Modelling the impact of the El Nino Southern Oscillation (ENSO) on the dynamics of Dengue outbreaks in Argentina between 2018 and 2024.

Background: Dengue fever has become an epidemiological concern worldwide, with 531,617 cases reported in Argentina during the 2023-2024 season. The El Nino Southern Oscillation (ENSO) phenomenon has been linked to changes in dengue dynamics. This study assessed the relationships among ENSO, local climatic variables, and dengue outbreaks in Argentina from 2018 to 2024. Methods: A nationwide spatiotemporal analysis was conducted using dengue cases and monthly mean temperature, relative humidity, and rainfall. The Southern Oscillation Index (SOI) was used as the ENSO indicator (El Nino phase=< minus0.5, neutral phase= minus0.5 to 0.5, and La Nina phase= > 0.5). We fitted Hierarchical Bayesian models with Integrated Nested Laplace Approximation (INLA). A Negative Binomial distribution of dengue cases was used to account for overdispersion. Spatial dependence was modeled by using the Besag York Mollie 2 (BYM2) structure, adjusted for population. Results: The final model explained 53.7% of the variation in Argentinas departmental monthly dengue cases during the study period. Exposure response curves of posterior probability and 95% credible intervals suggested that dengue risk increased when monthly mean temperatures were between 20 and 30 degrees Celsius during the El Nino or neutral phase, with less consistent effects during the La Nina phase. Humidity >50% was associated with increased dengue risk during the La Nina phase, with less steep or absent associations during El Nino or neutral phases. Negative SOI scores were associated with an increased risk of dengue after controlling for the covariates. Conclusion: Dengue outbreaks in Argentina were independently associated with temperatures between 20 and 30 degrees celsius during the El Nino and neutral phases, humidity > 50% during the La Nina phase, and the El Nino phase of the ENSO. Incorporating ENSO indices into predictive models could enhance early warning systems and timely public health interventions in Argentina.