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      Model-informed COVID-19 vaccine prioritization strategies by age and serostatus

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          Vaccine prioritization

          There is likely to be high demand for the limited supplies of vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), so how should vaccine distribution be prioritized? Bubar et al. modeled across countries how uncertainty about a vaccine's characteristics affects prioritization strategies for reducing deaths and transmission (see the Perspective by Fitzpatrick and Galvani). In the model, vaccine efficacy and its ability to reduce disease and/or block transmission was accounted for in relation to age-related variations in susceptibility, fatality rates, and immune decline. In almost all circumstances, reducing fatalities required distributing the vaccine to those who are most at risk of death, usually persons over 60 years of age and those with comorbidities. If a vaccine is leaky or poorly efficacious in older adults, then priority could be given to younger age groups. To increase the available doses, further priority should be given to seronegative individuals.

          Science, this issue p. 916; see also p. [Related article:]890

          Abstract

          To minimize mortality, vaccinate seronegative persons most at risk of death: those with comorbidities and those 60+ years of age.

          Abstract

          Limited initial supply of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine raises the question of how to prioritize available doses. We used a mathematical model to compare five age-stratified prioritization strategies. A highly effective transmission-blocking vaccine prioritized to adults ages 20 to 49 years minimized cumulative incidence, but mortality and years of life lost were minimized in most scenarios when the vaccine was prioritized to adults greater than 60 years old. Use of individual-level serological tests to redirect doses to seronegative individuals improved the marginal impact of each dose while potentially reducing existing inequities in COVID-19 impact. Although maximum impact prioritization strategies were broadly consistent across countries, transmission rates, vaccination rollout speeds, and estimates of naturally acquired immunity, this framework can be used to compare impacts of prioritization strategies across contexts.

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          Most cited references32

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          COVID-19 and Racial/Ethnic Disparities

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            Age-dependent effects in the transmission and control of COVID-19 epidemics

            The COVID-19 pandemic has shown a markedly low proportion of cases among children1-4. Age disparities in observed cases could be explained by children having lower susceptibility to infection, lower propensity to show clinical symptoms or both. We evaluate these possibilities by fitting an age-structured mathematical model to epidemic data from China, Italy, Japan, Singapore, Canada and South Korea. We estimate that susceptibility to infection in individuals under 20 years of age is approximately half that of adults aged over 20 years, and that clinical symptoms manifest in 21% (95% credible interval: 12-31%) of infections in 10- to 19-year-olds, rising to 69% (57-82%) of infections in people aged over 70 years. Accordingly, we find that interventions aimed at children might have a relatively small impact on reducing SARS-CoV-2 transmission, particularly if the transmissibility of subclinical infections is low. Our age-specific clinical fraction and susceptibility estimates have implications for the expected global burden of COVID-19, as a result of demographic differences across settings. In countries with younger population structures-such as many low-income countries-the expected per capita incidence of clinical cases would be lower than in countries with older population structures, although it is likely that comorbidities in low-income countries will also influence disease severity. Without effective control measures, regions with relatively older populations could see disproportionally more cases of COVID-19, particularly in the later stages of an unmitigated epidemic.
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              Sex differences in immune responses that underlie COVID-19 disease outcomes

              A growing body of evidence indicates sex differences in the clinical outcomes of coronavirus disease 2019 (COVID-19) 1–5 . However, whether immune responses against SARS-CoV-2 differ between sexes, and whether such differences explain male susceptibility to COVID-19, is currently unknown. In this study, we examined sex differences in viral loads, SARS-CoV-2-specific antibody titers, plasma cytokines, as well as blood cell phenotyping in COVID-19 patients. By focusing our analysis on patients with moderate disease who had not received immunomodulatory medications, our results revealed that male patients had higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes. In contrast, female patients mounted significantly more robust T cell activation than male patients during SARS-CoV-2 infection, which was sustained in old age. Importantly, we found that a poor T cell response negatively correlated with patients’ age and was associated with worse disease outcome in male patients, but not in female patients. Conversely, higher innate immune cytokines in female patients associated with worse disease progression, but not in male patients. These findings reveal a possible explanation underlying observed sex biases in COVID-19, and provide important basis for the development of sex-based approach to the treatment and care of men and women with COVID-19.

                Author and article information

                Journal
                Science
                Science
                SCIENCE
                science
                Science (New York, N.y.)
                American Association for the Advancement of Science
                0036-8075
                1095-9203
                26 February 2021
                21 January 2021
                : 371
                : 6532
                : 916-921
                Affiliations
                [1 ]Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO 80309, USA.
                [2 ]IQ Biology Program, University of Colorado Boulder, Boulder, CO 80303, USA.
                [3 ]Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA.
                [4 ]Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
                [5 ]Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
                [6 ]Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.
                [7 ]BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303, USA.
                Author notes
                [* ]Correspondng author. E-mail: kate.bubar@ 123456colorado.edu (K.M.B.); daniel.larremore@ 123456colorado.edu (D.B.L.)
                Author information
                https://orcid.org/0000-0002-1876-3132
                https://orcid.org/0000-0003-1504-9213
                https://orcid.org/0000-0001-5298-8979
                https://orcid.org/0000-0001-5646-1314
                https://orcid.org/0000-0001-5273-5234
                Article
                abe6959
                10.1126/science.abe6959
                7963218
                33479118
                8617e9ab-695a-4100-a550-d71c9dbeb1e6
                Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

                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 work is properly cited.

                History
                : 08 September 2020
                : 12 January 2021
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 3U24GM132013-02S2
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 3U24GM132013-02S2
                Funded by: Morris-Singer Fund;
                Funded by: Morris-Singer Fund;
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                Epidemiology
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