Dengue and chikungunya are increasing global public health concerns due to their rapid geographical spread and increasing disease burden. Knowledge of the contemporary distribution of their shared vectors, Aedes aegypti and Aedes albopictus remains incomplete and is complicated by an ongoing range expansion fuelled by increased global trade and travel. Mapping the global distribution of these vectors and the geographical determinants of their ranges is essential for public health planning. Here we compile the largest contemporary database for both species and pair it with relevant environmental variables predicting their global distribution. We show Aedes distributions to be the widest ever recorded; now extensive in all continents, including North America and Europe. These maps will help define the spatial limits of current autochthonous transmission of dengue and chikungunya viruses. It is only with this kind of rigorous entomological baseline that we can hope to project future health impacts of these viruses.DOI: http://dx.doi.org/10.7554/eLife.08347.001

Ebola virus disease (EVD) is a complex zoonosis that is highly virulent in humans. The largest recorded outbreak of EVD is ongoing in West Africa, outside of its previously reported and predicted niche. We assembled location data on all recorded zoonotic transmission to humans and Ebola virus infection in bats and primates (1976–2014). Using species distribution models, these occurrence data were paired with environmental covariates to predict a zoonotic transmission niche covering 22 countries across Central and West Africa. Vegetation, elevation, temperature, evapotranspiration, and suspected reservoir bat distributions define this relationship. At-risk areas are inhabited by 22 million people; however, the rarity of human outbreaks emphasises the very low probability of transmission to humans. Increasing population sizes and international connectivity by air since the first detection of EVD in 1976 suggest that the dynamics of human-to-human secondary transmission in contemporary outbreaks will be very different to those of the past.DOI: http://dx.doi.org/10.7554/eLife.04395.001

Aedes aegypti and Ae. albopictus are the main vectors transmitting dengue and chikungunya viruses. Despite being pathogens of global public health importance, knowledge of their vectors’ global distribution remains patchy and sparse. A global geographic database of known occurrences of Ae. aegypti and Ae. albopictus between 1960 and 2014 was compiled. Herein we present the database, which comprises occurrence data linked to point or polygon locations, derived from peer-reviewed literature and unpublished studies including national entomological surveys and expert networks. We describe all data collection processes, as well as geo-positioning methods, database management and quality-control procedures. This is the first comprehensive global database of Ae. aegypti and Ae. albopictus occurrence, consisting of 19,930 and 22,137 geo-positioned occurrence records respectively. Both datasets can be used for a variety of mapping and spatial analyses of the vectors and, by inference, the diseases they transmit.

BackgroundLassa fever is a viral haemorrhagic illness responsible for disease outbreaks across West Africa. It is a zoonosis, with the primary reservoir species identified as the Natal multimammate mouse, Mastomys natalensis. The host is distributed across sub-Saharan Africa while the virus' range appears to be restricted to West Africa. The majority of infections result from interactions between the animal reservoir and human populations, although secondary transmission between humans can occur, particularly in hospital settings.MethodsUsing a species distribution model, the locations of confirmed human and animal infections with Lassa virus (LASV) were used to generate a probabilistic surface of zoonotic transmission potential across sub-Saharan Africa.ResultsOur results predict that 37.7 million people in 14 countries, across much of West Africa, live in areas where conditions are suitable for zoonotic transmission of LASV. Four of these countries, where at-risk populations are predicted, have yet to report any cases of Lassa fever.ConclusionsThese maps act as a spatial guide for future surveillance activities to better characterise the geographical distribution of the disease and understand the anthropological, virological and zoological interactions necessary for viral transmission. Combining this zoonotic niche map with detailed patient travel histories can aid differential diagnoses of febrile illnesses, enabling a more rapid response in providing care and reducing the risk of onward transmission.

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BackgroundMarburg virus disease (MVD) describes a viral haemorrhagic fever responsible for a number of outbreaks across eastern and southern Africa. It is a zoonotic disease, with the Egyptian rousette (Rousettus aegyptiacus) identified as a reservoir host. Infection is suspected to result from contact between this reservoir and human populations, with occasional secondary human-to-human transmission.MethodsIndex cases of previous human outbreaks were identified and reports of infection in animals recorded. These data were modelled within a species distribution modelling framework in order to generate a probabilistic surface of zoonotic transmission potential of MVD across sub-Saharan Africa.ResultsAreas suitable for zoonotic transmission of MVD are predicted in 27 countries inhabited by 105 million people. Regions are suggested for exploratory surveys to better characterise the geographical distribution of the disease, as well as for directing efforts to communicate the risk of practices enhancing zoonotic contact.ConclusionsThese maps can inform future contingency and preparedness strategies for MVD control, especially where secondary transmission is a risk. Coupling this risk map with patient travel histories could be used to guide the differential diagnosis of highly transmissible pathogens, enabling more rapid response to outbreaks of haemorrhagic fever.

SummaryBackgroundPredicting when and where pathogens will emerge is difficult, yet, as shown by the recent Ebola and Zika epidemics, effective and timely responses are key. It is therefore crucial to transition from reactive to proactive responses for these pathogens. To better identify priorities for outbreak mitigation and prevention, we developed a cohesive framework combining disparate methods and data sources, and assessed subnational pandemic potential for four viral haemorrhagic fevers in Africa, Crimean–Congo haemorrhagic fever, Ebola virus disease, Lassa fever, and Marburg virus disease.MethodsIn this multistage analysis, we quantified three stages underlying the potential of widespread viral haemorrhagic fever epidemics. Environmental suitability maps were used to define stage 1, index-case potential, which assesses populations at risk of infection due to spillover from zoonotic hosts or vectors, identifying where index cases could present. Stage 2, outbreak potential, iterates upon an existing framework, the Index for Risk Management, to measure potential for secondary spread in people within specific communities. For stage 3, epidemic potential, we combined local and international scale connectivity assessments with stage 2 to evaluate possible spread of local outbreaks nationally, regionally, and internationally.FindingsWe found epidemic potential to vary within Africa, with regions where viral haemorrhagic fever outbreaks have previously occurred (eg, western Africa) and areas currently considered non-endemic (eg, Cameroon and Ethiopia) both ranking highly. Tracking transitions between stages showed how an index case can escalate into a widespread epidemic in the absence of intervention (eg, Nigeria and Guinea). Our analysis showed Chad, Somalia, and South Sudan to be highly susceptible to any outbreak at subnational levels.InterpretationOur analysis provides a unified assessment of potential epidemic trajectories, with the aim of allowing national and international agencies to pre-emptively evaluate needs and target resources. Within each country, our framework identifies at-risk subnational locations in which to improve surveillance, diagnostic capabilities, and health systems in parallel with the design of policies for optimal responses at each stage. In conjunction with pandemic preparedness activities, assessments such as ours can identify regions where needs and provisions do not align, and thus should be targeted for future strengthening and support.FundingPaul G Allen Family Foundation, Bill & Melinda Gates Foundation, Wellcome Trust, UK Department for International Development.

S U M M A R YThe domestic dog is the reservoir host of Leishmania infantum, the causative agent of zoonotic visceral leishmaniasis endemic in Mediterranean Europe. Targeted control requires predictive risk maps of canine leishmaniasis (CanL), which are now explored. We databased 2187 published and unpublished surveys of CanL in southern Europe. A total of 947 western surveys met inclusion criteria for analysis, including serological identification of infection (504, 369 dogs tested 1971-2006). Seroprevalence was 23·2% overall (median 10%). Logistic regression models within a GIS framework identified the main environmental predictors of CanL seroprevalence in Portugal, Spain, France and Italy, or in France alone. A 10-fold cross-validation approach determined model capacity to predict point-values of seroprevalence and the correct seroprevalence class (<5%, 5-20%, >20%). Both the four-country and France-only models performed reasonably well for predicting correctly the <5% and >20% seroprevalence classes (AUC >0·70). However, the France-only model performed much better for France than the four-country model. The four-country model adequately predicted regions of CanL emergence in northern Italy (<5% seroprevalence). Both models poorly predicted intermediate point seroprevalences (5-20%) within regional foci, because surveys were biased towards known rural foci and Mediterranean bioclimates. Our recommendations for standardizing surveys would permit higher-resolution risk mapping.