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Monitoring the evolution of COVID-19 Vaccines Trials
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change the section preprocessing

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# Newborn
(EUCTR2021-002613-34-NL','NCT04276896','NCT04299724','NCT04568811','NCT04796896','NCT04816643','NCT04992260','EUCTR2020-005442-42-PL','EUCTR2021-003277-55-AT','JPRN-UMIN000045492','NCT05193279')
# Infants (29 days to 1 year)
('EUCTR2021-002613-34-NL','NCT04276896','NCT04299724','NCT04568811','NCT04796896','NCT04816643','NCT04969601','NCT04992260','EUCTR2020-005442-42-PL','EUCTR2021-003277-55-AT','JPRN-UMIN000045492','NCT05193279')
# Children (1 to 9 years)
('ChiCTR2000031809','ChiCTR2100048439','EUCTR2021-002613-34-NL','ISRCTN15638344','NCT04276896','NCT04299724','NCT04551547','NCT04566770','NCT04568811','NCT04796896','NCT04816643','NCT04863638','NCT04869592','NCT04884685','NCT04895982','NCT04916886','NCT04917523','NCT04961359','NCT04969601','RPCEC00000374','RPCEC00000381','NCT04992260','EUCTR2020-005442-42-PL','NCT05003466','NCT05003479','NCT05013983','EUCTR2021-003277-55-AT','CTRI/2021/05/033752','RPCEC00000390','RPCEC00000391','RPCEC00000384','JPRN-UMIN000045492','NCT04918797','NCT05109598','NCT05107557','NCT05112913','NCT05137418','NCT05168709','NCT05169008','NCT05193279','NCT05198336','NCT05225285','NCT05231590','TCTR20220125002')
# Adolescents (10 to 17 years)
('ChiCTR2000031809','ChiCTR2100048439','CTRI/2020/09/027674','CTRI/2021/01/030416','EUCTR2021-002613-34-NL','EUCTR2021-003388-90-NL','ISRCTN15638344','JPRN-UMIN000044519','NCT04276896','NCT04299724','NCT04368728','NCT04471519','NCT04551547','NCT04566770','NCT04568811','NCT04611802','NCT04649151','NCT04683484','NCT04713553','NCT04761822','NCT04773067','NCT04796896','NCT04800133','NCT04816643','NCT04863638','NCT04864561','NCT04869592','NCT04884685','NCT04895982','NCT04916886','NCT04917523','NCT04951388','NCT04954092','NCT04955626','NCT04956224','NCT04961359','NCT04969601','NL9553','RPCEC00000374','RPCEC00000381','CTRI/2021/04/032942','NCT04992260','EUCTR2020-005442-42-PL','NCT05003466','NCT05003479','NCT05013983','EUCTR2021-003277-55-AT','CTRI/2021/05/033752','ISRCTN12348322','TCTR20210917004','TCTR20210920005','TCTR20210920006','TCTR20210923012','NCT05028361','RPCEC00000390','RPCEC00000391','RPCEC00000384','TCTR20210830002','ISRCTN15779782','EUCTR2021-005043-71-NL','JPRN-UMIN000045492','NCT04918797','NCT05109598','NCT05112913','IRCT20171122037571N3','NCT05007080','NCT05119855','NCT05137418','NCT05168709','NCT05169008','IRCT20150303021315N27','NCT05158855','NCT05193279','NCT05198336','NCT05225285','EUCTR2022-000074-25-ES','NCT05230940','NCT05231590','TCTR20220125002','IRCT20201214049709N5','NCT05249829','NCT05246137','NCT05239806','NCT05239923','NCT05239975','EUCTR2021-005197-25-DE')
# Immunosuppressed
('ACTRN12621000532808','ACTRN12621000661875','EUCTR2021-000175-37-SE','EUCTR2021-000291-11-AT','EUCTR2021-000412-28-BE','EUCTR2021-000461-33-BE','EUCTR2021-000515-24-NL','EUCTR2021-000905-26-SE','EUCTR2021-000930-32-BE','EUCTR2021-001414-10-NL','EUCTR2021-002014-14-CZ','EUCTR2021-002613-34-NL','EUCTR2021-002693-10-AT','EUCTR2021-003618-37-NO','NCT04754698','NCT04775069','NCT04780659','NCT04794946','NCT04805125','NCT04806113','NCT04839315','NCT04844489','NCT04847050','NCT04860258','NCT04860297','NCT04862806','NCT04869358','NCT04878211','NCT04885907','NCT04895982','NCT04904549','NCT04930770','NCT04954469','NCT04961229','NCT04969263','NCT04969601','NCT04977024','PACTR202105817814362','RBR-9ksh5f4','NCT05000216','NCT05022329','EUCTR2021-003277-55-AT','NCT05030974','NCT05047640','NCT05057897','ChiCTR2100049657','NCT05028374','NCT04801667','TCTR20210830002','NCT05075057','NCT05075083','NCT05075499','NCT05075538','EUCTR2021-001459-15-AT','NCT05081271','NCT05077254','NCT05085145','ACTRN12621001412820','ACTRN12621001465842','NCT04792567','TCTR20211102003','NCT05104333','NCT05104437','NCT05104216','NCT05112848','NCT05168813','NCT05170399','EUCTR2021-002927-39-AT','IRCT20140818018842N22','TCTR20211220004','TCTR20211228003','DRKS00027372','NCT05220397')
# Pregnant women
('EUCTR2021-000440-22-BE','EUCTR2021-000893-27-BE','EUCTR2021-002327-38-NL','NCT04754594','NCT04765384','TCTR20210923013','TCTR20210717002','ISRCTN15279830')
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Epidemiology/toward.aux
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......@@ -27,37 +27,37 @@
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\citation{chavalarias_phylomemetic_2013,chavalarias_draw_2021}
\citation{delanoe_mining_2021}
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\newlabel{fig:heterologous}{{3}{5}{A focus of \autoref {fig:phylomemy-randomized-unrandomized}. In red are highlighted all the trials evaluating heterologous primary vaccination and heterologous booster. We circle the first heterologous trials involving different platforms}{figure.3}{}}
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\newlabel{fig:phylomemy-randomized-publications}{{4}{6}{Phylomemy of the randomized only COVID-19 vaccines trials. In blue, we highlight all the trials with an associated publication (i.e., preprint or peer-reviewed articles)}{figure.4}{}}
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\bibstyle{cas-model2-names}
\bibdata{references}
\bibcite{boutron_nma_2020}{{1}{2020}{{Boutron et~al.}}{{Boutron, Chaimani, Meerpohl, Hróbjartsson, Devane, Rada, Tovey, Grasselli, Ravaud, Alawadhi, Amer-Yahia, Arienti, Auber, Ávila, Bafeta, Baldassarre, Banzi, Barnier, Baudry, Bergman, Bollig, Bonnet, Bouet, Boughanem, Boutron, Buckley, Cabanac, Chaiman, Charpy, Chavalarias, Chen, Chevance, Cohen-Boulakia, Cogo, Conil, Coquery, Davidson, De~Nale, Devane, Diard, Dkaki, Doreau, El~Asri, Evrenoglou, Fabbri, Featherstone, Feron, Ferrand, Fezeu, Fouet, Ghanawi, Ghosn El~Chall, Graña, Grasselli, Grolleau, Groz, Hacid, Hamel, Hansen, Henschke, Hohlfeld, Hróbjartsson, Julia, Mavridis, Meerpohl, Meyer, Minozzi, Moreno, Naidoo, Nguyen, Oikonomidi, Page, Petkovic, Pienaar, Pierre, Probyn, Quirke, Rada, Ravaud, Ripoll, Riveros, Rivière, Sauvant, Savovic, Schmucker, Sguassero, Sterne, Toumani, Tovey, Villanueva, Vuillemot, Xia, Yu, Zoletic and Zweigenbaum}}}
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Phylomemy of the randomized only COVID-19 vaccines trials. In blue, we highlight all the trials with an associated publication (i.e., preprint or peer-reviewed articles).}}{7}{figure.4}\protected@file@percent }
\newlabel{fig:phylomemy-randomized-publications}{{4}{7}{Phylomemy of the randomized only COVID-19 vaccines trials. In blue, we highlight all the trials with an associated publication (i.e., preprint or peer-reviewed articles)}{figure.4}{}}
\bibcite{chavalarias_phylomemetic_2013}{{2}{2013}{{Chavalarias and Cointet}}{{}}}
\bibcite{chavalarias_draw_2021}{{3}{2021}{{Chavalarias et~al.}}{{Chavalarias, Lobb{\'{e}} and Delanoë}}}
\bibcite{chumakove_old_2021}{{4}{2021}{{Chumakov et~al.}}{{Chumakov, Avidan, Benn, Bertozzi, Blatt, Chang, Jamison, Khader, Kottilil, Netea, Sparrow and Gallo}}}
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Epidemiology/toward.pdf
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Epidemiology/toward.tex
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......@@ -167,6 +167,8 @@ All authors contributed equally to the paper. G Ferrand was responsible for the
Over the past two years, the ongoing COVID-19 pandemic has impacted a wide number of human domains: from economy to education, from public health to politics. Among others, Science swung early on into action to find both a cure and an effective vaccine. This has resulted in an unprecedented volume of publications that have generated an information overload for the medical community. One of today's challenges is to synthesize this overwhelming amount of information about current COVID-19 research in order to improve coordination between the different research streams. Our paper thus proposes to address this issue by applying a new method for reconstructing the evolution of knowledge. We take as a case study the COVID-19 vaccines clinical trials from the \textit{COVID-NMA database} and use the \textit{phylomemy reconstruction process}~\cite{chavalarias_draw_2021}. The \textit{COVID-NMA database} stores the curated dataset of all the clinial trials available in the set of international primary and secondary trial registries~\cite{boutron_nma_2020,nguyen_research_2021} (~\textit{i.e.}, all trials registered in the International Clinical Trials Registry Platform (ICTRP), Clinicaltrials.gov and the EU clinical trials registry -- see~\ref{materials}). For the purpose of this study, the \textit{COVID-NMA database} has been reduced to a pruned corpus called~$\mathcal{D}_{vt}$ (see~\ref{pre-processing}). We then combine the expertise of epidemiologists and \textit{Complex Systems} researchers to interpret the resulting visualizations and reveal insights for upcoming COVID-19 research.
%\todo{la conjonction de la mutabilité et du flux de data a été nouveau}
%
%% Materials and methods
%
......@@ -178,14 +180,18 @@ Our paper aims at applying a new text mining method -- the \textit{phylomemy re
\subsection{The COVID-NMA database}
\label{materials}
The COVID-NMA project is an international initiative aimed at providing a living mapping and a living systematic review of all trials assessing treatments and preventive interventions for COVID-19~\cite{boutron_nma_2020,nguyen_research_2021}. The development of the COVID-NMA database relies on a full methodology designed to generate and make available a complete, comprehensive, integrated, non-redundant and carefully annotated data sets on clinical trials. We automatically extract data from clinical registries on a weekly basis and provide assistance to epidemiologists on the curation and annotation process. Raw data is extracted from the \href{https://www.clinicaltrialsregister.eu/}{EU clinical trials register}, from the \href{https://clinicaltrials.gov/}{ClinicalTrial registry} managed by the U.S. National Library of Medicine, from the \href{https://www.irct.ir/}{IRCT registry} and from the \href{https://www.who.int/ictrp/en/}{WHO International Clinical Trials Registry Platform} (ICTRP) -- an international registry that assembles information on clinical trials registered in 17 primary registries to identify new trial assessing COVID-19 vaccine and update of previously registered trial records. Data are extracted from registries, annotated by epidemiologists, then stored and made available through the COVID-NMA database. We here note that international trials registries can be post-updated by research teams, e.g. for post-adding a related publication.
The COVID-NMA project is an international initiative aimed at providing a living mapping and a living systematic review of all trials assessing treatments and preventive interventions for COVID-19~\cite{boutron_nma_2020,nguyen_research_2021}. The development of the COVID-NMA database relies on a full methodology designed to generate and make available a complete, comprehensive, integrated, non-redundant and carefully annotated data sets on clinical trials. We automatically extract data from clinical registries on a weekly basis and provide assistance to epidemiologists on the curation and annotation process. Raw data is extracted from the \href{https://www.clinicaltrialsregister.eu/}{EU clinical trials register}, from the \href{https://clinicaltrials.gov/}{ClinicalTrial registry} managed by the U.S. National Library of Medicine, from the \href{https://www.irct.ir/}{IRCT registry} and from the \href{https://www.who.int/ictrp/en/}{WHO International Clinical Trials Registry Platform} (ICTRP) -- an international registry that assembles information on clinical trials registered in 17 primary registries to identify new trial assessing COVID-19 vaccine and update of previously registered trial records. Data are extracted from registries, annotated \todo{and enriched} by epidemiologists, then stored and made available through the COVID-NMA database. We here note that international trials registries can be post-updated by research teams, e.g. for post-adding a related publication.
%Future versions of the phylomemies presented in this paper might thus be slightly different from the current ones. A promising way to get around this issue would be to archive every modifications of the original registries and then choose the version we want to integrate in the phylomemies.
\subsection{Pre-processing the database}
\subsection{\todo{Collaboratively pre-processing the COVID-NMA database}}
\label{pre-processing}
We have pre-processed the COVID-NMA database before using it for the phylomemy reconstruction to filter the 1,794 descriptions related to vaccines trials. The trials records have been first aggregated by publication week. Then, we have merged the sections `\textit{pharmacological treatment}', `\textit{treatment type}' and `\textit{treatment name}' together to shape the trial descriptions. These descriptions have also been enriched with extra-information such as trial phases, funding, involved countries or associated publications. The resulting corpus $\mathcal{D}_{vt}$ has latter been collectively and collaboratively curated by epidemiologists thanks to the free software \textit{Gargantext}~\cite{delanoe_mining_2021}. There, these experts have extracted and validated a core vocabulary as a list of 175 root terms.
\todo{In order to be used as input data for the phylomemy reconstruction process, the COVID-NMA database have had to be first pre-processed. But the unprecedented volume of trials challenged our capacity to build a time-consistent and insightful visualization. We have thus faced two main issues: 1) dealing with the mutable nature of the trials registries; 2) collaboratively curating a representative vocabulary of the trials descriptions.}
\todo{Contrary to regular scientific publications, the textual content of trials registries is mutable by nature: the description of a given trial can be updated weeks after having been recorded by detailing its protocol or simply by adding experimental results. As the tight temporality of the COVID-19 crisis forced us to monthly reconstruct our visualization, we have developed a time-consistent approach: for each recorded trial, we have made the decision not to update its textual descriptions but, in the same time, to keep the meta-data (i.e, phases, founders, associated publications, etc.) up to date with their most recent version. By doing so, we don't break the temporal continuity of the phylomemy reconstruction process as we preserve the natural evolution of the trials descriptions.}
\todo{We have then} filtered the 1,794 descriptions related to vaccines trials \todo{to isolate them from other preventive or post-treatments}. Next, the trials records have been aggregated by publication week and we have merged the sections `\textit{pharmacological treatment}', `\textit{treatment type}' and `\textit{treatment name}' together to shape \todo{a new field called} trial description. These descriptions have been enriched with \todo{meta-data} such as trial phases, funding, involved countries or associated publications. The resulting corpus $\mathcal{D}_{vt}$ has latter been collectively and collaboratively curated by epidemiologists thanks to the free software \textit{Gargantext}~\cite{delanoe_mining_2021}. There, these experts have extracted and validated a core vocabulary as a list of 175 root terms \todo{ready to be analyzed through the phylomemy}.
\subsection{The phylomemy reconstruction process}
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scripts/r/Process_CT_for_Gargantext.Rmd
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