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Kotsiou OS, Papagiannis D, Fradelos EC, Siachpazidou DI, Perlepe G, Miziou A, Kyritsis A, Vavougios GD, Kalantzis G, Gourgoulianis KI. Defining Antibody Seroprevalence and Duration of Humoral Responses to SARS-CoV-2 Infection and/or Vaccination in a Greek Community. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 19:407. [PMID: 35010667 PMCID: PMC8744770 DOI: 10.3390/ijerph19010407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 12/24/2021] [Accepted: 12/28/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND In this work, we aimed to evaluate antibody-response longevity to SARS-CoV-2 infection and/or vaccination in one of the Greek communities that was worst hit by the pandemic, Deskati, five months after a previous serosurveillance and nine months after the pandemic wave initiation (October 2020). METHODS The SARS-CoV-2 IgG II Quant method (Architect, Abbott, IL, USA) was used for antibody testing. RESULTS A total of 69 subjects, who previously tested positive or negative for COVID-19 antibodies, participated in the study. We found that 48% of participants turned positive due to vaccination. 27% of participants were both previously infected and vaccinated. However, all previously infected participants retained antibodies to the virus, irrespective of their vaccination status. The antibody titers were significantly higher in previously infected participants that had been vaccinated than those who were unvaccinated and in those that had been previously hospitalized for COVID-19 than those with mild disease. CONCLUSIONS Antibody responses to SARS-CoV-2 infection were maintained nine months after the pandemic. Vaccination alone had generated an immune response in almost half of the population. Higher antibody titers were found in the case of vaccination in previously infected subjects and especially in those with severe disease leading to hospitalization.
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Affiliation(s)
- Ourania S. Kotsiou
- Faculty of Nursing, School of Health Sciences, University of Thessaly, Gaiopolis, 41110 Larissa, Greece;
- Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41110 Larissa, Greece; (D.I.S.); (G.P.); (A.M.); (A.K.); (G.D.V.); (G.K.); (K.I.G.)
| | - Dimitrios Papagiannis
- Public Health & Vaccines Lab, Department of Nursing, School of Health Sciences, University of Thessaly, Gaiopolis, 41110 Larissa, Greece;
| | - Evangelos C. Fradelos
- Faculty of Nursing, School of Health Sciences, University of Thessaly, Gaiopolis, 41110 Larissa, Greece;
| | - Dimitra I. Siachpazidou
- Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41110 Larissa, Greece; (D.I.S.); (G.P.); (A.M.); (A.K.); (G.D.V.); (G.K.); (K.I.G.)
| | - Garifallia Perlepe
- Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41110 Larissa, Greece; (D.I.S.); (G.P.); (A.M.); (A.K.); (G.D.V.); (G.K.); (K.I.G.)
| | - Angeliki Miziou
- Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41110 Larissa, Greece; (D.I.S.); (G.P.); (A.M.); (A.K.); (G.D.V.); (G.K.); (K.I.G.)
| | - Athanasios Kyritsis
- Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41110 Larissa, Greece; (D.I.S.); (G.P.); (A.M.); (A.K.); (G.D.V.); (G.K.); (K.I.G.)
| | - George D. Vavougios
- Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41110 Larissa, Greece; (D.I.S.); (G.P.); (A.M.); (A.K.); (G.D.V.); (G.K.); (K.I.G.)
| | - Georgios Kalantzis
- Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41110 Larissa, Greece; (D.I.S.); (G.P.); (A.M.); (A.K.); (G.D.V.); (G.K.); (K.I.G.)
| | - Konstantinos I. Gourgoulianis
- Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, 41110 Larissa, Greece; (D.I.S.); (G.P.); (A.M.); (A.K.); (G.D.V.); (G.K.); (K.I.G.)
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Crawford FW, Jones SA, Cartter M, Dean SG, Warren JL, Li ZR, Barbieri J, Campbell J, Kenney P, Valleau T, Morozova O. Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.03.10.21253282. [PMID: 33758869 PMCID: PMC7987027 DOI: 10.1101/2021.03.10.21253282] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using anonymized mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021. Then we aggregated counts of contact events by area of residence to obtain an estimate of the total intensity of interpersonal contact experienced by residents of each town for each day. When incorporated into a susceptible-exposed-infective-removed (SEIR) model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact rate in Connecticut explains the large initial wave of infections during March-April, the subsequent drop in cases during June-August, local outbreaks during August-September, broad statewide resurgence during September-December, and decline in January 2021. Contact rate data can help guide public health messaging campaigns to encourage social distancing and in the allocation of testing resources to detect or prevent emerging local outbreaks more quickly than traditional case investigation. ONE SENTENCE SUMMARY Close interpersonal contact measured using mobile device location data explains dynamics of COVID-19 transmission in Connecticut during the first year of the pandemic.
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Affiliation(s)
- Forrest W Crawford
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, USA
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Yale School of Management, New Haven, CT, USA
| | - Sydney A Jones
- Epidemic Intelligence Service, Centers for Disease Control & Prevention, Atlanta, GA, USA
- Infectious Diseases Section, Connecticut Department of Public Health, New Haven, CT, USA
| | - Matthew Cartter
- Infectious Diseases Section, Connecticut Department of Public Health, New Haven, CT, USA
| | - Samantha G Dean
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Joshua L Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Zehang Richard Li
- Department of Statistics, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | | | | | | | - Olga Morozova
- Program in Public Health and Department of Family, Population and Preventive Medicine, Stony Brook University, NY, USA
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Galipeau Y, Greig M, Liu G, Driedger M, Langlois MA. Humoral Responses and Serological Assays in SARS-CoV-2 Infections. Front Immunol 2020; 11:610688. [PMID: 33391281 PMCID: PMC7775512 DOI: 10.3389/fimmu.2020.610688] [Citation(s) in RCA: 162] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 11/23/2020] [Indexed: 12/14/2022] Open
Abstract
In December 2019, the novel betacoronavirus Severe Acute Respiratory Disease Coronavirus 2 (SARS-CoV-2) was first detected in Wuhan, China. SARS-CoV-2 has since become a pandemic virus resulting in hundreds of thousands of deaths and deep socioeconomic implications worldwide. In recent months, efforts have been directed towards detecting, tracking, and better understanding human humoral responses to SARS-CoV-2 infection. It has become critical to develop robust and reliable serological assays to characterize the abundance, neutralization efficiency, and duration of antibodies in virus-exposed individuals. Here we review the latest knowledge on humoral immune responses to SARS-CoV-2 infection, along with the benefits and limitations of currently available commercial and laboratory-based serological assays. We also highlight important serological considerations, such as antibody expression levels, stability and neutralization dynamics, as well as cross-reactivity and possible immunological back-boosting by seasonal coronaviruses. The ability to accurately detect, measure and characterize the various antibodies specific to SARS-CoV-2 is necessary for vaccine development, manage risk and exposure for healthcare and at-risk workers, and for monitoring reinfections with genetic variants and new strains of the virus. Having a thorough understanding of the benefits and cautions of standardized serological testing at a community level remains critically important in the design and implementation of future vaccination campaigns, epidemiological models of immunity, and public health measures that rely heavily on up-to-date knowledge of transmission dynamics.
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Affiliation(s)
- Yannick Galipeau
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, Canada
| | - Matthew Greig
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, Canada
| | - George Liu
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, Canada
| | | | - Marc-André Langlois
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, Canada
- uOttawa Center for Infection, Immunity and Inflammation (CI3), Ottawa, ON, Canada
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Abstract
Herd immunity is a form of indirect protection that is offered to the community when a large proportion of individuals contained in the community are immune to a certain infection. This immunity can be due to vaccination or to the recovery post-disease. Effective herd immunity in SARS-CoV-2 infection has several hurdles upon achievement. Herd immunity cannot be obtained concomitantly in many geographical areas because the areas have different population density and the societal measures to contain the spreading are different. A proportion of 50-66% of the population needs to be immunized naturally or artificially in this SARS-Cov2 pandemic and this percentage is not easily achievable. The duration of herd immunity is another issue while information on the long-term immune response against SARS-CoV2 is yet scarce. Epitope stability, another issue to be solved when achieving herd immunity, is important. Mutation in the viral structure will call upon other sets of neutralizing antibodies and hence for other herd immunity type installment. The societal tactics to achieve the much-needed herd immunity should be developed keeping in mind the welfare of the population. Without being exhaustive, throughout our paper we will elaborate on each of the hurdles encountered in developing herd immunity to SARS-Cov2 infection.
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Affiliation(s)
- Monica Neagu
- Immunology Laboratory, Victor Babes National Institute of Pathology , Bucharest, Romania.,Pathology Department, Colentina Clinical Hospital , Bucharest, Romania
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Abstract
Introduction: A phenomenological approach is proposed to monitor the propagation of the first waves of the COVID-19 pandemic.Method: A large set of data collected during the first months of 2020 is compiled into a series of semi-logarithmic plots, for a selection of 32 countries from the five continents.Results: Three regimes are identified in the propagation of an epidemic wave: a pre-epidemic regime 1, an exponential-growth regime 2, and a resorption regime 3. A two-parameters scaling of the first-wave death variation reported in China is used to fit the first-wave data reported in other countries. Comparison is made between the propagation of the pandemic in different countries, which are classified into four groups, from Group A where the pandemic first waves were contained efficiently, to Group D where the pandemic first waves widely spread. All Asian countries considered here, where fast and efficient measures have been applied, are in Group A. Group D is composed of Western-European countries and the United States of America (USA), where late decisions and confused political communication (pandemic seriousness, protection masks, herd immunity, etc.) led to a large number of deaths.Discussion:The threat of resurging epidemic waves following a lift of lockdown measures is discussed. The results obtained in Asian countries from group A, as Hong Kong and South Korea, are highlighted, and the measures taken there are presented as examples that other countries may follow.
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