Search

Abstract We construct an effective field theory for complex Stueckelberg dark photon dark matter. Such an effective construction can be realized by writing down a complete set of operators up to dimension six built with the complex dark photon and Standard Model fields. Classifying the effective operators, we find that in order to properly take into account the non-renormalizable nature of an interacting massive vector, the size of the Wilson coefficients should be naturally smaller than naively expected. This can be consistently taken into account by a proper power counting, that we suggest. First we apply this to collider bounds on light dark matter, then to direct detection searches by extending the list of non-relativistic operators to include the case of complex vectors. In the former we correctly find scaling limits for small masses, while in the latter we mostly focus on electric dipole interactions, that are the signatures of this type of dark matter. Simple UV completions that effectively realize the above scenarios are also outlined.

Abstract We study how the coupling between axion-like particles (ALPs) and matter can be obtained at the level of on-shell scattering amplitudes. We identify three conditions that allow us to compute amplitudes that correspond to shift-symmetric Lagrangians, at the level of operators with dimension 5 or higher, and we discuss how they relate and extend the Adler’s zero condition. These conditions are necessary to reduce the number of coefficients consistent with the little-group scaling to the one expected from the Lagrangian approach. We also show how our formalism easily explains that the dimension-5 interaction involving one ALP and two massless spin-1 bosons receive corrections from higher order operators only when the ALP has a non-vanishing mass. As a direct application of our results, we perform a phenomenological study of the inelastic scattering ℓ + ℓ − → ϕh (with ℓ ± two charged leptons, ϕ the ALP and h the Higgs boson) for which, as a result of the structure of the 3-point and 4-point amplitudes, dimension-7 operators can dominate over the dimension-5 ones well before the energy reaches the cutoff of the theory.

Abstract We consider the minimal see-saw extension of the Standard Model with two right-handed singlet fermions N 1,2 with mass at the GeV scale, augmented by an effective dipole operator between the sterile states. We firstly review current bounds on this effective interaction from fixed-target and collider experiments, as well as from astrophysical and cosmological observations. We then highlight the prospects for testing the decay N 2 → N 1 γ induced by the dipole at future facilities targeting long-lived particles such as ANUBIS, CODEX-b, FACET, FASER 2, MAPP and SHiP.

Abstract We consider a dark sector containing a pair of almost degenerate states coupled to the Standard Model through a dark photon mediator. This set-up constitutes a simple realization of the inelastic dark matter scenario. The heaviest dark state is long-lived, in the limit of a small kinetic mixing among the dark photon and the Standard Model hypercharge gauge boson, and/or of a small mass splitting among the dark states. We study the prospects for detection of this scenario at proposed LHC experiments dedicated to search for long-lived particles, namely FASER, MATHUSLA, CODEX-b, AL3X, MAPP, ANUBIS and FACET. We consider both the cases of fermionic and scalar inelastic dark matter. We show that these experimental facilities can probe unexplored regions of the parameter space of this model, and we highlight their complementary roles.

Abstract We study an extension of the Standard Model with electroweak scale right-handed singlet fermions N that induces neutrino masses, plus a generic new physics sector at a higher scale Λ. The latter is parametrized in terms of effective operators in the language of the νSMEFT. We study its phenomenology considering operators up to d = 6, where additional production and decay modes for N are present in addition to those arising from the mixing with the active neutrinos. We focus on the production with four-Fermi operators and we identify the most relevant additional decay modes to be N → νγ and N → 3f. We assess the sensitivity of future Higgs factories on the νSMEFT in regions of the parameter space where the new states decay promptly, displaced or are stable on detector lengths. We show that new physics scale up to 5–60 TeV can be explored, depending on the collider considered.

The rapid development of intensive fish farming has been associated with the spreading of infectious diseases, pathogens and parasites. One such parasite is Sparicotyle chrysophrii (Platyhelminthes: Monogenea), which commonly infects cultured gilthead seabream (Sparus aurata)—a vital species in Mediterranean aquaculture. The parasite attaches to fish gills and can cause epizootics in sea cages with relevant consequences for fish health and associated economic losses for fish farmers. In this study, a novel stratified compartmental epidemiological model of S. chrysophrii transmission was developed and analysed. The model accounts for the temporal progression of the number of juvenile and adult parasites attached to each fish, as well as the abundance of eggs and oncomiracidia. We applied the model to data collected in a seabream farm, where the fish population and the number of adult parasites attached to fish gills were closely monitored in six different cages for 10 months. The model successfully replicated the temporal dynamics of the distribution of the parasite abundance within fish hosts and simulated the effects of environmental factors, such as water temperature, on the transmission dynamics. The findings highlight the potential of modelling tools for farming management, aiding in the prevention and control of S. chrysophrii infections in Mediterranean aquaculture.

Abstract Dark photons are massive abelian gauge bosons that interact with ordinary photons via a kinetic mixing with the hypercharge field strength tensor. This theory is probed by a variety of different experiments and limits are set on a combination of the dark photon mass and kinetic mixing parameter. These limits can however be strongly modified by the presence of additional heavy degrees of freedom. Using the framework of dark effective field theory, we study how robust are the current experimental bounds when these new states are present. We focus in particular on the possible existence of a dark dipole interaction between the Standard Model leptons and the dark photon. We show that, under certain assumptions, the presence of a dark dipole modifies existing supernovæ bounds for cut-off scales up to O $$ \mathcal{O} $$ (10–100 TeV). On the other hand, terrestrial experiments, such as LSND and E137, can probe cut-off scales up to O $$ \mathcal{O} $$ (3 TeV). For the latter experiment we highlight that the bound may extend down to vanishing kinetic mixing.

Abstract We consider a scenario in which the electroweak scale is stabilized via the relaxion mechanism during inflation, focussing on the case in which the back-reaction potential is generated by the confinement of new strongly interacting vector-like fermions. If the reheating temperature is sufficiently high to cause the deconfinement of the new strong interactions, the back-reaction barrier then disappears and the Universe undergoes a second relaxation phase. This phase stops when the temperature drops sufficiently for the back-reaction to form again. We identify the regions of parameter space in which the second relaxation phase does not spoil the successful stabilization of the electroweak scale. In addition, the generation of the back-reaction potential that ends the second relaxation phase can be associated to a strong first order phase transition. We then study when such transition can generate a gravitational wave signal in the range of detectability of future interferometer experiments.

Several prognostic indices are available to predict the long-term fate of emerging infectious diseases and the effect of their containment measures, including a variety of reproduction numbers. Here, the authors introduce the epidemicity index, a complementary index to evaluate the potential for transient increases of SARS-Cov-2 epidemics.

While campaigns of vaccination against SARS-CoV-2 are underway across the world, communities face the challenge of a fair and effective distribution of a limited supply of doses. Current vaccine allocation strategies are based on criteria such as age or risk. In the light of strong spatial heterogeneities in disease history and transmission, we explore spatial allocation strategies as a complement to existing approaches. Given the practical constraints and complex epidemiological dynamics, designing effective vaccination strategies at a country scale is an intricate task. We propose a novel optimal control framework to derive the best possible vaccine allocation for given disease transmission projections and constraints on vaccine supply and distribution logistics. As a proof-of-concept, we couple our framework with an existing spatially explicit compartmental COVID-19 model tailored to the Italian geographic and epidemiological context. We optimize the vaccine allocation on scenarios of unfolding disease transmission across the 107 provinces of Italy, from January to April 2021. For each scenario, the optimal solution significantly outperforms alternative strategies that prioritize provinces based on incidence, population distribution, or prevalence of susceptibles. Our results suggest that the complex interplay between the mobility network and the spatial heterogeneities implies highly non-trivial prioritization strategies for effective vaccination campaigns. Our work demonstrates the potential of optimal control for complex and heterogeneous epidemiological landscapes at country, and possibly global, scales.

Abstract We consider an extension of the Standard Model with two right-handed singlet fermions with mass at the electroweak scale that induce neutrino masses, plus a generic new physics sector at a higher scale Λ. We focus on the effective operators of lowest dimension d = 5, which induce new production and decay modes for the singlet fermions. We assess the sensitivity of future Higgs Factories, such as FCC-ee, CLIC-380, ILC and CEPC, to the coefficients of these operators for various center of mass energies. We show that future lepton colliders can test the cut-off of the theory up to Λ ≃ 500–1000 TeV, surpassing the reach of future indirect measurements of the Higgs and Z boson widths. We also comment on the possibility of determining the underlying model flavor structure should a New Physics signal be observed, and on the impact of higher dimensional d = 6 operators on the experimental signatures.

Abstract We investigate a dark sector containing a pair of light non-degenerate scalar particles, with masses in the MeV-GeV range, coupled to the visibile sector through heavier mediators. The heaviest dark state is long-lived, and its decays offer new testable signals. We analyze the prospects for detection with the proposed beam-dump facility SHiP, and the proposed LHC experiments FASER and MATHUSLA. Moreover, we consider bounds from the beam-dump experiment CHARM and from colliders (LEP, LHC and BaBar). We present our results both in terms of an effective field theory, where the heavy mediators have been integrated out, and of a simplified model containing a vector boson mediator, which can be heavy ≳ O 1 $$ \mathcal{O}(1) $$ TeV, or light O 10 $$ \mathcal{O}(10) $$ GeV. We show that future experiments can test large portions of the parameter space currently unexplored, and that they are complementary to future High-Luminosity LHC searches.

Releasing COVID-19 lockdown measures risks increases in transmission. Here, the authors estimate the increase in transmission rate for different regions in Italy and estimate that isolation of 5.5% exposed and highly infectious individuals would be needed to compensate for a 40% increase in transmission.

Abstract Several key processes in freshwater ecology are governed by the connectivity inherent to dendritic river networks. These have extensively been analyzed from a geomorphological and hydrological viewpoint, yet structures classically used in ecological modeling have been poorly representative of the structure of real river basins, often failing to capture well‐known scaling features of natural rivers. Pioneering work identified optimal channel networks (OCNs) as spanning trees reproducing all scaling features characteristic of natural stream networks worldwide. While OCNs have been used to create landscapes for studies on metapopulations, biodiversity, and epidemiology, their generation has not been generally accessible. Given the increasing interest in dendritic riverine networks by ecologists and evolutionary biologists, we here present a method to generate OCNs and, to facilitate its application, we provide the R‐package OCNet. Owing to the stochastic process generating OCNs, multiple network replicas spanning the same surface can be built; this allows performing computational experiments whose results are irrespective of the particular shape of a single river network. The OCN construct also enables the generation of elevational gradients derived from the optimal network configuration, which can constitute three‐dimensional landscapes for spatial studies in both terrestrial and freshwater realms. Moreover, the package provides functions that aggregate OCNs into an arbitrary number of nodes, calculate several descriptors of river networks, and draw relevant network features. We describe the main functionalities of the package and its integration with other R‐packages commonly used in spatial ecology. Moreover, we exemplify the generation of OCNs and discuss an application to a metapopulation model for an invasive riverine species. In conclusion, OCNet provides a powerful tool to generate realistic river network analogues for various applications. It thereby allows the design of spatially realistic studies in increasingly impacted ecosystems and enhances our knowledge on spatial processes in freshwater ecology in general.

Abstract We consider an extension of the Standard Model with two singlet leptons, with masses in the electroweak range, that induce neutrino masses via the see-saw mechanism, plus a generic new physics sector at a higher scale, Λ. We apply the minimal flavor violation (MFV) principle to the corresponding Effective Field Theory (νSMEFT) valid at energy scales E ≪ Λ. We identify the irreducible sources of lepton flavor and lepton number violation at the renormalizable level, and apply the MFV ansätz to derive the scaling of the Wilson coefficients of the νSMEFT operators up to dimension six. We highlight the most important phenomenological consequences of this hypothesis in the rates for exotic Higgs decays, the decay length of the heavy neutrinos, and their production modes at present and future colliders. We also comment on possible astrophysical implications.

We present a new bidimensional, spatially-explicit ecological model describing the dynamics of halophytic vegetation in tidal saline wetlands. Existing vegetation models employ relatively simple deterministic or stochastic mechanisms, and are driven by local environmental conditions. In the proposed model, in contrast, vegetation dynamics depend not only on the marsh local habitat, but also on spatially-explicit mechanisms of dispersal and competition among multiple interacting species. The role of habitat quality, here determined by the local elevation relative to the mean sea level as a proxy for environmental conditions, is mathematically modeled by a logistic function that represents the fundamental (theoretical) niche of each halophytic species. Hence, the model does not artificially impose any constraints to the ability of a species to colonize elevated areas where it is usually not observed: such limitations naturally arise through competition with fitter species across marsh topographic gradients. We qualitatively test our model against field data based on a suitable assemblage of focus species, and perform a sensitivity analysis aimed at determining how dynamic equilibria in vegetation distributions are affected by changes in model input parameters. Results indicate that the model is robust and can predict realistic vegetation distributions and species-richness patterns. More importantly, the model is also able to effectively reproduce the outcomes of classical ecological experiments, wherein a species is transplanted to an area outside its realized niche. A direct comparison shows that previous models not accounting for dispersal and interspecific competitions are unable to reproduce such dynamics. Our model can be easily integrated into virtually any existing morphodynamic model, thereby strengthening our ability to simulate the coupled biotic and abiotic evolution of salt marshes under changing climate forcings.

Freshwater systems are important components of the carbon cycle, but the extent of their role in CO2 fluxes is poorly understood. Here Horgby and colleagues show that mountain streams are a surprisingly large source of CO2 to the atmosphere, with annual emissions that belie their spatial extent.

Abstract High‐resolution data are improving our ability to resolve temporal patterns and controls on river productivity, but we still know little about the emergent patterns of primary production at river‐network scales. Here, we estimate daily and annual river‐network gross primary production (GPP) by applying characteristic temporal patterns of GPP (i.e., regimes) representing distinct river functional types to simulated river networks. A defined envelope of possible productivity regimes emerges at the network‐scale, but the amount and timing of network GPP can vary widely within this range depending on watershed size, productivity in larger rivers, and reach‐scale variation in light within headwater streams. Larger rivers become more influential on network‐scale GPP as watershed size increases, but small streams with relatively low productivity disproportionately influence network GPP due to their large collective surface area. Our initial predictions of network‐scale productivity provide mechanistic understanding of the factors that shape aquatic ecosystem function at broad scales.

Neutrinos may be the harbingers of new dark sectors, since the renormalizable neutrino portal allows for their interactions with hidden new physics. We propose here to use this fact to connect the generation of neutrino masses to a light dark sector, charged under a new U(1)D dark gauge symmetry. We introduce the minimal number of dark fields to obtain an anomaly free theory with spontaneous breaking of the dark symmetry, and obtain automatically the inverse seesaw Lagrangian. In addition, the so-called μ-term of the inverse seesaw is dynamically generated and technically natural in this framework. As a bonus, the new light dark gauge boson can provide a possible explanation to the MiniBooNE anomaly. Keywords: Neutrino masses, Inverse seesaw, Light dark sector

Abstract It is a distinct possibility that a Hidden Valley sector would have a spectrum of light particles consisting of both stable and unstable dark mesons. The simultaneous presence of these two types of particles can lead to novel mechanisms for generating the correct dark matter relic abundance, which in turn can reflect themselves into new exotic signatures at colliders. We study the viability of such sectors for various Hidden Valley models and map the valid parameter space to possible collider signatures. Mediators studied include various scalar bifundamentals and a heavy Z′. It is shown that in general bounds from direct and indirect detection can easily be avoided. In most of the allowed parameter space, the relic density is determined by stable mesons annihilating to unstable ones which in turn decay quickly to Standard Model particles. Dark mesons that decay mainly to heavier Standard Model fermions allow for more valid parameter space, though dark mesons are still allowed to decay exclusively to some of the lighter fermions. Possible exotic collider signatures include displaced vertices, emerging jets and semivisible jets.

Structural and functional attributes across fractal river networks have been characterized by well-established and consistent hierarchical, Hortonian scaling patterns. In most of the global river basins, spatial patterns of human settlements also conform to similar hierarchical scaling. However, emergent spatial hierarchical patterns and scaling of heterogeneous anthropogenic nutrient loads over a river basin are less known. As a case study, we examined here a large intensely managed river basin in Germany (Weser River; 46K km2; 8M population). Archived data for point-/diffuse-sources of total Phosphorus (Ptot) input loads were combined with numerical and analytical model simulations of coupled hydrological and biogeochemical processes for in-stream Ptot removal at the network scale. We find that Ptot input loads scale exponentially over stream-orders, with the larger scaling constant for point-source loads from urban agglomerations compared to those for diffuse-source contributions from agricultural and forested areas. These differences in scaling patterns result from hierarchical self-organization of human settlements, and the associated clustering of large-scale, altered land-cover. Fraction of Ptot loads removed through in-stream biogeochemical processes also manifests Hortonian scaling, consistent with predictions of an analytical model. Our analyses show that while smaller streams are more efficient in Ptot removal, in larger streams the magnitude of Ptot loads removed is higher. These trends are consistent with inverse scaling of nutrient removal rate constant with mean discharge, and downstream clustering of larger cumulative input loads. Analyses of six nested sub-basins within the Weser River Basin also reveal similar scaling patterns. Our findings are useful for projecting likely water-quality spatial patterns in similar river basins in Germany, and Central Europe. Extensions and generalizations require further examination of diverse basins with archetype spatial heterogeneities in anthropogenic pressures and hydroclimatic settings.

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.

Abstract Many models of Beyond the Standard Model physics contain particles that are charged under both Standard Model and Hidden Valley gauge groups, yet very little effort has been put into establishing their experimental signatures. We provide a general overview of the collider phenomenology of spin 0 or 1/2 mediators with non-trivial gauge numbers under both the Standard Model and a single new confining group. Due to the possibility of many unconventional signatures, the focus is on direct production with semivisible jets. For the mediators to be able to decay, a global U(1) symmetry must be broken. This is best done by introducing a set of operators explicitly violating this symmetry. We find that there is only a finite number of such renormalizable operators and that the phenomenology can be classified into five distinct categories. We show that large regions of the parameter space are already excluded, while others are unconstrained by current search strategies. We also discuss how searches could be modified to better probe these unconstrained regions by exploiting special properties of semivisible jets.

Abstract In Neutrinophilic Two Higgs Doublet Models, Dirac neutrino masses are obtained by forbidding a Majorana mass term for the right-handed neutrinos via a symmetry. We study a variation of such models in which that symmetry is taken to be a local U(1), leading naturally to the typical Lagrangian of the inverse seesaw scenario. The presence of a new gauge boson and of an extended scalar sector result in a rich phenomenology, including modifications to Z, Higgs and kaon decays as well as to electroweak precision parameters, and a pseudoscalar associated to the breaking of lepton number.

Abstract We use the framework of dark matter effective field theories to study the complementarity of bounds for a dark matter particle with mass in the MeV range. Taking properly into account the mixing between operators induced by the renormalization group running, we impose experimental constraints coming from the CMB, BBN, LHC, LEP, direct detection experiments and meson decays. In particular, we focus on the case of a vector coupling between the dark matter and the standard model fermions, and study to which extent future experiments can hope to probe regions of parameters space which are not already ruled out by current data.

Abstract We discuss the effect of Beyond the Standard Model charged current interactions on the detection of the Cosmic Neutrino Background by neutrino capture on tritium in a PTOLEMY-like detector. We show that the total capture rate can be substantially modified for Dirac neutrinos if scalar or tensor right-chiral currents, with strength consistent with current experimental bounds, are at play. We find that the total capture rate for Dirac neutrinos, Γ D BSM , can be between 0.3 to 2.2 of what is expected for Dirac neutrinos in the Standard Model, Γ D SM , so that it can be made as large as the rate expected for Majorana neutrinos with only Standard Model interactions. A non-negligible primordial abundance of right-handed neutrinos can only worsen the situation, increasing Γ D BSM by 30 to 90%. On the other hand, if a much lower total rate is measured than what is expected for Γ D SM , it may be a sign of new physics.

Abstract We investigate the effect of new physics interacting with both Dark Matter (DM) and neutrinos at DM direct detection experiments. Working within a simplified model formalism, we consider vector and scalar mediators to determine the scattering of DM as well as the modified scattering of solar neutrinos off nuclei. Using existing data from LUX as well as the expected sensitivity of LUX-ZEPLIN and DARWIN, we set limit on the couplings of the mediators to quarks, neutrinos and DM. Given the current limits, we also assess the true DM discovery potential of direct detection experiments under the presence of exotic neutrino interactions. In the case of a vector mediator, we show that the DM discovery reach of future experiments is affected for DM masses m χ ≲ 10 GeV or DM scattering cross sections σ χ ≲ 10−47 cm2. On the other hand, a scalar mediator will not affect the discovery reach appreciably.

Waterborne diseases are a diverse family of infections transmitted through ingestion of—or contact with—water infested with pathogens. Outbreaks of waterborne infections often show well-defined spatial signatures that are typically linked to local eco-epidemiological conditions, water-mediated pathogen transport and human mobility. In this work, we apply a spatially explicit network model describing the transmission cycle of waterborne pathogens to determine invasion conditions in metacommunities endowed with a realistic spatial structure. Specifically, we aim to define conditions under which pathogens can temporarily colonize a set of human communities, thus triggering a transient epidemic outbreak. To that end, we apply generalized reactivity analysis, a recently developed methodological framework for the study of transient dynamics in ecological systems subject to external perturbations. The study of pathogen invasion is complemented by the detection of the spatial signatures associated with the perturbations to a disease-free system that are expected to be amplified the most over different time scales. Understanding the drivers of waterborne disease dynamics over time scales that are relevant to epidemic and/or endemic transmission is a crucial, cross-disciplinary challenge, as large portions of the developing world still struggle to cope with the burden of these infections.

Recent studies have raised concerns about the potential reemergence of infectious diseases in Arctic regions associated with warming temperatures. Among these, particular attention has been devoted to anthrax, as a consequence of the outbreak that occurred in the Russian Yamalo-Nenets peninsula in 2016. Understanding how environmental change might influence the diffusion of this pathogen could allow informed decisions to prevent further zoonotic or epidemic episodes. To that end, the present study aims to identify and investigate the driving variables that may favor anthrax transmission within the Arctic, in order to build environmental niche maps describing the future suitability of these regions for the pathogen. To do so, we use the MaxEnt statistical learning tool informed by Arctic-specific variables, such as reindeer herd distribution and active-layer variation. Because of the relative lack of reliable georeferenced information in these regions, the resulting potential distribution maps are to be considered preliminary, but they can already provide a first assessment tool for local communities living in potential risk areas. They also indicate areas in which additional investigation is needed to improve the reliability of environmental niche modeling, hence the accuracy of risk mapping and the usefulness to Arctic communities.

A longstanding question in ecology concerns the prediction of the fate of mountain species under climate change, where climatic and geomorphic factors but also endogenous species characteristics are jointly expected to control species distributions. A significant step forward would single out reliably landscape effects, given their constraining role and relative ease of theoretical manipulation. Here, we address population dynamics in ecosystems where the substrates for ecological interactions are mountain landscapes subject to climate warming. We use a minimalist model of metapopulation dynamics based on virtual species (i.e. a suitable assemblage of focus species) where dispersal processes interact with the spatial structure of the landscape. Climate warming is subsumed by an upward shift of species habitat altering the metapopulation capacity of the landscape and hence species viability. We find that the landscape structure is a powerful determinant of species survival, owing to the specific role of the predictably evolving connectivity of the various habitats. Range shifts and lags in tracking suitable habitat experienced by virtual species under warming conditions are singled out in different landscapes. The range of parameters is identified for which these virtual species (characterized by comparable viability thus restricting their possible fitnesses and niche widths) prove unable to cope with environmental change. The statistics of the proportion of species bound to survive is identified for each landscape, providing the temporal evolution of species range shifts and the related expected occupation patterns. A baseline dynamic model for predicting species fates in evolving habitats is thus provided.

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated.

BACKGROUND:Cholera prevention and control interventions targeted to neighbors of cholera cases (case-area targeted interventions [CATIs]), including improved water, sanitation, and hygiene, oral cholera vaccine (OCV), and prophylactic antibiotics, may be able to efficiently avert cholera cases and deaths while saving scarce resources during epidemics. Efforts to quickly target interventions to neighbors of cases have been made in recent outbreaks, but little empirical evidence related to the effectiveness, efficiency, or ideal design of this approach exists. Here, we aim to provide practical guidance on how CATIs might be used by exploring key determinants of intervention impact, including the mix of interventions, "ring" size, and timing, in simulated cholera epidemics fit to data from an urban cholera epidemic in Africa. METHODS AND FINDINGS:We developed a micro-simulation model and calibrated it to both the epidemic curve and the small-scale spatiotemporal clustering pattern of case households from a large 2011 cholera outbreak in N'Djamena, Chad (4,352 reported cases over 232 days), and explored the potential impact of CATIs in simulated epidemics. CATIs were implemented with realistic logistical delays after cases presented for care using different combinations of prophylactic antibiotics, OCV, and/or point-of-use water treatment (POUWT) starting at different points during the epidemics and targeting rings of various radii around incident case households. Our findings suggest that CATIs shorten the duration of epidemics and are more resource-efficient than mass campaigns. OCV was predicted to be the most effective single intervention, followed by POUWT and antibiotics. CATIs with OCV started early in an epidemic focusing on a 100-m radius around case households were estimated to shorten epidemics by 68% (IQR 62% to 72%), with an 81% (IQR 69% to 87%) reduction in cases compared to uncontrolled epidemics. These same targeted interventions with OCV led to a 44-fold (IQR 27 to 78) reduction in the number of people needed to target to avert a single case of cholera, compared to mass campaigns in high-cholera-risk neighborhoods. The optimal radius to target around incident case households differed by intervention type, with antibiotics having an optimal radius of 30 m to 45 m compared to 70 m to 100 m for OCV and POUWT. Adding POUWT or antibiotics to OCV provided only marginal impact and efficiency improvements. Starting CATIs early in an epidemic with OCV and POUWT targeting those within 100 m of an incident case household reduced epidemic durations by 70% (IQR 65% to 75%) and the number of cases by 82% (IQR 71% to 88%) compared to uncontrolled epidemics. CATIs used late in epidemics, even after the peak, were estimated to avert relatively few cases but substantially reduced the number of epidemic days (e.g., by 28% [IQR 15% to 45%] for OCV in a 100-m radius). While this study is based on a rigorous, data-driven approach, the relatively high uncertainty about the ways in which POUWT and antibiotic interventions reduce cholera risk, as well as the heterogeneity in outbreak dynamics from place to place, limits the precision and generalizability of our quantitative estimates. CONCLUSIONS:In this study, we found that CATIs using OCV, antibiotics, and water treatment interventions at an appropriate radius around cases could be an effective and efficient way to fight cholera epidemics. They can provide a complementary and efficient approach to mass intervention campaigns and may prove particularly useful during the initial phase of an outbreak, when there are few cases and few available resources, or in order to shorten the often protracted tails of cholera epidemics.

Because of increasing global urbanization and its immediate consequences, including changes in patterns of food demand, circulation and land use, the next century will witness a major increase in the extent of paved roads built worldwide. To model the effects of this increase, it is crucial to understand whether possible self-organized patterns are inherent in the global road network structure. Here, we use the largest updated database comprising all major roads on the Earth, together with global urban and cropland inventories, to suggest that road length distributions within croplands are indistinguishable from urban ones, once rescaled to account for the difference in mean road length. Such similarity extends to road length distributions within urban or agricultural domains of a given area. We find two distinct regimes for the scaling of the mean road length with the associated area, holding in general at small and at large values of the latter. In suitably large urban and cropland domains, we find that mean and total road lengths increase linearly with their domain area, differently from earlier suggestions. Scaling regimes suggest that simple and universal mechanisms regulate urban and cropland road expansion at the global scale. As such, our findings bear implications for global road infrastructure growth based on land-use change and for planning policies sustaining urban expansions.

We study the geography of schistosomiasis across Burkina Faso by means of a spatially explicit model of water-based disease dynamics. The model quantitatively addresses the geographic stratification of disease burden in a novel framework by explicitly accounting for drivers and controls of the disease, including spatial information on the distributions of population and infrastructure, jointly with a general description of human mobility and climatic/ecological drivers. Spatial patterns of disease are analysed by the extraction and the mapping of suitable eigenvectors of the Jacobian matrix subsuming the stability of the disease-free equilibrium. The relevance of the work lies in the novel mapping of disease burden, a byproduct of the parametrization induced by regional upscaling, by model-guided field validations and in the predictive scenarios allowed by exploiting the range of possible parameters and processes. Human mobility is found to be a primary control at regional scales both for pathogen invasion success and the overall distribution of disease burden. The effects of water resources development highlighted by systematic reviews are accounted for by the average distances of human settlements from water bodies that are habitats for the parasite's intermediate host. Our results confirm the empirical findings about the role of water resources development on disease spread into regions previously nearly disease-free also by inspection of empirical prevalence patterns. We conclude that while the model still needs refinements based on field and epidemiological evidence, the proposed framework provides a powerful tool for large-scale public health planning and schistosomiasis management.

Despite major attempts to prevent cholera transmission, millions of people worldwide still must address this devastating disease. Cholera research has so far mainly focused on the causative agent, the bacterium Vibrio cholerae, or on disease treatment, but rarely were results from both fields interconnected. Indeed, the treatment of this severe diarrheal disease is mostly accomplished by oral rehydration therapy (ORT), whereby water and electrolytes are replenished. Commonly distributed oral rehydration salts also contain glucose. Here, we analyzed the effects of glucose and alternative carbon sources on the production of virulence determinants in the causative agent of cholera, the bacterium Vibrio cholerae during in vitro experimentation. We demonstrate that virulence gene expression and the production of cholera toxin are enhanced in the presence of glucose or similarly transported sugars in a ToxR-, TcpP- and ToxT-dependent manner. The virulence genes were significantly less expressed if alternative non-PTS carbon sources, including rice-based starch, were utilized. Notably, even though glucose-based ORT is commonly used, field studies indicated that rice-based ORT performs better. We therefore used a spatially explicit epidemiological model to demonstrate that the better performing rice-based ORT could have a significant impact on epidemic progression based on the recent outbreak of cholera in Haiti. Our results strongly support a change of carbon source for the treatment of cholera, especially in epidemic settings.

The temporal variability of streamflow is known to be a key feature structuring and controlling fluvial ecological communities and ecosystem processes. Although alterations of streamflow regime due to habitat fragmentation or other anthropogenic factors are ubiquitous, a quantitative understanding of their implications on ecosystem structure and function is far from complete. Here, by experimenting with two contrasting flow regimes in stream microcosms, we provide a novel mechanistic explanation for how fluctuating flow regimes may affect grazing of phototrophic biofilms (i.e., periphyton) by an invertebrate species (Ecdyonurus sp.). In both flow regimes light availability was manipulated as a control on autotroph biofilm productivity and grazer activity, thereby allowing the test of flow regime effects across various ratios of biofilm biomass to grazing activity. Average grazing rates were significantly enhanced under variable flow conditions and this effect was highest at intermediate light availability. Our results suggest that stochastic flow regimes, characterised by suitable fluctuations and temporal persistence, may offer increased windows of opportunity for grazing under favourable shear stress conditions. This bears important implications for the development of comprehensive schemes for water resources management and for the understanding of trophic carbon transfer in stream food webs.