Predicting SARS-CoV-2 variant spread in a completely seropositive population using semi-quantitative antibody measurements in blood donors

BackgroundSARS-CoV-2 serologic surveys estimate the proportion of the population with antibodies against historical variants which nears 100% in many settings. New analytic approaches are required to exploit the full information in serosurvey data. MethodUsing a SARS-CoV-2 anti-Spike (S) protein chemiluminescent microparticle assay, we attained a semi-quantitative measurement of population IgG titres in serial cross-sectional monthly samples of routine blood donations across seven Brazilian state capitals (March 2021-November 2021). In an ecological analysis (unit of analysis: age-city-calendar month) we assessed the relative contributions of prior attack rate and vaccination to antibody titre in blood donors. We compared blood donor anti-S titre across the seven cities during the growth phase of the Delta variant of concern (VOC) and use this to predict the resulting age-standardized incidence of severe COVID-19 cases. ResultsOn average we tested 780 samples per month in each location. Seroprevalence rose to >95% across all seven capitals by November 2021. Driven proximally by vaccination, mean antibody titre increased 16-fold over the study. The extent of prior natural infection shaped this process, with the greatest increases in antibody titres occurring in cities with the highest prior attack rates. Mean anti-S IgG was a strong predictor (adjusted R2 =0.89) of the number of severe cases caused by the Delta VOC in the seven cities. ConclusionsSemi-quantitative anti-S antibody titres are informative about prior exposure and vaccination coverage and can inform on the potential impact of future SARS-CoV-2 variants. SummaryIn the face of near 100% SARS-CoV-2 seroprevalence, we show that average semi-quantitative anti-S titre predicted the extent of the Delta variants spread in Brazil. This is a valuable metric for future seroprevalence studies.

Scale-free dynamics of Covid-19 in a Brazilian city

Mathematical models can provide insights into the control of pandemic COVID-19, which remains a global priority. The dynamics of directly-transmitted infectious diseases, such as COVID-19, are usually described by compartmental models where individuals are classified as susceptible, infected and removed. These SIR models typically assume homogenous transmission of infection, even in large populations, a simplification that is convenient but inconsistent with observations. Here we use original data on the dynamics of COVID-19 spread in a Brazilian city to investigate the structure of the transmission network. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5, which is independent of total population size. Compared with standard models of homogenous mixing, this scale-free, fractal infection process gives a better description of COVID-19 dynamics through time. In addition, the contact process explains the geographically localized clusters of disease seen in this Brazilian city. Our scale-free model can help refine criteria for physical and social distancing in order to more effectively mitigate the spread of COVID-19. We propose that scale-free COVID-19 dynamics could be a widespread phenomenon, a topic for further investigation.

Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation.

BackgroundBrazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions and predicting the course of the epidemic, but are often challenging due to different population sizes and structures. MethodsWe describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate reproduction number (Rt). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence. FindingsAfter an initial introduction in Sao Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to Northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11{middle dot}1 days [95% CI:13{middle dot}2,8{middle dot}9] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt) were largely driven by geographic location and the date of local onset. InterpretationThis study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics. FundingThis project was supported by a Medical Research Council UK (MRC-UK) -Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0)

Reinfection by the SARS-CoV-2 P.1 variant in blood donors in Manaus, Brazil

BackgroundThe city of Manaus, north Brazil, was stricken by a second epidemic wave of SARS-CoV-2 despite high seroprevalence estimates, coinciding with the emergence of the Gamma (P.1) variant. Reinfections were postulated as a partial explanation for the second surge. However, accurate calculation of reinfection rates is difficult when stringent criteria as two time-separated RT-PCR tests and/or genome sequencing are required. To estimate the proportion of reinfections caused by the Gamma variant during the second wave in Manaus and the protection conferred by previous infection, we analyzed a cohort of repeat blood donors to identify anti-SARS-CoV-2 antibody boosting as a means to infer reinfection. MethodsWe tested serial blood samples from unvaccinated repeat blood donors in Manaus for the presence of anti-SARS-CoV-2 IgG antibody. Donors were required to have three or more donations and at least one donation during each epidemic wave. Donors were tested with two assays that display waning in early convalescence, enabling the detection of reinfection-induced boosting. The serial samples were used to divide donors into six groups defined based on the inferred sequence of infection and reinfection with non-Gamma and Gamma variants. ResultsFrom 3,655 repeat blood donors, 238 met all inclusion criteria, and 223 had enough residual sample volume to perform both serological assays. Using a strict serological definition of reinfection, we found 13.6% (95% CI 7.0% - 24.5%) of all presumed Gamma infections that were observed in 2021 were reinfections. If we also include cases of probable or possible reinfections, these percentages increase respectively to 22.7% (95% CI 14.3% - 34.2%) and 39.3% (95% CI 29.5% - 50.0%). Previous infection conferred a protection against reinfection of 85.3% (95% CI 71.3% - 92.7%), decreasing to respectively 72.5% (95% CI 54.7% - 83.6%) and 39.5% (95% CI 14.1% - 57.8%) if probable and possible reinfections are included. ConclusionsReinfection due to Gamma is common and may play a significant role in epidemics where Gamma is prevalent, highlighting the continued threat variants of concern pose even to settings previously hit by substantial epidemics.

Performance of vaccination with CoronaVac in a cohort of healthcare workers (HCW) - preliminary report

BackgroundCoronaVac, a vaccine containing inactivated SARS-CoV-2, demonstrated efficacy of 50.39% 14 days or more after the 2nd dose. The objective of this study is to report the occurrence of symptomatic COVID-19 in a cohort of HCW vaccinated with CoronaVac and to estimate its effectiveness. MethodsCoronaVac was given to HCWs inHospital das Clinicas on 18-21 January, 2021 (epi week 3) (22,402 HCWs), and on 14-16 February, 2021 (epi week 7) (21,652 HCWs). Weekly cases of symptomatic COVID-19 were evaluated. Using the period from 2020 epi week 24 through 2021 epi week 2 (before vaccination), a Poisson regression was fit to model the HCWs with COVID-19 of the hospital, and the officially reported cases in the city of Sao Paulo. The predicted numbers of cases among HCWs for 2021 epi weeks 3-12 were then compared to the observed numbers of cases (after vaccination). Effectiveness was estimated for weeks 9-12 (2 to 5 weeks after the 2nd dose). 142 samples after vaccination were evaluated for SARS-CoV-2 variants of concern. ResultsSince the 1st dose there were 380 HCW diagnosed with COVID-19. On visual analysis, the number of cases of COVID-19 in the city increased sharply in 2021. The number of cases among the HCW did not follow. The estimated effectiveness 2 and 3 weeks after 2nd dose was 50.7% and 51.8%, respectively, and increased over the next 2 weeks. 67/142 samples (47%) were variants of concern, mostly P1 (57). ConclusionCoronavac is effective in preventing COVID-19.

PCR assay to enhance global surveillance for SARS-CoV-2 variants of concern

With the emergence of SARS-CoV-2 variants that may increase transmissibility and/or cause escape from immune responses1-3, there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant first detected in the UK4,5 could be serendipitously detected by the ThermoFisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike {Delta}69-70, would cause a "spike gene target failure" (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern that lack spike {Delta}69-70, such as B.1.351 (also 501Y.V2) detected in South Africa6 and P.1 (also 501Y.V3) recently detected in Brazil7. We identified a deletion in the ORF1a gene (ORF1a {Delta}3675-3677) in all three variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a {Delta}3675-3677 as the primary target and spike {Delta}69-70 to differentiate, we designed and validated an open source PCR assay to detect SARS-CoV-2 variants of concern8. Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence spread of B.1.1.7, B.1.351, and P.1.

Social and racial inequalities in COVID-19 risk of hospitalisation and death across Sao Paulo state, Brazil

BackgroundLittle evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in Sao Paulo state, Brazil and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. MethodsWe conducted a cross-sectional study using hospitalised severe acute respiratory infections (SARI) notified from March to August 2020, in the Sistema de Monitoramento Inteligente de Sao Paulo (SIMI-SP) database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple datasets for individual-level and spatio-temporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour, and comorbidities. FindingsThroughout the study period, patients living in the 40% poorest areas were more likely to die when compared to patients living in the 5% wealthiest areas (OR: 1{middle dot}60, 95% CI: 1{middle dot}48 - 1{middle dot}74) and were more likely to be hospitalised between April and July, 2020 (OR: 1{middle dot}08, 95% CI: 1{middle dot}04 - 1{middle dot}12). Black and Pardo individuals were more likely to be hospitalised when compared to White individuals (OR: 1{middle dot}37, 95% CI: 1{middle dot}32 - 1{middle dot}41; OR: 1{middle dot}23, 95% CI: 1{middle dot}21 - 1{middle dot}25, respectively), and were more likely to die (OR: 1{middle dot}14, 95% CI: 1{middle dot}07 - 1{middle dot}21; 1{middle dot}09, 95% CI: 1{middle dot}05 - 1{middle dot}13, respectively). InterpretationLow-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to healthcare, adherence to social distancing, and the higher prevalence of comorbidities. FundingThis project was supported by a Medical Research Council-Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0) (http://caddecentre.org/). This work received funding from the U.K. Medical Research Council under a concordat with the U.K. Department for International Development.

COVID-19 herd immunity in the Brazilian Amazon

The herd immunity threshold is the proportion of a population that must be immune to an infectious disease, either by natural infection or vaccination such that, in the absence of additional preventative measures, new cases decline and the effective reproduction number falls below unity. This fundamental epidemiological parameter is still unknown for the recently-emerged COVID-19, and mathematical models have predicted very divergent results. Population studies using antibody testing to infer total cumulative infections can provide empirical evidence of the level of population immunity in severely affected areas. Here we show that the transmission of SARS-CoV-2 in Manaus, located in the Brazilian Amazon, increased quickly during March and April and declined more slowly from May to September. In June, one month following the epidemic peak, 44% of the population was seropositive for SARS-CoV-2, equating to a cumulative incidence of 52%, after correcting for the false-negative rate of the antibody test. The seroprevalence fell in July and August due to antibody waning. After correcting for this, we estimate a final epidemic size of 66%. Although non-pharmaceutical interventions, plus a change in population behavior, may have helped to limit SARS-CoV-2 transmission in Manaus, the unusually high infection rate suggests that herd immunity played a significant role in determining the size of the epidemic.

A primary care approach to the COVID-19 pandemic: clinical features and natural history of 2,073 suspected cases in the Corona Sao Caetano programme, Sao Paulo, Brazil

BackgroundDespite most cases not requiring hospital care, there are limited community-based clinical data on COVID-19. Methods and findingsThe Corona Sao Caetano program is a primary care initiative offering COVID-19 care to all residents of Sao Caetano do Sul, Brazil. After triage of potentially severe cases, consecutive patients presenting between 13th April and 13th May 2020 were tested at home with SARS-CoV-2 reverse transcriptase (RT) PCR; positive patients were followed up for 14 days. RT-PCR-negative patients were offered SARS-CoV-2 serology. We describe the clinical features, virology and natural history of this prospective population-based cohort. Of 2,073 suspected COVID-19 cases, 1,583 (76{middle dot}4%) were tested by RT-PCR, of whom 444 (28{middle dot}0%, 95%CI: 25{middle dot}9% - 30{middle dot}3%) were positive; 604/1,136 (53%) RT-PCR-negative patients underwent serology, of whom 52 (8{middle dot}6%) tested SARS-CoV-2 seropositive. The most common symptoms of COVID-19 were cough, fatigue, myalgia and headache; whereas self-reported fever, anosmia, and ageusia were most associated with a positive COVID-19 diagnosis. RT-PCR cycle thresholds were lower in men, older patients, those with fever and arthralgia, and around symptom onset. The rates of hospitalization and death among 444 RT-PCR-positive cases were 6{middle dot}7% and 0{middle dot}7%, respectively, with older age and obesity more frequent in the hospitalized group. ConclusionsCOVID-19 presents similarly to other mild respiratory disease in primary care. Some symptoms assist the differential diagnosis. Most patients can be managed at home.

Routes for COVID-19 importation in Brazil

HighlightThe global outbreak caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been declared a pandemic by the WHO. As the number of imported SARS-CoV-2 cases is on the rise in Brazil, we use incidence and historical air travel data to estimate the most important routes of importation into the country.