ABSTRACT
BackgroundMuch of the worlds population has been infected with SARS-CoV-2. Thus, infection-induced immunity will play a critical role in future SARS-CoV-2 transmission. We investigated the impact of immunity from prior infection on viral shedding duration and viral load. MethodsWe conducted a household cohort study in Managua, Nicaragua with an embedded transmission study that closely monitors participants regardless of symptom status. Real-time reverse-transcription polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assays (ELISAs) were used to measure infections and seropositivity, respectively. Blood samples were collected in Feb/March and Oct/Nov 2020 and 2021, and surrounding household intensive monitoring periods. We used accelerated failure time models to compare shedding times. Participants vaccinated [≥]14 days prior to infection were excluded from primary analyses. ResultsThere were 600 RT-PCR-confirmed SARS-CoV-2 infections between May 1, 2020 and March 10, 2022 with ELISA data prior to infection. Prior infection was associated with 48% shorter shedding times, event time ratio (ETR) 0.52 (95% CI: 0.39-0.69, mean shedding: 13.7 vs 26.4 days). A 4-fold higher anti-SARS-CoV-2 spike titer was associated with 17% shorter shedding (ETR 0.83, 95% CI: 0.78-0.90). Similarly, maximum viral loads (lowest CT) were lower for previously infected individuals (mean CT 29.8 vs 28.0, p = 4.02x10-3). Shedding was shorter in previously infected adults and children [≥]10 years, but not in children 0-9 years; there was little difference in CT levels for previously infected vs naive adults above age 60. ConclusionsPrior infection-induced immunity was associated with shorter viral shedding and lower viral loads.
ABSTRACT
Accurate tracing of epidemic spread over space enables effective control measures. We examined three metrics of infection and disease in a pediatric cohort (N {approx} 3,000) over two chikungunya and one Zika epidemic, and in a household cohort (N=1,793) over one COVID-19 epidemic in Managua, Nicaragua. We compared spatial incidence rates (cases/total population), infection risks (infections/total population), and disease risks (cases/infected population). We used generalized additive and mixed-effects models, Kulldorfs spatial scan statistic, and intracluster correlation coefficients. Across different analyses and all epidemics, incidence rates considerably underestimated infection and disease risks, producing large and spatially non-uniform biases distinct from biases due to incomplete case ascertainment. Infection and disease risks exhibited distinct spatial patterns, and incidence clusters inconsistently identified areas of either risk. While incidence rates are commonly used to infer infection and disease risk in a population, we find that this can induce substantial biases and adversely impact policies to control epidemics. Article summary lineInferring measures of spatial risk from case-only data can substantially bias estimates, thereby weakening and potentially misdirecting measures needed to control an epidemic.
