Early detection of dengue outbreaks: Transmission model analysis of a dengue outbreak in a remote setting in Ecuador

Background: Pathogen transmission of an outbreak generally begins well before it is identified by a surveillance system, particularly for infectious diseases in which a high proportion of cases are subclinical, as is the case for arboviruses. We aimed to ascertain the most likely date of the primary case (the first infection, whether detected or not) in an outbreak. Methods: Using data from a 2019 dengue outbreak in a rural, riverine town in Northwestern Ecuador, we investigated potential undetected dengue virus transmission prior to the outbreak detected in mid-May. The outbreak was preceded by four reported cases on February 9^th, February 13^th, March 28^th, and May 2^nd. Using a hidden Markov model, we estimate the most likely date of the primary case for different assumed case reporting fractions. Results: For all reporting fractions, the most likely primary case occurred near the two February candidate index cases, ranging from February 7^thto February 12^th, over 2 months prior to the main outbreak. Individual simulations showed that earlier and later primary cases were also possible. Our results suggest that the dengue virus was circulating in the community for around 3 months before the outbreak. Conclusions: Surveillance systems that can detect low-level transmission in the early stages of an outbreak can provide time to intervene prior to the exponential phase of the outbreak, with the potential to substantially reduce transmission and disease burden.