will develop symptoms later? (3) What proportion of SARS-CoV-2 transmission is accounted for by people who are either asymptomatic throughout infection or presymptomatic? METHODS AND FINDINGS We searched PubMed, Embase, bioRxiv, and medRxiv using a database of SARS-CoV-2 literature that is updated daily, on 25 March 2020, 20 April 2020, and 10 June 2020. Studies of people with SARS-CoV-2 diagnosed by reverse transcriptase PCR (RT-PCR) that documented follow-up and symptom status at the beginning and end of follow-up or modelling studies were included. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with an adapted checklist for case series, and the relevance and credibility of modelling studies were assessed using a published checklist. We included a total of 94 studies. The overall estimate of the proportion of people who become infected with SARS-CoV-2 and remain asymptomatic throughout infection was 20% (95% confidence interval [CI] 17-25) with a prediction interval of 3%-67% in 79 studies that addressed this review question. There was some evidence that biases in the selection of participants influence the estimate. In seven studies of defined populations screened for SARS-CoV-2 and then followed, 31% (95% CI 26%-37%, prediction interval 24%-38%) remained asymptomatic. The proportion of people that is presymptomatic could not be summarised, owing to heterogeneity. The secondary attack rate was lower in contacts of people with asymptomatic infection than those with symptomatic infection (relative risk 0.35, 95% CI 0.10-1.27). Modelling studies fit to data found a higher proportion of all SARS-CoV-2 infections resulting from transmission from presymptomatic individuals than from asymptomatic individuals. Limitations of the review include that most included studies were not designed to estimate the proportion of asymptomatic SARS-CoV-2 infections and were at risk of selection biases; we did not consider the possible impact of false negative RT-PCR results, which would underestimate the proportion of asymptomatic infections; and the database does not include all sources. CONCLUSIONS The findings of this living systematic review suggest that most people who become infected with SARS-CoV-2 will not remain asymptomatic throughout the course of the infection. The contribution of presymptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention measures, with enhanced hand hygiene, masks, testing tracing, and isolation strategies and social distancing, will continue to be needed.
Keywords	610 Medicine & health ; 360 Social problems & social services ; covid19
Subject code	306
Language	English
Publisher	Public Library of Science
Publishing country	ch
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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