DOI: 10.5061/dryad.c59zw3r5n
title: Coronavirus prevalence in Brazilian Amazon and Sao Paulo city

description: SARS-CoV-2 spread rapidly in the Brazilian Amazon. Mortality was elevated,
 despite the young population, with the health services and cemeteries
 overwhelmed. The attack rate in this region is an estimate of the final
 epidemic size in an unmitigated epidemic. Here we show that by June, one
 month after the epidemic peak in Manaus, capital of the Amazonas state,
 44% of the population had detectable IgG antibodies. This equates to a
 cumulative incidence of 52% after correcting for the false-negative rate
 of the test. Further correcting for the effect of antibody waning we
 estimate that the final attack rate was 66%. This is higher than seen in
 other settings, but lower than the predicted final size for an unmitigated
 epidemic in a homogeneously mixed population. This discrepancy may be
 accounted for by population structure as well as some limited physical
 distancing and non-pharmaceutical measures adopted in the city.
description: <strong>Selection of blood samples for serology testing
 </strong> Both the FPS and HEMOAM blood centers
 routinely store residual blood samples for six months after donation. In
 order to cover a period starting from the introduction of SARSCoV-2 in
 both cities, we retrieved stored samples covering the months of February
 to May in São Paulo, and February to June in Manaus, at which point
 testing capacity became available. In subsequent months blood samples were
 prospectively selected for testing. The monthly target was to test 1,000
 samples at each study site. However, due to problems with purchasing the
 kits, supply chain issues, and the period of test validity, some months
 were under and others over the target (to avoid wasting kits soon to
 expire). We aimed to include donations starting from the second week of
 each month. Part of the remit of the wider project is to develop a system
 to prospectively select blood donation samples, based on the donor’s
 residential address, so as to capture a spatially representative sample of
 each participating city. For example, FPS receives blood donations from
 people living across the whole greater metropolitan region of São Paulo.
 The spatial distribution of donors does not follow the population density,
 with some areas over- and others under-represented. We used residential
 zip codes (recorded routinely at FPS) to select only individuals living
 within the city of São Paulo. We then further divided the city into 32
 regions (subprefeituras) and used their projected population sizes for
 2020 to define sampling weights, such that the number of donors selected
 in any given subprefeitura was proportional to the population size. We
 piloted this approach in São Paulo and have developed an information
 system to operationalize this process at the participating center.
 However, at the time of data collection the system was not implemented in
 HEMOAM and therefore it was not possible to use this sampling strategy. As
 such, we simply tested consecutive blood donations, beginning from the
 second week of each month until the target was reached.
 <strong>Quantifying antibody waning and rate of
 seroreversion </strong> We sought to quantify the
 rate of decline of the anti-nucleocapsid IgG antibody that is detected by
 the Abbott CMIA. We tested paired serum samples from our cohort of
 convalescent plasma donors (described above). We calculated the rate of
 signal decay as the difference in log2 S/C between the first and second
 time points divided by the number of days between the two visits. We used
 simple linear regression to determine the mean slope and 95% CI.
 <strong>Analysis of seroprevalence
 data</strong> Using the manufacturer's
 threshold of 1.4 S/C to define a positive result we first calculated the
 monthly crude prevalence of anti-SARS-CoV-2 antibodies as the number of
 positive samples/total samples tested. The 95% confidence intervals (CI)
 were calculated by the exact binomial method. We then re-weighted the
 estimates for age and sex to account for the different demographic make-up
 of blood donors compared to the underlying populations of São Paulo and
 Manaus (Fig. S4). Because only people aged between 16 and 70 years are
 eligible to donate blood, the re-weighting was based on the projected
 populations in the two cities in this age range only. The population
 projections for 2020 are available from
 (https://demografiaufrn.net/laboratorios/lepp/). We further adjusted these
 estimates for the sensitivity and specificity of the assay using the Rogan
 and Gladen method As a sensitivity analysis, we took two approaches to
 account for the effect of seroreversion through time. Firstly, the
 manufacturer's threshold of 1.4 optimizes specificity but misses many
 true-cases in which the S/C level is in the range of 0.4 – 1.4 (see ref
 and main text). In addition, individuals with waning antibody levels would
 be expected to fall initially into this range. Therefore, we present the
 results using an alternative threshold of 0.4 to define a positive result
 and adjust for the resultant loss in specificity. Secondly, we corrected
 the prevalence with a model-based method assuming that the probability of
 seroreversion for a given patient decays exponentially with time. In the
 model-based method for correcting the prevalence, only the months between
 March and August were considered. The measured prevalence used as input
 for this method was obtained using the manufacturer’s threshold of 1.4,
 and the correction based on the test specificity (99.9%) and sensitivity
 (84%) was applied, as well as the normalization by age and sex. Confidence
 intervals were calculated through bootstrapping, assuming a beta
 distribution for the input measured prevalence. It is worth noting that
 even though this model is limited by the exponential decay assumption,
 assuming distributions with more degrees of freedom may lead to
 overfitting due to the small number of samples of 9[7]. Finally, the
 obtained values for - and " must be interpreted as parameters for
 this model, and not estimates for the actual decay rate and seroreversion
 probability as they may absorb the effect of variables that are not taken
 into account by this model. <strong>Infection
 fatality ratio</strong> We calculated the global
 infection fatality ratio in Manaus and São Paulo. The total number of
 infections was estimated as the product of the population size in each
 city and the antibody prevalence in June (re-weighted and adjusted for
 sensitivity and specificity). The number of deaths were taken from the
 SIVEP-Gripe system, and we used both confirmed COVID-19 deaths, and deaths
 due to severe acute respiratory syndrome of unknown cause. The latter
 category likely represents COVID-19 cases in which access to diagnostic
 testing was limited , and more closely approximate the excess mortality.
 We calculated age-specific infection fatality ratios by assuming equal
 prevalence across all age groups.
 <strong>Effective reproduction
 number</strong> We calculated the effective
 reproduction number for São Paulo and Manaus using the renewal method9,
 with the serial interval as estimated by Ferguson (2020)10. Calculations
 were made using daily severe acute respiratory syndrome cases with
 PCR-confirmed COVID-19 in the SIVEP-Gripe system. Region-specific delays
 between the PCR result release and the date of symptom onset were
 accounted for using the technique proposed by Lawless (1994).