Journal of The Royal Society Interface · 2021 · 15 citations · 18 references
Veterinary MedicineBiostatisticsInfectious DiseasesEpidemiologic MethodsEpidemiologyInfectious Disease EpidemiologyDisease Trend AnalysisEpidemic ForecastingInfectious Disease DynamicsTime-series Analysis In EpidemiologyTransmission DynamicsVector-borne TransmissionBayesian Vector AutoregressionDisease Outbreak DynamicsForecasting Disease DynamicsInfectious Disease ModelsPopulation-level VariationStrain InteractionsVector-borne Disease DynamicsVector-borne Disease Transmission Dynamics
Over 105 million dengue infections are estimated to occur annually. Understanding the disease dynamics of dengue is often difficult due to multiple strains circulating within a population. Interactions between dengue serotype dynamics may result in complex cross-immunity dynamics at the population level and create difficulties in terms of formulating intervention strategies for the disease. In this study, a nationally representative 16-year time series with over 43 000 serotyped dengue infections was used to infer the long-run effects of between and within strain interactions and their impacts on past outbreaks. We used a novel identification strategy incorporating sign-identified Bayesian vector autoregressions, using structural impulse responses, historical decompositions and counterfactual analysis to conduct inference on dengue dynamics post-estimation. We found that on the population level: (i) across-serotype interactions on the population level were highly persistent, with a one time increase in any other serotype associated with long run decreases in the serotype of interest (range: 0.5-2.5 years) and (ii) over 38.7% of dengue cases of any serotype were associated with across-serotype interactions. The findings in this paper will substantially impact public health policy interventions with respect to dengue.
18