update 5 avril

Epidemiology/toward.abs

-This paper aims at reconstructing the evolution of all the available COVID-19 vaccines trials extracted from the \textit{COVID-NMA database} by applying the \textit{phylomemy reconstruction process}. We visualize the textual contents of 1,794 trials descriptions and explore their collective structure along with their semantic dynamics. We map the continuous progress of the main COVID-19 vaccine platforms from their early-stage trials in February 2020 to their most recent combinations driven by the rise of variants of concern, third dose issues and heterologous vaccinations. This paper brings insights for the global coordination between research teams especially in crisis situations such as the COVID-19 pandemic.
+ \textbf{Objective:}
+\todo{to visualize the evolution of all registered COVID-19 vaccines trials}\\
+\noindent \textbf{Study Design and Setting:}
+\todo{as part of the living mapping of the COVID-NMA initiative, we identify biweekly all COVID-19 vaccine trials and automatically extract data from the EU clinical trials registry, ClinicalTrials.gov, IRCT and the WHO International Clinical Trials Registry Platform. Data are curated and enriched by epidemiologists.
+We have used the phylomemy reconstruction process to visualize the temporal evolution of COVID-19 vaccines trials descriptions. We have analyzed the textual contents of 1,794 trials descriptions (last search in October 2021) and explored their collective structure along with their semantic dynamics.}\\
+\noindent \textbf{Results:}
+\todo{the structures highlighted by the phylomemy reconstruction processes synthesize the complexity of the knowledge produced by the research community. The reconstructed phylomemy clearly retrieves the five major COVID-19 vaccine platforms in the form of complete branches. The branches interactions reflect the exploration of a new approach to vaccine implementation moving from homologous prime vaccination to heterologous prime vaccination. Phylomemies also clearly identifies shifts in research questions; from vaccine efficacy to booster efficacy.}\\
+\noindent \textbf{Conclusion:}
+\todo{this new method provides important insights for the global coordination between research teams especially in crisis situations such as the COVID-19 pandemic.}

Epidemiology/toward.aux

@@ -19,26 +19,26 @@
 \citation{foster_phylogenetic_2020}
 \citation{aphp_indicators_2020}
 \citation{nguyen_research_2021}
-\citation{chavalarias_draw_2021}
 \citation{boutron_nma_2020,nguyen_research_2021}
+\citation{chavalarias_draw_2021}
 \@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}\protected@file@percent }
 \newlabel{introduction}{{1}{1}{Introduction}{section.1}{}}
 \citation{boutron_nma_2020,nguyen_research_2021}
 \citation{delanoe_mining_2021}
 \citation{chavalarias_phylomemetic_2013,chavalarias_draw_2021}
-\citation{delanoe_mining_2021}
 \@writefile{toc}{\contentsline {section}{\numberline {2}Materials and method}{2}{section.2}\protected@file@percent }
 \@writefile{toc}{\contentsline {subsection}{\numberline {2.1}The COVID-NMA database}{2}{subsection.2.1}\protected@file@percent }
 \newlabel{materials}{{2.1}{2}{The COVID-NMA database}{subsection.2.1}{}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {2.2}Pre-processing the COVID-NMA database}{2}{subsection.2.2}\protected@file@percent }
 \newlabel{pre-processing}{{2.2}{2}{Pre-processing the COVID-NMA database}{subsection.2.2}{}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {2.3}The phylomemy reconstruction process}{2}{subsection.2.3}\protected@file@percent }
+\citation{delanoe_mining_2021}
 \citation{dias2008mapping}
 \citation{uno2004lcm}
-\citation{chavalarias_draw_2021}
-\citation{lobbe_exploring_2021}
 \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces The four operators of the phylomemy reconstruction process: 1. terms indexation, 2. similarity measures, 3. fields detection, 4. inter-temporal matching}}{3}{figure.1}\protected@file@percent }
 \newlabel{fig:reconstruction}{{1}{3}{The four operators of the phylomemy reconstruction process: 1. terms indexation, 2. similarity measures, 3. fields detection, 4. inter-temporal matching}{figure.1}{}}
+\citation{chavalarias_draw_2021}
+\citation{lobbe_exploring_2021}
 \@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Phylomemy of 1,794 COVID-19 vaccines trials recorded between February 2020 and October 2021 in the COVID-NMA database. Online and interactive version available at \href  {http://maps.gargantext.org/unpublished_maps_phylo/vaccines_publications_10_2021/}{maps.gargantext.org/publications}}}{4}{figure.2}\protected@file@percent }
 \newlabel{fig:phylomemy-randomized-unrandomized}{{2}{4}{Phylomemy of 1,794 COVID-19 vaccines trials recorded between February 2020 and October 2021 in the COVID-NMA database. Online and interactive version available at \href {http://maps.gargantext.org/unpublished_maps_phylo/vaccines_publications_10_2021/}{maps.gargantext.org/publications}}{figure.2}{}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {2.4}Visualizing phylomemies}{4}{subsection.2.4}\protected@file@percent }
@@ -51,23 +51,23 @@
 \@writefile{toc}{\contentsline {subsection}{\numberline {4.1}General observations}{5}{subsection.4.1}\protected@file@percent }
 \@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Repurposing non-COVID vaccines}{5}{subsection.4.2}\protected@file@percent }
 \citation{krause_considerations_2021}
+\@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).}}{6}{figure.4}\protected@file@percent }
+\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}{}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Heterologous vaccination}{6}{subsection.4.3}\protected@file@percent }
 \@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Boosters}{6}{subsection.4.4}\protected@file@percent }
-\@writefile{toc}{\contentsline {subsection}{\numberline {4.5}Filters and \leavevmode {\color  {red}\textbf  {specific}} research questions}{6}{subsection.4.5}\protected@file@percent }
-\newlabel{filter}{{4.5}{6}{Filters and \todo {specific} research questions}{subsection.4.5}{}}
-\@writefile{toc}{\contentsline {section}{\numberline {5}\leavevmode {\color  {red}\textbf  {Discussion}}}{6}{section.5}\protected@file@percent }
-\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}\leavevmode {\color  {red}\textbf  {Generalization}}}{6}{subsection.5.1}\protected@file@percent }
 \citation{chavalarias_draw_2021}
-\@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}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {4.5}Filters and \leavevmode {\color  {red}\textbf  {specific}} research questions}{7}{subsection.4.5}\protected@file@percent }
+\newlabel{filter}{{4.5}{7}{Filters and \todo {specific} research questions}{subsection.4.5}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {5}\leavevmode {\color  {red}\textbf  {Discussion}}}{7}{section.5}\protected@file@percent }
+\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}\leavevmode {\color  {red}\textbf  {Generalization}}}{7}{subsection.5.1}\protected@file@percent }
 \citation{nguyen_research_2021}
 \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}}}
-\bibcite{chavalarias_phylomemetic_2013}{{2}{2013}{{Chavalarias and Cointet}}{{}}}
-\bibcite{chavalarias_draw_2021}{{3}{2021}{{Chavalarias et~al.}}{{Chavalarias, Lobb{\'{e}} and Delanoë}}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {5.2}\leavevmode {\color  {red}\textbf  {Prediction}}}{8}{subsection.5.2}\protected@file@percent }
 \@writefile{toc}{\contentsline {section}{\numberline {6}Perspectives and insights for COVID-19 research}{8}{section.6}\protected@file@percent }
+\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}}}
 \bibcite{aphp_indicators_2020}{{5}{2020}{{the COVID-19 APHP-Universities-INRIA-INSERM~Group}}{{}}}
 \bibcite{delanoe_mining_2021}{{6}{forthcoming 2021}{{Delanoë and Chavalarias}}{{}}}