RESUMO
BackgroundRespiratory syncytial virus (RSV) is a leading cause of respiratory tract infections and bronchiolitis in young children. The seasonal pattern of RSV is shaped by short-lived immunity, seasonally varying contact rates and pathogen viability. The magnitude of each of these parameters is not fully clear. The disruption of the regular seasonality of RSV during the COVID pandemic in 2020 due to control measures, and the ensuing delayed surge in RSV cases provides an opportunity to disentangle these factors and to understand the implication for vaccination strategies. A better understanding of the drivers of RSV seasonality is key for developing future vaccination strategies. MethodsWe developed a mathematical model of RSV transmission, which simulates the sequential re-infection (SEIRRS4) and uses a flexible Von Mises function to model the seasonal forcing. Using MCMC we fit the model to laboratory confirmed RSV data from 2010-2022 from NSW while accounting for the reduced contact rates during the pandemic with Google mobility data. We estimated the baseline transmission rate, its amplitude and shape during RSV season as well as the duration of immunity. The resulting parameter estimates were compared to a fit to pre-pandemic data only, and to a fit with a cosine forcing function. We then simulated the expected shifts in peak timing and amplitude under two vaccination strategies: continuous and seasonal vaccination. ResultsWe estimate that RSV dynamics in NSW can be best explained by a high effective baseline transmission rate (2.94/d, 95% CrI 2.72-3.19) and a narrow peak with a maximum 13% increase compared to the baseline transmission rate. We also estimate the duration of post infection temporary but sterilizing immunity to be 412 days (95% CrI 391-434). A cosine forcing resulted in a similar fit and posterior estimates. Excluding the data from the pandemic period in the fit increased parameter correlation and yielded less informative posterior distributions. The continuous vaccination strategy led to more extreme seasonal incidence with a delay in the peak timing and a higher amplitude whereas seasonal vaccination flattened the incidence curves. ConclusionQuantifying the parameters that govern RSV seasonality is key in determining potential indirect effects from immunization strategies as those are being rolled out in the next few years.
RESUMO
IntroductionIn countries with weak surveillance systems confirmed COVID-19 deaths are likely to underestimate the death toll of the pandemic. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data on burial patterns in Mogadishu, Somalia during 2020 to estimate the date of introduction, transmissibility and other epidemiologic characteristics of SARS-CoV-2 in this low-income, crisis-affected setting. MethodsWe performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number (R0) and the effect of non-pharmaceutical interventions in Mogadishu up to September 2020. ResultsUnder the assumption that excess deaths in Mogadishu February-September 2020 were directly attributable to SARS-CoV-2 infection we arrived at median estimates of October-November 2019 for the date of introduction and low R0 estimates (1.3-1.5) stemming from the early and slow rise of excess deaths. The effect of control measures on transmissibility appeared small. ConclusionSubject to study assumptions, a very early SARS-CoV-2 introduction event may have occurred in Somalia. Estimated transmissibility in the first epidemic wave was lower than observed in European settings.
