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.
Since the first outbreaks of Ebola virus disease in 1976, there have been numerous other outbreaks in humans across Africa with fatality rates ranging from 50% to 90%. Humans can become infected with the Ebola virus after direct contact with blood or bodily fluids from an infected person or animal. The virus also infects and kills other primates—such as chimpanzees or gorillas—though Old World fruit bats are suspected to be the most likely carriers of the virus in the wild.
The largest recorded outbreak of Ebola virus disease is ongoing in West Africa: more people have been infected in this current outbreak than in all previous outbreaks combined. The current outbreak is also the first to occur in West Africa—which is outside the previously known range of the Ebola virus.
Pigott et al. have now updated predictions about where in Africa wild animals may harbour the virus and where the transmission of the virus from these animals to humans is possible. As such, the map identifies the regions that are most at risk of a future Ebola outbreak. The data behind these new maps include the locations of all recorded primary cases of Ebola in human populations—the ‘index’ cases—many of which have been linked to animal sources. The data also include the locations of recorded cases of Ebola virus infections in wild bats and primates from the last forty years. The maps, which were modelled using more flexible methods than previous predictions, also include new information—collected using satellites—about environmental factors and new predictions of the range of wild fruit bats.
Pigott et al. report that the transmission of Ebola virus from animals to humans is possible in 22 countries across Central and West Africa—and that 22 million people live in the areas at risk. However, outbreaks in human populations are rare and the likelihood of a human getting the disease from an infected animal still remains very low. The updated map does not include data about how infections spread from one person to another, so the next challenge is to use existing data on human-to-human transmission to better understand the likely size and extent of current and future outbreaks. As more people live in, and travel to and from, the at-risk regions than ever before, Pigott et al. note that new outbreaks of Ebola virus disease are likely to be very different to those of the past.
Ebola; species distribution modelling; boosted regression trees; disease mapping; Ebola virus; niche based modelling; human; viruses
The WHO declared the 2014 west African Ebola epidemic a public health emergency of international concern in view of its potential for further international spread. Decision makers worldwide are in need of empirical data to inform and implement emergency response measures. Our aim was to assess the potential for Ebola virus to spread across international borders via commercial air travel and assess the relative efficiency of exit versus entry screening of travellers at commercial airports.
We analysed International Air Transport Association data for worldwide flight schedules between Sept 1, 2014, and Dec 31, 2014, and historic traveller flight itinerary data from 2013 to describe expected global population movements via commercial air travel out of Guinea, Liberia, and Sierra Leone. Coupled with Ebola virus surveillance data, we modelled the expected number of internationally exported Ebola virus infections, the potential effect of air travel restrictions, and the efficiency of airport-based traveller screening at international ports of entry and exit. We deemed individuals initiating travel from any domestic or international airport within these three countries to have possible exposure to Ebola virus. We deemed all other travellers to have no significant risk of exposure to Ebola virus.
Based on epidemic conditions and international flight restrictions to and from Guinea, Liberia, and Sierra Leone as of Sept 1, 2014 (reductions in passenger seats by 51% for Liberia, 66% for Guinea, and 85% for Sierra Leone), our model projects 2·8 travellers infected with Ebola virus departing the above three countries via commercial flights, on average, every month. 91 547 (64%) of all air travellers departing Guinea, Liberia, and Sierra Leone had expected destinations in low-income and lower-middle-income countries. Screening international travellers departing three airports would enable health assessments of all travellers at highest risk of exposure to Ebola virus infection.
Decision makers must carefully balance the potential harms from travel restrictions imposed on countries that have Ebola virus activity against any potential reductions in risk from Ebola virus importations. Exit screening of travellers at airports in Guinea, Liberia, and Sierra Leone would be the most efficient frontier at which to assess the health status of travellers at risk of Ebola virus exposure, however, this intervention might require international support to implement effectively.
Canadian Institutes of Health Research.
Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled datasets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.
The Mediterranean fruit fly (Medfly) is one of the world's most economically damaging pests. It displays highly seasonal population dynamics, and the environmental conditions suitable for its abundance are not constant throughout the year in most places. An extensive literature search was performed to obtain the most comprehensive data on the historical and contemporary spatio-temporal occurrence of the pest globally. The database constructed contained 2328 unique geo-located entries on Medfly detection sites from 43 countries and nearly 500 unique localities, as well as information on hosts, life stages and capture method. Of these, 125 localities had information on the month when Medfly was recorded and these data were complemented by additional material found in comprehensive databases available online. Records from 1980 until present were used for medfly environmental niche modeling. Maximum Entropy Algorithm (MaxEnt) and a set of seasonally varying environmental covariates were used to predict the fundamental niche of the Medfly on a global scale. Three seasonal maps were also produced: January-April, May-August and September-December. Models performed significantly better than random achieving high accuracy scores, indicating a good discrimination of suitable versus unsuitable areas for the presence of the species.
cellphone; disease model; disease outbreak; ebola; mobility; travel
Individual-based models of infectious disease transmission depend on accurate quantification of fine-scale patterns of human movement. Existing models of movement either pertain to overly coarse scales, simulate some aspects of movement but not others, or were designed specifically for populations in developed countries. Here, we propose a generalizable framework for simulating the locations that an individual visits, time allocation across those locations, and population-level variation therein. As a case study, we fit alternative models for each of five aspects of movement (number, distance from home and types of locations visited; frequency and duration of visits) to interview data from 157 residents of the city of Iquitos, Peru. Comparison of alternative models showed that location type and distance from home were significant determinants of the locations that individuals visited and how much time they spent there. We also found that for most locations, residents of two neighbourhoods displayed indistinguishable preferences for visiting locations at various distances, despite differing distributions of locations around those neighbourhoods. Finally, simulated patterns of time allocation matched the interview data in a number of ways, suggesting that our framework constitutes a sound basis for simulating fine-scale movement and for investigating factors that influence it.
activity space; agent-based model; co-location and contact networks; human mobility; simulation; synthetic population
The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.
Africa; urban growth; modelling; spatial pattern; boosted regression trees
Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain ‘hotspots’ of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected.
National census data were used to analyse and model cross-border migration and movement, using East Africa as an example. ‘Hotspots’ of origin-specific immigrants from neighbouring countries were identified for Kenya, Tanzania and Uganda. Populations of origin-specific migrants were compared to distance from origin country borders and population size at destination, and regression models were developed to quantify and compare differences in migration patterns. Migration data were then combined with existing spatially-referenced malaria data to compare the relative propensity for cross-border malaria movement in the region.
The spatial patterns and processes for immigration were different between each origin and destination country pair. Hotspots of immigration, for example, were concentrated close to origin country borders for most immigrants to Tanzania, but for Kenya, a similar pattern was only seen for Tanzanian and Ugandan immigrants. Regression model fits also differed between specific migrant groups, with some migration patterns more dependent on population size at destination and distance travelled than others. With these differences between immigration patterns and processes, and heterogeneous transmission risk in East Africa and the surrounding region, propensities to import malaria infections also likely show substantial variations.
This was a first attempt to quantify and model cross-border movements relevant to malaria transmission and control. With national census available worldwide, this approach can be translated to construct a cross-border human and malaria movement evidence base for other malaria endemic countries. The outcomes of this study will feed into wider efforts to quantify and model human and malaria movements in endemic regions to facilitate improved intervention planning, resource allocation and collaborative policy decisions.
Mosquito-borne diseases pose some of the greatest challenges in public health, especially
in tropical and sub-tropical regions of the world. Efforts to control these diseases have
been underpinned by a theoretical framework developed for malaria by Ross and Macdonald,
including models, metrics for measuring transmission, and theory of control that
identifies key vulnerabilities in the transmission cycle. That framework, especially
Macdonald's formula for R0 and its entomological derivative,
vectorial capacity, are now used to study dynamics and design interventions for many
mosquito-borne diseases. A systematic review of 388 models published between 1970 and 2010
found that the vast majority adopted the Ross–Macdonald assumption of homogeneous
transmission in a well-mixed population. Studies comparing models and data question these
assumptions and point to the capacity to model heterogeneous, focal transmission as the
most important but relatively unexplored component in current theory. Fine-scale
heterogeneity causes transmission dynamics to be nonlinear, and poses problems for
modeling, epidemiology and measurement. Novel mathematical approaches show how
heterogeneity arises from the biology and the landscape on which the processes of mosquito
biting and pathogen transmission unfold. Emerging theory focuses attention on the
ecological and social context for mosquito blood feeding, the movement of both hosts and
mosquitoes, and the relevant spatial scales for measuring transmission and for modeling
dynamics and control.
Dengue; Filariasis; Malaria; Mosquito-borne pathogen transmission; Vector control; West Nile virus
As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.
Here, using the example of Namibia, a method for targeting of interventions using surveillance data, satellite imagery, and mobile phone call records to support elimination planning is described. One year of aggregated movement patterns for over a million people across Namibia are analyzed, and linked with case-based risk maps built on satellite imagery. By combining case-data and movement, the way human population movements connect transmission risk areas is demonstrated. Communities that were strongly connected by relatively higher levels of movement were then identified, and net export and import of travellers and infection risks by region were quantified. These maps can aid the design of targeted interventions to maximally reduce the number of cases exported to other regions while employing appropriate interventions to manage risk in places that import them.
The approaches presented can be rapidly updated and used to identify where active surveillance for both local and imported cases should be increased, which regions would benefit from coordinating efforts, and how spatially progressive elimination plans can be designed. With improvements in surveillance systems linked to improved diagnosis of malaria, detailed satellite imagery being readily available and mobile phone usage data continually being collected by network providers, the potential exists to make operational use of such valuable, complimentary and contemporary datasets on an ongoing basis in infectious disease control and elimination.
Human mobility; Plasmodium falciparum malaria; Malaria elimination; Migration; Disease mapping; Spatial analysis; Satellite imagery; Mobile phones
Changes in the geographical distribution of genetic disorders are often thought to happen slowly, especially when compared with infectious diseases. Whereas mutations, genetic drift, and natural selection take place over many generations, epidemics can spread through large populations within a few days or weeks. Nevertheless, population movements can interfere with these processes, and few studies have been done of their effect on genetic disorders. We aimed to investigate the effect of global migration on the distribution of the sickle-cell gene—the most common and clinically significant haemoglobin structural variant.
For each country, we extracted data from the World Bank's Global Bilateral Migration Database about international human migrations between 1960 and 2000. We combined this information with evidence-based estimates of national HbS allele frequencies, generated within a Bayesian geostatistical framework, to analyse temporal changes in the net numbers of migrants, and classified countries with an index summarising these temporal trends.
The number of international migrants increased from 92·6 million in 1960, to 165·2 million in 2000. The estimated global number of migrants with HbS increased from about 1·6 million in 1960, to 3·6 million in 2000. This increase was largely due to an increase in the number of migrants from countries with HbS allele frequencies higher than 10%, from 3·1 million in 1960, to 14·2 million in 2000. Additionally, the mean number of countries of origin for each destination country increased from 70 (SE 46) in 1960, to 98 (48) in 2000, showing an increasing diversity in the network of international migrations between countries. Our index of change map shows a patchy distribution of the magnitude of temporal changes, with the highest positive and negative values scattered across all continents.
Global human population movements have had a substantial effect on the distribution of the HbS gene. Population movements can create a long-term burden on health-care systems. Our findings, which emphasise countries in which migration fluxes are changing the most, should increase awareness about the global burden of haemoglobinopathies and encourage policy makers to implement specific public health interventions, such as screening programmes and genetic counselling.
Wellcome Trust, European Research Council, Bill & Melinda Gates Foundation, National Institute of Allergy and Infectious Diseases–National Institutes of Health, the Research and Policy for Infectious Disease Dynamics program, Fogarty International Center.
The health and survival of women and their new-born babies in low income countries has been a key priority in public health since the 1990s. However, basic planning data, such as numbers of pregnancies and births, remain difficult to obtain and information is also lacking on geographic access to key services, such as facilities with skilled health workers. For maternal and newborn health and survival, planning for safer births and healthier newborns could be improved by more accurate estimations of the distributions of women of childbearing age. Moreover, subnational estimates of projected future numbers of pregnancies are needed for more effective strategies on human resources and infrastructure, while there is a need to link information on pregnancies to better information on health facilities in districts and regions so that coverage of services can be assessed.
This paper outlines demographic mapping methods based on freely available data for the production of high resolution datasets depicting estimates of numbers of people, women of childbearing age, live births and pregnancies, and distribution of comprehensive EmONC facilities in four large high burden countries: Afghanistan, Bangladesh, Ethiopia and Tanzania. Satellite derived maps of settlements and land cover were constructed and used to redistribute areal census counts to produce detailed maps of the distributions of women of childbearing age. Household survey data, UN statistics and other sources on growth rates, age specific fertility rates, live births, stillbirths and abortions were then integrated to convert the population distribution datasets to gridded estimates of births and pregnancies.
Results and conclusions
These estimates, which can be produced for current, past or future years based on standard demographic projections, can provide the basis for strategic intelligence, planning services, and provide denominators for subnational indicators to track progress. The datasets produced are part of national midwifery workforce assessments conducted in collaboration with the respective Ministries of Health and the United Nations Population Fund (UNFPA) to identify disparities between population needs, health infrastructure and workforce supply. The datasets are available to the respective Ministries as part of the UNFPA programme to inform midwifery workforce planning and also publicly available through the WorldPop population mapping project.
The quantification of parasite movements can provide valuable information for control strategy planning across all transmission intensities. Mobile parasite carrying individuals can instigate transmission in receptive areas, spread drug resistant strains and reduce the effectiveness of control strategies. The identification of mobile demographic groups, their routes of travel and how these movements connect differing transmission zones, potentially enables limited resources for interventions to be efficiently targeted over space, time and populations.
National population censuses and household surveys provide individual-level migration, travel, and other data relevant for understanding malaria movement patterns. Together with existing spatially referenced malaria data and mathematical models, network analysis techniques were used to quantify the demographics of human and malaria movement patterns in Kenya, Uganda and Tanzania. Movement networks were developed based on connectivity and magnitudes of flow within each country and compared to assess relative differences between regions and demographic groups. Additional malaria-relevant characteristics, such as short-term travel and bed net use, were also examined.
Patterns of human and malaria movements varied between demographic groups, within country regions and between countries. Migration rates were highest in 20–30 year olds in all three countries, but when accounting for malaria prevalence, movements in the 10–20 year age group became more important. Different age and sex groups also exhibited substantial variations in terms of the most likely sources, sinks and routes of migration and malaria movement, as well as risk factors for infection, such as short-term travel and bed net use.
Census and survey data, together with spatially referenced malaria data, GIS and network analysis tools, can be valuable for identifying, mapping and quantifying regional connectivities and the mobility of different demographic groups. Demographically-stratified HPM and malaria movement estimates can provide quantitative evidence to inform the design of more efficient intervention and surveillance strategies that are targeted to specific regions and population groups.
Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies specific importation routes that contribute to malaria epidemiology on regional spatial scales.
In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
Metapopulation rescue effects are thought to be key to the persistence of many acute immunizing infections. Yet the enhancement of persistence through spatial coupling has not been previously quantified. Here we estimate the metapopulation rescue effects for four childhood infections using global WHO reported incidence data by comparing persistence on island countries vs all other countries, while controlling for key variables such as vaccine cover, birth rates and economic development. The relative risk of extinction on islands is significantly higher, and approximately double the risk of extinction in mainland countries. Furthermore, as may be expected, infections with longer infectious periods tend to have the strongest metapopulation rescue effects. Our results quantitate the notion that demography and local community size controls disease persistence.
Malaria eradication involves eliminating malaria from every country where transmission occurs. Current theory suggests that the post-elimination challenges of remaining malaria-free by stopping transmission from imported malaria will have onerous operational and financial requirements. Although resurgent malaria has occurred in a majority of countries that tried but failed to eliminate malaria, a review of resurgence in countries that successfully eliminated finds only four such failures out of 50 successful programmes. Data documenting malaria importation and onwards transmission in these countries suggests malaria transmission potential has declined by more than 50-fold (i.e. more than 98%) since before elimination. These outcomes suggest that elimination is a surprisingly stable state. Elimination's ‘stickiness’ must be explained either by eliminating countries starting off qualitatively different from non-eliminating countries or becoming different once elimination was achieved. Countries that successfully eliminated were wealthier and had lower baseline endemicity than those that were unsuccessful, but our analysis shows that those same variables were at best incomplete predictors of the patterns of resurgence. Stability is reinforced by the loss of immunity to disease and by the health system's increasing capacity to control malaria transmission after elimination through routine treatment of cases with antimalarial drugs supplemented by malaria outbreak control. Human travel patterns reinforce these patterns; as malaria recedes, fewer people carry malaria from remote endemic areas to remote areas where transmission potential remains high. Establishment of an international resource with backup capacity to control large outbreaks can make elimination stickier, increase the incentives for countries to eliminate, and ensure steady progress towards global eradication. Although available evidence supports malaria elimination's stickiness at moderate-to-low transmission in areas with well-developed health systems, it is not yet clear if such patterns will hold in all areas. The sticky endpoint changes the projected costs of maintaining elimination and makes it substantially more attractive for countries acting alone, and it makes spatially progressive elimination a sensible strategy for a malaria eradication endgame.
malaria elimination; malaria eradication; backwards bifurcation
Air travel has expanded at an unprecedented rate and continues to do so. Its effects have been seen on malaria in rates of imported cases, local outbreaks in non-endemic areas and the global spread of drug resistance. With elimination and global eradication back on the agenda, changing levels and compositions of imported malaria in malaria-free countries, and the threat of artemisinin resistance spreading from Southeast Asia, there is a need to better understand how the modern flow of air passengers connects each Plasmodium falciparum- and Plasmodium vivax-endemic region to the rest of the world.
Recently constructed global P. falciparum and P.vivax malaria risk maps, along with data on flight schedules and modelled passenger flows across the air network, were combined to describe and quantify global malaria connectivity through air travel. Network analysis approaches were then utilized to describe and quantify the patterns that exist in passenger flows weighted by malaria prevalence. Finally, the connectivity within and to the Southeast Asia region where the threat of imported artemisinin resistance arising is highest, was examined to highlight risk routes for its spread.
The analyses demonstrate the substantial connectivity that now exists between and from malaria-endemic regions through air travel. While the air network provides connections to previously isolated malarious regions, it is clear that great variations exist, with significant regional communities of airports connected by higher rates of flow standing out. The structures of these communities are often not geographically coherent, with historical, economic and cultural ties evident, and variations between P. falciparum and P. vivax clear. Moreover, results highlight how well connected the malaria-endemic areas of Africa are now to Southeast Asia, illustrating the many possible routes that artemisinin-resistant strains could take.
The continuing growth in air travel is playing an important role in the global epidemiology of malaria, with the endemic world becoming increasingly connected to both malaria-free areas and other endemic regions. The research presented here provides an initial effort to quantify and analyse the connectivity that exists across the malaria-endemic world through air travel, and provide a basic assessment of the risks it results in for movement of infections.
The Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress toward their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, and this has prompted the development of sophisticated cartographic techniques for mapping and modeling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition over large areas is lacking, prompting many influential studies that have rigorously accounted for health risk heterogeneities to overlook the substantial demographic variations that exist subnationally and merely apply national-level adjustments.
Here we outline the development of high resolution age- and sex-structured spatial population datasets for Africa in 2000-2015 built from over a million measurements from more than 20,000 subnational units, increasing input data detail from previous studies by over 400-fold. We analyze the large spatial variations seen within countries and across the continent for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and find that substantial differences in health and development indicators can result through using only national level statistics, compared to accounting for subnational variation.
Progress toward meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on derived metrics through accounting for spatial demographic heterogeneities that exist within nations across Africa.
Population; Demography; Mapping; Millenium development goals
Changes in Dengue virus (DENV) disease patterns in the Americas over recent decades have been attributed, at least in part, to repeated introduction of DENV strains from other regions, resulting in a shift from hypoendemicity to hyperendemicity. Using newly sequenced DENV-1 and DENV-3 envelope (E) gene isolates from 11 Caribbean countries, along with sequences available on GenBank, we sought to document the population genetic and spatiotemporal transmission histories of the four main invading DENV genotypes within the Americas and investigate factors that influence the rate and intensity of DENV transmission. For all genotypes, there was an initial invasion phase characterized by rapid increases in genetic diversity, which coincided with the first confirmed cases of each genotype in the region. Rapid geographic dispersal occurred upon each genotype's introduction, after which individual lineages were locally maintained, and gene flow was primarily observed among neighboring and nearby countries. There were, however, centers of viral diversity (Barbados, Puerto Rico, Colombia, Suriname, Venezuela, and Brazil) that were repeatedly involved in gene flow with more distant locations. For DENV-1 and DENV-2, we found that a “distance-informed” model, which posits that the intensity of virus movement between locations is inversely proportional to the distance between them, provided a better fit than a model assuming equal rates of movement between all pairs of countries. However, for DENV-3 and DENV-4, the more stochastic “equal rates” model was preferred.
dengue virus; gene flow; Bayesian phylogeography; Americas; population dynamics; evolution; coalescent
Experience gained from the Global Malaria Eradication Program (1955–72) identified a set of shared technical and operational factors that enabled some countries to successfully eliminate malaria. Spatial data for these factors were assembled for all malaria-endemic countries and combined to provide an objective, relative ranking of countries by technical, operational, and combined elimination feasibility. The analysis was done separately for Plasmodium falciparum and Plasmodium vivax, and the limitations of the approach were discussed. The relative rankings suggested that malaria elimination would be most feasible in countries in the Americas and Asia, and least feasible in countries in central and west Africa. The results differed when feasibility was measured by technical or operational factors, highlighting the different types of challenge faced by each country. The results are not intended to be prescriptive, predictive, or to provide absolute assessments of feasibility, but they do show that spatial information is available to facilitate evidence-based assessments of the relative feasibility of malaria elimination by country that can be rapidly updated.
The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data.
Mathematical models of mosquito-borne pathogen transmission originated in the early twentieth century to provide insights into how to most effectively combat malaria. The foundations of the Ross–Macdonald theory were established by 1970. Since then, there has been a growing interest in reducing the public health burden of mosquito-borne pathogens and an expanding use of models to guide their control. To assess how theory has changed to confront evolving public health challenges, we compiled a bibliography of 325 publications from 1970 through 2010 that included at least one mathematical model of mosquito-borne pathogen transmission and then used a 79-part questionnaire to classify each of 388 associated models according to its biological assumptions. As a composite measure to interpret the multidimensional results of our survey, we assigned a numerical value to each model that measured its similarity to 15 core assumptions of the Ross–Macdonald model. Although the analysis illustrated a growing acknowledgement of geographical, ecological and epidemiological complexities in modelling transmission, most models during the past 40 years closely resemble the Ross–Macdonald model. Modern theory would benefit from an expansion around the concepts of heterogeneous mosquito biting, poorly mixed mosquito-host encounters, spatial heterogeneity and temporal variation in the transmission process.
infectious disease dynamics; vector-borne disease; epidemiology; dengue; West Nile; filariasis
Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.
Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.