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Cooperation among kin is common in animal societies. Kin groups may form by individuals directly discriminating relatives based on kin recognition cues, or form passively through natal philopatry and limited dispersal. We describe the genetic landscape for a primitively eusocial wasp, Polistes dominula, and ask whether individuals choose cooperative partners that are nearby and/or that are genetic relatives. Firstly, we genotyped an entire sub-population of 1361 wasps and found genetic structuring on an extremely fine scale: the probability of finding genetic relatives decreases exponentially within just a few meters of an individual’s nest. At the same time, however, we found a lack of genetic structuring between natural nest aggregations within the population. Secondly, in a separate dataset where ~2000 wasps were genotyped, we show that wasps forced experimentally to make a new nest choice tended to choose new nests near to their original nests, and that these nests tended to contain some full sisters. However, a significant fraction of wasps chose nests that did not contain sisters, despite sisters being present in nearby nests. Although we cannot rule out a role for direct kin recognition or natal nest-mate recognition, our data suggest that kin groups may form via a philopatric rule-of-thumb, whereby wasps simply select groups and nesting sites that are nearby. The result is that most subordinate helpers obtain indirect fitness benefits by breeding cooperatively.

Novel chemistry for vector control is urgently needed to counter insecticide resistance in mosquitoes. Here a new meta-diamide insecticide, broflanilide (TENEBENALTM), was evaluated in East African experimental huts in Moshi, northern Tanzania. Two consecutive experimental hut trials with broflanilide 50WP were conducted; the first evaluating the efficacy of three concentrations, 50 mg/m2, 100 mg/m2, and 200 mg/m2 using a prototype formulation, and the second trial evaluating an improved formulation. The IRS treatments were applied on both mud and concrete surfaces and efficacy was monitored over time. The mortality, blood-feeding inhibition and exiting behaviour of free-flying wild mosquitoes was compared between treatment arms. Additionally, cone assays with pyrethroid-susceptible and resistant mosquito strains were conducted in the huts to determine residual efficacy. The first trial showed a dosage-mortality response of the prototype formulation and 3–8 months of residual activity, with longer activity on concrete than mud. The second trial with an improved formulation showed prolonged residual efficacy of the 100 mg/m2 concentration to 5–6 months on mud, and mosquito mortality on the concrete surface ranged between 94–100% for the full duration of the trial. In both trials, results with free-flying, wild Anopheles arabiensis echoed the mortality trend shown in cone assays, with the highest dose inducing the highest mortality and the improved formulation showing increased mortality rates. No blood-feeding inhibition or insecticide-induced exiting effects were observed with broflanilide. Broflanilide 50WP was effective against both susceptible and pyrethroid-resistant mosquito strains, demonstrating an absence of cross resistance between broflanilide and pyrethroids. The improved formulation, which has now been branded VECTRONTM T500, resulted in a prolonged residual efficacy. These results indicate the potential of this insecticide as an addition to the arsenal of IRS products needed to maintain both control of malaria and resistance management of malaria-transmitting mosquitoes.

Globally, the economic losses due to hard ticks infestation and the control of the associated diseases have been calculated at USD $13.9–18.7 billion per year. The economic impact is related to its direct damage to the skins, blood loss, anemia, severe immunological reactions and indirect losses that related to the effects of hemoparasites, cost of treatment for clinical cases and expenses incurred in the control of ticks. The current study evaluated the acaricidal activities of fennel Foeniculum vulgare essential oil and its main components; trans-anethole and fenchone; against R. annulatus. GC–MS analysis revealed that this oil contained 16 components representing 99.9% of the total identified compounds with E-anethole being the predominant component(64.29%), followed by fenchone (9.94%). The fennel oil and trans-anethole showed significant acaricidal activities. The LC50 of the fennel oil was attained at concentrations of 12.96% for adult ticks and 1.75% for tick larvae meanwhile the LC50 of trans-anethole was reached at concentrations of 2.36% for adult tick and 0.56% for tick larvae. On the contrary, fenchone showed no any significant adulticidal activities and its LC50 attained at a concentration of 9.11% for tick larvae. Regarding repellence activities, trans-anethole achieved 100% repellency at the concentration of 10% while fennel showed 86% repellency at the same concentration. Fenchone showed no repellency effect. Treatment of larvae with fennel, trans-anethole, and fenchone LC50 concentrations significantly inhibited the acetylcholinesterase activity. Meanwhile, glutathione s-transferase activity was significantly decreased in fennel treated larvae but no significant effect was found in the larvae of trans-anethole and fenchone groups. These results indicate that the acaricide effect of fennel oil may attributed to its high content of trans-anethole. This was supported by potent adulticidal, larvicidal, and repellency effects of trans-anethole against Rhipeciphalus annulatus tick and therefore it could be included in the list of acaricide of plant origin.

Seed germination traits are key drivers of population dynamics, yet they are under-represented in community ecology studies, which have predominately focussed on adult plant and seed morphological traits. We studied the seed traits and germination strategy of eight woody plant species to investigate regeneration strategies in the arid zone of eastern Australia. To cope with stochastic and minimal rainfall, we predict that arid seeds will either have rapid germination across a wide range of temperatures, improved germination under cooler temperatures, or dormancy and/or longevity traits to delay or stagger germination across time. To understand how temperature affects germination responses, seeds of eight keystone arid species were germinated under laboratory conditions, and under three diurnal temperatures (30/20°C, 25/15°C and 17/7°C) for 30 days. Seeds of species in this study are currently stored for minesite restoration projects, hence we tested for decline in seed viability across 24 months in dry storage at similar storage conditions (≈20°C). Six of the eight arid species studied had non-dormant, rapidly germinating seeds, and only two species had physiological dormancy traits. Seed longevity differed widely between species, from one recalcitrant species surviving only months in storage (P50 =

BACKGROUND: Different setups and protocols have been developed for investigating insecticide effects on Anopheles (An.) mosquitoes, vectors of malaria. However, chemical uptake resulting from their tarsal contact with insecticide-treated material has seldom been investigated. To address the challenges encountered in the interpretation of bioassay data, a high throughput method for chemical analysis on malaria vectors was developed and validated for five selected insecticides including alpha-cypermethrin (aCYP), deltamethrin (DM), etofenprox (EPX), permethrin (PM), pirimiphos-methyl (PPM). METHODS: The method includes a single chemical extraction step via an ultrasound probe on mosquito samples and analysis via liquid chromatography coupled to high-resolution tandem mass spectrometry (UHPLC-MS/MS). The protocol was established for two malaria vector species, Anopheles gambiae senso stricto (s.s.) and An. stephensi, both males and females. Recovery rates ranged from 70 to 100% without any influence of sex or species. The method was efficiently applied to female An. gambiae s.s. of the KISUMU1 reference strain, after susceptibility tests using the World Health Organization´s standard protocol. RESULTS: Susceptibility tests revealed 13.4-18.4 minutes knockdown times for 50% mosquitoes during exposure to EPX and pyrethroids. The mortality rates 24 hours post-exposure to insecticides were mostly 99-100%, except in two PM and three PPM assays suggesting possible or confirmed resistance to these insecticides. The mean insecticide uptake in dead mosquitoes ranged from 23 pg (aCYP) to 1812 pg (EPX) per specimen. However, the mean uptake in survivors to PM and PPM was reduced by at least 25%, suggesting that acute doses were not achieved in these specimens during bioassays. CONCLUSIONS: The developed and validated UHPLC-MS/MS method could be used to address some limitations of bioassays or to assess the penetration of insecticides in mosquito matrix with reference to cuticle thickness and other insecticide resistance mechanisms.

Vermicomposting has been recommended as an eco-friendly method to transform organic waste into nutrient resources with minimum energy input. However, odor and pest issues associated with this method limit the use of vermicomposting, especially in indoor conditions. This study evaluated the effectiveness of applying hypochlorous acid (HOCl) to deodorize the vermicomposting process and improve the breeding environment for earthworms (Eisenia fetida). The deodorization performance of HOCl was compared by measuring the amount of ammonia (NH3) and amine (R-NH2) released from the decaying process of two types of food waste: HOCl-treated (HTW) waste and non-treated waste (NTW). The total and individual weights of earthworms in the waste treated with HOCl was measured to evaluate the impact on earthworm reproduction after applying HOCl. The results showed that HOCl application could reduce NH3 by 40% and R-NH2 by 80%, and increase the earthworm population size and total weight by up to 29% and 92%, respectively, compared to the control group. These results suggest that HOCl application is potentially an efficient method to control the odor and to boost earthworm reproduction and thus facilitate vermicomposting for improved food waste treatment and environmental quality.

Extensive research shows that residential segregation has severe health consequences for racial and ethnic minorities. Most research to date has operationalized segregation in terms of either poverty or race/ethnicity rather than a synergy of these factors. A novel version of the Index of Concentration at the Extremes (ICERace-Income) specifically assesses racialized economic segregation in terms of spatial concentrations of racial and economic privilege (e.g., wealthy white people) versus disadvantage (e.g., poor Black people) within a given area. This multidimensional measure advances a more comprehensive understanding of residential segregation and its consequences for racial and ethnic minorities. The aim of this paper is to critically review the evidence on the association between ICERace-Income and health outcomes. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a rigorous search of academic databases for papers linking ICERace-Income with health. Twenty articles were included in the review. Studies focused on the association of ICERace-Income with adverse birth outcomes, cancer, premature and all-cause mortality, and communicable diseases. Most of the evidence indicates a strong association between ICERace-Income and each health outcome, underscoring income as a key mechanism by which segregation produces health inequality along racial and ethnic lines. Two of the reviewed studies examined racial disparities in comorbidities and health care access as potential explanatory factors underlying this relationship. We discuss our findings in the context of the extant literature on segregation and health and propose new directions for future research and applications of the ICERace-Income measure.

The rapid development of urbanization has had a dramatic impact on the economy, society and environment in China. In this context, the coordination relationship between population urbanization and land urbanization is essential for achieving sustainable urbanization. Based on the statistical data from 2007–2017 in the Yangtze River Economic Belt (YEB), this paper established the multi-dimensional coordination evaluation (MDCE) model by using the speed coordination evaluation (SCE) model, the level consistency evaluation (LCE) model, the entropy method and the space matching evaluation (SME) model to evaluate the coordination relationship between population urbanization and land urbanization from the speed-level-space perspective. The results showed that from 2007 to 2017: 1) the development speed of population urbanization and land urbanization in the YEB were more and more coordinated, and the speed of population urbanization lagged behind that of land urbanization. In addition, the overall development speed of the 11 provinces declined, and most of them were characterized by excessive development of land urbanization. 2) the development level of population urbanization and land urbanization in the YEB were all high, but the development level of population urbanization was lower than that of land urbanization. Further, the development level of the 11 provinces remained stable and high, and continuously improved. 3) the space matching of population urbanization and land urbanization in the YEB had a high degree of coordination, and the space matching degree of population urbanization was higher than that of land urbanization. Moreover, the space matching of most provinces in the region had declined, but the change was small. Finally, this paper proposes the policy recommendations on the coordinated development of population and land urbanization at the institutional, market and management levels to achieve coordinated and sustainable urbanization.

The COVID-19 pandemic stimulated the interest of scientists, decision makers and the general public in short-term mortality fluctuations caused by epidemics and other natural or man-made disasters. To address this interest and provide a basis for further research, in May 2020, the Short-term Mortality Fluctuations data series was launched as a new section of the Human Mortality Database. At present, this unique data resource provides weekly mortality death counts and rates by age and sex for 38 countries and regions. The main objective of this paper is to detail the web-based application for visualizing and analyzing the excess mortality based on the Short-term Mortality Fluctuation data series. The application yields a visual representation of the database that enhances the understanding of the underlying data. Besides, it enables the users to explore data on weekly mortality and excess mortality across years and countries. The contribution of this paper is twofold. First, to describe a visualization tool that aims to facilitate research on short-term mortality fluctuations. Second, to provide a comprehensive open-source software solution for demographic data to encourage data holders to promote their datasets in a visual framework.

Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the research area, this paper used OD cluster analysis based on Baidu migration data from January 11 to January 25 (before the sealing-off of Wuhan) and concluded that there is a significant correlation 1the migration level from Wuhan to the GBA and the epidemic severity index. This paper also analyzed the migration levels and diffusivity of the outer and inner cities of the GBA. Lastly, four evaluation indexes were selected to research the possibility of work resumption and the rating of epidemic prevention and control through kernel density estimation. According to the study, the amount of migration depends on the geographical proximity, relationship and economic development of the source region, and the severity of the epidemic depends mainly on the migration volume and the severity of the epidemic in the source region. The epidemic risk is related not only to the severity of the epidemic in the source region but also to the degree of urban traffic development and the degree of urban openness. After the resumption of work, the pressure of epidemic prevention and control has been concentrated mainly in Shenzhen and Canton; the further away a region is from the core cities, the lower the pressure in that region. The mass migration of the population makes it difficult to control the epidemic effectively. The study of the relationship between migration volume, epidemic severity and epidemic risk is helpful to further analyze transmission types and predict the trends of the epidemic.

Background Displaced femoral neck fractures in geriatric patients are typically treated with either hemiarthroplasty or total hip arthroplasty. The choice between hemiarthroplasty and total hip arthroplasty requires a good estimate of the patient’s life expectancy, as the recent HEALTH trial suggests that the benefits of the two operations do not diverge, if at all, until the second year post-operatively. A systematic review was this performed to determine if there sufficient information in the medical literature to estimate a patient’s life expectancy beyond two years and to identify those patient variables affecting survival of that duration. Methods Pubmed, Embase, and Cochrane databases were queried for articles reporting survival data for at least two years post-operatively for at least 100 patients, age 65 or greater, treated surgically for an isolated hip fracture. A final set of 43 papers was created. The methods section of all selected papers was then reviewed to determine which variables were collected in the studies and the results section was reviewed to note whether an effect was reported for all collected variables. Results There were 43 eligible studies with 25 unique variables identified. Only age, gender, comorbidities, the presence of dementia and fracture type were collected in a majority of studies, and within that, only age and gender were reported in a majority of the results. Most (15/ 25) variables were reported in 5 or fewer of the studies. Discussion There are important deficiencies in the literature precluding the evidence-based estimation of 2 year life expectancy. Because the ostensible advantages of total hip arthroplasty are reaped only by those who survive two years or more, there is a need for additional data collection, analysis and reporting regarding survival after geriatric hip fracture.

Knowing how many donkeys there are in specific countries where welfare is compromised is a key concern for targeting efforts to improve donkey welfare. Additionally, accurate population estimates are vital for providing evidence and addressing the impact of population threats. The FAO annually report the number of donkeys and mules in each country. The last paper to investigate global and region trends dates back to 2000 and used FAO data from 1961 to 1997. This paper is an update focusing on global, regional and country level donkey and mule populations to understand if there have been any changes in the trends reported by the previous study between 1997 and 2018. Results show that the general trend identified between 1961 and 1997 is continuing with the number of donkeys globally increasing at a rate of ~1% per annum whilst mule populations are in decline at a rate of ~2% per annum. Results also suggest that the trend identified in the original paper are still evident today with the largest increases in donkey population seen in the sub-Saharan African region and greatest reduction noted in Eastern Europe with these two regions having different socio-economic drivers influencing these changes. These results highlight the multifaceted socio-economic drivers influence changes in donkey and mule populations demonstrating the complexity of designing targeted one-welfare approaches. Whilst the FAO donkey and mule datasets are the best available for understanding spatial-temporal distributions in populations there needs to be greater effort to promote the communication of information from the country level to the FAO. This can be directly supported by NGO’s by promoting the robustness of the FAO process for collating and disseminating this information. NGO’s should also seek to highlight the importance of this information for understanding global regional and country level drivers for equid population changes and potential threats to welfare as well as using this information to facilitate projects that support one-welfare approaches.

The aims of this study were to achieve a quantitative assessment of the severity of accidents involving roadside trees on highways and to propose corresponding safety measures to reduce accident losses. This paper used the acceleration severity index (ASI), head injury criteria (HIC) and chest resultant acceleration (CRA) as indicators of occupant injuries and horizontal radii, vehicle departure speeds, tree diameters and roadside tree spacing as research variables to carry out bias collision tests between cars, trucks and trees by constructing a vehicle rigid body system and an occupant multibody system in PC-crash 10.0® simulation software. A total of 2,256 data points were collected. For straight and curved segments of highways, the occupant injury evaluation models of cars were fitted based on the CRA, and occupant injury evaluation models of trucks and cars were fitted based on the ASI. According to the Fisher optimal segmentation method, reasonable classification standards of severities of accidents involving roadside trees and the corresponding ASI and CRA thresholds were determined, and severity assessment methods for accidents involving roadside trees based on the CRA and ASI were provided. Additionally, a new index by which to evaluate the accuracy of the accident severity classification and the degree of misclassification was built and applied for the validity verification of the proposed severity assessment methods. A proportion of trucks was introduced to further improve the ASI evaluation model. For the same simulation conditions, the results show that driver chest injuries are more serious than driver head injuries and that the average ASI of cars is greater than that of trucks. The CRA and ASI have a positive linear correlation with the departure speed and a logarithmic correlation with the roadside tree diameters. The larger the spacing of roadside trees is and the smaller the horizontal radius is, the smaller the chance that a vehicle will experience a second collision and the lower the risk of occupant injury. In method validation, the evaluation results from two proposed severity assessment methods based on the CRA and ASI are consistent, and the degrees of misclassification are 4.65% and 4.26%, respectively, which verifies the accuracy of the methods proposed in this paper and confirms that the ASI can be employed as an effective index for evaluating occupant injuries in accidents involving roadside trees.

Novel Covid-19 has had a huge impact on the world’s population since December 2019. The very rapid spreading of the virus worldwide, with its heavy toll of death and overload of the healthcare systems, induced the scientific community to focus on understanding, monitoring and foreseeing the epidemic evolution, weighing up the impact of different containment measures. An immense literature was produced in few months. Many papers were focused on predicting the peak features through a variety of different models. In the present paper, combining the surveillance data-set with data on mobility and testing, we develop a deterministic compartment model aimed at performing a retrospective analysis to understand the main modifications occurred to the characteristic parameters that regulate the epidemic spreading. We find that, besides self-protective behaviors, a reduction of susceptibility should have occurred in order to explain the fast descent of the epidemic after the peak. A sensitivity analysis of the basic reproduction number, in response to variations of the epidemiological parameters that can be influenced by policy-makers, shows the primary importance of a rigid isolation procedure for the diagnosed cases, combined with an intensive effort in performing extended testing campaigns. Future scenarios depend on the ability to protect the population from the injection of new cases from abroad, and to pursue in applying rigid self-protective measures.

Through the online booking platform, 10,543 big data of spatial and temporal distribution of Beijing hotel industry has been obtained in this paper. Then, the methods of GIS and the geographical detector are used to study the spatiotemporal evolution process and the influencing factors of Beijing hotel industry during 2003-2018. The results are as follows: a. During the period of 2003-2018, the hotel industry in Beijing maintained a high growth rate and had three growth peaks in 2008, 2010 and 2014. Meanwhile, major historical events, such as the Olympic Games had a significant influence on the development of the hotel industry. b. Between 2003 and 2018, the hotel industry in Beijing gradually developed from the centripetal agglomeration to aggregation + diffusion, and also from the single center to the multi-center. Besides, various hotels presented two characteristics of city orientation and scenic orientation. c. The natural geographical environment had shaped the overall pattern and characteristics of the spatial distribution of the hotel industry in Beijing, and the socio-economic factors such as commercial activities, public facilities, tourism services and traffic conditions significantly influenced the location selection of the hotel industry. Therefore, the urban center is the ideal area for the spatial layout of the hotel industry. d. Geographical detector research showed that the factors, such as administrative organs, road network density, leisure and recreational facilities, and companies have strong explanatory power for hotel location selection, which is an important reference index for hotels to select the micro location. This paper is a beneficial supplement to the existing research and has certain guiding significance for the sustainable development of Beijing hotel industry.

The paper introduces structural diversity as a new approach to quantify the complexity of technologies. By modeling technologies as combinatorial networks, a measure of technological complexity is derived that represents the diversity of (sub-)network topologies in these networks. It is further argued that this measure can be empirically approximated with the Network Diversity Score (NDS). The paper also presents an application of this approach to European patent data from 1980 to 2015. On this basis, the measure of structural diversity is shown to replicate a number of stylized facts commonly associated with technological complexity: Complexity increases over time and younger technologies are more complex than older technologies. Complex technologies are also associated to larger R&D efforts and require more collaborative R&D activities. Lastly, when controlling for technologies’ size, technologies scoring high on structural diversity are also shown to concentrate in space.

Co-diagnosis of HIV and tuberculosis presents a treatment dilemma. Starting both treatments at the same time can cause a flood of immune response called immune reconstitution inflammatory syndrome (IRIS) which can be lethal. But, how long to delay HIV treatment is less understood. In 2011, based on the conclusions of three separate studies, WHO recommended starting HIV treatment earlier for those with later HIV disease progression. This paper conducts a replication study of one of the three studies, by Havlir and colleagues. Using their publicly available data, we were able to replicate most of the results presented in the original paper. In our measurement and estimation analyses we use different estimation techniques to assess the robustness of the results. We find that adjusting for loss to follow-up does not affect the main results of the paper. However, an ANCOVA estimation and an instrumental variable model weaken the main result of the paper of better outcomes with early HIV treatment only for those who are sicker, reducing significance from the 5% to the 10% level. A change-point analysis also detects no changes in effect by timing of HIV treatment initiation or different thresholds of CD4 count for the primary outcome. This result suggests that the choice of start time for HIV treatment initiation should be based on other factors including potential drug interactions, overlapping side effects, a high pill burden and severity of illness rather than CD4 threshold and preset timeframes. While we caution against overgeneralizing, the result of this replication is aligned with more recent studies that show no evidence that early initiation of HIV treatment reduces mortality for any patients.

Open access publication rates have been steadily increasing over time. In spite of this growth, academics in low income settings struggle to gain access to the full canon of research literature. While the vast majority of open access repositories and funding organizations with open access policies are based in high income countries, the geographic patterns of open access publication itself are not well characterized. In this study, we developed a computational approach to better understand the topical and geographical landscape of open access publications in the biomedical research literature. Surprisingly, we found a strong negative correlation between country per capita income and the percentage of open access publication. Open access publication rates were particularly high in sub-Saharan Africa, but vastly lower in the Middle East and North Africa, South Asia, and East Asia and the Pacific. These effects persisted when considering papers only bearing authors from within each region and income group. However, papers resulting from international collaborations did have a higher percentage of OA than single-country papers, and inter-regional collaboration increased OA publication for all world regions. There was no clear relationship between the number of open access policies in a region and the percentage of open access publications in that region. To understand the distribution of open access across topics of biomedical research, we examined keywords that were most enriched and depleted in open access papers. Keywords related to genomics, computational biology, animal models, and infectious disease were enriched in open access publications, while keywords related to the environment, nursing, and surgery were depleted in open access publications. This work identifies geographic regions and fields of research that could be priority areas for open access advocacy. The finding that open access publication rates are highest in sub-Saharan Africa and low income countries suggests that factors other than open access policy strongly influence authors' decisions to make their work openly accessible. The high proportion of OA resulting from international collaborations indicates yet another benefit of collaborative research. Certain applied fields of medical research, notably nursing, surgery, and environmental fields, appear to have a greater proportion of fee-for-access publications, which presumably creates barriers that prevent researchers and practitioners in low income settings from accessing the literature in those fields.

This paper examines the levels of health system efficiency and their possible determinants across Latin American and Caribbean (LAC) countries using national-level data for those countries, as well as for other emerging and developed countries. The data are analyzed using data envelopment analyses and econometric advances that yield reliable estimations of the relationship between system efficiency and its potential determinants. We find that there is substantial room for efficiency improvements in the health system of most LAC countries. For example, LAC countries could improve life expectancy at birth by about five years on average at current public spending levels if they followed best practices. Furthermore, the paper assesses what factors amenable to policy act as the main possible levers for some countries to be able to translate a given level of health financing into better performance on access to care and health outcomes. Our econometric analyses suggest that efforts to increase health system efficiency could be focused in a few key policy areas associated with broader access to health services and better outcomes. These areas include general governance aspects, in addition to improvements in specific dimensions of the quality of health system institutions, notably stronger reliance on results-based management in the production of healthcare goods and services.

BackgroundFloating catchment methods have recently been applied to identify priority regions for Automated External Defibrillator (AED) deployment, to aid in improving Out of Hospital Cardiac Arrest (OHCA) survival. This approach models access as a supply-to-demand ratio for each area, targeting areas with high demand and low supply for AED placement. These methods incorporate spatial covariates on OHCA occurrence, but do not provide precise AED locations, which are critical to the initial intent of such location analysis research. Exact AED locations can be determined using optimisation methods, but they do not incorporate known spatial risk factors for OHCA, such as income and demographics. Combining these two approaches would evaluate AED placement impact, describe drivers of OHCA occurrence, and identify areas that may not be appropriately covered by AED placement strategies. There are two aims in this paper. First, to develop geospatial models of OHCA that account for and display uncertainty. Second, to evaluate the AED placement methods using geospatial models of accessibility. We first identify communities with the greatest gap between demand and supply for allocating AEDs. We then use this information to evaluate models for precise AED location deployment.MethodsCase study data set consisted of 2802 OHCA events and 719 AEDs. Spatial OHCA occurrence was described using a geospatial model, with possible spatial correlation accommodated by introducing a conditional autoregressive (CAR) prior on the municipality-level spatial random effect. This model was fit with Integrated Nested Laplacian Approximation (INLA), using covariates for population density, proportion male, proportion over 65 years, financial strength, and the proportion of land used for transport, commercial, buildings, recreation, and urban areas. Optimisation methods for AED locations were applied to find the top 100 AED placement locations. AED access was calculated for current access and 100 AED placements. Priority rankings were then given for each area based on their access score and predicted number of OHCA events.ResultsOf the 2802 OHCA events, 64.28% occurred in rural areas, and 35.72% in urban areas. Additionally, over 70% of individuals were aged over 65. Supply of AEDs was less than demand in most areas. Priority regions for AED placement were identified, and access scores were evaluated for AED placement methodology by ranking the access scores and the predicted OHCA count. AED placement methodology placed AEDs in areas with the highest priority, but placed more AEDs in areas with more predicted OHCA events in each grid cell.ConclusionThe methods in this paper incorporate OHCA spatial risk factors and OHCA coverage to identify spatial regions most in need of resources. These methods can be used to help understand how AED allocation methods affect OHCA accessibility, which is of significant practical value for communities when deciding AED placements.

Rationale The lifecycle perspective reminds us that the roots of adult ill-health may start in-utero or in early childhood. Nutritional and infectious disease insults in early life, the critical first 1000 days, are associated with stunting in childhood, and subsequent short adult stature. There is limited or no opportunity for stunted children above 2 years of age to experience catch-up growth. Some previous research has shown short maternal height to lead to adverse birth outcomes. In this paper, we document the association between maternal height and caesarean section, and between maternal height and neonatal mortality in 34 sub-Saharan African countries. We also explore the appropriate height cut-offs to use. Our paper contributes arguments to support a focus on preventing non-communicable risk factors, namely early childhood under-nutrition, as part of the fight to reduce caesarean section rates and other adverse maternal and newborn health outcomes, particularly neonatal mortality. We focus on the Sub-Saharan Africa region because it carries the highest burden of maternal and neonatal ill-health. Methods We used the most recent Demographic and Health Survey for 34 sub-Saharan African countries. The distribution of heights of women who had given birth in the 5 years before the survey was explored. We adopted the following cut-offs: Very Short (

Introduction Despite evidence of the high prevalence of antibiotic resistant infections in developing countries, studies on the clinical and economic impact of antibiotic resistance (ABR) to inform interventions to contain its emergence and spread are limited. The aim of this study was to analyze the published literature on the clinical and economic implications of ABR in developing countries. Methods A systematic search was carried out in Medline via PubMed and Web of Sciences and included studies published from January 01, 2000 to December 09, 2016. All papers were considered and a quality assessment was performed using the Newcastle-Ottawa quality assessment scale (NOS). Results Of 27 033 papers identified, 40 studies met the strict inclusion and exclusion criteria and were finally included in the qualitative and quantitative analysis. Mortality was associated with resistant bacteria, and statistical significance was evident with an odds ratio (OR) 2.828 (95%CI, 2.231–3.584; p = 0.000). ESKAPE pathogens was associated with the highest risk of mortality and with high statistical significance (OR 3.217; 95%CIs; 2.395–4.321; p = 0.001). Eight studies showed that ABR, and especially antibiotic-resistant ESKAPE bacteria significantly increased health care costs. Conclusion ABR is associated with a high mortality risk and increased economic costs with ESKAPE pathogens implicated as the main cause of increased mortality. Patients with non-communicable disease co-morbidities were identified as high-risk populations.

Introduction This paper studies the relationship between the risk of child social exclusion, as measured by the Child Social Exclusion (CSE) index and its individual domains, and child health outcomes at the small area level in Australia. The CSE index is Australia’s only national small-area index of the risk of child social exclusion. It includes five domains that capture different components of social exclusion: socio-economic background, education, connectedness, housing and health services. Methods The paper used data from the National Centre for Social and Economic Modelling (NATSEM), University of Canberra for the CSE Index and its domains and two key Australian Institute of Health and Welfare (AIHW) data sources for the health outcome measures: the National Hospital Morbidity Database and the National Mortality Database. Results The results show positive associations between rates of both of the negative health outcomes: potentially preventable hospitalisations (PPH) and avoidable deaths, and the overall risk of child social exclusion as well as with the index domains. This analysis at the small-area level can be used to identify and study areas with unexpectedly good or bad health outcomes relative to their estimated risk of child social exclusion. We show that children’s health outcomes are worse in remote parts of Australia than what would be expected solely based on the CSE index. Conclusions The results of this study suggest that developing composite indices of the risk of child social exclusion can provide valuable guidance for local interventions and programs aimed at improving children’s health outcomes. They also indicate the importance of taking a small-area approach when conducting geographic modelling of disadvantage.

Introduction From 1990–2010, worldwide child mortality declined by 43%, and maternal mortality declined by 40%. This paper compares two sources of progress: improvements in societal coverage of health determinants versus improvements in the impact of health determinants as a result of technical change. Methods This paper decomposes the progress made by 146 low- and middle-income countries (LMICs) in lowering childhood and maternal mortality into one component due to better health determinants like literacy, income, and health coverage and a second component due to changes in the impact of these health determinants. Health determinants were selected from eight distinct health-impacting sectors. Health determinants were selected from eight distinct health-impacting sectors. Regression models are used to estimate impact size in 1990 and again in 2010. Changes in the levels of health determinants were measured using secondary data. Findings The model shows that respectively 100% and 89% of the reductions in maternal and child mortality since 1990 were due to improvements in nationwide coverage of health determinants. The relative share of overall improvement attributable to any single determinant varies by country and by model specification. However, in aggregate, approximately 50% of the mortality reductions were due to improvements in the health sector, and the other 50% of the mortality reductions were due to gains outside the health sector. Conclusions Overall, countries improved maternal and child health (MCH) from 1990 to 2010 mainly through improvements in the societal coverage of a broad array of health system, social, economic and environmental determinants of child health. These findings vindicate efforts by the global community to obtain such improvements, and align with the post-2015 development agenda that builds on the lessons from the MDGs and highlights the importance of promoting health and sustainable development in a more integrated manner across sectors.

In the paper the costs of Polish county hospitals in 2015–2018 are studied using behavioral cost function. The set of variables combines hospitals’ characteristics which may determine their level of costs, such as the form of ownership, bed turnover rate, number of patient-days and share of beds in emergency department with environment characteristics which may influence both outsourcing costs and patients’ health. In 2017 the system of basic hospital service provision (hospital network) was introduced in Poland. Dummy variables included in the model represent the category of hospital in the system. The results show that the costs may be described using fixed effect panel model. Positive impact of percentage of emergency department patients transferred to other departments and of wages is found. Higher ratio of residents and interns to doctors is found to decrease costs. Dummy variable for the period after the introduction of hospital network assumed a negative sign with costs, but the parameter remained insignificant.

Biocapacity of a region exhibits spatial differences owing to the limitations of regional scale and natural conditions. Based on the multi-scale perspective, we comprehensively studied and analyzed the temporal and spatial differences of the biocapacity of a region in an attempt to establish the groundwork for optimizing urban development and its utilization framework. By adopting the ecological footprint model along with multi-scale difference evaluation method, the municipal and county scales are incorporated into a unified analysis framework in this paper, thereby facilitating the exploration of the temporal and spatial differences in the biocapacity of Shenyang—a city in China—from 2005 to 2019. The results demonstrated that: 1) At the municipal scale, the biocapacity per capita fluctuated between 1.35 hm2/person and 2.22 hm2/person. It revealed an “up-down-up” trend, which appeared consistent with the Kuznets cycle; at the county scale, the biocapacity depicted spatial differences, while those of downtown and surrounding districts/counties developed a two-level ascending hierarchical structure. 2) The time series of footprint size and depth first ascended and then declined, and can be classified into four types: closed type, inverted U-type, S-type, and M-type. Among them, S-type and M-type have the phenomenon of over-utilizing the stock capital. 3) For a long time, the regional difference of biocapacity has mostly dwelt on two scales with an evident scale effect, and the biocapacity of Liaozhong District was the worst.

Objective The Spanish health system is made up of seventeen regional health systems. Through the official reporting systems, some inconsistencies and differences in case fatality rates between Autonomous Communities (CC.AA.) have been observed. Therefore the objective of this paper is to compare COVID-19 case fatality rates across the Spanish CC.AA. Material and methods Observational descriptive study. The COVID-19 case fatality rate (CFR) was estimated according to the official records (CFR-PCR+), the daily mortality monitory system (MoMo) record (CFR-Mo), and the seroprevalence study ENE-COVID-19 (Estudio Nacional de sero Epidemiologia Covid-19) according to sex, age group and CC.AA. between March and June 2020. The main objective is to detect whether there are any differences in CFR between Spanish Regions using two different register systems, i. e., the official register of the Ministry of Health and the MoMo. Results Overall, the CFR-Mo was higher than the CFR-PCR+, 1.59% vs 0.98%. The differences in case fatality rate between both methods were significantly higher in Castilla La Mancha, Castilla y León, Cataluña, and Madrid. The difference between both methods was higher in persons over 74 years of age (CFR-PCR+ 7.5% vs 13.0% for the CFR-Mo) but without statistical significance. There was no correlation of the estimated prevalence of infection with CFR-PCR+, but there was with CFR-Mo (R2 = 0.33). Andalucía presented a SCFR below 1 with both methods, and Asturias had a SCFR higher than 1. Cataluña and Castilla La Mancha presented a SCFR greater than 1 in any scenario of SARS-CoV-2 infection calculated with SCFR-Mo. Conclusions The PCR+ case fatality rate underestimates the case fatality rate of the SARS-CoV- 2 virus pandemic. It is therefore preferable to consider the MoMo case fatality rate. Significant differences have been observed in the information and registration systems and in the severity of the pandemic between the Spanish CC.AA. Although the infection prevalence correlates with case fatality rate, other factors such as age, comorbidities, and the policies adopted to address the pandemic can explain the differences observed between CC.AA.

In pan Pamir Plateau countries, Peste des petits ruminants (PPR) has brought huge losses to the livestock industry and threaten the endangered wildlife. In unknown regions, revealing PPRV transmission among countries is the premise of effective prevention and control, therefore calls for quantified monitoring on disease communication among countries. In this paper, a MaxEnt model was built for the first time to predict the PPR risk within the research area. The least cost path (LCP) for PPR transboundary communication were calculated and referred to as the maximum available paths (MAP). The results show that there are many places with high-risk in the research area, and the domestic risk in China is lower than that in foreign countries and is mainly determined by human activities. Five LCPs representing corridors among Kazakhstan, Tajikistan, Pakistan, India and China were obtained. This study proves for the first time that there is the possibility of cross-border transmission of diseases by wild and domestic animals. In the future, it will play an important role in monitoring the PPR epidemic and blocking-up its cross-border transmission.

Why have the effects of COVID-19 been so unevenly geographically distributed in the United States? This paper investigates the role of social capital as a mediating factor for the spread of the virus. Because social capital is associated with greater trust and relationships within a community, it could endow individuals with a greater concern for others, thereby leading to more hygienic practices and social distancing. Using data for over 2,700 US counties, we investigate how social capital explains the level and growth rate of infections. We find that moving a county from the 25th to the 75th percentile of the distribution of social capital would lead to a 18% and 5.7% decline in the cumulative number of infections and deaths, as well as suggestive evidence of a lower spread of the virus. Our results are robust to many demographic characteristics, controls, and alternative measures of social capital.

Since the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and construct a short-term forecast of 60 days in the near time horizon, in relation to the expected total duration of the pandemic.

The second wave of COVID-19 in Malaysia is largely attributed to a four-day mass gathering held in Sri Petaling from February 27, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting from March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model disease dynamics before and after intervention which is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implementation within a short time period. The models and methodology used provided important insights into the nature of local transmissions to decision makers in the Ministry of Health, Malaysia.

Removal models were proposed over 80 years ago as a tool to estimate unknown population size. More recently, they are used as an effective tool for management actions for the control of non desirable species, or for the evaluation of translocation management actions. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Within this paper we review the literature of removal modelling and highlight the methodological developments for the analysis of removal data, in order to provide a unified resource for ecologists wishing to implement these approaches. Models for removal data have developed to better accommodate important features of the data and we discuss the shift in the required assumptions for the implementation of the models. The relative simplicity of this type of data and associated models mean that the method remains attractive and we discuss the potential future role of this technique.

Injuries have become devastating and often under-recognized public health concerns. In Canada, injuries are the leading cause of potential years of life lost before the age of 65. The geographical patterns of injury, however, are evident both over space and time, suggesting the possibility of spatial optimization of policies at the neighborhood scale to mitigate injury risk, foster prevention, and control within metropolitan regions. In this paper, Canada’s National Ambulatory Care Reporting System is used to assess unintentional and intentional injuries for Toronto between 2004 and 2010, exploring the spatial relations of injury throughout the city, together with Wellbeing Toronto data. Corroborating with these findings, spatial autocorrelations at global and local levels are performed for the reported over 1.7 million injuries. The sub-categorization for Toronto’s neighborhood further distills the most vulnerable communities throughout the city, registering a robust spatial profile throughout. Individual neighborhoods pave the need for distinct policy profiles for injury prevention. This brings one of the main novelties of this contribution. A comparison of the three regression models is carried out. The findings suggest that the performance of spatial regression models is significantly stronger, showing evidence that spatial regressions should be used for injury research. Wellbeing Toronto data performs reasonably well in assessing unintentional injuries, morbidity, and falls. Less so to understand the dynamics of intentional injuries. The results enable a framework to allow tailor-made injury prevention initiatives at the neighborhood level as a vital source for planning and participatory decision making in the medical field in developed cities such as Toronto.

This paper adopts models from epidemiology to account for the development and decline of neologisms based on internet usage. The research design focuses on the issue of whether a host-driven epidemic model is well-suited to explain human behavior regarding neologisms. We extracted the search frequency data from Google Trends that covers the ninety most influential Chinese neologisms from 2008-2016 and found that the majority of them possess a similar rapidly rising-decaying pattern. The epidemic model is utilized to fit the evolution of these internet-based neologisms. The epidemic model not only has good fitting performance to model the pattern of rapid growth, but also is able to predict the peak point in the neologism’s life cycle. This result underlines the role of human agents in the life cycle of neologisms and supports the macro-theory that the evolution of human languages mirrors the biological evolution of human beings.

Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator “rand/1” of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.

Archaeological research has by now revealed a great deal of variation in the way early complex societies, or chiefdoms, developed. This variation is widely recognized, but our understanding of the forces that produced it remains relatively undeveloped. This paper takes aim at such understanding by exploring variation in the local economies of six early chiefdoms; it considers what implications this variation had for trajectories of chiefdom development, as well as the source of that variation. Economic exchange is a primary form of local interaction in all societies. Because of distance-interaction principles, closer household spacing within local communities facilitated more frequent interaction and thus encouraged productive differentiation, economic interdependence, and the development of well-integrated local economies. Well-integrated local economies, in turn, provided ready opportunities for aspiring leaders to accumulate wealth and fund political economies, and pursuit of these opportunities led to societies with leaders whose power had a direct economic base. Wider household spacing, on the other hand, impeded interaction and the development of well-integrated local economies. In such contexts, aspiring leaders were able to turn to ritual and religion as a base of social power. Even when well-integrated local economies offered opportunities for wealth accumulation and a ready source of funding for political economies, these opportunities were not always taken advantage of. That variation in the shapes of early chiefdoms can be traced back to patterns of household spacing highlights the importance of settlement and interaction in explaining not just chiefdom development, but societal change more generally.

Gut physiology and the bacterial community play crucial roles in insect susceptibility to infections and insecticides. Interactions among Colorado potato beetle Leptinotarsa decemlineata (Say), its bacterial associates, pathogens and xenobiotics have been insufficiently studied. In this paper, we present our study of the survival, midgut histopathology, activity of digestive enzymes and bacterial communities of L. decemlineata larvae under the influence of Bacillus thuringiensis var. tenebrionis (morrissoni) (Bt), a natural complex of avermectins and a combination of both agents. Moreover, we estimated the impact of culturable enterobacteria on the susceptibility of the larvae to Bt and avermectins. An additive effect between Bt and avermectins was established regarding the mortality of the larvae. Both agents led to the destruction of midgut tissues, a decrease in the activity of alpha-amylases and alkaline proteinases, a decrease in the Spiroplasma leptinotarsae relative abundance and a strong elevation of Enterobacteriaceae abundance in the midgut. Moreover, an elevation of the enterobacterial CFU count was observed under the influence of Bt and avermectins, and the greatest enhancement was observed after combined treatment. Insects pretreated with antibiotics were less susceptible to Bt and avermectins, but reintroduction of the predominant enterobacteria Enterobacter ludwigii, Citrobacter freundii and Serratia marcescens increased susceptibility to both agents. We suggest that enterobacteria play an important role in the acceleration of Bt infection and avermectin toxicoses in L. decemlineata and that the additive effect between Bt and avermectin may be mediated by alterations in the bacterial community.

Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to train and evaluate the machine learning models for polyp classification. We have also compared the performance of eight state-of-the-art deep learning-based object detection models. The results demonstrate that deep CNN models are promising in CRC screening. This work can serve as a baseline for future research in polyp detection and classification.

Background To mitigate the spread of the COVID-19 coronavirus, some countries have adopted more stringent non-pharmaceutical interventions in contrast to those widely used. In addition to standard practices such as enforcing curfews, social distancing, and closure of non-essential service industries, other non-conventional policies also have been implemented, such as the total lockdown of fragmented regions, which are composed of sparsely and highly populated areas. Methods In this paper, we model the movement of a host population using a mechanistic approach based on random walks, which are either diffusive or super-diffusive. Infections are realised through a contact process, whereby a susceptible host is infected if in close spatial proximity of the infectious host with an assigned transmission probability. Our focus is on a short-time scale (∼ 3 days), which is the average time lag time before an infected individual becomes infectious. Results We find that the level of infection remains approximately constant with an increase in population diffusion, and also in the case of faster population dispersal (super-diffusion). Moreover, we demonstrate how the efficacy of imposing a lockdown depends heavily on how susceptible and infectious individuals are distributed over space. Conclusion Our results indicate that on a short-time scale, the type of movement behaviour does not play an important role in rising infection levels. Also, lock-down restrictions are ineffective if the population distribution is homogeneous. However, in the case of a heterogeneous population, lockdowns are effective if a large proportion of infectious carriers are distributed in sparsely populated sub-regions.

open access article This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.

When meteorological conditions deviate from the optimal range for human well-being, the risks of illness, injury, and death increase, and such impacts are feared in particular with more frequent and intense extreme weather conditions resulting from climate change. Thermal indices, such as the universal thermal climate index (UTCI), can better assess human weather-related stresses by integrating multiple weather components. This paper quantifies and compares the seasonal and spatial association of UTCI with mortality, morbidity, and road accidents in the federal state of Bavaria, Germany. Linear regression was applied to seasonally associate daily 56 million hospital admissions and 2.5 million death counts (1995-2015) as well as approximately 930,000 road accidents and 1.7 million people injured (2002-2015) with spatially interpolated same day- and lagged- (up to 14 days) average UTCI values. Additional linear regressions were performed stratifying by age, gender, region, and district. UTCI effects were clear in all three health outcomes studied: Increased UTCI resulted in immediate (1-2 days) rises in morbidity and even more strongly in mortality in summer, and lagged (up to 14 days) decreases in fall, winter, and spring. The strongest UTCI effects were found for road accidents where increasing UTCI led to immediate decreases in daily road accidents in winter but pronounced increases in all other seasons. Differences in UTCI effects were observed e.g. between in warmer north-western regions (Franconia, more districts with heat stress-related mortality, but hospital admissions for lung, heart and external reasons decreasing with summer heat stress), the touristic alpine regions in the south (immediate effect of increasing UTCI on road accidents in summer), and the colder south-eastern regions (increasing hospital admissions for lung, heart and external reasons in winter with UTCI). Districts with high percentages of elderly suffered from higher morbidity and mortality, particularly in winter. The influences of UTCI as well as the spatial and temporal patterns of this influence call for improved infrastructure planning and resource allocation in the health sector.

Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70–0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.

Background Common variable immunodeficiency disorders (CVID), the most common form of primary antibody deficiency, are rare conditions associated with considerable morbidity and mortality. The clinical benefit of immunoglobulin replacement therapy (IgGRT) is substantial: timely treatment with appropriate doses significantly reduces mortality and the incidence of CVID-complications such as major infections and bronchiectasis. Unfortunately, CVID-patients still face a median diagnostic delay of 4 years. Their disease burden, expressed in annual loss of disability-adjusted life years, is 3-fold higher than in the general population. Hurdles to treatment access and reimbursement by healthcare payers may exist because the value of IgGRT is poorly documented. This paper aims to demonstrate cost-effectiveness and cost-utility (on life expectancy and quality) of IgGRT in CVID. Methods and findings With input from a literature search, we built a health-economic model for cost-effectiveness and cost-utility assessment of IgGRT in CVID. We compared a mean literature-based dose (≥450mg/kg/4wks) to a zero-or-low dose (0 to ≤100 mg/kg/4wks) in a simulated cohort of adult patients from time of diagnosis until death; we also estimated the economic impact of diagnostic delay in this simulated cohort. Compared to no or minimal treatment, IgGRT showed an incremental benefit of 17 life-years (LYs) and 11 quality-adjusted life-years (QALYs), resulting in an incremental cost-effectiveness ratio (ICER) of €29,296/LY and €46,717/QALY. These results were robust in a sensitivity analysis. Reducing diagnostic delay by 4 years provided an incremental benefit of six LYs and four QALYs compared to simulated patients with delayed IgGRT initiation, resulting in an ICER of €30,374/LY and €47,495/QALY. Conclusions The health-economic model suggests that early initiation of IgGRT compared to no or delayed IgGRT is highly cost-effective. CVID-patients’ access to IgGRT should be facilitated, not only because of proven clinical efficacy, but also due to the now demonstrated cost-effectiveness.

This paper estimates the benefits and costs of state suppression policies to “bend the curve” during the initial outbreak of COVID-19 in the United States. We employ an approach that values benefits and costs in terms of additions or subtractions to total production. Relative to a baseline in which only the infected and at-risk populations mitigate the spread of coronavirus, we estimate that total benefits of suppression policies to economic output are between $632.5 billion and $765.0 billion from early March 2020 to August 1, 2020. Relative to private mitigation, output lost due to suppression policies is estimated to be between $214.2 billion and $331.5 billion. The cost estimate is based on the duration of nonessential business closures and stay-at-home orders, which were enforced between 42 and 65 days. Our results indicate that the net benefits of suppression policies to slow the spread of COVID-19 are positive and may be substantial. We discuss uncertainty surrounding several parameters and employ alternative methods for valuing mortality benefits, which also suggest that suppression measures had positive net benefits.

Urban street networks take many forms, from the circular streets in Black Rock City (which is built and removed every year as part of the Burning Man Festival) to the streets and avenues in the Manhattan grid. This paper compares the traits of cities with different street networks using the tools of urban economics. When both cities have commuting arteries of the same length, cities with circular streets have higher population densities unless access to these arteries is expensive and the number of arteries is large. Cities with arteries set at a 45oangle to the grid have smaller population densities under all circumstances.

An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.

Ever since the outbreak of the COVID-19 epidemic, various public health control strategies have been proposed and tested against the coronavirus SARS-CoV-2. We study three specific COVID-19 epidemic control models: the susceptible, exposed, infectious, recovered (SEIR) model with vaccination control; the SEIR model withshield immunitycontrol; and the susceptible, un-quarantined infected, quarantined infected, confirmed infected (SUQC) model with quarantine control. We express the control requirement inmetric temporal logic(MTL) formulas (a type of formal specification languages) which can specify the expected control outcomes such as “the deaths from the infection should never exceed one thousand per day within the next three months” or “the population immune from the disease should eventually exceed 200 thousand within the next 100 to 120 days”. We then develop methods for synthesizing control strategies with MTL specifications. To the best of our knowledge, this is the first paper to systematically synthesize control strategies based on the COVID-19 epidemic models with formal specifications. We provide simulation results in three different case studies: vaccination control for the COVID-19 epidemic with model parameters estimated from data in Lombardy, Italy; shield immunity control for the COVID-19 epidemic with model parameters estimated from data in Lombardy, Italy; and quarantine control for the COVID-19 epidemic with model parameters estimated from data in Wuhan, China. The results show that the proposed synthesis approach can generate control inputs such that the time-varying numbers of individuals in each category (e.g., infectious, immune) satisfy the MTL specifications. The results also show that early intervention is essential in mitigating the spread of COVID-19, and more control effort is needed for morestringentMTL specifications. For example, based on the model in Lombardy, Italy, achieving less than 100 deaths per day and 10000 total deaths within 100 days requires 441.7% more vaccination control effort than achieving less than 1000 deaths per day and 50000 total deaths within 100 days.

The number of suicides has increased in the last decades in several developed countries. For instance, Spain has experienced a gradual but steady increase in suicides since the 80’s and it is currently the leading external cause of death in the country. At the same time, the dualisation of the labor market, with a strong and persistent incidence of temporary contracts, has increased the instability of employment conditions. Both developments have a stronger incidence for individuals with lower levels of education. Therefore, in this paper we use rich administrative data in order to estimate the impact of the wide spread use of temporary contracts on suicides. In order to do that we exploit a reform that liberalised the use of fix-term contracts in Spain in 1984. Our results show strong long-term effects of the reform, which increased the suicide mortality rate of affected cohorts (those entering the labor market just after the liberalisation) by at least 25.3%. We believe that this result has important policy implications and should be taken into account in the design of the national suicide prevention plans.

There is evidence that adoption of non-pharmaceutical containment measures (NPMs) may have had a major impact on Covid-19 epidemic dynamics, and mitigated its effect on healthcare system. Optimal timing of implementation of these measures however is not known. In Italy, a national lockdown was decided on March 11th 2020 and ended 4th of May. At that time, cumulative incidence (CI) was different in Italian regions which ranged from 11 cases/100,000 inhabitants. In this paper, we aim to evaluate how level of incidence in different regions at the time of implementation of NPMs affected CI and had an impact on the healthcare system in terms of ICU bed occupancy and mortality rates. We used regional daily new COVID-19 diagnosed cases as well number of people hospitalized in ICU and number of deaths for period February 24-May 11 from all the 19 Italian regions and two autonomous provinces. For each region we calculated: temporal daily trend of cumulative cases of Covid-19/100,000 inhabitants, daily trend of ICU bed occupancy and mortality rate at the end of period. We found that the epidemic curves show similar trends for all regions and all tend to flatten between 11–32 days. However, after 2 months, regions with lower CI at lockdown remained at substantially lower CI (

Notable cross-country differences exist in the diffusion of the Covid-19 and in its lethality. Contact patterns in populations, and in particular intergenerational contacts, have been argued to be responsible for the most vulnerable, the elderly, getting infected more often and thus driving up mortality in some context, like in the southern European one. This paper asks a simple question: is it between whom contacts occur that matters or is it simply how many contacts people have? Due to the high number of confounding factors, it is extremely difficult to empirically assess the impact of single network features separately. This is why we rely on a simulation exercise in which we counterfactually manipulate single aspects of countries’ age distribution and network structures. We disentangle the contributions of the kind and of the number of contacts while holding constant the age structure. More precisely, we isolate the respective effects of inter-age contact patterns, degree distribution and clustering on the virus propagation across age groups. We use survey data on face-to-face contacts for Great Britain, Italy, and Germany, to reconstruct networks that mirror empirical contact patterns in these three countries. It turns out that the number of social contacts (degree distribution) largely accounts for the higher infection rates of the elderly in the Italian context, while differences in inter-age contacts patterns are only responsible for minor differences. This suggests that policies specifically targeting inter-age contacts would be little effective.