Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986–1990

Abstract

Predicting arbovirus re-emergence remains challenging in regions with limited off-season transmission and intermittent epidemics. Current mathematical models treat the depletion and replenishment of susceptible (non-immune) hosts as the principal drivers of re-emergence, based on established understanding of highly transmissible childhood diseases with frequent epidemics. We extend an analytical approach to determine the number of ‘skip’ years preceding re-emergence for diseases with continuous seasonal transmission, population growth and under-reporting. Re-emergence times are shown to be highly sensitive to small changes in low R 0 (secondary cases produced from a primary infection in a fully susceptible population). We then fit a stochastic Susceptible–Infected–Recovered (SIR) model to observed case data for the emergence of dengue serotype DENV1 in Rio de Janeiro. This aggregated city-level model substantially over-estimates observed re-emergence times either in terms of skips or outbreak probability under forward simulation. The inability of susceptible depletion and replenishment to explain re-emergence under ‘well-mixed’ conditions at a city-wide scale demonstrates a key limitation of SIR aggregated models, including those applied to other arboviruses. The predictive uncertainty and high skip sensitivity to epidemiological parameters suggest a need to investigate the relevant spatial scales of susceptible depletion and the scaling of microscale transmission dynamics to formulate simpler models that apply at coarse resolutions.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections

“…The collection of datasets considered also spans a range of transmission intensities, from low transmission rates manifested in intermittent large epidemics ( 44 ) to higher values under more endemic conditions with seasonally recurrent outbreaks. Examples of these respective dynamics for dengue are found in a city such as Rio de Janeiro (Brazil) versus the island of Puerto Rico in South America and, more broadly, most of Thailand.…”

Informing policy via dynamic models: Cholera in Haiti

“…Including white noise in the rate function enables the possibility of an overdispersed Markov counting system . Since rates should be non-negative, Gaussian noise is not appropriate and gamma noise is a convenient option that has found various applications (Romero-Severson et al, 2015;Subramanian et al, 2020). Specifically, we consider a model given by…”

Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics

“…Although dengue is a vector-borne disease, for simplicity we omitted the explicit representation of the dynamics of the mosquito population, and treated vector transmission via the seasonality of the transmission rate (25). Thus, for each unit u, the deterministic SIR model is based on the following traditional differential equations:…”

“…The parameters of the model are given in Table S1. We relied on parameters estimated for dengue transmission in Rio de Janeiro by (25). Those estimates were obtained for the aggregated city and for the emergence of DENV1.…”

“…A recent modeling study of the emergence of DENV1 in the megacity of Rio de Janeiro, Brazil, illustrates the challenge. A well-mixed model for the city as a whole clearly failed to predict time to re-emergence (25), possibly due to its inability to accurately track the build-up of herd immunity at this coarse resolution.…”