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Dengue is a mosquito-borne viral infection that has spread throughout the tropical world over the past 60 years and now affects over half the world’s population. The geographical range of dengue is expected to further expand due to ongoing global phenomena including climate change and urbanization. We applied statistical mapping techniques to the most extensive database of case locations to date to predict global environmental suitability for the virus as of 2015. We then made use of climate, population and socioeconomic projections for the years 2020, 2050 and 2080 to project future changes in virus suitability and human population at risk. This study is the first to consider the spread of Aedes mosquito vectors to project dengue suitability. Our projections provide a key missing piece of evidence for the changing global threat of vector-borne disease and will help decision-makers worldwide to better prepare for and respond to future changes in dengue risk. Statistical mapping techniques provide insights into the current geographical spread of the mosquito-borne dengue virus infection and predict changes in the areas that will be environmentally suitable to the virus for the years 2020, 2050 and 2080.

Introduction Diabetes and chronic kidney diseases are associated with a large health burden in the USA and globally. Objective To estimate age-standardized mortality rates by county from diabetes mellitus and chronic kidney disease. Design and setting Validated small area estimation models were applied to de-identified death records from the National Center for Health Statistics (NCHS) and population counts from the census bureau, NCHS, and the Human Mortality Database to estimate county-level mortality rates from 1980 to 2014 from diabetes mellitus and chronic kidney disease (CKD). Exposures County of residence. Main outcomes and measures Age-standardized mortality rates by county, year, sex, and cause. Results Between 1980 and 2014, 2,067,805 deaths due to diabetes were recorded in the USA. The mortality rate due to diabetes increased by 33.6% (95% UI: 26.5%–41.3%) between 1980 and 2000 and then declined by 26.4% (95% UI: 22.8%–30.0%) between 2000 and 2014. Counties with very high mortality rates were found along the southern half of the Mississippi river and in parts of South and North Dakota, while very low rates were observed in central Colorado, and select counties in the Midwest, California, and southern Florida. A total of 1,659,045 deaths due to CKD were recorded between 1980 and 2014 (477,332 due to diabetes mellitus, 1,056,150 due to hypertension, 122,795 due to glomerulonephritis, and 2,768 due to other causes). CKD mortality varied among counties with very low mortality rates observed in central Colorado as well as some counties in southern Florida, California, and Great Plains states. High mortality rates from CKD were observed in counties throughout much of the Deep South, and a cluster of counties with particularly high rates was observed around the Mississippi river. Conclusions and relevance This study found large inequalities in diabetes and CKD mortality among US counties. The findings provide insights into the root causes of this variation and call for improvements in risk factors, access to medical care, and quality of medical care.

Onchocerciasis is a neglected tropical disease with numerous symptoms and side effects, and when left untreated can lead to permanent blindness or skin disease. This database is an attempt to combine onchocerciasis prevalence data from peer-reviewed publications into a single open-source dataset. The process followed to extract and format the information has been detailed in this paper. A total of 14,043 unique location, diagnostic, age and sex-specific records from 1975–2017 have been collected, organized and marked for collapse where a single geo-position is shared between multiple records. The locations vary from single villages up to smaller administrative units and onchocerciasis control program-defined foci. This resulting database can be used to by the global health community to advance understanding of the distribution of onchocerciasis infection and disease.Design Type(s)data integration objective • disease analysis objectiveMeasurement Type(s)Scientific PublicationTechnology Type(s)digital curationFactor Type(s)geographic location • temporal_intervalSample Characteristic(s)Homo sapiensMachine-accessible metadata file describing the reported data (ISA-Tab format)

The global population at risk from mosquito-borne diseases—including dengue, yellow fever, chikungunya and Zika—is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus . The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally. Statistical mapping techniques provide insights into the spread of two key arbovirus vectors in Europe and the United States, and predict the future distributions of both mosquitoes in response to accelerating urbanization, connectivity and climate change.

In the version of this Article originally published, the affiliation for author Catherine Linard was incorrectly stated as ‘ 6 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK’. The correct affiliation is ‘ 9 Spatial Epidemiology Lab (SpELL), Universite Libre de Bruxelles, Brussels, Belgium’. The affiliation for author Hongjie Yu was also incorrectly stated as ‘ 11 Department of Statistics, Harvard University, Cambridge, MA, USA’. The correct affiliation is ‘ 15 School of Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China’. This has now been amended in all versions of the Article.

The rise of generative AI requires a research agenda grounded in the African context to determine locally relevant strategies for its development and use. With a critical mass of evidence on the risks and benefits that generative AI poses to African societies, the scaled use of this new technology might help to reduce rising global inequities.

As a World Health Organization Research and Development Blueprint priority pathogen, there is a need to better understand the geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and its potential to infect mammals and humans. This database documents cases of MERS-CoV globally, with specific attention paid to zoonotic transmission. An initial literature search was conducted in PubMed, Web of Science, and Scopus; after screening articles according to the inclusion/exclusion criteria, a total of 208 sources were selected for extraction and geo-positioning. Each MERS-CoV occurrence was assigned one of the following classifications based upon published contextual information: index, unspecified, secondary, mammal, environmental, or imported. In total, this database is comprised of 861 unique geo-positioned MERS-CoV occurrences. The purpose of this article is to share a collated MERS-CoV database and extraction protocol that can be utilized in future mapping efforts for both MERS-CoV and other infectious diseases. More broadly, it may also provide useful data for the development of targeted MERS-CoV surveillance, which would prove invaluable in preventing future zoonotic spillover.

This Article was mistakenly not made Open Access when originally published; this has now been amended, and information about the Creative Commons Attribution 4.0 International License has been added into the ‘Additional information’ section.