Probabilistic seasonal dengue forecasting in Vietnam using superensembles

Abstract Timely information is key for decision-making. The ability to predict dengue transmission ahead of time would significantly benefit planners and decision-makers. Dengue is climate-sensitive. Monitoring climate variability could provide advance warning about dengue risk. Multiple dengue early warning systems have been proposed. Often, these systems are based on deterministic models that have limitations for quantifying the probability that a public health event may occur. We introduce an operational seasonal dengue forecasting system where Earth observations and seasonal climate forecasts are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to six months ahead. We demonstrate that the system has skill and relative economic value at multiple forecast horizons, seasons, and locations. The superensemble generated, on average, more accurate forecasts than those obtained from the models used to create it. We argue our system provides a useful tool for the development and deployment of targeted vector control interventions, and a more efficient allocation of resources in Vietnam.