Thin oceanic crust is formed by decompression melting of the upper mantle at mid-ocean ridges, but the origin of the thick and buoyant continental crust is enigmatic. Juvenile continental crust may form from magmas erupted above intraoceanic subduction zones, where oceanic lithosphere subducts beneath other oceanic lithosphere. However, it is unclear why the subduction of dominantly basaltic oceanic crust would result in the formation of andesitic continental crust at the surface. Here we use geochemical and geophysical data to reconstruct the evolution of the Central American land bridge, which formed above an intra-oceanic subduction system over the past 70 Myr. We find that the geochemical signature of erupted lavas evolved from basaltic to andesitic about 10 Myr ago-coincident with the onset of subduction of more oceanic crust that originally formed above the Galápagos mantle plume. We also find that seismic P-waves travel through the crust at velocities intermediate between those typically observed for oceanic and continental crust. We develop a continentality index to quantitatively correlate geochemical composition with the average P-wave velocity of arc crust globally. We conclude that although the formation and evolution of continents may involve many processes, melting enriched oceanic crust within a subduction zone-a process probably more common in the Archaean-can produce juvenile continental crust.

ObjectivesThis research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its residents.MethodsWe use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza.ResultsSimulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%–55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums.ConclusionsUnique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.

BackgroundInfluenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact.ObjectiveDescribe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions.MethodsWe describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You).ResultsWISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information.ConclusionsWhile the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world.