94.1% (89.8–97.6) at ⩾35 days
Single dose:
92.6% (69.0–98.3) between days 14–28
CI, confidence interval; COVID-19, coronavirus disease 2019; IM, intramuscular.
With the endurance of the COVID-19 vaccine still being heavily researched, a chief concern is the sustainability of the vaccine-mediated immune response. This is important in the consideration of whether vaccinated individuals could still contract, transmit, or be carriers of SARS-CoV-2 virus. Vaccinated individuals currently may not understand the rationale behind why social restriction rules still apply to them. Most COVID-19 mRNA vaccines require at least 3 weeks to mount an immunological response and create the required antibodies and proliferate accessory cells of the adaptive immune system of the appropriate recognition repertoire. 50 This may be particularly relevant in the context of travel, as the World Health Organization (WHO) states that a proof of vaccination should not exempt international travelers from complying with social restrictions and risk-reduction measures. 51
All vaccines are contraindicated in cases of documented hypersensitivity to the active substance or any of the excipients. There are a set of general guidelines relative to patients which must be adhered to until further information is provided; predominantly regarding groups such as pregnant or lactating women and immunodeficient patients. The Centers for Disease Control and Prevention (CDC) considers absolute contraindications to patients who have had severe anaphylactic reactions to a previous dose of an mRNA COVID-19 vaccine or PEG, a component of the vaccine. Moreover, immediate allergic reactions of any severity to polysorbate are also a significant contraindication. Importantly, there are many precautions which are not classified as contraindications but must be considered, such as patients who have had allergic reactions to any vaccine or injectable therapy. In the cases of patients with a precaution to the vaccine, they should be counseled on the benefits and risks, but are not contraindicated from vaccination. 15 In the instance of patients with autoimmune diseases, there is currently insubstantial data regarding the efficacy of the vaccine; however, current guidelines suggest that individuals with autoimmune conditions may take the vaccine if they do not have any absolute contraindications. In the case of patients with HIV, limited data from COVID-19 mRNA vaccination trials suggest that they can receive the vaccine barring any contraindications.
Prior to discussing the relationship between the current vaccines for COVID-19 and pregnancy, it is crucial to gain an insight of the relationship between pregnancy and COVID-19 itself. Adhikari et al. showed that there was no difference in the frequency of Caesarean section, pre-eclampsia, preterm births, and abnormal fetal cardiotocography in pregnant women with and without SARS-CoV-2 infection. In addition, examination of the placenta revealed were no abnormalities, which were initially suspected due to the cross-matching between the SARS-CoV-2 spike protein and the placental synctyin-1 protein. 52 Similarly, there was no association found between COVID-19 and first-trimester spontaneous abortions. 53 A systematic review and meta-analysis revealed that COVID-19 leads to higher preterm deliveries (odds ratio (OR): 3.01, 95% CI: 1.16–7.85) and an increase in the ICU admission rates (OR: 71.63, 95% CI: 9.81–523.06) in pregnant women. 54
Pregnancy remained an exclusion criterion for all the COVID-19 vaccine trial; therefore, the efficacy of the COVID-19 vaccines in pregnant women is unavailable. However, given the effectiveness of the influenza vaccines elucidated in a meta-analysis conducted by Quach et al ., it can be hypothesized that the effects of pregnancy on the vaccine would be minimal, but more data would be needed for confirmation. 55 Pfizer’s animal studies revealed antibodies in the maternal rats, fetus, and offspring, in addition to no effects on fertility pregnancy or fetal development. 56 A similar study was conducted with the Moderna vaccine which led the US FDA to conclude that the vaccine did not have any adverse effects on female reproduction, fetal development, or postnatal development. 34 Furthermore, the Oxford-AstraZeneca vaccine animal studies are still pending. However, as a precaution, the National Immunization Advisory Committee (NIAC) has recommended for the two-dose schedule to not commence before 14 weeks of gestation and to be completed by week 33 of gestation. This precaution reduces any potential associations with miscarriage or pre-term birth. 57
Despite the exclusion of pregnancy in the preliminary stages of the trials, 23 Pfizer, 13 Moderna, and 21 AstraZeneca subjects became pregnant after enrolment into the trial. Among this cohort, there was one miscarriage part of the Pfizer control group, no miscarriages part of the Pfizer vaccine group, one miscarriage part of the Moderna control group, no miscarriages part of the Moderna vaccine group, three miscarriages part of the AstraZeneca control group, and two miscarriages part of the AstraZeneca vaccine group. While these preliminary numbers support the current guidelines regarding the vaccines being safe in pregnancy, it is crucial to be aware of the ongoing studies as new data emerges.
The CDC v-safe COVID-19 Pregnancy Study explored the effect of mRNA vaccine (Pfizer-BioNTech or Moderna) on the pregnancy. The pregnancy loss within those with a completed pregnancy included a spontaneous abortion (<20 weeks) rate of 12.6% (104 out of 827) and stillbirth (⩾20 weeks) incidence of 0.1% (1 out of 725). 58 The neonatal outcomes within the live birth infant cohort showed preterm birth (<37 weeks) incidence at 9.4% (60 out of 636), small for gestational age incidence of 3.2% (23 out of 724), and congenital anomalies were seen in 2.2% (16 out of 724). 58 No neonatal deaths were observed in this study.
Limited vaccine resources have caused some governments to extend the date of the second dose beyond the recommended manufacturer date. On December 30, NHS England had made the decision to prioritize the administration of the first doses, and to extend the second doses of the vaccine to the end of 12 weeks, rather than the recommended 3–4 weeks as shown in the clinical phase-3 trial. Pfizer-BioNTech at the time had no data to support this decision, and thus stated that the safety and efficacy of the vaccine had not been evaluated on different dosing schedules, and importantly, the second dose should not be administered later than 42 days. 59
Newly accrued evidence might warrant changes in the landscape of this vaccination program. Estimation of the effectiveness of the Pfizer-BioNTech after a single dose from the primary data from Israeli population (n = 500,000) showed that from day 0 to day 8 post–vaccination, the likelihood of contracting COVID-19 infection doubled. 60 This result may appear counterintuitive, but it takes 3 weeks for the vaccine to instill efficacy during which this real-world population could have not maintained the stringent public health measures which lead to the increased incidence in COVID-19 in this time-period. Then from day 8 to day 21 the incidence of COVID-19 declined and at day 21 the vaccine effectiveness was documented at 91%. 60 This efficacy was seen to stabilize at 90% for the duration of the study (9 weeks), and the authors of this study extrapolate this stability up to 6 months. 60 This concludes that the single dose of Pfizer-BioNTech is highly protective from day 21 onwards and supports the NHS England’s vaccination policy for extending gaps between the doses. The data from the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) trial in the Scottish population revealed that a single dose of Pfizer (n = 650,000) and Oxford-AstraZeneca (n = 490,000) vaccines resulted in a decline in hospitalization at 4 weeks by 84% and 94%, respectively. 61
However, the trials for the Oxford-AstraZeneca vaccine included varied spacing schedules between doses. The findings from these trials displayed that a greater space between the first and second dose provided a superior immune response. This is supported by a combined trial between a UK and Brazil study, which demonstrated a higher VE 14 days after a second dose in patients who had greater than 6 weeks between their first and second dose than patients who had less than 6 weeks by 53.4%. 17 , 62
It was also proposed that to meet the supply shortage that vaccine dose can be halved. Half-dose of Moderna vaccine (50ug) was in a phase-IIa trial. Immune response in the half-dose group compared to those that received a full dose were the same. Therefore, this dosing strategy is supported from an immunogenicity perspective. It is reasonable to infer that the immunogenicity would translate to immune protection, but unfortunately no clinical trial has validated the immune protection for this dosing strategy.
Mutations are changes in the SARS-CoV-2 viral genome that occur naturally over time. These mutations from the parent SARS-CoV-2 virus create variants. A certain amount of genetic variation is expected as SARS-CoV-2 replicates as such it is important to monitor circulating viral variants to collate key mutations. Fortunately, coronaviruses have a slower rate of mutation of 1 to 2 nucleotides per month. 63 These definitions become complicated when environmental factors apply selective pressures on these variants that enable them to express distinct phenotypes that may facilitate viral fitness. This ability of a variant to express distinct phenotypes is termed as a strain. A compilation of beneficial lineage defining mutations can create a strain that has a higher transmission rate or induce severe disease. This raises the question: will the current vaccines or convalescent immunity from a non-variant SARS-CoV-2 infection provide adequate immunological protection against these new variants?
Coronaviruses mutate spontaneously via antigenic drift. This process typically utilizes the virus-specific transcription regulatory network (TRN) sequence to initiate the change, resulting in a new mRNA sequence virus being formed. Homologous and genetic recombination allows for the virus to gain more ecological features and has been speculated to be the reason why SARS-CoV-2 was zoonotic in origin. 64 A variant of the original SARS-CoV-2 virus with a D614G substitution in the spike protein encoding gene emerged in early February 2020, and by June 2020, D614G became the dominant form of the virus circulating globally. 65 Studies have shown that the D614G mutation resulted in increased infectivity and transmissibility. 66 Since then, there have been many viral lineages to note, most notable VOC include the B.1.1.7/20I/501.Y.V1 variant that was first detected in the United Kingdom in October 2020, the B.1.351/20 H/501Y.V2 variant that was detected in South Africa in December 2020, and the Lineage P.1. (B.1.1.28.1) variant that was detected in Tokyo in January 2021 but is believed to have originated from Brazil.
Currently, there exists two open-source real-time software tools to analyze and assign nomenclature of genetic variations in the SARS-CoV-2 virus: Nextstrain and PANGOLIN. 64 , 67 Both refer to the GISAID (Global Initiative on Sharing All Influenza Data) genomic database but have slight differences with regards to their nomenclature to describe various lineages of the virus. The COVID-19 Genomics UK Consortium has also developed CoV-GLUE, an open-source browser application that allows for easy referral of all sequenced SARS-CoV-2 genetic replacements, insertions, and deletions. 68 Therefore, sequencing every local infection will yield a repository to track the development of new mutations and variants.
Before diving deeper into these variants, it is important to understand the physical alteration in the S-protein at a molecular level and the perceived functional advantages that the SARS-CoV-2 gains. Table 2 highlights some of the notable S-protein mutations as they evolve amid the pandemic.
Summary of the physical and functional alterations of S-protein due to notable amino acid substitutions.
Mutation | Alterations in S-protein structure | Functional Consequences for VOC | Distribution Earliest/latest (frequency) | Notes/references |
---|---|---|---|---|
D614G | • Substitution of aspartate to glycine at site 614. • Open conformity of S1 spike protein | • Increased transmissibility • Increased vulnerability to host immune attack (speculated) • Higher nasal viral loads and correlates with the prevalence of anosmia | 1/3/2020 Switzerland (0.4) UK (0.19) France (0.15) Italy (0.11) | , |
E484K | • Substitution of glutamate to lysine at site 484 • This change takes place in the receptor-binding motif on RBD | • Increased ACE2 binding • Potential for escaping recognition from S-protein neutralizing antibodies. • Documented case of reinfection • Moderna and Pfizer have shown small but significant reduction in neutralization | 12/10/2020 Brazil (0.08) South Africa (0.06) 08/02/2021 Brazil (0.58) France (0.22) | , |
N501Y | • Substitution from asparagine to tyrosine at position 501 • This change takes place at the RBD of the S-protein | • Increase ACE2 binding due to increase duration in open conformation. • Potential for escaping recognition from S-protein neutralizing antibodies. • >Moderna and Pfizer have shown small but significant reduction in neutralization. | 01/06/2020 Netherlands (0.01) 08/02/2021 UK (0.87) France (0.53) Australia (0.43) Brazil (0.42) | – |
HV69-70 del | • Deletion of histidine at site 69 and valine at site 70 in the S1 domain of S-protein. • Predicted altered structure will be a ‘tucked in’ spike N-terminal domain | • Potential for escaping recognition from S-protein neutralizing antibodies. The serum virus neutralization (SVN) assay showed reduced neutralization to human SARS-CoV-2 convalescent plasma. | 10/08/2020 Switzerland (0.04) Denmark (0.01) 08/02/2021 UK (0.87) Australia (0.45) France (0.36) Singapore (0.26) | , |
P681H | • Substitution of proline to histidine at position 681 is immediately adjacent to the furin cleavage site between S1 and S2 in S-protein | • Enables increased cleavage activity by TMPRSS2. Therefore, increase SARS-CoV-2 entry | 12/10/2020 Nigeria (NA) UK (NA) |
RBD, receptor-binding domain; VOC, variants of concern.
Newly emerged variants of SARS-CoV-2 have now become VOC which can be attributed to their new ability of increased transmission and infectivity. Therefore, it is important to collate the data on the mutations they acquired, the extend of spread, and the efficacy of different vaccines to create a repository for further analysis ( Table 3 ).
Summary of data on features, acquired cluster of S-protein mutations, and vaccine efficacy studies for the major COVID-19 variants of concern.
Names (PANGOLIN, Nexstrain, Media) | Features | Notable mutations in S-Protein | Vaccine efficacy reduction | Countries reported (n) as of August 17, 2021 | References |
---|---|---|---|---|---|
B.1.1.7, 20I/501.Y.V1, VOC/20201201, UK strain (Alpha Variant) | • Increased binding to ACE2 receptor • 30–70% increased transmissibility • Realistic possibility of increased severity Reproduction rate [range 1.5–1.7] • Higher nasal viral load and increased shedding, prolonged viral shedding, and heighten stability in the current environment • Decreased neutralization | • N501Y • HV69-70 del • P681H • Y144 del, • A570D • E484K • D614G | • Efficacy data • Novavax 86% • Pfizer/BioNTech Single dose 47.5% (95% CI: 41.6–52.8) • Pfizer/BioNTech Dual dose 93.7% (95% CI: 91.6–95.3) • AstraZeneca Single dose 48.7% (95% CI: 45.2–51.9) • AstraZeneca dual dose 74.5% (95% CI: 68.4–79.4) • Mean loss in neutralization: • At day 43 after dual doses at day 28 • Moderna (n = 12): 1.8-fold • Pfizer/BioNTech (n = 10): 2-fold | 190 | , – |
B.1.351, 20H/501.Y.V2, South African strain (Beta Variant) | • Increased severity • Increased transmission • Reinfection is possible as the convalescent immunity cannot mount a response against this new variant | • E484K • K417N • N501Y • D614G orf1b deletion | • Efficacy data • Janssen Vaccine (moderate to severe at day 28) 64.0% (95% CI: 41.2–78.7) • Janssen Vaccine (Severe at day 28) 81.7% (95% CI: 46.2–95.4) • Novavax Dual dose 60.1% (95%CI: 19.9–80.1) • AstraZeneca Dual dose 10.4% (95% CI: −76.8 to 54.8) • Mean loss in neutralization compared to wild type: • Moderna (n = 12): 8.6-fold • Pfizer/BioNTech (n = 10): 6.5-fold • BBIBP-CorV: 10-fold | 138 | , , , – |
Lineage P.1, B.1.1.28.1, Brazilian strain (Gamma Variant) | • Increased severity • Increased transmissibility • Documented case of reinfection | • N501Y • E484K • D614G • K417N/T • orf1b deletion | • Pfizer/BioNTech: Significant reduction in neutralization • Moderna: Significant reduction in neutralization • SinoVac: seroconversion and geometric mean titres in the neutralizing antibody assays | 82 | , , , |
B.1.617.2 Indian Strain (Delta Variant) | • Increase transmission • Decrease neutralization | • L452R • D614R • P681R | • Efficacy data • Pfizer/BioNTech Single dose 35.6% (95% CI: 22.7–46.4) • Pfizer/BioNTech Dual dose 88.0% (95% CI: 85.3–90.1) • AstraZeneca Single dose 30.0% (95% CI: 24.3–35.3) • AstraZeneca Dual dose 67.0% (95% CI: 61.3–71.8) • BBV152 Dual Dose 65·2% (95% CI: 33·1–83·0) • Mean loss in neutralization compared to wild type: • BBIBP-CorV: 1.38- fold | 148 | , , |
CI, confidence interval; COVID-19, coronavirus disease 2019; VOC, variants of concern.
There are more variants emerging as the pandemic progresses, but it is important to note that there is still a myriad of available vaccines in our armamentarium that are adequately efficacious in the performed neutralization assays as well as the real-world data. Furthermore, while vaccines induce the antibody-dependent immunity, they can also stimulate other components of the adaptative immune system such as the Memory B-cells, CD8+ Tc cells, and CD4+ Th cells to mount their own response against the viral variants. This can compensate for the reduction in neutralization rate by the vaccine induced antibodies. Interestingly, the adaptative immune system can proliferate libraries of memory B-cells with mutated antibody repertoires that can predict viral variants. Therefore, it is prudent to commence vaccinations in accordance with the local public health bodies. This combined with the continued implementation of public health measures until target level of herd immunity is acquired can lead toward mitigating the prevalence and incidence of COVID-19 variants.
This review highlighted the current available vaccines and candidates being rolled out amid the ongoing prevention measures and summarized the documented findings with regards to their efficacies, side-effects, and storage requirements. An overview of the physiology of immunogenic responses against the disease provided by the more prominent vaccines were discussed, alongside questions regarding the implementation of vaccines; heterologous prime-boosting, vaccine contraindications, dosing strategies, side effects, and the presence of SARS-CoV-2 mutations and variants.
There are still many unanswered questions that need to be addressed with regards to antibodies produced in individuals including their impact on the clinical course and severity of the disease, how long will they remain in the body to protect from the disease, and if what we have is enough to deal with newly emerging variants. Studies on these topics are rapidly being conducted and published on a global scale, and scientific communities are working on the clock to produce as much information to bring us a better understanding on how to deal with this disease.
For this global pandemic to end, it is imperative that people are vaccinated as quickly as possible until herd immunity can be achieved. One aspect of achieving this, in the face of vaccine hesitancy, is to address the lack of community understanding on how vaccines work, the risks, and the factors that keep this area of research volatile and distribution policies ever-changing. In addition, it is important to remain cautious about the information being released and to trust the accredited sources and experts, rather than the aberrant rumors being spread through social media. Nonetheless, the COVID-19 vaccines have shown to be highly promising and we recommend for everyone that is eligible to take the vaccine at the correct dosing interval when they are given the chance as this would potentiate a positive trend toward pandemic resolution.
Authors’ contributions: CY, AA, Amogh P, Akul P, AP performed acquisition and curation of the data; CY, AA, Amogh P, Akul P, AP, YYL and PK analyzed the data, performed interpretation of the data, and wrote of the original draft; YYL and PK performed the critical revision; All authors have read and approved the final manuscript.
Conflict of interest statement: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
Charles Yap, School of Medicine, National University of Ireland, Galway, Ireland.
Abulhassan Ali, School of Medicine, National University of Ireland, Galway, Ireland.
Amogh Prabhakar, School of Medicine, National University of Ireland, Galway, Ireland.
Akul Prabhakar, School of Medicine, National University of Ireland, Galway, Ireland.
Aman Pal, School of Medicine, National University of Ireland, Galway, Ireland.
Ying Yi Lim, School of Medicine, National University of Ireland, Galway, Ireland.
Pramath Kakodkar, School of Medicine, National University of Ireland, Galway, University Road, Galway H91 TK33, Ireland.
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Collection 29 March 2022
The 25 most downloaded Nature Communications articles* on COVID-19 published in 2021 illustrate the collaborative efforts of the international community to combat the ongoing pandemic. These papers highlight valuable research into the biology of coronavirus infection, its detection, treatment as well as into vaccine development and the epidemiology of the disease.
Browse all Top 25 subject area collections here .
*Data obtained from SN Insights (based on Digital Science's Dimensions) and normalised to account for articles published later in the year.
Most people who are infected with SARS-CoV-2 seroconvert within a few weeks, but the determinants and duration of the antibody response are not known. Here, the authors characterise these features of the immune response using data from a large representative community sample of the UK population.
Hydroxychloroquine and chloroquine have been investigated as a potential treatment for Covid-19 in several clinical trials. Here the authors report a meta-analysis of published and unpublished trials, and show that treatment with hydroxychloroquine for patients with Covid-19 was associated with increased mortality, and there was no benefit from chloroquine.
Vaccination is an effective strategy in suppressing COVID-19 pandemic, but rare adverse effects have been reported, including cerebral venous thrombosis. Here the authors report two cases of middle cerebral artery infarct within 9-10 days following ChAdOx1 nCov-19 vaccination that also manifest pulmonary and portal vein thrombosis.
The duration of effectiveness of SARS-CoV-2 vaccination is not yet known. Here, the authors present preliminary evidence of BNT162b2 vaccine waning across all age groups above 16, with a higher incidence of infection in people who received their second dose early in 2021 compared to later in the year.
Emerging SARS-CoV-2 variants contain mutations in the spike protein that may affect vaccine efficacy. Here, Jalkanen et al . show, using sera from 180 BNT162b2-vaccinated health care workers, that neutralization of SARS-CoV2 variant B.1.1.7 is not affected, while neutralization of B.1.351 variant is five-fold reduced.
T cells compose a critical component of the immune response to coronavirus infection with SARS-CoV-2. Here the authors characterise the T cell response to SARS CoV-2 in patients and their close contacts, and show the presence of SARS-CoV-2 specific T cells in the absence of detectable virus infection.
The humoral immune response to SARS-CoV-2 infection is not yet fully understood. Here, Marot et al. monitor the longitudinal profile and neutralizing activity of IgG, IgA, and IgM among 26 healthcare workers and provide evidence for a short-lasting humoral immune protection due to a decrease of neutralizing antibody titers within 3 months.
In this single-arm clinical trial, the authors show that treatment of COVID-19 patients with bevacizumab, an anti-vascular endothelial growth factor drug, can improve PaO 2 /FiO 2 ratios and oxygen-support status. Relative to an external control group, bevacizumab shows clinical efficacy by improving oxygenation.
A bat origin for SARS-CoV-2 has been proposed. Here, by sampling wild Rhinolophus acuminatus bats from Thailand, the authors identified a SARS-CoV-2-related coronavirus (SC2r-CoV), designated as RacCS203, with 91.5% genome similarity to SARS-CoV-2, and show that sera obtained from bats and Malayan pangolin neutralize SARS-CoV-2.
The SARS-CoV-2 gene set remains unresolved, hindering dissection of COVID-19 biology. Comparing 44 Sarbecovirus genomes provides a high-confidence protein-coding gene set. The study characterizes protein-level and nucleotide-level evolutionary constraints, and prioritizes functional mutations from the ongoing COVID-19 pandemic.
Antibody responses are critical for protection from developing severe COVID-19 following SARS-CoV-2 infection. Here the authors show that antibody responses against SARS-CoV-2 spike protein correlate with neutralizing capacity and protection, are not affected by heterologous boosting of influenza or common cold immunity, and can last up to 8 months.
Infection with SARS-CoV2 and the development of Coronavirus disease 2019 (COVID-19) has been linked to induction of autoimmunity and autoantibody production. Here the authors characterise the new-onset IgG autoantibody response in hospitalised patients with COVID-19 which they correlate to the magnitude of the SARS-CoV2 response.
Several COVID-19 vaccines have shown good efficacy in clinical trials. Here, the authors provide real world effectiveness data in a group of BNT162b2 vaccinated health care workers and find that breakthrough infections are asymptomatic or mild.
Duration of infectious SARS-CoV-2 shedding is an important measure for improved disease control. Here, the authors use virus cultures of respiratory tract samples from COVID-19 patients and observe a median shedding duration of 8 days and a drop below 5% after 15,2 days post onset of symptoms.
In this study, Delaune et al., isolate and characterise a SARS-CoV-2-related coronavirus from two bats sampled in Cambodia. Their findings suggest that the geographic distribution of SARS-CoV-2-related viruses is wider than previously reported.
Here, the authors perform plaque reduction neutralization (PRNT) assays quantitating SARS-CoV-2 specific neutralizing antibodies from 195 patients in different disease states and find that patients with severe disease exhibit higher peaks of neutralizing antibody titres than patients with mild or asymptomatic infections and that serum neutralizing antibody persists for over 6 months in most people.
Vo’, Italy, is a unique setting for studying SARS-CoV-2 antibody dynamics because mass testing was conducted there early in the pandemic. Here, the authors perform two follow-up serological surveys and estimate seroprevalence, the extent of within-household transmission, and the impact of contact tracing.
The extent of antibody protection against SARS-CoV-2 remains unclear. Here, using a cohort of 120 seroconverted individuals, the authors longitudinally characterize neutralization, Fc-function, and SARS-CoV-2 specific T cell responses, which they show to be prominent only in those subjects that elicited receptor-binding domain (RBD)-specific antibody titers above a certain threshold, suggesting that development of T cell responses to be related to anti-RBD Ab production.
Shark antibodies (Variable New Antigen Receptors, VNARs) are the smallest naturally occurring antibody fragments. Here, the authors screen a VNAR phage display library against the SARS-CoV2 receptor binding domain (RBD) and identify VNARs that neutralize the SARSCoV-2 virus and discuss their mechanisms of viral neutralization.
New Zealand has been relatively successful in controlling COVID-19 due to implementation of strict non-pharmaceutical interventions. Here, the authors demonstrate a striking decline in reports of influenza and other non-influenza respiratory pathogens over winter months in which the interventions have been in place.
Neutralizing nanobodies (Nb) are of considerable interest as therapeutic agents for COVID-19 treatment. Here, the authors functionally and structurally characterize Nbs that bind with high affinity to the receptor binding domain of the SARS-CoV-2 spike protein and show that an engineered homotrimeric Nb prevents disease progression in a Syrian hamster model of COVID-19 when administered intranasally.
Cas13b can be harnessed to target and degrade RNA transcripts inside a cellular environment. Here the authors reprogram Cas13b to target SARSCoV-2 transcripts in infected mammalian cells and reveal its resilience to variants thanks to single mismatch tolerance.
T cells are instrumental to protective immune responses against SARS-CoV-2, the pathogen responsible for the COVID-19 pandemic. Here the authors show that, in convalescent COVID-19 patients, memory T cell responses are detectable up to 317 days post-symptom onset, in which the presence of stem cell-like memory T cells further hints long-lasting immunity.
Long-term characterisation of SARS-CoV-2 antibody kinetics is needed to understand the protective role of the immune response. Here the authors describe antibody levels and neutralisation activity in healthcare workers over seven months and investigate the role of immunity to endemic human coronaviruses.
Remdesivir is a nucleoside analog that inhibits the SARS-CoV-2 RNA dependent RNA polymerase (RdRp) and is used as a drug to treat COVID19 patients. Here, the authors provide insights into the mechanism of remdesivir-induced RdRp stalling by determining the cryo-EM structures of SARS-CoV-2 RdRp with bound RNA molecules that contain remdesivir at defined positions and observe that addition of the fourth nucleotide following remdesivir incorporation into the RNA product is impaired by a barrier to further RNA translocation.
Jhu has stopped collecting data as of.
After three years of around-the-clock tracking of COVID-19 data from...
The Johns Hopkins Coronavirus Resource Center has collected, verified, and published local, regional, national, and international pandemic data since it launched in March 2020. From the beginning, the information has been freely available to all — researchers, institutions, the media, the public, and policymakers. As a result, the CRC and its data have been cited in many published research papers and reports. Here we have gathered publications authored by CRC team members that focus on the CRC or its data.
July 14, 2022
CDC underreports cases and deaths among African American and Hispanic or Latino individuals.
February 17, 2022
Johns Hopkins team highlighted the urgent need for better COVID data collection.
Researchers from disparate fields join to chart a new path for formulating policies in response to future pandemics.
Study found that physical distancing was an effective COVID mitigation strategy.
Lancet Infectious Diseases published first paper detailing how the global map was built.
Johns Hopkins team conducted an analysis of state-published demographic data
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In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of February 14, 2020, 49,053 laboratory-confirmed and 1,381 deaths have been reported globally. Perceived risk of acquiring disease has led many governments to institute a variety of control measures. We conducted a literature review of publicly available information to summarize knowledge about the pathogen and the current epidemic. In this literature review, the causative agent, pathogenesis and immune responses, epidemiology, diagnosis, treatment and management of the disease, control and preventions strategies are all reviewed.
Keywords: 2019-nCoV; COVID-19; Novel coronavirus; Outbreak; SARS-CoV-2.
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March 19, 2020
Updated January 12, 2024
COVID-19 Research Guide Home
Important announcement:
The CDC Database of COVID-19 Research Articles became a collaboration with the WHO to create the WHO COVID-19 database during the pandemic to make it easier for results to be searched, downloaded, and used by researchers worldwide.
The last version of the CDC COVID-19 database was archived and remain available on this website. Please note that it has stopped updating as of October 9, 2020 and all new articles were integrated into the WHO COVID-19 database . The WHO Covid-19 Research Database was a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued.
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Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings.
Below are options to download the archive of COVID-19 research articles. You can search the database of citations by author, keyword (in title, author, abstract, subject headings fields), journal, or abstract when available. DOI, PMID, and URL links are included when available.
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Detailed search strategy for gathering COVID-19 articles, updated October 9, 2020 [PDF – 135 KB]
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Open Access
Peer-reviewed
Research Article
Roles Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing
Affiliation AXES, IMT School for Advanced Studies Lucca, Lucca, Italy
Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing
* E-mail: [email protected]
Affiliation Chair of Systems Design D-MTEC, ETH Zürich, Zurich, Switzerland
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 allocate resources toward research areas strictly related to the COVID-19 emergency. However, research in many fields not directly related to the pandemic has been displaced. In this paper, we assess the impact of COVID-19 on world scientific production in the life sciences and find indications that the usage of medical subject headings (MeSH) has changed following the outbreak. We estimate through a difference-in-differences approach the impact of the start of the COVID-19 pandemic on scientific production using the PubMed database (3.6 Million research papers). We find that COVID-19-related MeSH terms have experienced a 6.5 fold increase in output on average, while publications on unrelated MeSH terms dropped by 10 to 12%. The publication weighted impact has an even more pronounced negative effect (-16% to -19%). Moreover, COVID-19 has displaced clinical trial publications (-24%) and diverted grants from research areas not closely related to COVID-19. Note that since COVID-19 publications may have been fast-tracked, the sudden surge in COVID-19 publications might be driven by editorial policy.
Citation: Riccaboni M, Verginer L (2022) The impact of the COVID-19 pandemic on scientific research in the life sciences. PLoS ONE 17(2): e0263001. https://doi.org/10.1371/journal.pone.0263001
Editor: Florian Naudet, University of Rennes 1, FRANCE
Received: April 28, 2021; Accepted: January 10, 2022; Published: February 9, 2022
Copyright: © 2022 Riccaboni, Verginer. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The processed data, instructions on how to process the raw PubMed dataset as well as all code are available via Zenodo at https://doi.org/10.5281/zenodo.5121216 .
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
The COVID-19 pandemic has mobilized the world scientific community in 2020, especially in the life sciences [ 1 , 2 ]. In the first three months after the pandemic, the number of scientific papers about COVID-19 was fivefold the number of articles on H1N1 swine influenza [ 3 ]. Similarly, the number of clinical trials related to COVID-19 prophylaxis and treatments skyrocketed [ 4 ]. Thanks to the rapid mobilization of the world scientific community, COVID-19 vaccines have been developed in record time. Despite this undeniable success, there is a rising concern about the negative consequences of COVID-19 on clinical trial research, with many projects being postponed [ 5 – 7 ]. 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-19 in April 2020 [ 8 , 9 ] reporting a total of 1,200 trials suspended as of July 2020. As a consequence, clinical researchers have been impaired by reduced access to healthcare research infrastructures. Particularly, the COVID-19 outbreak took a tall on women and early-career scientists [ 10 – 13 ]. On a different ground, Shan and colleagues found that non-COVID-19-related articles decreased as COVID-19-related articles increased in top clinical research journals [ 14 ]. Fraser and coworker found that COVID-19 preprints received more attention and citations than non-COVID-19 preprints [ 1 ]. More recently, Hook and Porter have found some early evidence of ‘covidisation’ of academic research, with research grants and output diverted to COVID-19 research in 2020 [ 15 ]. 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 [ 4 , 5 , 16 ].
Against this background, in this paper, we investigate if the COVID-19 pandemic has induced a shift in biomedical publications toward COVID-19-related scientific production. The objective of the study is to show that scientific articles listing covid-related Medical Subject Headings (MeSH) when compared against covid-unrelated MeSH have been partially displaced. Specifically, we look at several indicators of scientific production in the life sciences before and after the start of the COVID-19 pandemic: (1) number of papers published, (2) impact factor weighted number of papers, (3) opens access, (4) number of publications related to clinical trials, (5) number of papers listing grants, (6) number of papers listing grants existing before the pandemic. Through a natural experiment approach, we analyze the impact of the pandemic on scientific production in the life sciences. We consider COVID-19 an unexpected and unprecedented exogenous source of variation with heterogeneous effects across biomedical research fields (i.e., MeSH terms).
Based on the difference in difference results, we document the displacement effect that the pandemic has had on several aspects of scientific publishing. The overall picture that emerges from this analysis is that there has been a profound realignment of priorities and research efforts. This shift has displaced biomedical research in fields not related to COVID-19.
The rest of the paper is structured as follows. First, we describe the data and our measure of relatedness to COVID-19. Next, we illustrate the difference-in-differences specification we rely on to identify the impact of the pandemic on scientific output. In the results section, we present the results of the difference-in-differences and network analyses. We document the sudden shift in publications, grants and trials towards COVID-19-related MeSH terms. Finally, we discuss the findings and highlight several policy implications.
The present analysis is based primarily on PubMed and the Medical Subject Headings (MeSH) terminology. This data is used to estimate the effect of the start of the COVID 19 pandemic via a difference in difference approach. This section is structured as follows. We first introduce the data and then the econometric methodology. This analysis is not based on a pre-registered protocol.
We rely on PubMed, a repository with more than 34 million biomedical citations, for the analysis. Specifically, we analyze the daily updated files up to 31/06/2021, extracting all publications of type ‘Journal Article’. For the principal analysis, we consider 3,638,584 papers published from January 2019 to December 2020. We also analyze 11,122,017 papers published from 2010 onwards to identify the earliest usage of a grant and infer if it was new in 2020. We use the SCImago journal ranking statistics to compute the impact factor weighted number (IFWN) of papers in a given field of research. To assign the publication date, we use the ‘electronically published’ dates and, if missing, the ‘print published’ dates.
We rely on the Medical Subject Headings (MeSH) terminology to approximate narrowly defined biomedical research fields. This terminology is a curated medical vocabulary, which is manually added to papers in the PubMed corpus. The fact that MeSH terms are manually annotated makes this terminology ideal for classification purposes. However, there is a delay between publication and annotation, on the order of several months. To address this delay and have the most recent classification, we search for all 28 425 MeSH terms using PubMed’s ESearch utility and classify paper by the results. The specific API endpoint is https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi , the relevant scripts are available with the code. For example, we assign the term ‘Ageusia’ (MeSH ID D000370) to all papers listed in the results of the ESearch API. We apply this method to the whole period (January 2019—December 2020) and obtain a mapping from papers to the MeSH terms. For every MeSH term, we keep track of the year they have been established. For instance, COVID-19 terms were established in 2020 (see Table 1 ): in January 2020, the WHO recommended 2019-nCoV and 2019-nCoV acute respiratory disease as provisional names for the virus and disease. The WHO issued the official terms COVID-19 and SARS-CoV-2 at the beginning of February 2020. By manually annotating publications, all publications referring to COVID-19 and SARS-CoV-2 since January 2020 have been labelled with the related MeSH terms. Other MeSH terms related to COVID-19, such as coronavirus, for instance, have been established years before the pandemic (see Table 2 ). We proxy MeSH term usage via search terms using the PubMed EUtilities API; this means that we are not using the hand-labelled MeSH terms but rather the PubMed search results. This means that the accuracy of the MeSH term we assign to a given paper is not perfect. In practice, this means that we have assigned more MeSH terms to a given term than a human annotator would have.
https://doi.org/10.1371/journal.pone.0263001.t001
The list contains only terms with at least 100 publications in 2020.
https://doi.org/10.1371/journal.pone.0263001.t002
We classify publications using PubMed’s ‘PublicationType’ field in the XML baseline files (There are 187 publication types, see https://www.nlm.nih.gov/mesh/pubtypes.html ). We consider a publication to be related to a clinical trial if it lists any of the following descriptors:
In our analysis of the impact of COVID-19 on publications related to clinical trials, we only consider MeSH terms that are associated at least once with a clinical trial publication over the two years. We apply this restriction to filter out MeSH terms that are very unlikely to be relevant for clinical trial types of research.
We proxy the availability of a journal article to the public, i.e., open access, if it is available from PubMed Central. PubMed Central archives full-text journal articles and provides free access to the public. Note that the copyright license may vary across participating publishers. However, the text of the paper is for all effects and purposes freely available without requiring subscriptions or special affiliation.
We infer if a publication has been funded by checking if it lists any grants. We classify grants as either ‘old’, i.e. existed before 2019, or ‘new’, i.e. first observed afterwards. To do so, we collect all grant IDs for 11,122,017 papers from 2010 on-wards and record their first appearance. This procedure is an indirect inference of the year the grant has been granted. The basic assumption is that if a grant number has not been listed in any publication since 2010, it is very likely a new grant. Specifically, an old grant is a grant listed since 2019 observed at least once from 2010 to 2018.
Note that this procedure is only approximate and has a few shortcomings. Mistyped grant numbers (e.g. ‘1234-M JPN’ and ‘1234-M-JPN’) could appear as new grants, even though they existed before, or new grants might be classified as old grants if they have a common ID (e.g. ‘Grant 1’). Unfortunately, there is no central repository of grant numbers and the associated metadata; however, there are plans to assign DOI numbers to grants to alleviate this problem (See https://gitlab.com/crossref/open_funder_registry for the project).
In our analysis, we consider two measures of scientific output. First, we simply count the number of publications by MeSH term. However, since journals vary considerably in terms of impact factor, we also weigh the number of publications by the impact factor of the venue (e.g., journal) where it was published. Specifically, we use the SCImago journal ranking statistics to weigh a paper by the impact factor of the journal it appears in. We use the ‘citation per document in the past two years’ for 45,230 ISSNs. Note that a journal may and often has more than one ISSN, i.e., one for the printed edition and one for the online edition. SCImago applies the same score for a venue across linked ISSNs.
For the impact factor weighted number (IFWN) of publication per MeSH terms, this means that all publications are replaced by the impact score of the journal they appear in and summed up.
To measure how closely related to COVID-19 is a MeSH term, we introduce an index of relatedness to COVID-19. First, we identify the focal COVID-19 terms, which appeared in the literature in 2020 (see Table 1 ). Next, for all other pre-existing MeSH terms, we measure how closely related to COVID-19 they end up being.
Our aim is to show that MeSH terms that existed before and are related have experienced a sudden increase in the number of (impact factor weighted) papers.
Intuitively we can read this measure as: what is the probability in 2020 that a COVID-19 MeSH term is present given that we chose a paper with MeSH term i ? For example, given that in 2020 we choose a paper dealing with “Ageusia” (i.e., Complete or severe loss of the subjective sense of taste), there is a 96% probability that this paper also lists COVID-19, see Table 1 .
Note that a paper listing a related MeSH term does not imply that that paper is doing COVID-19 research, but it implies that one of the MeSH terms listed is often used in COVID-19 research.
In sum, in our analysis, we use the following variables:
The difference-in-differences (DiD) method is an econometric technique to imitate an experimental research design from observation data, sometimes referred to as a quasi-experimental setup. In a randomized controlled trial, subjects are randomly assigned either to the treated or the control group. Analogously, in this natural experiment, we assume that medical subject headings (MeSH) have been randomly assigned to be either treated (related) or not treated (unrelated) by the pandemic crisis.
Before the COVID, for a future health crisis, the set of potentially impacted medical knowledge was not predictable since it depended on the specifics of the emergency. For instance, ageusia (loss of taste), a medical concept existing since 1991, became known to be a specific symptom of COVID-19 only after the pandemic.
Specifically, we exploit the COVID-19 as an unpredictable and exogenous shock that has deeply affected the publication priorities for biomedical scientific production, as compared to the situation before the pandemic. In this setting, COVID-19 is the treatment, and the identification of this new human coronavirus is the event. We claim that treated MeSH terms, i.e., MeSH terms related to COVID-19, have experienced a sudden increase in terms of scientific production and attention. In contrast, research on untreated MeSH terms, i.e., MeSH terms not related to COVID-19, has been displaced by COVID-19. Our analysis compares the scientific output of COVID-19 related and unrelated MeSH terms before and after January 2020.
In our case, some of the terms turn out to be related to COVID-19 in 2020, whereas most of the MeSH terms are not closely related to COVID-19.
Thus β 1 identifies the overall effect on the control group after the event, β 2 the difference across treated and control groups before the event (i.e. the first difference in DiD) and finally the effect on the treated group after the event, net of the first difference, β 3 . This last parameter identifies the treatment effect on the treated group netting out the pre-treatment difference.
For the DiD to have a causal interpretation, it must be noted that pre-event, the trends of the two groups should be parallel, i.e., the common trend assumption (CTA) must be satisfied. We will show that the CTA holds in the results section.
To specify the DiD model, we need to define a period before and after the event and assign a treatment status or level of exposure to each term.
The pre-treatment period is defined as January 2019 to December 2019. The post-treatment period is defined as the months from January 2020 to December 2020. We argue that the state of biomedical research was similar in those two years, apart from the effect of the pandemic.
The treatment is determined by the COVID-19 relatedness index σ i introduced earlier. Specifically, this number indicates the likelihood that COVID-19 will be a listed MeSH term, given that we observe the focal MeSH term i . To show that the effect becomes even stronger the closer related the subject is, and for ease of interpretation, we also discretize the relatedness value into three levels of treatment. Namely, we group MeSH terms with a σ between, 0% to 20%, 20% to 80% and 80% to 100%. The choice of alternative grouping strategies does not significantly affect our results. Results for alternative thresholds of relatedness can be computed using the available source code. We complement the dichotomized analysis by using the treatment intensity (relatedness measure σ ) to show that the result persists.
In this work, we estimate a random effects panel regression where the units of analysis are 28 318 biomedical research fields (i.e. MeSH terms) observed over time before and after the COVID-19 pandemic. The time resolution is at the monthly level, meaning that for each MeSH term, we have 24 observations from January 2019 to December 2020.
The outcome variable Y it identifies the outcome at time t (i.e., month), for MeSH term i . As before, P t identifies the period with P t = 0 if the month is before January 2020 and P t = 1 if it is on or after this date. In (3) , the treatment level is measure by the relatedness to COVID-19 ( σ i ), where again the γ 1 identifies pre-trend (constant) differences and δ 1 the overall effect.
In total, we estimate six coefficients. As before, the δ l coefficient identifies the DiD effect.
We show that the CTA holds for this model by comparing the pre-event trends of the control group to the treated groups (COVID-19 related MeSH terms). Namely, we show that the pre-event trends of the control group are the same as the pre-event trends of the treated group.
To investigate if the pandemic has caused a reconfiguration of research priorities, we look at the MeSH term co-occurrence network. Precisely, we extract the co-occurrence network of all 28,318 MeSH terms as they appear in the 3.3 million papers. We considered the co-occurrence networks of 2018, 2019 and 2020. Each node represents a MeSH term in these networks, and a link between them indicates that they have been observed at least once together. The weight of the edge between the MeSH terms is given by the number of times those terms have been jointly observed in the same publications.
Medical language is hugely complicated, and this simple representation does not capture the intricacies, subtle nuances and, in fact, meaning of the terms. Therefore, we do not claim that we can identify how the actual usage of MeSH terms has changed from this object, but rather that it has. Nevertheless, the co-occurrence graph captures rudimentary relations between concepts. We argue that absent a shock to the system, their basic usage patterns, change in importance (within the network) would essentially be the same from year to year. However, if we find that the importance of terms changes more than expected in 2020, it stands to reason that there have been some significant changes.
To show that that MeSH usage has been affected, we compute for each term in the years 2018, 2019 and 2020 their PageRank centrality [ 17 ]. The PageRank centrality tells us how likely a random walker traversing a network would be found at a given node if she follows the weights of the empirical edges (i.e., co-usage probability). Specifically, for the case of the MeSH co-occurrence network, this number represents how often an annotator at the National Library of Medicine would assign that MeSH term following the observed general usage patterns. It is a simplistic measure to capture the complexities of biomedical research. Nevertheless, it captures far-reaching interdependence across MeSH terms as the measure uses the whole network to determine the centrality of every MeSH term. A sudden change in the rankings and thus the position of MeSH terms in this network suggests that a given research subject has risen as it is used more often with other important MeSH terms (or vice versa).
We then compare the growth for each MeSH i term in g i (2019), i.e. before the the COVID-19 pandemic, with the growth after the event ( g i (2020)).
Before we show the regression results, we provide descriptive evidence that publications from 2019 to 2020 have drastically increased. By showing that this growth correlates strongly with a MeSH term’s COVID-19 relatedness ( σ ), we demonstrate that (1) σ captures an essential aspect of the growth dynamics and (2) highlight the meteoric rise of highly related terms.
We look at the year over year growth in the number of the impact weighted number of publications per MeSH term from 2018 to 2019 and 2019 to 2020 as defined in the methods section.
Fig 1 shows the yearly growth of the impact weighted number of publications per MeSH term. By comparing the growth of the number of publications from the years 2018, 2019 and 2020, we find that the impact factor weighted number of publications has increased by up to a factor of 100 compared to the previous year for Betacoronavirus, one of the most closely related to COVID-19 MeSH term.
Each dot represents, a MeSH term. The y axis (growth) is in symmetric log scale. The x axis shows the COVID-19 relatedness, σ . Note that the position of the dots on the x-axis is the same in the two plots. Below: MeSH term importance gain (PageRank) and their COVID-19 relatedness.
https://doi.org/10.1371/journal.pone.0263001.g001
Fig 1 , first row, reveals how strongly correlated the growth in the IFWN of publication is to the term’s COVID-19 relatedness. For instance, we see that the term ‘Betacoronavirus’ skyrocketed from 2019 to 2020, which is expected given that SARS-CoV-2 is a species of the genus. Conversely, the term ‘Alphacoronavirus’ has not experienced any growth given that it is twin a genus of the Coronaviridae family, but SARS-CoV-2 is not one of its species. Note also the fast growth in the number of publications dealing with ‘Quarantine’. Moreover, MeSH terms that grew significantly from 2018 to 2019 and were not closely related to COVID-19, like ‘Vaping’, slowed down in 2020. From the graph, the picture emerges that publication growth is correlated with COVID-19 relatedness σ and that the growth for less related terms slowed down.
To show that the usage pattern of MeSH terms has changed following the pandemic, we compute the PageRank centrality using graph-tool [ 18 ] as discussed in the Methods section.
Fig 1 , second row, shows the change in the PageRank centrality of the MeSH terms after the pandemic (2019 to 2020, right plot) and before (2018 to 2019, left plot). If there were no change in the general usage pattern, we would expect the variance in PageRank changes to be narrow across the two periods, see (left plot). However, PageRank scores changed significantly more from 2019 to 2020 than from 2018 to 2019, suggesting that there has been a reconfiguration of the network.
To further support this argument, we carry out a DiD regression analysis.
As discussed in the Methods section, we need to show that the CTA assumption holds for the DiD to be defined appropriately. We do this by estimating for each month the number of publications and comparing it across treatment groups. This exercise also serves the purpose of a placebo test. By assuming that each month could have potentially been the event’s timing (i.e., the outbreak), we show that January 2020 is the most likely timing of the event. The regression table, as noted earlier, contains over 70 estimated coefficients, hence for ease of reading, we will only show the predicted outcome per month by group (see Fig 2 ). The full regression table with all coefficients is available in the S1 Table .
The y axis is in log scale. The dashed vertical line identifies January 2020. The dashed horizontal line shows the publications in January 2019 for the 0–20% group before the event. This line highlights that the drop happens after the event. The bands around the lines indicate the 95% confidence interval of the predicted values. The results are the output of the Stata margins command.
https://doi.org/10.1371/journal.pone.0263001.g002
Fig 2 shows the predicted number per outcome variable obtained from the panel regression model. These predictions correspond to the predicted value per relatedness group using the regression parameters estimated via the linear panel regression. The bands around the curves are the 95% confidence intervals.
All outcome measures depict a similar trend per month. Before the event (i.e., January 2020), there is a common trend across all groups. In contrast, after the event, we observe a sudden rise for the outcomes of the COVID-19 related treated groups (green and red lines) and a decline in the outcomes for the unrelated group (blue line). Therefore, we can conclude that the CTA assumption holds.
Table 3 shows the DiD regression results (see Eq (3) ) for the selected outcome measures: number of publications (Papers), impact factor weighted number of publications (Impact), open access (OA) publications, clinical trial related publications, and publications with existing grants.
https://doi.org/10.1371/journal.pone.0263001.t003
Table 3 shows results for the discrete treatment level version of the DiD model (see Eq (4) ).
Note that the outcome variable is in natural log scale; hence to get the effect of the independent variable, we need to exponentiate the coefficient. For values close to 0, the effect is well approximated by the percentage change of that magnitude.
In both specifications we see that the least related group, drops in the number of publications between 10% and 13%, respectively (first row of Tables 3 and 4 , exp(−0.102) ≈ 0.87). In line with our expectations, the increase in the number of papers published by MeSH term is positively affected by the relatedness to COVID-19. In the discrete model (row 2), we note that the number of documents with MeSH terms with a COVID-19 relatedness between 20 and 80% grows by 18% and highly related terms by a factor of approximately 6.6 (exp(1.88)). The same general pattern can be observed for the impact weighted publication number, i.e., Model (2). Note, however, that the drop in the impact factor weighted output is more significant, reaching -19% for COVID-19 unrelated publications, and related publications growing by a factor of 8.7. This difference suggests that there might be a bias to publish papers on COVID-19 related subjects in high impact factor journals.
https://doi.org/10.1371/journal.pone.0263001.t004
By looking at the number of open access publications (PMC), we note that the least related group has not been affected negatively by the pandemic. However, the number of COVID-19 related publications has drastically increased for the most COVID-19 related group by a factor of 6.2. Note that the substantial increase in the number of papers available through open access is in large part due to journal and editorial policies to make preferentially COVID research immediately available to the public.
Regarding the number of clinical trial publications, we note that the least related group has been affected negatively, with the number of publications on clinical trials dropping by a staggering 24%. At the same time, publications on clinical trials for COVID-19-related MeSH have increased by a factor of 2.1. Note, however, that the effect on clinical trials is not significant in the continuous regression. The discrepancy across Tables 3 and 4 highlights that, especially for trials, the effect is not linear, where only the publications on clinical trials closely related to COVID-19 experiencing a boost.
It has been reported [ 19 ] that while the number of clinical trials registered to treat or prevent COVID-19 has surged with 179 new registrations in the second week of April 2020 alone. Only a few of these have led to publishable results in the 12 months since [ 20 ]. On the other hand, we find that clinical trial publications, considering related MeSH (but not COVID-19 directly), have had significant growth from the beginning of the pandemic. These results are not contradictory. Indeed counting the number of clinical trial publications listing the exact COVID-19 MeSH term (D000086382), we find 212 publications. While this might seem like a small number, consider that in 2020 only 8,485 publications were classified as clinical trials; thus, targeted trials still made up 2.5% of all clinical trials in 2020 . So while one might doubt the effectiveness of these research efforts, it is still the case that by sheer number, they represent a significant proportion of all publications on clinical trials in 2020. Moreover, COVID-19 specific Clinical trial publications in 2020, being a delayed signal of the actual trials, are a lower bound estimate on the true number of such clinical trials being conducted. This is because COVID-19 studies could only have commenced in 2020, whereas other studies had a head start. Thus our reported estimates are conservative, meaning that the true effect on actual clinical trials is likely larger, not smaller.
Research funding, as proxied by the number of publications with grants, follows a similar pattern, but notably, COVID-19-related MeSH terms list the same proportion of grants established before 2019 as other unrelated MeSH terms, suggesting that grants which were not designated for COVID-19 research have been used to support COVID-19 related research. Overall, the number of publications listing a grant has dropped. Note that this should be because the number of publications overall in the unrelated group has dropped. However, we note that the drop in publications is 10% while the decline in publications with at least one grant is 15%. This difference suggests that publications listing grants, which should have more funding, are disproportionately COVID-19 related papers. To further investigate this aspect, we look at whether the grant was old (pre-2019) or appeared for the first time in or after 2019. It stands to reason that an old grant (pre-2019) would not have been granted for a project dealing with the pandemic. Hence we would expect that COVID-19 related MeSH terms to have a lower proportion of old grants than the unrelated group. In models (6) in Table 4 we show that the number of old grants for the unrelated group drops by 13%. At the same time, the number of papers listing old grants (i.e., pre-2019) among the most related group increased by a factor of 3.1. Overall, these results suggest that COVID-19 related research has been funded largely by pre-existing grants, even though a specific mandate tied to the grants for this use is unlikely.
The scientific community has swiftly reallocated research efforts to cope with the COVID-19 pandemic, mobilizing knowledge across disciplines to find innovative solutions in record time. We document this both in terms of changing trends in the biomedical scientific output and the usage of MeSH terms by the scientific community. The flip side of this sudden and energetic prioritization of effort to fight COVID-19 has been a sudden contraction of scientific production in other relevant research areas. All in all, we find strong support to the hypotheses that the COVID-19 crisis has induced a sudden increase of research output in COVID-19 related areas of biomedical research. Conversely, research in areas not related to COVID-19 has experienced a significant drop in overall publishing rates and funding.
Our paper contributes to the literature on the impact of COVID-19 on scientific research: we corroborate previous findings about the surge of COVID-19 related publications [ 1 – 3 ], partially displacing research in COVID-19 unrelated fields of research [ 4 , 14 ], particularly research related to clinical trials [ 5 – 7 ]. The drop in trial research might have severe consequences for patients affected by life-threatening diseases since it will delay access to new and better treatments. We also confirm the impact of COVID-19 on open access publication output [ 1 ]; also, this is milder than traditional outlets. On top of this, we provide more robust evidence on the impact weighted effect of COVID-19 and grant financed research, highlighting the strong displacement effect of COVID-19 on the allocation of financial resources [ 15 ]. We document a substantial change in the usage patterns of MeSH terms, suggesting that there has been a reconfiguration in the way research terms are being combined. MeSH terms highly related to COVID-19 were peripheral in the MeSH usage networks before the pandemic but have become central since 2020. We conclude that the usage patterns have changed, with COVID-19 related MeSH terms occupying a much more prominent role in 2020 than they did in the previous years.
We also contribute to the literature by estimating the effect of COVID-19 on biomedical research in a natural experiment framework, isolating the specific effects of the COVID-19 pandemic on the biomedical scientific landscape. This is crucial to identify areas of public intervention to sustain areas of biomedical research which have been neglected during the COVID-19 crisis. Moreover, the exploratory analysis on the changes in usage patterns of MeSH terms, points to an increase in the importance of covid-related topics in the broader biomedical research landscape.
Our results provide compelling evidence that research related to COVID-19 has indeed displaced scientific production in other biomedical fields of research not related to COVID-19, with a significant drop in (impact weighted) scientific output related to non-COVID-19 and a marked reduction of financial support for publications not related to COVID-19 [ 4 , 5 , 16 ]. The displacement effect is persistent to the end of 2020. As vaccination progresses, we highlight the urgent need for science policy to re-balance support for 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 trials activities that have been postponed or suspended for reasons related to COVID-19 is a priority that should be considered in the national vaccination plans. Moreover, since grants have been diverted and financial incentives have been targeted to sustain COVID-19 research leading to an excessive entry in COVID-19-related clinical trials and the ‘covidisation’ of research, there is a need to reorient incentives to basic research and otherwise neglected or temporally abandoned areas of biomedical research. Without dedicated support in the recovery plans for neglected research of the COVID-19 era, there is a risk that more medical needs will be unmet in the future, possibly exacerbating the shortage of scientific research for orphan and neglected diseases, which do not belong to COVID-19-related research areas.
Our empirical approach has some limits. First, we proxy MeSH term usage via search terms using the PubMed EUtilities API. This means that the accuracy of the MeSH term we assign to a given paper is not fully validated. More time is needed for the completion of manually annotated MeSH terms. Second, the timing of publication is not the moment the research has been carried out. There is a lead time between inception, analysis, write-up, review, revision, and final publication. This delay varies across disciplines. Nevertheless, given that the surge in publications happens around the alleged event date, January 2020, we are confident that the publication date is a reasonable yet imperfect estimate of the timing of the research. Third, several journals have publicly declared to fast-track COVID-19 research. This discrepancy in the speed of publication of COVID-19 related research and other research could affect our results. Specifically, a surge or displacement could be overestimated due to a lag in the publication of COVID-19 unrelated research. We alleviate this bias by estimating the effect considering a considerable time after the event (January 2020 to December 2020). Forth, on the one hand, clinical Trials may lead to multiple publications. Therefore we might overestimate the impact of COVID-19 on the number of clinical trials. On the other hand, COVID-19 publications on clinical trials lag behind, so the number of papers related COVID-19 trials is likely underestimated. Therefore, we note that the focus of this paper is scientific publications on clinical trials rather than on actual clinical trials. Fifth, regarding grants, unfortunately, there is no unique centralized repository mapping grant numbers to years, so we have to proxy old grants with grants that appeared in publications from 2010 to 2018. Besides, grant numbers are free-form entries, meaning that PubMed has no validation step to disambiguate or verify that the grant number has been entered correctly. This has the effect of classifying a grant as new even though it has appeared under a different name. We mitigate this problem by using a long period to collect grant numbers and catch many spellings of the same grant, thereby reducing the likelihood of miss-identifying a grant as new when it existed before. Still, unless unique identifiers are widely used, there is no way to verify this.
So far, there is no conclusive evidence on whether entry into COVID-19 has been excessive. However, there is a growing consensus that COVID-19 has displaced, at least temporally, scientific research in COVID-19 unrelated biomedical research areas. Even though it is certainly expected that more attention will be devoted to the emergency during a pandemic, the displacement of biomedical research in other fields is concerning. Future research is needed to investigate the long-run structural consequences of the COVID-19 crisis on biomedical research.
S1 table. common trend assumption (cta) regression table..
Full regression table with all controls and interactions.
https://doi.org/10.1371/journal.pone.0263001.s001
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FDA News Release
Today, the U.S. Food and Drug Administration approved and granted emergency use authorization (EUA) for updated mRNA COVID-19 vaccines (2024-2025 formula) to include a monovalent (single) component that corresponds to the Omicron variant KP.2 strain of SARS-CoV-2. The mRNA COVID-19 vaccines have been updated with this formula to more closely target currently circulating variants and provide better protection against serious consequences of COVID-19, including hospitalization and death. Today’s actions relate to updated mRNA COVID-19 vaccines manufactured by ModernaTX Inc. and Pfizer Inc.
In early June, the FDA advised manufacturers of licensed and authorized COVID-19 vaccines that the COVID-19 vaccines (2024-2025 formula) should be monovalent JN.1 vaccines. Based on the further evolution of SARS-CoV-2 and a rise in cases of COVID-19, the agency subsequently determined and advised manufacturers that the preferred JN.1-lineage for the COVID-19 vaccines (2024-2025 formula) is the KP.2 strain, if feasible.
“Vaccination continues to be the cornerstone of COVID-19 prevention,” said Peter Marks, M.D., Ph.D., director of the FDA’s Center for Biologics Evaluation and Research. “These updated vaccines meet the agency’s rigorous, scientific standards for safety, effectiveness, and manufacturing quality. Given waning immunity of the population from previous exposure to the virus and from prior vaccination, we strongly encourage those who are eligible to consider receiving an updated COVID-19 vaccine to provide better protection against currently circulating variants.”
The updated mRNA COVID-19 vaccines include Comirnaty and Spikevax, both of which are approved for individuals 12 years of age and older, and the Moderna COVID-19 Vaccine and Pfizer-BioNTech COVID-19 Vaccine, both of which are authorized for emergency use for individuals 6 months through 11 years of age.
What You Need to Know
Individuals who receive an updated mRNA COVID-19 vaccine may experience similar side effects as those reported by individuals who previously received mRNA COVID-19 vaccines and as described in the respective prescribing information or fact sheets. The updated vaccines are expected to provide protection against COVID-19 caused by the currently circulating variants. Barring the emergence of a markedly more infectious variant of SARS-CoV-2, the FDA anticipates that the composition of COVID-19 vaccines will need to be assessed annually, as occurs for seasonal influenza vaccines.
For today’s approvals and authorizations of the mRNA COVID-19 vaccines, the FDA assessed manufacturing and nonclinical data to support the change to include the 2024-2025 formula in the mRNA COVID-19 vaccines. The updated mRNA vaccines are manufactured using a similar process as previous formulas of these vaccines. The mRNA COVID-19 vaccines have been administered to hundreds of millions of people in the U.S., and the benefits of these vaccines continue to outweigh their risks.
On an ongoing basis, the FDA will review any additional COVID-19 vaccine applications submitted to the agency and take appropriate regulatory action.
The approval of Comirnaty (COVID-19 Vaccine, mRNA) (2024-2025 Formula) was granted to BioNTech Manufacturing GmbH. The EUA amendment for the Pfizer-BioNTech COVID-19 Vaccine (2024-2025 Formula) was issued to Pfizer Inc.
The approval of Spikevax (COVID-19 Vaccine, mRNA) (2024-2025 Formula) was granted to ModernaTX Inc. and the EUA amendment for the Moderna COVID-19 Vaccine (2024-2025 Formula) was issued to ModernaTX Inc.
The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nation’s food supply, cosmetics, dietary supplements, radiation-emitting electronic products, and for regulating tobacco products.
IMAGES
COMMENTS
of COVID-19 can explain 40% of the delayed graduation gap (as well as a substantial part of the gap for other outcomes) between lower- and higher-income students. To our knowledge, this is the rst paper to shed light on the e ects of COVID-19 on college students' experiences. The treatment e ects that we nd are large in economic terms.
The spread of the "Severe Acute Respiratory Coronavirus 2" (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [].The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [], causing massive economic strain ...
Transmission. The role of the Huanan Seafood Wholesale Market in propagating disease is unclear. Many initial COVID-19 cases were linked to this market suggesting that SARS-CoV-2 was transmitted from animals to humans .However, a genomic study has provided evidence that the virus was introduced from another, yet unknown location, into the market where it spread more rapidly, although human-to ...
There have been around 96,000 reported cases of coronavirus disease 2019 (COVID-2019) and 3300 reported deaths to date (05/03/2020). The disease is transmitted by inhalation or contact with infected droplets and the incubation period ranges from 2 to 14 d. The symptoms are usually fever, cough, sore throat, breathlessness, fatigue, malaise ...
The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical ...
A two-dose regimen of BNT162b2 (30 μg per dose, given 21 days apart) was found to be safe and 95% effective against Covid-19. The vaccine met both primary efficacy end points, with more than a 99 ...
Introduction. The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in over 192 million cases and 4.1 million deaths as of July 22, 2021. 1 This pandemic has brought along a massive burden in morbidity and mortality in the healthcare systems. Despite the implementation of stringent public health measures, there ...
in the wake of COVID-19, including unemployment, mental illness, and addiction; and, (3) the importance of moderating factors (e.g., age, race and ethnicity, gender, personality, family status, and culture) for which there are likely to be disparate COVID-19 impacts.
In this rapid living systematic evidence synthesis and meta-analysis, we searched EMBASE and the US National Institutes of Health's iSearch COVID-19 Portfolio, supplemented by manual searches of COVID-19-specific sources, until Dec 1, 2022, for studies that reported vaccine effectiveness immediately and at least 112 days after a primary vaccine series or at least 84 days after a booster dose.
The 25 most downloaded Nature Communications articles* on COVID-19 published in 2021 illustrate the collaborative efforts of the international community to combat the ongoing pandemic.These papers ...
The Johns Hopkins Coronavirus Resource Center has collected, verified, and published local, regional, national and international pandemic data since it launched in March 2020. From the beginning, the information has been freely available to all — researchers, institutions, the media, the public, and policymakers. As a result, the CRC and its data have been cited in many published research ...
The costs of COVID-19 in 3 stages The COVID-19 shock can be interpreted as a combination of supply and demand shocks (Baqaee and Farhi, 2020; Caballero and Simsek, 2020; Guerrieri et al., 2020).
rly-stage effects of COVID-19 on small business owners from April 2020 CPS microdata. I find that the number of working business owners plummeted from 15.0 million in Febru. ry 2020 to 11.7 million in April 2020 because of COVID-19 mandates and demand shifts. T. e loss of 3.3 million business owners (or 22 percent) was the largest drop on ...
Abstract. In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern.
According to a study published in 2019, An-. Corona virus causes respiratory infection including pneumonia, cold, sneezing and coughing while in animal it causes. diarrhea and upper respiratory ...
1. INTRODUCTION. The COVID-19 pandemic has caused an unprecedented policy response from public health. officials and governments. As of September 16, 2021, approximately 4.7 million deaths. worldwide and 669,000 deaths in the United States were attributed to COVID-19 (Johns Hopkins. Coronavirus Resource Center 2022).
Detailed search strategy for gathering COVID-19 articles, updated October 9, 2020 [PDF - 135 KB] The CDC Database of COVID-19 Research Articles is now a part of the WHO COVID-19 database. Our new search results are now being sent to the WHO COVID-19 Database to make it easier for them to be searched, downloaded, and used by researchers ...
We designed a time-use survey to study whether and how the transition towards "work-. from-home" arrangements (WFH), and away from the office, caused by the COVID-19. pandemic affected the use of time of knowledge workers. Specifically, this study. addresses the following research questions:
DRAFT Prepared by the SAGE Working Group on COVID-19 Vaccines 22 December 2020 5 . even death. Several occupational risks for health workers emerged or were amplified by the COVID-19 response, including (1) occupational infections with COVID-19, (2) skin disorders and heat stress
WHO has described four levels of COVID-19 transmission with varying public health and social measures depending on the local evolution of the COVID-19 pandemic. For more details, please see 'Subject in Focus'. Figure 1. Countries, territories or areas with reported confirmed cases of COVID-19, 1 April 2020 SITUATION IN NUMBERS Globally
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 allocate resources toward research areas strictly related to the COVID-19 emergency. However, research in many fields not directly related to the pandemic has been displaced. In this paper, we assess the impact of COVID-19 on ...
The novel coronavirus disease 2019 (COVID-19, caused by SARS-CoV-2) has spread globally since its first report in Wuhan, China on December 31, 2019. On January 30, the Philippines reported its first two imported cases of COVID-19 in a couple from Wuhan. One of them died on February 1st, becoming the first COVID-19 death outside China.
Abstract: The study of online consumer behavior is especially pertinent today given the development of the COVID-19 epidemic and the growing significance of e-commerce. The goal of this study was to provide a systematic framework for evaluating the linkages and degree of influence of the elements influencing online consumers' purchase behavior against the backdrop of the COVID-19 epidemic. The ...
The updated mRNA COVID-19 vaccines include Comirnaty and Spikevax, both of which are approved for individuals 12 years of age and older, and the Moderna COVID-19 Vaccine and Pfizer-BioNTech COVID ...