The public health burden of dengue is unknown; here cartographic approaches are used to provide insight into the global, regional and national burden of dengue, with the finding that the global number of infections per year is around 390 million, more than three times the estimate of the World Health Organization. The mosquito-borne viral infection dengue is found in tropical and subtropical regions worldwide, predominantly in urban and semi-urban areas. The incidence of dengue is on the increase, but the current global distribution is poorly known. Simon Hay and colleagues have applied novel mapping techniques to an extensive evidence base of nearly 10,000 case records. The outcome is an estimate of around 390 million new infections per year, more than double the most recent estimate from the World Health Organization. Importantly, this work extends the mapping to provide global estimates of the symptomatic and asymptomatic dengue case burden, at 96 and 294 million, respectively. Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes1. For some patients, dengue is a life-threatening illness2. There are currently no licensed vaccines or specific therapeutics, and substantial vector control efforts have not stopped its rapid emergence and global spread3. The contemporary worldwide distribution of the risk of dengue virus infection4 and its public health burden are poorly known2,5. Here we undertake an exhaustive assembly of known records of dengue occurrence worldwide, and use a formal modelling framework to map the global distribution of dengue risk. We then pair the resulting risk map with detailed longitudinal information from dengue cohort studies and population surfaces to infer the public health burden of dengue in 2010. We predict dengue to be ubiquitous throughout the tropics, with local spatial variations in risk influenced strongly by rainfall, temperature and the degree of urbanization. Using cartographic approaches, we estimate there to be 390 million (95% credible interval 284–528) dengue infections per year, of which 96 million (67–136) manifest apparently (any level of disease severity). This infection total is more than three times the dengue burden estimate of the World Health Organization2. Stratification of our estimates by country allows comparison with national dengue reporting, after taking into account the probability of an apparent infection being formally reported. The most notable differences are discussed. These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue. We anticipate that they will provide a starting point for a wider discussion about the global impact of this disease and will help to guide improvements in disease control strategies using vaccine, drug and vector control methods, and in their economic evaluation.

Significance: Forecasting reservoirs of zoonotic disease is a pressing public health priority. We apply machine learning to datasets describing the biological, ecological, and life history traits of rodents, which collectively carry a disproportionate number of zoonotic pathogens. We identify particular rodent species predicted to be novel zoonotic reservoirs and geographic regions from which new emerging pathogens are most likely to arise. We also describe trait profiles—complexes of biological features—that distinguish reservoirs from nonreservoirs. Generally, the most permissive rodent reservoirs display a fast-paced life history strategy, maximizing near-term fitness by having many altricial young that begin reproduction early and reproduce frequently. These findings may constitute an important lead in guiding the search for novel disease reservoirs in the wild.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Summary: The ecological niche is a fundamental biological concept. Modelling species’ niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species’ ranges. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species’ environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine‐learning algorithms can estimate the support of such high‐dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre‐processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo‐absence data and are comparable in empirical tests. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine‐learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species’ potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.