Synthetic Population Research Articles

Modelling the epidemiological implications for SARS-CoV-2 of Christmas household bubbles in England

The emergence of SARS-CoV-2 saw severe detriments to public health being inflicted by COVID-19 disease throughout 2020. In the lead up to Christmas 2020, the UK Government sought an easement of social restrictions that would permit spending time with others over the Christmas period, whilst limiting the risk of spreading SARS-CoV-2. In November 2020, plans were published to allow individuals to socialise within ‘Christmas bubbles’ with friends and family. This policy involved a planned easing of restrictions in England between 23–27 December 2020, with Christmas bubbles allowing people from up to three households to meet throughout the holiday period. We estimated the epidemiological impact of both this and alternative bubble strategies that allowed extending contacts beyond the immediate household. We used a stochastic individual-based model for a synthetic population of 100,000 households, with demographic and SARS-CoV-2 epidemiological characteristics comparable to England as of November 2020. We evaluated five Christmas bubble scenarios for the period 23–27 December 2020, assuming our populations of households did not have symptomatic infection present and were not in isolation as the eased social restrictions began. Assessment comprised incidence and cumulative infection metrics. We tested the sensitivity of the results to a situation where it was possible for households to be in isolation at the beginning of the Christmas bubble period and also when there was lower adherence to testing, contact tracing and isolation interventions. We found that visiting family and friends over the holiday period for a shorter duration and in smaller groups was less risky than spending the entire five days together. The increases in infection from greater amounts of social mixing disproportionately impacted the eldest. We provide this account as an illustration of a real-time contribution of modelling insights to a scientific advisory group, the Scientific Pandemic Influenza Group on Modelling, Operational sub-group (SPI-M-O) for the Scientific Advisory Group for Emergencies (SAGE) in the UK, during the COVID-19 pandemic. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling

This paper presents a novel activity-based demand model that combines an optimisation framework for continuous temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dimensions (i.e. activity participation, number and type of tours, and destinations). The central idea of our approach is that individuals resolve temporal scheduling conflicts that arise from overlapping activities, e.g. needing to work and desiring to shop at the same time, in order to maximise their daily utility. Flexibility parameters capture behavioural preferences that penalise deviations from desired timings. This framework has three advantages over existing activity-based modelling approaches: (i) the time conflicts between different temporal scheduling decisions including the activity sequence are treated jointly; (ii) flexibility parameters follow a utility maximisation approach; and (iii) the framework can be used to estimate and simulate a city-scale case study in reasonable time. We introduce an estimation routine that allows flexibility parameters to be estimated using real-world observations as well as a simulation routine to efficiently resolve temporal conflicts using an optimisation model. The framework is applied to the full-time workers of a synthetic population for the city of Lausanne, Switzerland. We validate the model results against reported schedules. The results demonstrate the capabilities of our approach to reproduce empirical observations in a real-world case study.

Quality changes of Longissimus dorsi and Semimembranosus muscles and perirenal adipose tissue during frozen storage of lambs fed dihydroquercetin or dry distilled rose petals supplemented diet

The aim of study was to determine the changes of frozen stored of Longissimus dorsi and Semimembranosus muscles and perirenal adipose tissue from Bulgarian Dairy Synthetic population lambs, fed a diet supplemented by 7.5 mg dihydroquercetin (DQ) or 545 mg dry distilled rose petals (DDRP)/kg life weight/d. Three groups of 10 male lambs aged 65 days were fed 50 days ad libitum: a control (ground alfalfa + granular compound feed) and two experimental groups (the same diet + the addition of phytonutrients). Samples were collected 1 d post mortem. A half of them were analyzed immediately. Another half was vacuum-packed, quickly frozen at -40°C and stored for 365 d at -18°C. Supplementing of the lamb’s diet with polyphenol-rich phytonutrients reduces with 6.9 -12.9% the aerobic plate count, 9.3 - 25.3% the α-aminoacidic nitrogen, 31.8 - 45.9% the protein carbonyls and 38.9 - 63.4% TBARS (p ≤ 0.05) more pronounced in the m. Longissimus dorsi from lambs fed with 545 mg DDRP/kg/d. Further studies are needed to understand whether the use of higher doses of DDRP or DQ as supplements in the small ruminant’s diet will be able to provide a more pronounced inhibitory effect on oxidation processes in frozen lamb.

A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles

Due to the unavailability of prototypes, the early adopters of novel products actively seek information from multiple sources (e.g., media and social networks) to minimize the potential risk. The existing behavior models not only fail to capture the information propagation within the individual's social network, but also they do not incorporate the impact of such word-of-mouth (WOM) dissemination on the consumer's risk preferences. Moreover, even cutting-edge forecasting models rely on crude/synthetic consumer behavior models. We propose a general framework to forecast the adoption of novel products by developing a new consumer behavior model and integrating it into a population-level agent-based model. Specifically, we extend the hybrid choice model to estimate consumer behavior, which incorporates social network effects and interplay between WOM and risk aversion. The calibrated consumer behavior model and synthetic population are passed through the agent-based model for forecasting the product market share. We apply the proposed framework to forecast the adoption of autonomous vehicles (AVs) in Nashville, USA. The consumer behavior model is calibrated with a stated preference survey data of 1,495 Nashville residents. The output of the agent-based model provides the effect of the purchase price, post-purchase satisfaction, and safety measures/regulations on the forecasted AV market share. With an annual AV price reduction of 5% at the initial purchase price of $60,000 and 90% of satisfied adopters, AVs are forecasted to attain around 80% market share in thirty-one years. These findings are crucial for policymakers to develop infrastructure plans and manufacturers to conduct an after-sales cost-benefit analysis.

In-silico study of influence of HLA heterogeneity on CTL responses across ethnicities to SARS-CoV-2

Differences in outcome to COVID-19 infection in different individuals is largely attributed to genetic heterogeneity leading to differential immune responses across individuals and populations. HLA is one such genetic factor that varies across individuals leading to differences in how T-cell responses are triggered against SARS-CoV-2, directly influencing disease susceptibility. HLA alleles that influence COVID-19 outcome, by virtue of epitope binding and presentation, have been identified in cohorts worldwide. However, the heterogeneity in HLA distribution across ethnic groups limits the generality of such association. In this study, we address this limitation by comparing the recognition of CTL epitopes across HLA genotypes and ethnic groups. Using HLA allele frequency data for ethnic groups from Allele Frequency Net Database (AFND), we construct synthetic populations for each ethnic group and show that CTL epitope strength varies across HLA genotypes and populations. We also observe that HLA genotypes, in certain cases, can have high CTL epitope strengths in the absence of top-responsive HLA alleles. Finally, we show that the theoretical estimate of responsiveness and hence protection offered by a HLA allele is bound to vary across ethnic groups, due to the influence of other HLA alleles within the HLA genotype on CTL epitope recognition. This emphasizes the need for studying HLA-disease associations at the genotype level rather than at a single allele level.

Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis

Agent-based microsimulation has become the standard to analyze intelligent transportation systems, using disaggregate travel demand data for entire populations, data that are not typically readily available. Population synthesis approaches are thus needed. We present Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution of a population, that is capable of reconstructing composite synthetic agents having tabular (e.g. age and sex) as well as sequential mobility data (e.g. trip trajectory and sequence). The CTGAN model is compared with other recently proposed methods such as the Variational Autoencoders (VAE) method, which has shown success in high dimensional tabular population synthesis. We evaluate the performance of the synthesized outputs based on distribution similarity, multi-variate correlations and spatio-temporal metrics. The results show the consistent and accurate generation of synthetic populations and their tabular and spatially sequential attributes, generated over varying spatial scales and dimensions.

Population synthesis of pulsar wind nebulae and pulsar halos in the Milky Way

Context.The discovery of extended gamma-ray emission toward a number of middle-aged pulsars suggests the possibility of long-lived particle confinement beyond the classical pulsar wind nebula (PWN) stage. How this emerging source class can be extrapolated to a Galactic population remains unclear.Aims.We aim to evaluate how pulsar halos fit in existing TeV observations, under the assumption that all middle-aged pulsars develop halos similar to those observed toward the J0633+1746 or B0656+14 pulsars.Methods.We modeled the populations of supernova remnants, PWNe, and pulsar halos in the Milky Way. The PWN-halo evolutionary sequence is described in a simple yet coherent framework, and both kinds of objects are assumed to share the same particle injection properties. We then assessed the contribution of the different source classes to the very-high-energy emission from the Galaxy.Results.The synthetic population can be made consistent with the flux distribution of all known objects, including unidentified objects, for a reasonable set of parameters. The fraction of the populations predicted to be detectable in surveys of the Galactic plane with HESS and HAWC is then found to be in good agreement with their actual outcome, with a number of detectable halos ranging from 30 to 80% of the number of detectable PWNe. Prospects for CTA involve the detection of 250 − 300 sources in the Galactic Plane Survey, including 170 PWNe and up to 100 halos. The extent of diffusion suppression in halos has a limited impact on such prospects but its magnitude has a strong influence. The level of diffuse emission from unresolved populations in each survey is found to be dominated by halos and comparable to large-scale interstellar radiation powered by cosmic rays above 0.1–1 TeV.Conclusions.Pulsar halos are shown to be viable counterparts to a fraction of the currently unidentified sources if they develop around most middle-aged pulsars. Yet, if the phenomenon is rare, with an occurrence rate of 5 − 10% as suggested in a previous work from the local positron flux constraint, the total number of currently known TeV sources including unidentified ones cannot be accounted for in our model from young PWNe only. This calls for continued efforts to model pulsar-powered emission along the full evolutionary path, including the late stages past the young nebula phase.

The family pictures of our neighbours: investigating the mass function and dynamical parameters of nearby open clusters

ABSTRACT We determine the mass functions (MFs) and the dynamical parameters of 15 nearby open clusters (OCs), using the unprecedented data set of the Gaia Early Data Release 3. We select the members of each cluster by combining the photometric (colour and magnitude) and astrometric (parallax and proper motions) parameters of stars, minimizing the contamination from Galactic field interlopers. By comparing the observed distribution of stars along the cluster main sequence with the best-fitting synthetic population, we find the present-day MF and the binary fraction of the OCs, along with their dynamical parameters like mass, half-mass radius, and half-mass relaxation time. We found that the global present-day MF of OCs are consistent with a single power-law function, F(m) ∝ mα, with slopes −3 < α < −0.6 including both subsolar, 0.2 < m/M⊙ < 1, and supersolar mass regimes. A significant correlation between the MF slope and the ratio of age to half-mass relaxation time is evidenced, similarly to the same conclusion already observed among Galactic globular clusters. However, OCs evolve along different tracks in comparison with the globular clusters, possibly indicating primordial differences in their initial mass function (IMF). The comparison with Monte Carlo simulations suggests that all the analysed OCs could be born with an IMF with slope αIMF < −2.3. We also show that the less evolved OCs have an MF consistent with that of the solar neighbourhood, indicating a possible connection between the dissolution of OCs and the formation of the Galactic disc.

Anticipated cancer burden of low individual fruit and vegetable consumption under climate change: A modelling study in China.

Dietary patterns with a high intake of fruits and vegetables (FV) are associated with a reduced risk of various cancers. It is not yet clear where and to what extent a decline in crop productivity caused by climate change may modify the distribution of related cancer burdens through reducing FV consumption in China. To design policies and interventions aimed at improving FV intake, regional monitoring is required on how consumption-changing factors might impact the associated cancer burdens by socio-demographic subpopulations. A microsimulation study was conducted from a societal perspective to project the effects of cancers associated with inadequate FV intake attributable to climate change. We linked the International Model for Policy Analysis of Agricultural Commodities and Trade to a health modelling framework for obesity, gastric cancer, lung cancer, and oesophageal cancer in a close-to-reality synthetic population. In the presence of climate constantly change, the relative reduction in FV consumption would induce an additional 9.73 million disability-adjusted life years (DALYs) nationally over the period 2010-2050 ([CrI]: 7.83-12.13). The climate change-induced cancer burden is projected to disproportionately affect socio-demographic index regions from 0.65 to 5.06 million DALYs. Effects of climate change on FV consumption are anticipated to exacerbate intra-regional inequalities in the associated cancer burdens of China by 2050. By quantitatively analysing the impact of such dietary changes on regional health in light of climate change, our research can inform the design of public health interventions for heterogeneous populations, as health impact assessments based solely on the population as a whole cannot reflect significant differences across subpopulations.

Signature of a chemical spread in the open cluster M37

ABSTRACT Recent Gaia photometry of the open cluster M37 has disclosed the existence of an extended main sequence turn-off – like in Magellanic clusters younger than about 2 Gyr – and a main sequence that is broadened in colour beyond what is expected from the photometric errors, at magnitudes well below the region of the extended turn-off, where neither age differences nor rotation rates (the candidates to explain the extended turn-off phenomenon) are expected to play a role. Moreover, not even the contribution of unresolved binaries can fully explain the observed broadening. We investigated the reasons behind this broadening by making use of synthetic stellar populations and differential colour–colour diagrams, using a combination of Gaia and Sloan filters. From our analysis, we have concluded that the observed colour spread in the Gaia colour–magnitude diagram can be reproduced by a combination of either a metallicity spread $\Delta \rm [Fe/H] \sim 0.15$ plus a differential reddening across the face of the cluster spanning a total range ΔE(B − V) ∼ 0.06, or a spread of the initial helium mass fraction ΔY ∼ 0.10 plus a smaller range of reddening ΔE(B − V) ∼ 0.03. High-resolution differential abundance determinations of a sizeable sample of cluster stars are necessary to confirm or exclude the presence of a metal abundance spread. Our results raise the possibility that also individual open clusters, like globular clusters and massive star clusters, host stars born with different initial chemical compositions.

Statistical Properties of a Virtual Cohort for In Silico Trials Generated with a Statistical Anatomy Atlas

Osteoporosis-related hip fragility fractures are a catastrophic event for patient lives but are not frequently observed in prospective studies, and therefore phase III clinical trials using fractures as primary clinical endpoint require thousands of patients enrolled for several years to reach statistical significance. A novel answer to the large number of subjects needed to reach the desired evidence level is offered by In Silico Trials, that is, the simulation of a clinical trial on a large cohort of virtual patients, monitoring the biomarkers of interest. In this work we investigated if statistical aliasing from a custom anatomy atlas could be used to expand the patient cohort while retaining the original biomechanical characteristics. We used a pair-matched cohort of 94 post-menopausal women (at the time of the CT scan, 47 fractured and 47 not fractured) to create a statistical anatomy atlas through principal component analysis, and up-sampled the atlas in order to obtain over 1000 synthetic patient models. We applied the biomechanical computed tomography pipeline to the resulting virtual cohort and compared its fracture risk distribution with that of the original physical cohort. While the distribution of femoral strength values in the non-fractured sub-group was nearly identical to that of the original physical cohort, that of the fractured sub-group was lower than in the physical cohort. Nonetheless, by using the classification threshold used for the original population, the synthetic population was still divided into two parts of approximatively equal number.

Anatomically Parameterized Statistical Shape Model: Explaining Morphometry Through Statistical Learning.

Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature. This limits the use of SSMs to subjective evaluations in clinical practices. We propose a novel SSM controlled by anatomical parameters derived from morphometric analysis. The proposed anatomically parameterized SSM (ANAT[Formula: see text]) is based on learning a linear mapping between shape coefficients (latent space) and selected anatomical parameters (anatomical space). This mapping is learned from a synthetic population generated by the standard SSM. Determining the pseudo-inverse of the mapping allows us to build the ANAT[Formula: see text]. We further impose orthogonality constraints to the anatomical parameterization (OC-ANAT[Formula: see text]) to obtain independent shape variation patterns. The proposed contribution was evaluated on two skeletal databases of femoral and scapular bone shapes using clinically relevant anatomical parameters within each (five for femoral and six for scapular bone). Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved moderate to excellent performance in predicting anatomical parameters on unseen shapes. This study demonstrates the use of anatomical representation for creating anatomically parameterized SSMs and as a result, removes the limited clinical interpretability of standard SSMs. The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment.

Analysis of MATSim Modeling of Road Infrastructure in Cyclists’ Choices in the Case of a Hilly Relief

For too long, many refined transportation models have focused solely on private and public transportation, assuming that bicycles only require simple models, such as bird flight distance or trips on horizontal tracks at a constant speed. This paper aims to study the impact of the road characteristics, such as road gradient, type of road and pavement surface of the road, on cyclists’ behavior using dedicated modules of MATSim. For that, we compare two approaches: a standard approach which does not consider the road characteristics, and a second approach that uses MATSim bicycle extension of Ziemke et al. The two approaches are analyzed over a sub-regional area around a district, focusing on a suburban city with an undulating relief made of average-to-steep hills. The focus is on the bicycle transportation model because the catchment area has a particularly challenging altitude profile and a large variety of roads, whether in type—from residential to national highway—or in pavement surface due to the number of green areas, such as parks and forests. This area is defined as a rather large 7 × 12 km, including five suburban cities in the South of Paris, France. A synthetic population of 126,000 agents was generated at a regional scale, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, with activity-time choice, location choice and modal choice. We wanted to know how accurately a standard model of bicycle travels can be made with a 2D flat Earth assumption by comparing it to an algorithm extension that explicitly considers road characteristics in cyclists’ route choices. Our finding is that the MATSim bicycle extension model impacts mainly the long trips. Otherwise, the differences are minimal between the two models in terms of travel time and travel distance.

Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2.

Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes-with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity.

Allocating synthetic population to a finer spatial scale: An integer quadratic programming formulation

Agent-Based Models (ABMs) are being increasingly used to evaluate urban systems, urban policies and environmental impacts. One prerequisite for using the ABM framework consists of generating a synthetic population representative of the actual population, featuring the appropriate attributes with respect to model objectives. A precise spatial positioning of the synthetic population agents is often key to ensuring ABM modeling quality. This paper considers the problem of allocating synthetic population agents to a finer spatial scale. Such an allocation process is performed from a higher-level statistical area where a synthetic population can be generated, that is, a container statistical area (CSA), to several nested non-overlapping elementary statistical areas (ESAs), where only marginals are available. This allocation step relies not only on common attributes between CSA and ESA, but also on additional discriminatory attributes, that is, attributes of interest, estimated from external data sources. The case study examined herein is based on French census and fiscal data. Common attributes include eight socio-demographic variables, totaling 17 modalities. An additional attribute of interest, that is, income, has also been added. The allocation problem at hand is modeled as an integer quadratic programming problem. An exact algorithm is first applied to solve the problem; the applicability of this algorithm proves to be limited to small-size synthetic populations. A heuristic is proposed to handle the allocation of larger-size synthetic populations. Tests carried out on the case study show that this heuristic yields near-optimal solutions; it is also computationally efficient and may fulfill the needs of a majority of users.

Genetic dissection of innate immune memory in Drosophila melanogaster.

Current studies have demonstrated that innate immunity possesses memory characteristics. Although the molecular mechanisms underlying innate immune memory have been addressed by numerous studies, genetic variations in innate immune memory and the associated genes remain unclear. Here, we explored innate immune memory in 163 lines of Drosophila melanogaster from the Drosophila Synthetic Population Resource. In our assay system, prior training with low pathogenic bacteria (Micrococcus luteus) increased the survival rate of flies after subsequent challenge with highly pathogenic bacteria (Staphylococcus aureus). This positive training effect was observed in most lines, but some lines exhibited negative training effects. Survival rates under training and control conditions were poorly correlated, suggesting that distinct genetic factors regulate training effects and normal immune responses. Subsequent quantitative trait loci analysis suggested that four loci containing 80 genes may be involved in regulating innate immune memory. Among them, Adgf-A, which encodes an extracellular adenosine deaminase-related growth factor, was shown to be associated with training effects. Our study findings help to elucidate the genetic architecture of innate immune memory in Drosophila and may provide insight for new therapeutic treatments aimed at boosting immunity.

An agent-based model to assess coronavirus disease 19 spread and health systems burden

The present pandemic has tremendously raised the health systems’ burden around the globe. It is important to understand the transmission dynamics of the infection and impose localized strategies across different geographies to curtail the spread of the infection. The present study was designed to assess the transmission dynamics and the health systems’ burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based modeling (ABM) approach. The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana, India. The effects of imposing and lifting lockdowns, nonpharmaceutical interventions, and the role of immunity were analyzed. The distribution of people in different health states was measured separately for each district of Telangana. The spread dramatically increased and reached a peak soon after the lockdowns were relaxed. It was evident that is the protection offered is higher when a higher proportion of the population is exposed to the interventions. ABMs help to analyze grassroots details compared to compartmental models. Risk estimates provide insights on the proportion of the population protected by the adoption of one or more of the control measures, which is of practical significance for policymaking.

Large Interferometer For Exoplanets (LIFE)

Context.One of the long-term goals of exoplanet science is the atmospheric characterization of dozens of small exoplanets in order to understand their diversity and search for habitable worlds and potential biosignatures. Achieving this goal requires a space mission of sufficient scale that can spatially separate the signals from exoplanets and their host stars and thus directly scrutinize the exoplanets and their atmospheres.Aims.We seek to quantify the exoplanet detection performance of a space-based mid-infrared (MIR) nulling interferometer that measures the thermal emission of exoplanets. We study the impact of various parameters and compare the performance with that of large single-aperture mission concepts that detect exoplanets in reflected light.Methods.We have developed an instrument simulator that considers all major astrophysical noise sources and coupled it with Monte Carlo simulations of a synthetic exoplanet population around main-sequence stars within 20 pc of the Sun. This allows us to quantify the number (and types) of exoplanets that our mission concept could detect. Considering single visits only, we discuss two different scenarios for distributing 2.5 yr of an initial search phase among the stellar targets. Different apertures sizes and wavelength ranges are investigated.Results.An interferometer consisting of four 2 m apertures working in the 4–18.5 μ.m wavelength range with a total instrument throughput of 5% could detect up to ≈550 exoplanets with radii between 0.5 and 6R⊕with an integratedS/N≥ 7. At least ≈160 of the detected exoplanets have radii ≤1.5R⊕. Depending on the observing scenario, ≈25–45 rocky exoplanets (objects with radii between 0.5 and 1.5R⊕) orbiting within the empirical habitable zone (eHZ) of their host stars are among the detections. With four 3.5 m apertures, the total number of detections can increase to up to ≈770, including ≈60–80 rocky eHZ planets. With four times 1 m apertures, the maximum detection yield is ≈315 exoplanets, including ≤20 rocky eHZ planets. The vast majority of small, temperate exoplanets are detected around M dwarfs. The impact of changing the wavelength range to 3–20 μm or 6–17 μm on the detection yield is negligible.Conclusions.A large space-based MIR nulling interferometer will be able to directly detect hundreds of small, nearby exoplanets, tens of which would be habitable world candidates. This shows that such a mission can compete with large single-aperture reflected light missions. Further increasing the number of habitable world candidates, in particular around solar-type stars, appears possible via the implementation of a multi-visit strategy during the search phase. The high median S/N of most of the detected planets will allow for first estimates of their radii and effective temperatures and will help prioritize the targets for a second mission phase to obtain high-S/N thermal emission spectra, leveraging the superior diagnostic power of the MIR regime compared to shorter wavelengths.

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia.

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

Access to community pharmacies: A nationwide geographic information systems cross-sectional analysis

BackgroundPharmacy accessibility is key for the emerging role of community pharmacists as providers of patient-centered, medication management services in addition to traditional dispensing roles. ObjectiveTo quantify population access to community pharmacies across the United States. MethodsWe obtained addresses for pharmacy locations in the United States from the National Council for Prescription Drug Programs and geocoded each. For a 1% sample of a U.S. synthetic population, we calculated the driving distance to the closest pharmacy using ArcGIS. We estimated the proportion of population living within 1, 2, 5, and 10 miles of a community pharmacy. We quantified the role of chain vs regional franchises or independently owned pharmacies in providing access across degrees of urbanicity. ResultsWe identified 61,715 pharmacies, including 37,954 (61.5%) chains, 23,521 (38.1%) regional franchises or independently owned pharmacies, and 240 (0.4%) government pharmacies. In large metropolitan areas, 62.8% of the pharmacies were chains; however, in rural areas, 76.5% of pharmacies were franchises or independent pharmacies. Across the overall U.S. population, 48.1% lived within 1 mile of any pharmacy, 73.1% within 2 miles, 88.9% within 5 miles, and 96.5% within 10 miles. Across the United States, 8.3% of counties had at least 50% of residents with a distance greater than 10 miles. These low-access counties were concentrated in Alaska, South Dakota, North Dakota, and Montana. ConclusionsCommunity pharmacies may serve as accessible locations for patient-centered, medication management services that enhance the health and wellness of communities. Although chain pharmacies represent the majority of pharmacy locations across the country, access to community pharmacies in rural areas predominantly relies on regional franchises and independently owned pharmacies.