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There is increasing evidence that the risk of SARS-CoV-2 infection among vaccinated individuals is variant-specific, suggesting that protective immunity against SARS-CoV-2 may differ by variant. We enrolled vaccinated (n = 39) and unvaccinated (n = 11) individuals with acute, symptomatic SARS-CoV-2 Delta or Omicron infection and performed SARS-CoV-2 viral load quantification, whole-genome sequencing, and variant-specific antibody characterization at the time of acute illness and convalescence. Viral load at the time of infection was inversely correlated with antibody binding and neutralizing antibody responses. Increases in antibody titers and neutralizing activity occurred at convalescence in a variant-specific manner. Across all variants tested, convalescent neutralization titers in unvaccinated individuals were markedly lower than in vaccinated individuals. For individuals infected with the Delta variant, neutralizing antibody responses were weakest against BA.2, whereas infection with Omicron BA.1 variant generated a broader response against all tested variants, including BA.2.
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Clinical features of SARS-CoV-2 Omicron variant infection, including incubation period and transmission rates, distinguish this variant from preceding variants. However, whether the duration of shedding of viable virus differs between omicron and previous variants is not well understood. To characterize how variant and vaccination status impact shedding of viable virus, we serially sampled symptomatic outpatients newly diagnosed with COVID-19. Anterior nasal swabs were tested for viral load, sequencing, and viral culture. Time to PCR conversion was similar between individuals infected with the Delta and the Omicron variant. Time to culture conversion was also similar, with a median time to culture conversion of 6 days (interquartile range 4-8 days) in both groups. There were also no differences in time to PCR or culture conversion by vaccination status.
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Despite the advent of safe and highly effective COVID-19 vaccines1-4, pervasive inequities in global distribution persist5. In response, multinational partners have proposed programs to allocate vaccines to low- and middle-income countries (LMICs)6. Yet, there remains a substantial funding gap for such programs7. Further, the optimal vaccine supply is unknown and the cost-effectiveness of investments into global vaccination programs has not been described. We used a validated COVID-19 simulation model8 to project the health benefits and costs of reaching 20%-70% vaccine coverage in 91 LMICs. We show that funding 20% vaccine coverage over one year among 91 LMICs would prevent 294 million infections and 2 million deaths, with 26 million years of life saved at a cost of US$6.4 billion, for an incremental cost effectiveness ratio (ICER) of US$250/year of life saved (YLS). Increasing vaccine coverage up to 50% would prevent millions more infections and save hundreds of thousands of additional lives, with ICERs below US$8,000/YLS. Results were robust to variations in vaccine efficacy and hesitancy, but were more sensitive to assumptions about epidemic pace and vaccination costs. These results support efforts to fund vaccination programs in LMICs and complement arguments about health equity9, economic benefits10, and pandemic control11.
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BackgroundWeeks after issuing social distancing orders, all U.S. states and the District of Columbia at least partially relaxed these measures. Critical unanswered questions remain about the timing of relaxation, and if and how unregulated social distancing measures can be sustained while effectively maintaining epidemic control. MethodsWe identified all statewide social distancing measures that were implemented and/or relaxed in the U.S. between March 10-July 15, 2020, triangulating data from state government and third-party sources. Using segmented linear regression, we evaluated the extent to which social distancing measure relaxation affected epidemic control, as indicated by the time-varying, state-specific effective reproduction number (Rt). ResultsIn the eight weeks prior to relaxation, mean Rt declined by 0.012 units per day (95% CI, -0.013 to -0.012), and 46/51 jurisdictions achieved Rt < 1.0 by the date of relaxation. After relaxation of social distancing, Rt reversed course and began increasing by 0.007 units per day (95% CI, 0.006-0.007), reaching a mean Rt of 1.16 eight weeks later, with only 9/51 jurisdictions maintaining Rt <1.0. Indicators often used to motivate relaxation at the time of relaxation (e.g. test positivity rate <5%) predicted greater post-relaxation epidemic growth. ConclusionsWe detected an immediate and significant reversal in epidemic growth gains after relaxation of social distancing measures across the U.S. These results illustrate the potential pitfalls of premature relaxation of social distancing measures in the U.S.
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BackgroundSocial distancing measures to address the U.S. coronavirus disease 2019 (COVID-19) epidemic may have notable health and social impacts. Methods and FindingsWe conducted a longitudinal pretest-posttest comparison group study to estimate the change in COVID-19 case growth before versus after implementation of statewide social distancing measures in the U.S. The primary exposure was time before (14 days prior to, and up to 3 days after) versus after (beginning 4 days after, and up to 21 days after) implementation of the first statewide social distancing measures. Statewide restrictions on internal movement were examined as a secondary exposure. The primary outcome was the COVID-19 case growth rate. The secondary outcome was the COVID-19-attributed mortality growth rate. All states initiated social distancing measures between March 10-25, 2020. The mean daily COVID-19 case growth rate decreased beginning four days after implementation of the first statewide social distancing measures, by 0.9% per day (95% confidence interval [CI], -1.3% to -0.4%; P<0.001). We did not estimate a statistically significant difference in the mean daily case growth rate before versus after implementation of statewide restrictions on internal movement (0.1% per day; 95% CI, -0.04% to 0.3%, P=0.14), but there is significant difficulty in disentangling the unique associations with statewide restrictions on internal movement from the unique associations with the first social distancing measures. Beginning seven days after social distancing, the COVID-19-attributed mortality growth rate decreased by 1.7% per day (95% CI, -3.0% to -0.7%; P<0.001). Our analysis is susceptible to potential bias resulting from the aggregate nature of the ecological data, potential confounding by contemporaneous changes (e.g., increases in testing), and potential underestimation of social distancing due to spillovers across neighboring states. ConclusionsStatewide social distancing measures were associated with a decrease in the COVID-19 epidemic case growth rate that was statistically significant and a decrease in the COVID-19-attributed mortality growth rate that was not statistically significant. Author SummaryO_ST_ABSWhy was the study doneC_ST_ABSThere are few empirical data about the population health benefits of imposing statewide social distancing measures to reduce transmission of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). What did the researchers findWe compared data from each state before vs. after implementation of statewide social distancing measures to estimate changes in mean COVID-19 daily case growth rates. Growth rates declined by approximately 1% per day beginning four days (approximately one incubation period) after statewide social distancing measures were implemented. Stated differently, our model implies that social distancing reduced the total number of COVID-19 cases by approximately 1,600 reported cases at 7 days after implementation, by approximately reported 55,000 cases at 14 days after implementation, and by approximately reported 600,000 cases at 21 days after implementation. What do these findings meanStatewide social distancing measures were associated with a reduction in the growth rate of COVID-19 cases in the U.S. However, our analysis is susceptible to potential bias resulting from the aggregate nature of the data, potential confounding by other changes that occurred during the study period (e.g., increases in testing), and potential underestimation of social distancing due to spillovers across neighboring states.