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Neutralizing antibody plays a key role in protective immunity against COVID-19. As increasingly distinct variants circulate, debate continues regarding the value of adding novel variants to SARS-CoV-2 vaccines. In this study, we have analyzed live virus neutralization titers against WA1, Delta, BA.1, BA.2, and BA.5 in 187 hospitalized patients infected with Delta or Omicron strains. This information will be useful in selection of the SARS-CoV-2 strains to include in an updated vaccine. Our results show that unvaccinated Delta infected patients made a highly biased neutralizing antibody response towards the infecting Delta strain with slightly lower responses against the WA1 strain, but with strikingly lower titers against BA.1, BA.2, and BA.5. Delta infected patients that had been previously vaccinated with the WA1 containing COVID vaccine made equivalent responses to WA1 and Delta strains, but still had very low neutralizing antibody responses to Omicron strains. In striking contrast, both unvaccinated and vaccinated Omicron patients exhibited a more balanced ratio of Omicron virus neutralization compared to neutralization of ancestral strains. Interestingly, Omicron patients infected with BA.1 or BA.2 had detectable neutralizing antibody titers to BA.5, but these titers were lower than neutralization titers to BA.1 and BA.2. Taken together, these results suggest that inclusion of the Omicron BA.5 strain in a SARS-CoV-2 vaccine would be beneficial in protection against the widely circulating BA.5 variant. DisclaimerThe findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
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BackgroundSerology tests can identify previous infections and facilitate estimation of the number of total infections. However, immunoglobulins targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported to wane below the detectable level of serological assays. We estimate the cumulative incidence of SARS-CoV-2 infection from serology studies, accounting for expected levels of antibody acquisition (seroconversion) and waning (seroreversion), and apply this framework using data from New York City (NYC) and Connecticut. MethodsWe estimated time from seroconversion to seroreversion and infection fatality ratio (IFR) using mortality data from March-October 2020 and population-level cross-sectional seroprevalence data from April-August 2020 in NYC and Connecticut. We then estimated the daily seroprevalence and cumulative incidence of SARS-CoV-2 infection. FindingsThe estimated average time from seroconversion to seroreversion was 3-4 months. The estimated IFR was 1.1% (95% credible interval: 1.0-1.2%) in NYC and 1.4% (1.1-1.7%) in Connecticut. The estimated daily seroprevalence declined after a peak in the spring. The estimated cumulative incidence reached 26.8% (24.2-29.7%) and 8.8% (7.1-11.3%) at the end of September in NYC and Connecticut, higher than maximum seroprevalence measures (22.1% and 6.1%), respectively. InterpretationThe cumulative incidence of SARS-CoV-2 infection is underestimated using cross-sectional serology data without adjustment for waning antibodies. Our approach can help quantify the magnitude of underestimation and adjust estimates for waning antibodies. FundingThis study was supported by the US National Science Foundation and the National Institute of Allergy and Infectious Diseases.
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BackgroundBeginning in early February 2020, COVID-19 spread across the state of Georgia, leading to 258,354 cumulative cases as of August 25, 2020. The time scale of spreading (i.e., serial interval) and magnitude of spreading (i.e., Rt or reproduction number) for COVID-19, were observed to be heterogenous by demographic characteristics, region and time period. In this study, we examined the COVID-19 transmission in the state of Georgia, United States. MethodsDuring February 1 to July 13, 2020, we identified 4080 transmission pairs using contact information from reports of COVID-19 cases from the Georgia Department of Public Health. We examined how various transmission characteristics were affected by disease symptoms, demographics (age, gender, and race), and time period (during shelter-in-place and after reopening). In addition, we estimated the time course of reproduction numbers during early February-mid-June for all 159 counties in the state of Georgia, using a total of 118,491 reported COVID-19 cases. FindingsOver this period, the serial interval appeared to decrease from 5.97 days in February-April to 4.40 days in June-July. With regard to age, transmission was assortative and patterns of transmission changed over time. COVID-19 mainly spread from adults to all age groups; transmission among and between children and the elderly was found less frequently. Younger adults (20- 50 years old) were involved in the majority of transmissions occurring during or after reopening subsequent to the shelter-in-place period. By mid-July, two waves of COVID-19 transmission were apparent, separated by the shelter-in-place period in the state of Georgia. Counties around major cities and along interstate highways had more intense transmission. InterpretationThe transmission of COVID-19 in the state of Georgia had been heterogeneous by area and changed over time. The shelter-in-place was not long enough to sufficiently suppress COVID-19 transmission in densely populated urban areas connected by major transportation links. Studying local transmission patterns may help in predicting and guiding states in prevention and control of COVID-19 according to population and region. FundingEmory COVID-19 Response Collaborative. Research in context Evidence before this studyThe ongoing COVID-19 pandemic has caused 37,109,581 cases and 1,070,355 deaths worldwide as of October 11, 2020. We searched PubMed for articles published on and before October 11, 2020 using keywords "novel coronavirus", "SARS-nCoV-2", "COVID-19", "transmission", "serial interval", "reproduction number", and "shelter-in-place". Few published studies have estimated the serial interval but no study was found that examined the time-varying serial interval. Few studies have examined the transmission patterns between groups with different characteristics. And no study has examined the timevarying reproduction number for COVID-19 and impact of shelter-in-place order at the county level in the United States. Added value of this studyTo our knowledge, this is the first study showing the multiple aspects of COVID-19 transmission, including serial interval, transmission patterns between age, gender, or race groups, and spatiotemporal patterns, based on data from 118,491 confirmed COVID-19 cases and 4080 tracked pairs of infector and infectee. We found that during February-July the serial interval for symptom onset shortened, and the major contribution to the spread of COVID-19 shifted to younger ages (from 40-70 years old in February-April to 20-50 years old in June-July). We also found three to four weeks of the shelter-in-place slowed transmission but was insufficient to prevent transmission into urban and peri-urban counties connected with major transportation. Implications of all the available evidenceThe contracting serial intervals and increasing spread by younger generation show the COVID-19 transmission at county level changes over time. The spatiotemporal patterns of transmission in county level further provide important evidence to guide effective COVID-19 prevention and control measures (e.g., shelter-in-place) in different areas.
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It is imperative to advance our understanding of heterogeneities in the transmission of SARS-CoV-2 such as age-specific infectiousness and super-spreading. To this end, it is important to exploit multiple data streams that are becoming abundantly available during the pandemic. In this paper, we formulate an individual-level spatio-temporal mechanistic framework to integrate individual surveillance data with geo-location data and aggregate mobility data, enabling a more granular understanding of the transmission dynamics of SARS-CoV-2. We analyze reported cases, between March and early May 2020, in five (urban and rural) counties in the State of Georgia USA. First, our results show that the reproductive number reduced to below 1 in about two weeks after the shelter-in-place order. Super-spreading appears to be widespread across space and time, and it may have a particularly important role in driving the outbreak in rural areas and an increasing importance towards later stages of outbreaks in both urban and rural settings. Overall, about 2% of cases were directly responsible for 20% of all infections. We estimate that the infected non-elderly cases (<60) may be 2.78 [2.10, 4.22] times more infectious than the elderly, and the former tend to be the main driver of super-spreading. Our results improve our understanding of the natural history and transmission dynamics of SARS-CoV-2. More importantly, we reveal the roles of age-specific infectiousness and characterize systematic variations and associated risk factors of super-spreading. These have important implications for the planning of relaxing social distancing and, more generally, designing optimal control measures. Significance StatementThere is still considerable scope for advancing our understanding of the epidemiology and ecology of COVID-19. In particular, much is unknown about individual-level transmission heterogeneities such as super-spreading and age-specific infectiousness. We statistically synthesize multiple valuable datastreams, including surveillance data and mobility data, that are available during the current COVID-19 pandemic. We show that age is an important factor in the transmission of the virus. Super-spreading is ubiquitous over space and time, and has particular importance in rural areas and later stages of an outbreak. Our results improve our understanding of the natural history the virus and have important implications for designing optimal control measures.