Future Spread Research Articles

Accounting downside risk measures and credit spreads

PurposeThis study aims to examine the association between predictive accounting downside risk measures and changes in credit spreads. Building upon the earnings downside risk (EDR) measure developed in prior literature, this paper introduces cash flow downside risk (CFDR).Design/methodology/approachThis study modifies an existing empirical framework (root lower partial moment) to calculate CFDR and applies it to a sample of firms between 2002 and 2013 for which credit default swap data are available.FindingsAfter validating the measure, this study identifies a positive association between CFDR and changes in credit spreads. This paper further shows the association between CFDR and credit spread changes is stronger than that between EDR and credit spread changes. Financial stability moderates the relationship between CFDR and credit spreads.Originality/valueThis study proposes a novel measure of accounting downside risk, CFDR and demonstrates a negative association between this measure and future cash flow and a positive association between this measure and future credit spreads.

Hydroxycinnamic Acids and Derivatives Formulations for Skin Damages and Disorders: A Review.

Alterations of skin homeostasis are widely diffused in our everyday life both due to accidental injuries, such as wounds and burns, and physiological conditions, such as late-stage diabetes, dermatitis, or psoriasis. These events are locally characterized by an intense inflammatory response, a high generation of harmful free radicals, or an impairment in the immune response regulation, which can profoundly change the skin tissue’ repair process, vulnerability, and functionality. Moreover, diabetes diffusion, antibiotic resistance, and abuse of aggressive soaps and disinfectants following the COVID-19 emergency could be causes for the future spreading of skin disorders. In the last years, hydroxycinnamic acids and derivatives have been investigated and applied in several research fields for their anti-oxidant, anti-inflammatory, and anti-bacterial activities. First, in this study, we give an overview of these natural molecules’ current source and applications. Afterwards, we review their potential role as valid alternatives to the current therapies, supporting the management and rebalancing of skin disorders and diseases at different levels. Also, we will introduce the recent advances in the design of biomaterials loaded with these phenolic compounds, specifically suitable for skin disorders treatments. Lastly, we will suggest future perspectives for introducing hydroxycinnamic acids and derivatives in treating skin disorders.

Long-Term Projections of the Natural Expansion of the Pine Wood Nematode in the Iberian Peninsula

The invasive pine wood nematode (PWN), Bursaphelenchus xylophilus, causal agent of pine wilt disease, was first reported in Europe, near Lisbon, in 1999, and has since then spread to most of Portugal. We here modelled the spatiotemporal patterns of future PNW natural spread in the Iberian Peninsula, as dispersed by the vector beetle Monochamus galloprovincialis, using a process-based and previously validated network model. We improved the accuracy, informative content, forecasted period and spatial drivers considered in previous modelling efforts for the PWN in Southern Europe. We considered the distribution and different susceptibility to the PWN of individual pine tree species and the effect of climate change projections on environmental suitability for PWN spread, as we modelled the PWN expansion dynamics over the long term (>100 years). We found that, in the absence of effective containment measures, the PWN will spread naturally to the entire Iberian Peninsula, including the Pyrenees, where it would find a gateway for spread into France. The PWN spread will be relatively gradual, with an average rate of 0.83% of the total current Iberian pine forest area infected yearly. Climate was not found to be an important limiting factor for long-term PWN spread, because (i) there is ample availability of alternative pathways for PWN dispersal through areas that are already suitable for the PWN in the current climatic conditions; and (ii) future temperatures will make most of the Iberian Peninsula suitable for the PWN before the end of this century. Unlike climate, the susceptibility of different pine tree species to the PWN was a strong determinant of PWN expansion through Spain. This finding highlights the importance of accounting for individual tree species data and of additional research on species-specific susceptibility for more accurate modelling of PWN spread and guidance of related containment efforts.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

A study on the efficiency of the estimation models of COVID-19

Today, the world is fighting against a dangerous epidemic caused by the novel coronavirus, also known as COVID-19. All have been impacted and countries are trying to recover from the social, economic, and health devastations of COVID-19. Recent epidemiology research has concentrated on using different prediction models to estimate the numbers of infected, recovered, and deceased cases around the world. This study is primarily focused on evaluating two common prediction models: Susceptible – Infected – Recovered (SIR) and Susceptible – Exposed – Infected – Recovered (SEIR). The SIR and SEIR models were compared in estimating the outbreak and identifying the better fitting model for forecasting future spread in Kuwait. Based on the results of the comparison, the SEIR model was selected for predicting COVID-19 infected, recovered, and cumulative cases. The data needed for estimation was collected from official sites of the Kuwait Government between 24 February and 1 December 2020. This study presents estimated values for peak dates and expected eradication of COVID-19 in Kuwait. The proposed estimation model is simulated using the Python Programming language on the collected data. The simulation was performed with various basic reproduction numbers (between 5.2 and 3), the initial exposed population, and the incubation rate. The results show that the SEIR model was better suited than the SIR model for predicting both infection and recovery cases with R0 values ranging from 3 to 4, E0=80 and α=0.2.

Iteratively forecasting biological invasions with PoPS and a little help from our friends.

Ecological forecasting has vast potential to support environmental decision making with repeated, testable predictions across management‐relevant timescales and locations. Yet resource managers rarely use co‐designed forecasting systems or embed them in decision making. Although prediction of planned management outcomes is particularly important for biological invasions to optimize when and where resources should be allocated, spatial–temporal models of spread typically have not been openly shared, iteratively updated, or interactive to facilitate exploration of management actions. We describe a species‐agnostic, open‐source framework – called the Pest or Pathogen Spread (PoPS) Forecasting Platform – for co‐designing near‐term iterative forecasts of biological invasions. Two case studies are presented to demonstrate that iterative calibration yields higher forecast skill than using only the earliest‐available data to predict future spread. The PoPS framework is a primary example of an ecological forecasting system that has been both scientifically improved and optimized for real‐world decision making through sustained participation and use by management stakeholders.

The dispersal-related traits of an invasive plant Galinsoga quadriradiata correlate with elevation during range expansion into mountain ranges.

Detecting shifts in trait values among populations of an invasive plant is important for assessing invasion risks and predicting future spread. Although a growing number of studies suggest that the dispersal propensity of invasive plants increases during range expansion, there has been relatively little attention paid to dispersal patterns along elevational gradients. In this study, we tested the differentiation of dispersal-related traits in an invasive plant, Galinsoga quadriradiata, across populations at different elevations in the Qinling and Bashan Mountains in central China. Seed mass–area ratio (MAR), an important seed dispersal-related trait, of 45 populations from along an elevational gradient was measured, and genetic variation of 23 populations was quantified using inter-simple sequence repeat (ISSR) markers. Individuals from four populations were then planted in a greenhouse to compare their performance under shared conditions. Changing patterns of seed dispersal-related traits and populations genetic diversity along elevation were tested using linear regression. Mass–area ratio of G. quadriradiata increased, while genetic diversity decreased with elevation in the field survey. In the greenhouse, populations of G. quadriradiata sourced from different elevations showed a difference response of MAR. These results suggest that although rapid evolution may contribute to the range expansion of G. quadriradiata in mountain ranges, dispersal-related traits will also likely be affected by phenotypic plasticity. This challenges the common argument that dispersal ability of invasive plants increases along dispersal routes. Furthermore, our results suggest that high-altitude populations would be more effective at seed dispersal once they continue to expand their range downslope on the other side. Our experiment provides novel evidence that the spread of these high-altitude populations may be more likely than previously theorized and that they should thus be cautiously monitored.

Covid-19 Re-infection vs Prolonged Viral Shedding


 Since December 2019, COVID 19 pandemic has devastated communities across the world. As number of patients recovered from COVID 19 continue to rise, question of acquired immunity versus chances of re-infection becomes critical to understand the future spread of infection. Here, we present a case of a patient previously recovered from COVID-19, develops new symptoms concerning for possible re-infection with positive reverse transcriptase-polymerase chain reaction (RT-PCR) after few months of initial infection.

Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

Ageratina adenophora and Lantana camara in Kailash Sacred Landscape, India: Current distribution and future climatic scenarios through modeling.

The Himalayan region is one of the global biodiversity hotspots. However, its biodiversity and ecosystems are threatened due to abiotic and biotic drivers. One of the major biotic threats to biodiversity in this region is the rapid spread of Invasive Alien Species (IAS). Natural forests and grasslands are increasingly getting infested by IAS affecting regeneration of native species and decline in availability of bio-resources. Assessing the current status of IAS and prediction of their future spread would be vital for evolving specific species management interventions. Keeping this in view, we conducted an in-depth study on two IASs, viz., Ageratina adenophora and Lantana camara in the Indian part of Kailash Sacred Landscape (KSL), Western Himalaya. Intensive field surveys were conducted to collect the presence of A. adenophora (n = 567) and L. camara (n = 120) along an altitudinal gradient between 300 and 3000 m a.s.l. We performed Principal Component Analysis to nullify the multi-colinearity effects of the environmental predictors following MaxEnt species distribution model in the current and future climatic scenarios for both the species. All current and future model precision (i.e., Area Under the Curve; AUC) for both species was higher than 0.81. It is predicted that under the current rate of climate change and higher emission (i.e., RCP 8.5 pathway), A. adenophora will spread 45.3% more than its current distribution and is likely to reach up to 3029 m a.s.l., whereas, L. camara will spread 29.8% more than its current distribution range and likely to reach up to 3018 m a.s.l. Our results will help in future conservation planning and participatory management of forests and grasslands in the Kailash Sacred Landscape–India.

Wide and increasing suitability for Aedes albopictus in Europe is congruent across distribution models

The Asian tiger mosquito (Aedes albopictus), a vector of dengue, Zika and other diseases, was introduced in Europe in the 1970s, where it is still widening its range. Spurred by public health concerns, several studies have delivered predictions of the current and future distribution of the species for this region, often with differing results. We provide the first joint analysis of these predictions, to identify consensus hotspots of high and low suitability, as well as areas with high uncertainty. The analysis focused on current and future climate conditions and was carried out for the whole of Europe and for 65 major urban areas. High consensus on current suitability was found for the northwest of the Iberian Peninsula, southern France, Italy and the coastline between the western Balkans and Greece. Most models also agree on a substantial future expansion of suitable areas into northern and eastern Europe. About 83% of urban areas are expected to become suitable in the future, in contrast with ~ 49% nowadays. Our findings show that previous research is congruent in identifying wide suitable areas for Aedes albopictus across Europe and in the need to effectively account for climate change in managing and preventing its future spread.

Non‐native species outperform natives in coastal marine ecosystems subjected to warming and freshening events

AbstractAimsContemporary climate change and biological invasions are two main drivers of biodiversity redistribution. Interactive effects between these drivers have been reported in a variety of studies, yet results are conflicting. Some studies find that contemporary climate change facilitates the spread and success of non‐native species, especially those with broad physiological tolerances. Other studies conclude that non‐natives are vulnerable to current and future changes in climatic conditions. Given that most studies have focused on terrestrial species, here we contribute to this debate by analysing responses of marine native and non‐native fauna and flora to key climate‐related stressors, namely increased temperature (warming) and decreased salinity (freshening).LocationGlobal.Time period2002–2019.Major taxa studiedMarine benthic macrophytes and invertebrates.MethodsWe conducted a meta‐analysis of experiments investigating the performance (e.g. growth, survival and reproduction) of benthic species in response to warming and freshening.ResultsWe found that non‐native species tended to respond positively to elevated temperature, whereas the performance of native species declined. Similarly, decreased salinity negatively affected the biological processes of native species, but non‐natives showed neutral or negative overall responses to freshening.Main conclusionsWe find evidence that non‐native species outperform natives under a wide variety of warming and freshening conditions. The growth and reproduction of non‐natives are enhanced by warmer temperatures, and thus ocean warming is expected to facilitate future spread and success of non‐native species. Increased freshening along future coastal areas, however, will likely have a negative impact in both native and non‐native species and thus is expected to be a driver of significant change in coastal marine ecosystems. Our comprehensive analysis highlighted the need to expand our understanding of climate change effects beyond warming and specifically, studies focusing on salinity changes.

Tracking Error and Portfolio of Futures ETFs According to Futures Contracts Maturity

Purpose - The purpose of this research is to confirm the tracking error of futures ETFs according to their portfolio weights with the most recent and next month’s maturity futures contracts. Design/Methodology/Approach - This research used futures indexes, futures contracts, ETF prices, and net asset value data of ETFs from the Fn Data Guide. Tracking error of futures ETFs, volatility, spread rate, and weight error variables were calculated from the data, and panel regression models from Hausman (1978) were used for empirical analysis. Findings - This research has two findings. First, the tracking error from D-3 (criterion: most recent maturity of futures contracts) to D-day (criterion: most recent maturity of futures contracts) was higher than the other trading days’ tracking errors. This is because the futures index is calculated with the most recent and next month’s maturity futures contracts, and the weight is different. Second, if the weight error between the most recent month’s maturity and next month’s maturity futures contracts is high in ETF portfolios, the tracking error is high. Research Implications - The results from the empirical analysis are as follows. When portfolio managers in futures ETFs do not adjust the futures contract weights between the most recent month’s maturity and next month’s maturity exactly, the tracking error is high. Therefore, managers should reflect the weight of the portfolio exactly.

Forecasting COVID-19 Confirmed Cases in Major Indian Cities and Their Connectedness with Mobility and Weather-related Parameters

Coronavirus disease 2019 (COVID-19) outbreak that was declared as a pandemic by the World Health Organization (WHO) on 11 March 2020 has already had severe consequences in all aspects of people’s lives worldwide. The pandemic has affected over 200 countries and has become a major concern. India also faced a stiff challenge in terms of controlling the virus outbreak and through some strict measures such as nationwide lockdown was able to control the further spread of COVID-19 towards the latter part of 2020. Therefore, it is imperative to predict the spread of this virus along with causality analysis of parameters that play a significant role in its spread. The present study employs a series of univariate and multivariate time series forecasting techniques namely MSARIMA, ARMAX and extended VAR models to predict COVID-19 cases in New Delhi, Mumbai and Bengaluru. Besides, providing a robust forecasting performance for COVID-19 cases, the study also deals with finding the causal relationship of the spread of COVID-19 with various mobility and weather parameters. Outcomes of our study establish that the spread of COVID-19 can be associated with mobility and weather parameters apart from the various precautions that are taken by the people to reduce community transmission. However, the type of mobility (residential, retail and workplace) and type of weather conditions (air quality, temperature and humidity) associated with the causality differ with cities. For New Delhi, air quality, residential, retail are the parameters affecting the spread of the COVID-19 cases, whereas masks, temperature, residential and workplace were the significant mobility and weather parameters for Mumbai. In addition, for Bengaluru, the statistically significant causal variables were air quality, masks and residential. Outcomes of this study would help the concerned authorities to predict and contain future COVID-19 spreads in Indian cities efficiently.

Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts.

The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive cases and more than 1.4 million deaths by the end of November 2020. As long as effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, self-isolation and quarantine as well as far-reaching shutdowns of economic activity and public life are the only available strategies to prevent the virus from spreading. These interventions must meet conflicting requirements where some objectives, like the minimization of disease-related deaths or the impact on health systems, demand for stronger counter-measures, while others, such as social and economic costs, call for weaker counter-measures. Therefore, finding the optimal compromise of counter-measures requires the solution of a multi-objective optimization problem that is based on accurate prediction of future infection spreading for all combinations of counter-measures under consideration. We present a strategy for construction and solution of such a multi-objective optimization problem with real-world applicability. The strategy is based on a micro-model allowing for accurate prediction via a realistic combination of person-centric data-driven human mobility and behavior, stochastic infection models and disease progression models including micro-level inclusion of governmental intervention strategies. For this micro-model, a surrogate macro-model is constructed and validated that is much less computationally expensive and can therefore be used in the core of a numerical solver for the multi-objective optimization problem. The resulting set of optimal compromises between counter-measures (Pareto front) is discussed and its meaning for policy decisions is outlined.

Kentucky Bluegrass Invasion in the Northern Great Plains and Prospective Management Approaches to Mitigate Its Spread.

Kentucky bluegrass (Poa pratensis L.) is one of the most aggressive grasses invading Northern Great Plains (NGP) grasslands, resulting in substantial native species losses. Highly diverse grasslands dominated by native species are gradually transforming into rangelands largely dominated by non-native Kentucky bluegrass. Several factors potentially associated with Kentucky bluegrass invasions, including high propagule pressure, thatch formation, climate change, and increasing nitrogen deposition, could determine the future dominance and spread of Kentucky bluegrass in the NGP. Because atmospheric CO2 is amplifying rapidly, a C3 grass like Kentucky bluegrass might be photosynthetically more efficient than native C4 grasses. As this exotic species shares similar morphological and phenological traits with many native cool-season grasses, controlling it with traditional management practices such as prescribed fire, grazing, herbicides, or combinations of these practices may also impair the growth of native species. Thus, developing effective management practices to combat Kentucky bluegrass spread while facilitating the native species cover is essential. Modifying traditional techniques and embracing science-based adaptive management tools that focus on the ecological interactions of Kentucky bluegrass with the surrounding native species could achieve these desired management goals. Enhancement of the competitiveness of surrounding native species could also be an important consideration for controlling this invasive species.

Markovian Analysis of Covid-19 Dynamics

Covid-19 is an emergency and viral infection with its outbreak being termed as one of the great epidemics in the 21st century causing so many deaths, which made WHO declare it as a pandemic emergency. This virus is new and comes with its characteristics of which randomness and uncertainty are among its common features. In this paper, we developed a model for carrying out an analysis of COVID-19 dynamics using Markov-chain theory methodology. Here, we employed the use of conditional probability distribution as embedded in the Markov property of our chain to construct the transition probabilities that were used in determining the probability distributions of COVID-19 patients as well as predicting its future spread dynamics. We provide a step-by-step approach to obtaining probability distributions of infected and recovered individuals, of infected and recovering and of a recovered patient being getting infected again. This study reveals that irrespective of the initial state of health of an individual, we will always have probabilities P_RI/〖(P〗_IR+P_RI) of an individual being infected and P_RI/〖(P〗_IR+P_RI) of an individual recovering from this disease. Also, with increasing ‘n’, we have an equilibrium that does not depend on the initial conditions, the implication of which is that at some point in time, the situation stabilizes and the distribution X_(n+1) is the same as that of X_n. We envision that the output of this model will assist those in the health system and related fields to plan for the potential impact of the pandemic and its peak.

Development and application of a new framework for infectious disease management at the early stage of new epidemics: Taking COVID-19 outbreak in China as an example.

BackgroundThe outbreak of coronavirus disease 2019 (COVID‐19) rapidly spread across worldwide, posing a significant challenge to public health. Several shortcomings in the existing infectious disease management system were exposed during the pandemic, which hindered the control of the disease globally. To cope with this issue, we propose a window‐period framework to reveal the general rule of the progression of management of infectious diseases and to help with decision making at the early stage of epidemics with a focus on healthcare provisions.MethodsThe framework has two significant periods (dark‐window period and bright‐window period). Outbreak of COVID‐19 in China was used as an example for the application of the framework.ResultsThe framework could reflect the progression of the epidemic objectively. The spread increased slowly in the dark‐window period, but rocketed up in the bright‐window period. The beginning of the bright‐window period was the time when healthcare personnel were exposed to a substantially high risk of nosocomial infection. Additionally, proper and prompt preventive actions during the dark‐window and bright‐window periods were substantially important to reduce the future spreading of the disease.ConclusionsIt was recommended that when possible healthcare provisions should upgrade to the highest level of alert for the control of an unknown epidemic in the dark‐window period, while countermeasures in the bright‐window period could be accordingly adjusted with full exploration and considerations. The framework may provide some insights into how to accelerate the control of future epidemics promptly and effectively.

Surveillance of the major pathogenic arboviruses of public health concern in Gabon, Central Africa: increased risk of West Nile virus and dengue virus infections

BackgroundIncreasing arbovirus infections have been a global burden in recent decades. Many countries have experienced the periodic emergence of arbovirus diseases. However, information on the prevalence of arboviruses is largely unknown or infrequently updated because of the lack of surveillance studies, especially in Africa.MethodsA surveillance study was conducted in Gabon, Central Africa, on arboviruses, which are a major public health concern in Africa, including: West Nile virus (WNV), dengue virus (DENV), Zika virus (ZIKV), yellow fever virus (YFV), chikungunya virus (CHIKV), and Rift Valley fever virus (RVFV). Serological and molecular assays were performed to investigate past infection history and the current status of infection, using serum samples collected from healthy individuals and febrile patients, respectively.ResultsThe overall seroprevalence during 2014˗2017 was estimated to be 25.3% for WNV, 20.4% for DENV, 40.3% for ZIKV, 60.7% for YFV, 61.2% for CHIKV, and 14.3% for RVFV. No significant differences were found in the seroprevalence of any of the viruses between the male and female populations. However, a focus on the mean age in each arbovirus-seropositive individual showed a significantly younger age in WNV- and DENV-seropositive individuals than in CHIKV-seropositive individuals, indicating that WNV and DENV caused a relatively recent epidemic in the region, whereas CHIKV had actively circulated before. Of note, this indication was supported by the detection of both WNV and DENV genomes in serum samples collected from febrile patients after 2016.ConclusionsThis study revealed the recent re-emergence of WNV and DENV in Gabon as well as the latest seroprevalence state of the major arboviruses, which indicated the different potential risks of virus infections and virus-specific circulation patterns. This information will be helpful for public health organizations and will enable a rapid response towards these arbovirus infections, thereby preventing future spread in the country.

Hospital-wide surveillance-based antimicrobial treatments: A Monte-Carlo look-ahead method

Background and objectives. We present a heuristic solution method to the problem of choosing hospital-wide antimicrobial treatments that minimize the cumulative infected patient-days in the long run in a health care facility. Methods. Our solution method is a rollout algorithm. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in the health care facility and the emergence and spread of resistance to two drugs. We assume that the parameters of the model are known. Treatments are chosen at the beginning of each period based on the count of patients with each health status, and on stochastic simulations of the future emergence and spread of antimicrobial resistance. The same treatment is then administered to all patients, including uninfected patients, during the period and cannot be adjusted until the next period. Results. In our simulations, our algorithm allows to reduce the average cumulative infected patient-days over two years by 47.0% compared to the best standard therapy, and by 32.2% compared to a similar heuristic algorithm not using surveillance data (significantly at the 95% threshold). Conclusion. Our heuristic solution method is simple yet flexible. We explain how it can be used either to perform online optimization, or to produce data for quantitative analysis. Its performance is illustrated using a relatively simple infectious disease transmission model, but it is compatible with more advanced epidemiological models.