The Impact of the COVID-19 Pandemic on Scientific Research in the Life Sciences
MMassimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research The Impact of the COVID-19 Pandemicon Scientific Research in the Life Sciences
Massimo Riccaboni , ∗ , Luca Verginer Piazza S. Francesco, 19, Lucca, 55100, Italy Chair of Systems Design, ETH Zurich, Weinbergstrasse 58, 8092 Zurich, Switzerland ∗ corresponding author [email protected] Abstract
The COVID-19 outbreak has posed an unprecedented challenge to humanity and science.On the one side, public and private incentives have been put in place to promptly allocateresources toward research areas strictly related to the COVID-19 emergency. But on the flipside, research in many fields not directly related to the pandemic has lagged behind. In thispaper, we assess the impact of COVID-19 on world scientific production in the life sciences.We investigate how the usage of medical subject headings (MeSH) has changed following theoutbreak. We estimate through a difference-in-differences approach the impact of COVID-19on scientific production through PubMed. We find that COVID-related research topics haverisen to prominence, displaced clinical publications, diverted funds away from research areasnot directly related to COVID-19 and that the number of publications on clinical trials inunrelated fields has contracted. Our results call for urgent targeted policy interventions toreactivate biomedical research in areas that have been neglected by the COVID-19 emergency.
Introduction
The COVID-19 pandemic has mobilized the world scientific community in 2020, especially in thelife sciences [6, 12]. In the first three months after the pandemic, the number of scientific papersabout COVID was fivefold the number of articles on H1N1 swine influenza [5]. Similarly, thenumber of clinical trials for COVID prophylaxis and treatments skyrocketed [3]. Thanks to therapid mobilization of the world scientific community, COVID-19 vaccines have been developedin record time. Despite this undeniable success, there is a rising concern about the negativeconsequences of COVID-19 on clinical trial research with many projects being postponed [4, 10,11]. According to Evaluate Pharma, clinical trials were one of the pandemic’s first casualties,with a record number of 160 studies suspended for reasons related to COVID in April 2020[2]. As a consequence, clinical researchers have been impaired by reduced access to healthcareresearch infrastructures. Particularly, the COVID-19 outbreak took a tall on women and early-career scientists [7, 9, 13, 15]. On a different ground, Shan and colleagues found that non-COVID-related articles decreased as COVID-related articles increased in top clinical researchjournals [14]. Fraser and coworker found that COVID preprints received more attention and1/11 a r X i v : . [ ec on . GN ] J a n assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research citations than non-COVID preprints [6]. More recently, Hook and Porter have found some earlyevidence of “covidisation” of academic research, with research grants and output diverted toCOVID research in 2020 [8]. How much should scientists switch their efforts toward SARS-CoV-2 prevention, treatment, or mitigation? There is a growing consensus that the current level of“covidisation” of research can be wasteful [1, 3, 4].Against this background, in this paper we investigate if the COVID-19 pandemic has induceda bias in in biomedical publications toward COVID-related scientific production. We classifyscientific articles of the last three years in PubMed in about 19 000 “research fields” using thecurated Medical Subject Headings (MeSH) terminology. For each research field, we compute ameasure of relatedness to COVID-19 (including COVID-19 Testing, Serological Testing, NucleicAcid Testing, and Vaccines) and SARS-CoV-2 MeSH terms, which first appeared in the scientificliterature in December 2019. A sample of the MeSH terms most closely related to COVID-19 isshown Figure 1.We first look at how the COVID emergency has caused a profound change in the importance ofresearch topics (as proxied through MeSH) shifting attention to COVID-19 related research. Wethen look through a natural-experiment approach on the impact of the pandemic on scientificoutput. We consider COVID-19 as a transparent exogenous source of variation across researchfields. By applying a difference-in-differences regression analysis, we find that COVID-relatedMeSH terms have been much more likely to be published in high-impact journals in the af-termath of the COVID-19 pandemic. The publication bias is even more pronounced for openaccess research. We also document a significant contraction of non-COVID related clinical trialpublications and a negative funding effect for COVID unrelated research fields, with no sign ofre-balancing at the end of 2020.The overall picture that emerges is that there has been a profound realignment of prioritiesand research efforts within the scientific community and that this shift has displaced unrelatedresearch.The rest of the paper is structured as follows. We introduce first the data, our measure of relat-edness as well as the econometric specification we rely on to identify the impact of the pandemicon scientific output. We show the difference in difference analysis highlighting the sudden shiftin publications, grants and trial towards COVID related topics and show through a networkapproach how the MeSH usage patterns of COVID related terms have risen to prominence andalso affected the focus of many other fields of research.2/11 assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research Betacoronavirus
SARS Virus
Coronavirus Infections
Coronavirus
Pneumonia, Viral
Pandemics
Physical Distancing
Coronavirus 3C Proteases
Severe Acute Respiratory Syndrome Angiotensin-Converting Enzyme 2
Coronavirus Nucleocapsid ProteinsAgeusia Spike Glycoprotein, Coronavirus
Infectious Disease Transmission, Patient-to-ProfessionalInfectious Disease Transmission, Professional-to-Patient
Disease Outbreaks
Middle East Respiratory Syndrome Coronavirus
LopinavirPneumonia C h il b l a i n s Respiratory Tract InfectionsCoronavirus, Feline
Peptidyl-Dipeptidase A C a r b o x y p e p t i d a s e s Hydroxychloroquine
Contact Tracing
Personal Protective EquipmentRespiratory Protective DevicesQuarantineSurge Capacity Infectious Disease Incubation PeriodFeline Infectious Peritonitis
Dipeptidyl-Peptidases and Tripeptidyl-PeptidasesRNA Viruses Cytokine Release SyndromeEye Infections, Viral RNA Virus InfectionsHand SanitizersRitonavir
MasksConjunctivitis, Viral Viral Fusion ProteinsProtective Devices H e a l t h C a r e R a t i o n i n g V e n t il a t o r s , M e c h a n i c a l Chloroquine Dysgeusia V i r u s D i s e a s e s Lung Diseases C o m p a ss i o n a t e U s e T r i a l s Basic Reproduction NumberAminoquinolinesEquipment and Supplies, Hospital Asymptomatic InfectionsHealthcare-Associated Pneumonia LymphopeniaRespiratory Distress SyndromeOlfaction DisordersImmunity, Herd FomitesFurinViruses Taste Disorders F i b r i n F i b r i n o g e n D e g r a d a t i o n P r o d u c t s Hospital Bed Capacity Equipment Reuse VideoconferencingUniversal Precautions Acute Chest Syndrome R e s p i r a t o r y T r a c t D i s e a s e s Nucleocapsid Receptors, Angiotensin T r a n s m i ss i b l e g a s t r o e n t e r i t i s v i r u s Virus SheddingSocial Isolation FractalsInfectious bronchitis virus Gloves, ProtectiveHand Disinfection I n S u e n z a P a n d e m i c , - C o n t a i n m e n t o f B i o h a z a r d s G l o v e s , S u r g i c a l F r o s t b i t e Remote Consultation Telemedicine
Antibody-Dependent Enhancement C i v il D e f e n s e Azithromycin Acute Radiation SyndromeConvalescencePorcine epidemic diarrhea virusEye Protective Devices Viral Envelope ProteinsVirus Internalization H I V P r o t e a s e I n h i b i t o r s D i s e a s e T r a n s m i ss i o n , I n f e c t i o u s Reverse Transcriptase Polymerase Chain ReactionMurine hepatitis virusCoughVisitors to Patients SneezingMiller Fisher Syndrome Angiotensin-Converting Enzyme Inhibitors Hand HygieneCrisis Intervention Drug RepositioningBed Occupancy A i r T r a v e l Patient IsolationTriagePsychological Distance Emergency ShelterDental OWcesMacrophage Activation Syndrome MyalgiaAirportsAntibodies, ViralLivedo Reticularis Disposable Equipment N a s o p h a r y n x Amebicides Hotlines Economic Recession P r o t e i n C o r o n a Respiratory Center Medical Laboratory PersonnelVirology Papain Disseminated Intravascular CoagulationCommunicable Diseases, Imported Viral TropismOtolaryngologists D a r u n a v i r Infections PanicPregnancy Complications, InfectiousVirus InactivationRenin-Angiotensin System Antimalarials Equipment Safety Acute DiseaseAerosols Virus ReleaseRNA, ViralLaboratory Personnel Respiratory Therapy E m e r g e n c i e s V i r a l S t r u c t u r a l P r o t e i n s ProcalcitoninUnited States Occupational Safety and Health AdministrationFamotidineAgeism Oseltamivir MyocarditisHemodialysis Units, Hospital Viral Vaccines Teleradiology Infectious Disease Transmission, VerticalHealth Facility Closure Asymptomatic DiseasesInfectious Disease MedicineHomoharringtonine Disaster MedicineReceptor, Angiotensin, Type 2 MetapneumovirusTelerehabilitation Chiroptera Eye InfectionsEpitopes, B-LymphocyteCytopathogenic Effect, Viral D i s a s t e r P l a nn i n g Neutralization Tests Vero CellsCommunicable Diseases, EmergingCritical Illness Virus Attachment SyndemicElective Surgical ProceduresPolyproteinsBeds Radiography, Thoracic ReninGuillain-Barre SyndromeCommunicable Disease Control Angiotensin Receptor AntagonistsResource Allocation Respiratory Rate BoredomMediastinal Emphysema M u c o c u t a n e o u s L y m p h N o d e S y n d r o m e Frameshifting, RibosomalSmell Viral Nonstructural Proteins Skin Diseases, ViralDental Staff Infection Control Respiration, ArtiXcialProne PositionHospital Rapid Response Team Loneliness
Figure 1: The 200 MeSH terms used most frequently with COVID-specific MeSH Terms areshown as word-cloud. The size of the term is proportional to its relatedness to COVID-19MeSH terms.
Materials and Methods
Data
For the analysis, we use PubMed, specifically the daily updated files up to 31/12/2020. Weconsider 3 360 248 papers published between January 2018 and December 2020 for the mainanalysis, as well as 6 389 974 papers published from 2015 on-wards to classify grants. We useSCImago to weigh the papers by the impact factor of the journal they appear in.
Medical Subject Headings (MeSH)
We rely on the MeSH terminology in this work to approximate “fields” of research. This ter-minology is a curated medical vocabulary, which is manually added to Papers in the PubMedcorpus. The fact that MeSH terms are manually added makes this terminology ideal for classi-fication purposes. However, there is a delay between publication and annotation (on the orderof months). To address this delay and have the most recent classification, i.e., December 2020,we search for all 28 425 MeSH terms using the ESearch facility of PubMed and classify paperby the results. We apply this method to the whole period (January 2018 to December 2020).Similarly, we classify papers on clinical trials through ESearch, searching for the specific clinicaltrial MeSH terms. 3/11 assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research COVID-Relatedness
We estimate the relatedness of a focal MeSH term to COVID-19 MeSH terms as the conditionalprobability that a paper contains a COVID-19 term given that it includes the focal term. Formallysim i = P ( COVID | MeSH i ) = P ( COVID ∩ MeSH i ) /P ( MeSH i ) (1)We define a dichotomous variable COVID i that takes value 1 for MeSH terms highly relatedto COVID-19 (larger than the threshold value τ ) and 0 otherwise. In our primary analysis weselected the value of τ , which minimizes the pre-2020 distance between the two groups. Model
To estimate the impact of the COVID pandemic (treatment) on scientific production, we esti-mate a random effects panel regression where the units of analysis are about 19 000 researchfields observed over time before and after the COVID-19 pandemic. Specifically, we estimate thefollowing model ln Y it = β + β COVID i + β T t + β COVID i × T t + ν i + (cid:15) it Where Y it identifies one of the four outcome measures. The variable COVID i is 1 if term i isclosely related to COVID-19 and 0 otherwise, specifically COVID i = 1( sim i > τ ) . The variable T t identifies the period, i.e., month or year. ν i is the MeSH specific error term, and (cid:15) it is theoverall error term. The saturation in COVID and T means that β can be interpreted as theDiff-in Diff effect. In the yearly specification, 2018 is the base year. In the monthly specification,January 2019 is the base month. The errors are clustered at the MeSH term level. We drop MeSHterms which have fewer than five papers per month or 60 (=5*12) per year, to exclude infrequentterms. Additional Robustness Checks
We have carried out the regression analysis on the unweighted analogs of the dependant variables(i.e., Number of Papers, not IFWN) and raw counts, i.e., not log-transformed. Moreover, we havecarried out the analysis using the continuous COVID-Relatedness measure and a wide range ofother τ values. Finally, we considered an alternative model by dropping MeSH terms with anintermediate level of relatedness to COVID-19. The results are qualitatively identical. With theprovided code and data all the mentioned robustness checks can be replicated.4/11 assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research Results
Impact on Publications, Funding and Clinical Trials
In January 2020, researchers discovered the cause of a severe respiratory disease of unknown ori-gin: the novel coronavirus SARS-CoV-2. Since then the pandemic has shuffled the research prior-ities of the worldwide scientific community. We consider SARS-CoV-2 and the related COVID-19disease as a natural experiment that suddenly affected the scientific community at the beginningof 2020. PubMed scientific publications of the last three years have been classified into about19 000 areas of research. We defined a measure of relatedness to COVID-19 as the probabilitythat a paper in a given domain contains SARS-CoV-2 or COVID-19 MeSH terms. Research fieldshave been divided into two groups: COVID-related areas of research (treated) and other researchfields (control). Different thresholds of relatedness to COVID-19 have been used to separate thetwo groups.In Table 1 we report the result of a difference-in-differences regression to estimate the impact ofCOVID-19 on the journal impact factor weighted number (IFWN) of publications, open accesspublications, articles related to clinical trials, and publications with grants. And in Figure 2we show the margins of this regression, which highlight that the common trend assumption issatisfied since the scientific output grew at the same rate from 2018 to 2019 across both groups.Then in 2020 the two groups significantly diverged: COVID-related publications experience asignificant increase, whereas publications in other fields halted, with a marked drop of clinicaltrials publications and publications listing grants.Figure 3 shows a similar analysis on a monthly basis. The monthly analysis reveals that totalscientific output and open access publications in non-COVID research areas bounce back topre-COVID-19 levels after a dip in May. Conversely, we do not detect any sign of recoveryfor publication related to non-COVID clinical trials and granted publications. To understand ifnon-COVID grants have been diverted to COVID-related research, we classify the type of grantslisted in the publications as “new grants” (appear first in 2020) and “old grants” (appeared before2020). Obviously, old grants could not have been meant for COVID research. The usage of thesetwo types of grants by the two grups is shown in Figure 4. We notice that old grants are useddisproportionately more often in COVID-related areas of research. This implies that grants whichwere not meant to sustain COVID research have been diverted to COVID publications. This ison top of the ad-hoc financial support by new grants for COVID-19 in 2020.
Changes in MeSH usage
The sudden surge in publications on COVID-19 related terms documented above has also affectedthe way MeSH terms are used and, by extension, the Biomedical research landscape’s focus. To5/11 assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research Table 1: Panel RegressionImpact weighted No. of (1) (2) (3) (4)scientific publications: ln(Total) ln(OA) ln(Grants) ln(Trials)Covid-Related 0.001 -0.008 -0.020 0.012(0.02) (-0.13) (-0.30) (0.09)2019 (Other) 0.222 ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (106.11) (52.38) (54.61) (52.97)2019 × Covid-Related -0.011 -0.020 0.020 -0.033(-1.19) (-1.26) (1.40) (-0.86)2020 (Other) 0.194 ∗∗∗ ∗∗∗ -0.121 ∗∗∗ -0.102 ∗∗∗ (49.23) (25.91) (-23.38) (-15.19) × Covid-Related ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (24.75) (29.96) (20.91) (12.01)Constant 7.396 ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (580.26) (473.59) (364.94) (240.27)Observations 52,597 52,597 52,597 52,597MeSH Terms 18,926 18,926 18,926 18,926 R within 0.204 0.137 0.140 0.127 R overall 0.002 0.003 0.004 0.004COVID Relatedness ( τ ) 0.05 0.05 0.05 0.11 t the statistics in parentheses are clustered at MeSH term level. ∗ p < . , ∗∗ p < . , ∗∗∗ p < . highlight this shift in focus, we proxy the usage and changing interdependence of MeSH termsby looking at their co-occurrence on all publications in the years 2018, 2019 and 2020. Precisely,we extract the co-occurrence matrix of all 28,000 MeSH terms as they appear on the 3.3 millionpapers by year. These networks summarise their complex interdependence and usage by thescientific community. We proxy the changes in this network by looking at the change in PageRankcentralities across years. The PageRank centrality tells us how likely a random walker traversinga network would be found at a given node if he follows the edges. Specifically, for the case of theMeSH co-occurrence network, this number represents how often an author would include a givenMeSH term following the observed general usage patterns. It is a simple measure to capture the6/11 assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research COVID related
MeSH
Other
MeSH
95% CI (a) (b)(c) (d) l n ( To t a l ) l n ( O p e n A ccess ) l n ( G r a n t s ) l n ( T r i a l s ) . . Figure 2: Results of the difference-in-differences regression analysis on the impact of related-ness to COVID-19 and Sars-Cov-2 (treatment) on the scientific outcome in MeSH researchdomains defined as (1) the yearly impact factor weighted number (IFWN) of publications inyears 2019 and 2020 as compared to 2018. A similar analysis is performed for (2) the IFWN ofopen access publications in PubMed; (3) the IFWN of publications that acknowledged grantsand (4) the IFWN of publications related to clinical trials. The figures show that the com-mon trend assumption is satisfied up to 2019. In 2020 there has been a significant increasein COVID-related scientific productions. Non-COVID research lagged behind. All outcomemeasures are in natural logarithm.complexities of biomedical research. Nevertheless, it captures far reaching interdependence acrossMeSH terms as the measure uses the whole network to determine the centrality of every MeSHterm. A sudden change in the rankings and thus the position of MeSH terms in this networksuggests that a given research topic has risen from obscurity to prominence as it is used moreoften with other important MeSH terms (or vice versa).To show that COVID-related research has profoundly impacted the way MeSH terms are used,we compute for each MeSH how many ranks it gained from 2019 to 2020. To have a caparison,we compute the gain for the pre-COVID period from 2018 to 2018.In figure Figure 5 we see that MeSH terms with high COVID-19 similarity have risen quicklyfrom obscurity to become central to much of the published research. We find that the Pearsoncorrelation between rank gain and COVID relatedness is very high at 0.5. We also note that this7/11 assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research
56 34523
Jan2019 Jan2020 Dec2020
COVID related
MeSH
Other
MeSH
95% CI
Jan2019 Jan2020 Dec2020 l n ( To t a l ) l n ( O p e n A ccess ) l n ( G r a n t s ) l n ( T r i a l s ) (b)(a) (d)(c) Figure 3: Sub-figures (a-d) are the monthly analogs to the marginals in 2. The last two months(Nov and Dec 2020) are grayed out to highlight that MeSH terms and other manually addedmetadata are added with a delay by PubMed. . . . . . l n ( N e w G r a n t s ) Jan Dec . . l n ( O l d G r a n t s ) Jan Dec (a) (b)
Figure 4: Sub-Figure (a) and (c) show respectively the log of the IWTN of papers listing a newgrant (first appearance in 2020) and an old grant (appeared before 2020)effect was completely absent in 2019, as indicated by the blue point cloud with a correlation of0.003. Note that the same effect can be replicated for other centrality measures (i.e., betweenness)8/11 assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research Discussion
The scientific community has swiftly reallocated research efforts to cope with the COVID-19pandemic, mobilizing knowledge across disciplines to find innovative solutions in record time.We document this both in the scientific output as well as in the usage of MeSH terms of thescientific community. The flip side of this sudden and energetic prioritization of effort has causeda sudden contraction of scientific production in other relevant areas of research.Our results provide compelling evidence that research related to COVID has indeed displacedscientific production in other research fields, with a significant drop of scientific output relatedto non-COVID clinical trials and a marked reduction of financial support for publications notrelated to COVID. The displacement effect is persistent to the end of 2020. Heading into 2021,as vaccination progresses, we highlight the urgent need for science policy to re-balance supportfor research activity that was put on pause because of the COVID-19 pandemic.We find that COVID-19 dramatically impacted clinical research. Reactivation of clinical trialsactivities that have been postponed or suspended for reasons related to COVID is a priority thatshould be considered in the national vaccination plans by including patients and researchers inthe high priority groups. Moreover, since grants have been diverted and financial incentives havebeen targeted to sustain COVID-19 research leading to an excessive entry in COVID-related COVID19 Relatedness R an k G a i n Figure 5: MeSH term importance gain (PageRank) and their COVID relatedness. The red dotsshow for each MeSH term how many ranks it has gained from 2019 to 2020 as a function ofits relatedness to COVID-19 MeSH terms. Similarly, the rank-gain for the period 2018 to 2019as a reference is shown. The vertical blue line at 0.05 relatedness is the cut-off we use in ourpreferred specification of the Difference in Difference regression.9/11 assimo Riccaboni , ∗ , Luca Verginer :The Impact of the COVID-19 Pandemic on Scientific Research clinical trials and the “covidisation” of research, there is a need to reorient incentives to basicresearch and otherwise neglected or temporally abandoned areas of research. Without dedicatedsupport in the recovery plans for neglected research of the COVID-19 era, there is a risk thatmore medical needs will be unmet in the future, possibly exacerbating the shortage of scientificresearch for orphan and neglected diseases, which do not belong to COVID-related research areas. References [1] Abi Younes, G.; Ayoubi, C.; Ballester, O.; Cristelli, G.; de Rassenfosse, G.; Foray, D.; Gaulé, P.;Pellegrino, G.; van den Heuvel, M.; Webster, E.; et al. (2020). COVID-19: Insights from innovationeconomists.
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