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Jie Li; Matteo Convertino. Author Correction: Inferring ecosystem networks as information flows. Scientific Reports 2021, 11, 1 -1.
AMA StyleJie Li, Matteo Convertino. Author Correction: Inferring ecosystem networks as information flows. Scientific Reports. 2021; 11 (1):1-1.
Chicago/Turabian StyleJie Li; Matteo Convertino. 2021. "Author Correction: Inferring ecosystem networks as information flows." Scientific Reports 11, no. 1: 1-1.
Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics – underpinning persistent criticality and neutrality or independence among areas – is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.
M. Convertino; A. Reddy; Y. Liu; C. Munoz-Zanzi. Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts. Science of The Total Environment 2021, 799, 149102 .
AMA StyleM. Convertino, A. Reddy, Y. Liu, C. Munoz-Zanzi. Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts. Science of The Total Environment. 2021; 799 ():149102.
Chicago/Turabian StyleM. Convertino; A. Reddy; Y. Liu; C. Munoz-Zanzi. 2021. "Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts." Science of The Total Environment 799, no. : 149102.
Social media can forecast disease dynamics, but infoveillance remains focused on infection spread, with little consideration of media content reliability and its relationship to behavior-driven epidemiological outcomes. Sentiment-encoded social media indicators have been poorly developed for expressed text to forecast healthcare pressure and infer population risk perception patterns. Here we introduce Infodemic Tomography (InTo) as the first web-based interactive infoveillance cybertechnology that forecasts and visualizes spatio-temporal sentiments and healthcare pressure as a function of social media positivity (i.e., Twitter here), considering both epidemic information and potential misinformation. Information spread is measured on volume and retweets and the Value of Misinformation (VoMi) is introduced as the impact on forecast accuracy where misinformation has the highest dissimilarity in information dynamics. We validate InTo for COVID-19 in New Delhi and three other SE Asian cities. We forecast weekly hospitalization and cases using ARIMA models and interpolate spatial hospitalization using geostatistical kriging on inferred risk perception curves between tweet positivity and epidemiological outcomes. Geospatial tweet positivity tracks accurately ~60% of hospitalizations and forecasts hospitalization risk hotspots along risk aversion gradients. VoMi is higher for risk-prone areas and time periods, where misinformation has the highest predictability, with high incidence and positivity manifesting popularity-seeking social dynamics. Hospitalization gradients, VoMi, effective healthcare pressure and spatial model-data gaps can be used to predict hospitalization fluxes, misinformation, capacity gaps and surveillance uncertainty. Thus, InTo is a participatory instrument to better prepare and respond to public health crises by extracting and combining salient epidemiological and social surveillance at any desired space-time scale.
Elroy Galbraith; Matteo Convertino; Jie Li; Victor Del-Rio Vilas. In.To. COVID-19 Socio-epidemiological Co-causality. 2021, 1 .
AMA StyleElroy Galbraith, Matteo Convertino, Jie Li, Victor Del-Rio Vilas. In.To. COVID-19 Socio-epidemiological Co-causality. . 2021; ():1.
Chicago/Turabian StyleElroy Galbraith; Matteo Convertino; Jie Li; Victor Del-Rio Vilas. 2021. "In.To. COVID-19 Socio-epidemiological Co-causality." , no. : 1.
The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and $$\alpha$$ α -diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective $$\alpha$$ α -diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.
Jie Li; Matteo Convertino. Inferring ecosystem networks as information flows. Scientific Reports 2021, 11, 1 -22.
AMA StyleJie Li, Matteo Convertino. Inferring ecosystem networks as information flows. Scientific Reports. 2021; 11 (1):1-22.
Chicago/Turabian StyleJie Li; Matteo Convertino. 2021. "Inferring ecosystem networks as information flows." Scientific Reports 11, no. 1: 1-22.
Contaminants of emerging concern (CECs) include a variety of pharmaceuticals, personal care products, and hormones commonly detected in surface waters. Human activities, such as wastewater treatment and discharge, contribute to the distribution of CECs in water, but other sources and pathways are less frequently examined. This study aimed to identify anthropogenic activities and environmental characteristics associated with the presence of CECs, previously determined to be of high priority for further research and mitigation, in rural inland lakes in northeastern Minnesota, United States. The setting for this study consisted of 21 lakes located within both the Grand Portage Indian Reservation and the 1854 Ceded Territory, where subsistence hunting and fishing are important to the cultural heritage of the indigenous community. We used data pertaining to numbers of buildings, healthcare facilities, wastewater treatment plants, impervious surfaces, and wetlands within defined areas surrounding the lakes as potential predictors of the detection of high priority CECs in water, sediment, and fish. Separate models were run for each contaminant detected in each sample media. We used least absolute shrinkage and selection operator (LASSO) models to account for both predictor selection and parameter estimation for CEC detection. Across contaminants and sample media, the percentage of impervious surface was consistently positively associated with CEC detection. Number of buildings in the surrounding area was often negatively associated with CEC detection, though nonsignificant. Surrounding population, presence of wastewater treatment facilities, and percentage of wetlands in surrounding areas were positively, but inconsistently, associated with CECs, while catchment area and healthcare centers were generally not associated. The results of this study highlight human activities and environmental characteristics associated with CEC presence in a rural area, informing future work regarding specific sources and transport pathways. We also demonstrate the utility of LASSO modeling in the identification of these important relationships.
Joseph L. Servadio; Jessica R. Deere; Mark D. Jankowski; Mark Ferrey; E.J. Isaac; Yvette Chenaux-Ibrahim; Alexander Primus; Matteo Convertino; Nicholas B.D. Phelps; Summer Streets; Dominic A. Travis; Seth Moore; Tiffany M. Wolf. Anthropogenic factors associated with contaminants of emerging concern detected in inland Minnesota lakes (Phase II). Science of The Total Environment 2021, 772, 146188 .
AMA StyleJoseph L. Servadio, Jessica R. Deere, Mark D. Jankowski, Mark Ferrey, E.J. Isaac, Yvette Chenaux-Ibrahim, Alexander Primus, Matteo Convertino, Nicholas B.D. Phelps, Summer Streets, Dominic A. Travis, Seth Moore, Tiffany M. Wolf. Anthropogenic factors associated with contaminants of emerging concern detected in inland Minnesota lakes (Phase II). Science of The Total Environment. 2021; 772 ():146188.
Chicago/Turabian StyleJoseph L. Servadio; Jessica R. Deere; Mark D. Jankowski; Mark Ferrey; E.J. Isaac; Yvette Chenaux-Ibrahim; Alexander Primus; Matteo Convertino; Nicholas B.D. Phelps; Summer Streets; Dominic A. Travis; Seth Moore; Tiffany M. Wolf. 2021. "Anthropogenic factors associated with contaminants of emerging concern detected in inland Minnesota lakes (Phase II)." Science of The Total Environment 772, no. : 146188.
Contaminants of emerging concern (CECs), such as pharmaceuticals, personal care products, and hormones, are frequently found in aquatic ecosystems around the world. Information on sublethal effects from exposure to commonly detected concentrations of CECs is lacking and the limited availability of toxicity data makes it difficult to interpret the biological significance of occurrence data. However, the ability to evaluate the effects of CECs on aquatic ecosystems is growing in importance, as detection frequency increases. The goal of this study was to prioritize the chemical hazards of 117 CECs detected in subsistence species and freshwater ecosystems on the Grand Portage Indian Reservation and adjacent 1854 Ceded Territory in Minnesota, USA. To prioritize CECs for management actions, we adapted Minnesota Pollution Control Agency's Aquatic Toxicity Profiles framework, a tool for the rapid assessment of contaminants to cause adverse effects on aquatic life by incorporating chemical-specific information. This study aimed to 1) perform a rapid-screening assessment and prioritization of detected CECs based on their potential environmental hazard; 2) identify waterbodies in the study region that contain high priority CECs; and 3) inform future monitoring, assessment, and potential remediation in the study region. In water samples alone, 50 CECs were deemed high priority. Twenty-one CECs were high priority among sediment samples and seven CECs were high priority in fish samples. Azithromycin, DEET, diphenhydramine, fluoxetine, miconazole, and verapamil were high priority in all three media. Due to the presence of high priority CECs throughout the study region, we recommend future monitoring of particular CECs based on the prioritization method used here. We present an application of a chemical hazard prioritization process and identify areas where the framework may be adapted to meet the objectives of other management-related assessments.
Jessica R. Deere; Summer Streets; Mark D. Jankowski; Mark Ferrey; Yvette Chenaux-Ibrahim; Matteo Convertino; E.J. Isaac; Nicholas B.D. Phelps; Alexander Primus; Joseph L. Servadio; Randall S. Singer; Dominic A. Travis; Seth Moore; Tiffany M. Wolf. A chemical prioritization process: Applications to contaminants of emerging concern in freshwater ecosystems (Phase I). Science of The Total Environment 2021, 772, 146030 .
AMA StyleJessica R. Deere, Summer Streets, Mark D. Jankowski, Mark Ferrey, Yvette Chenaux-Ibrahim, Matteo Convertino, E.J. Isaac, Nicholas B.D. Phelps, Alexander Primus, Joseph L. Servadio, Randall S. Singer, Dominic A. Travis, Seth Moore, Tiffany M. Wolf. A chemical prioritization process: Applications to contaminants of emerging concern in freshwater ecosystems (Phase I). Science of The Total Environment. 2021; 772 ():146030.
Chicago/Turabian StyleJessica R. Deere; Summer Streets; Mark D. Jankowski; Mark Ferrey; Yvette Chenaux-Ibrahim; Matteo Convertino; E.J. Isaac; Nicholas B.D. Phelps; Alexander Primus; Joseph L. Servadio; Randall S. Singer; Dominic A. Travis; Seth Moore; Tiffany M. Wolf. 2021. "A chemical prioritization process: Applications to contaminants of emerging concern in freshwater ecosystems (Phase I)." Science of The Total Environment 772, no. : 146030.
The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed Optimal Information Flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and α-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective α-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.
Jie Li; Matteo Convertino. Inferring Ecosystem Networks as Information Flows. 2021, 1 .
AMA StyleJie Li, Matteo Convertino. Inferring Ecosystem Networks as Information Flows. . 2021; ():1.
Chicago/Turabian StyleJie Li; Matteo Convertino. 2021. "Inferring Ecosystem Networks as Information Flows." , no. : 1.
Fish ecosystems perform ecological functions that are critically important for the sustainability of marine ecosystems, as well as for global food security. During the 21st century, significant global warming caused by climate change has created novel challenges for fish ecosystems that threaten the global environmental and human health. Here, we study a coastal fish community in Maizuru Bay, Japan, and investigate the relationships between fluctuations of sea temperature and fish biodiversity and abundance. The global increase of temperature from 2002 to 2014 reduces fish diversity, while some species become more abundant and that causes ecological productivity to grow exponentially. The fish community is analyzed considering five temperature ranges: ≤10° C, 10-15° C, 15-20° C, 20-25° C, ≥25° C. In order to infer bidirectional interactions between species, an optimal information flow model is introduced in this study. We detect interdependencies between species and reconstruct species interaction networks that are functionally different for each temperature range. Networks for lower and higher temperature ranges are more scale-free compared to networks for the intermediate 15-20° C range in which the fish ecosystem experiences a first order phase transition from a locally stable state to a metastable state. Species-specific analysis is conducted by calculating the link salience and total outgoing information flow. Native species whose abundance is distributed more uniformly have a higher total outgoing information flow, and are the reference species (nodes in networks) of the most salient links. These species play an important role in maintaining the fish ecosystem stability. Species diversity, total interactions and entropy of species abundance in the fish community grow with the increase of temperature. This work provides a data-driven tool for analyzing and monitoring fish ecosystems under the pressure of global warming or other stressors. Macroecological and network-based analyses are useful to formulate science-based and accurate fishery policy to maintain marine fish ecosystems stable and sustainable.
Jie Li; Matteo Convertino. Temperature-driven Organization of Fish Ecosystems and Fishery Implications. 2021, 1 .
AMA StyleJie Li, Matteo Convertino. Temperature-driven Organization of Fish Ecosystems and Fishery Implications. . 2021; ():1.
Chicago/Turabian StyleJie Li; Matteo Convertino. 2021. "Temperature-driven Organization of Fish Ecosystems and Fishery Implications." , no. : 1.
Pharmaceuticals, personal care products, hormones, and other chemicals lacking water quality standards are frequently found in surface water. While evidence is growing that these contaminants of emerging concern (CECs) – those previously unknown, unrecognized, or unregulated – can affect the behavior and reproduction of fish and wildlife, little is known about the distribution of these chemicals in rural, tribal areas. Therefore, we surveyed the presence of CECs in water, sediment, and subsistence fish species across various waterbodies, categorized as undeveloped (i.e., no human development along shorelines), developed (i.e., human development along shorelines), and wastewater effluent-impacted (i.e., contain effluence from wastewater treatment plants), within the Grand Portage Indian Reservation and 1854 Ceded Territory in northeastern Minnesota, U.S.A. Overall, in 28 sites across three years (2016–2018), 117 of the 158 compounds tested were detected in at least one form of medium (i.e., water, sediment, or fish). CECs were detected most frequently at wastewater effluent-impacted sites, with up to 83 chemicals detected in one such lake, while as many as 17 were detected in an undeveloped lake. Although there was no statistically significant difference between the number of CECs present in developed versus undeveloped lakes, a range of 3–17 CECs were detected across these locations. Twenty-two CECs were detected in developed and undeveloped sites that were not detected in wastewater effluent-impacted sites. The detection of CECs in remote, undeveloped locations, where subsistence fish are harvested, raises scientific questions about the safety and security of subsistence foods for indigenous communities. Further investigation is warranted so that science-based solutions to reduce chemical risks to aquatic life and people can be developed locally and be informative for indigenous communities elsewhere.
Jessica R. Deere; Seth Moore; Mark Ferrey; Mark D. Jankowski; Alexander Primus; Matteo Convertino; Joseph L. Servadio; Nicholas B.D. Phelps; M. Coreen Hamilton; Yvette Chenaux-Ibrahim; Dominic A. Travis; Tiffany M. Wolf. Occurrence of contaminants of emerging concern in aquatic ecosystems utilized by Minnesota tribal communities. Science of The Total Environment 2020, 724, 138057 -138057.
AMA StyleJessica R. Deere, Seth Moore, Mark Ferrey, Mark D. Jankowski, Alexander Primus, Matteo Convertino, Joseph L. Servadio, Nicholas B.D. Phelps, M. Coreen Hamilton, Yvette Chenaux-Ibrahim, Dominic A. Travis, Tiffany M. Wolf. Occurrence of contaminants of emerging concern in aquatic ecosystems utilized by Minnesota tribal communities. Science of The Total Environment. 2020; 724 ():138057-138057.
Chicago/Turabian StyleJessica R. Deere; Seth Moore; Mark Ferrey; Mark D. Jankowski; Alexander Primus; Matteo Convertino; Joseph L. Servadio; Nicholas B.D. Phelps; M. Coreen Hamilton; Yvette Chenaux-Ibrahim; Dominic A. Travis; Tiffany M. Wolf. 2020. "Occurrence of contaminants of emerging concern in aquatic ecosystems utilized by Minnesota tribal communities." Science of The Total Environment 724, no. : 138057-138057.
Ecosystems’ microbiome organization is the epitomic feature of ecosystem function and an incredibly fascinating system considering its complexity, ecology and evolution, and practical applications for individual and population health. Due to its ‘’unknowns’’ the microbiome also provides the opportunity to test and develop information theoretic models that mimic and predict its dynamics. A novel information and network theoretic model that predicts microbiome network organization, diversity, dynamics and stability for the human gut microbiome is presented. The model is able to classify health states based on microbiome entropic patterns, that, in the optimal biological function are related to neutral scale-free information organization of species interactions. The healthy state is characterized by an optimal metabolic function that is predicted by macroecological quintessential indicators whose variability is meaningful of state transitions. Information propagation analyses detect total species importance, proportional to outgoing information flow, which can be use for microbial engineering or disease diagnosis and etiognosis. Finally a link with ocean microbial ecosystems is highlighted as well as the collectivity-diversity-dynamics triality.
Matteo Convertino. Multispecies Emergence of Collective Behavior: Microbiome Connectome, Diversity and Services. Proceedings of 5th International Electronic Conference on Entropy and Its Applications 2019, 1 .
AMA StyleMatteo Convertino. Multispecies Emergence of Collective Behavior: Microbiome Connectome, Diversity and Services. Proceedings of 5th International Electronic Conference on Entropy and Its Applications. 2019; ():1.
Chicago/Turabian StyleMatteo Convertino. 2019. "Multispecies Emergence of Collective Behavior: Microbiome Connectome, Diversity and Services." Proceedings of 5th International Electronic Conference on Entropy and Its Applications , no. : 1.
The concept of resilience occupies an increasingly prominent position within contemporary efforts to confront many of modernity’s most pressing challenges, including global environmental change, famine, infrastructure, poverty, and terrorism, to name but a few. Received views of resilience span a broad conceptual and theoretical terrain, with a diverse range of application domains and settings. In this paper, we identify several foundational tenets—dealing primarily with intent/intentionality and uncertainty—that are seen to underlie a number of recent accounts of resilience, and we explore the implications of these tenets for ongoing attempts to articulate the rudiments of an overarching resilience paradigm. Firstly, we explore the complemental nature of risk and resilience, looking, initially, at the role that linearity assumptions play in numerous resilience frameworks found in the literature. We then explore the limitations of these assumptions for efforts directed at modeling risk and resilience in complex domains. These discussions are then used to motivate a pluralistic conception of resilience, drawing inspiration and content from a broad range of sources and empirical domains, including information, network, and decision theories. Secondly, we sketch the rudiments of a framework for engineered resilience, the primary focus of which is the exploration of the fundamental challenges that system design and system performance pose for resilience managers. The conception of engineered resilience set forth here also considers how challenges concerning time and predictability should factor explicitly into the formal schemes that are used to represent and model resilience. Finally, we conclude with a summary of our findings, and we provide a brief sketch of possible future research directions.
Matteo Convertino; L. James Valverde. Toward a pluralistic conception of resilience. Ecological Indicators 2019, 107, 105510 .
AMA StyleMatteo Convertino, L. James Valverde. Toward a pluralistic conception of resilience. Ecological Indicators. 2019; 107 ():105510.
Chicago/Turabian StyleMatteo Convertino; L. James Valverde. 2019. "Toward a pluralistic conception of resilience." Ecological Indicators 107, no. : 105510.
The increasing impact of flooding urges more effective flood management strategies to guarantee sustainable ecosystem development. Recent catastrophes underline the importance of avoiding local flood management, but characterizing large scale basin wide approaches for systemic flood risk management. Here we introduce an information-theoretic Portfolio Decision Model (iPDM) for the optimization of a systemic ecosystem value at the basin scale by evaluating all potential flood risk mitigation plans. iPDM calculates the ecosystem value predicted by all feasible combinations of flood control structures (FCS) considering environmental, social and economical asset criteria. A multi-criteria decision analytical model evaluates the benefits of all FCS portfolios at the basin scale weighted by stakeholder preferences for assets’ criteria as ecosystem services. The risk model is based on a maximum entropy model (MaxEnt) that predicts the flood susceptibility, the risk of floods based on the exceedance probability distribution, and its most important drivers. Information theoretic global sensitivity and uncertainty analysis is used to select the simplest and most accurate model based on a flood return period. A stochastic optimization algorithm optimizes the ecosystem value constrained to the budget available and provides Pareto frontiers of optimal FCS plans for any budget level. Pareto optimal solutions maximize FCS diversity and minimize the criticality of floods manifested by the scaling exponent of the Pareto distribution of flood size that links management and hydrogeomorphological patterns. The proposed model is tested on the 17,000 km2 Tiber river basin in Italy. iPDM allows stakeholders to identify optimal FCS plans in river basins for a comprehensive evaluation of flood effects under future ecosystem trajectories.
Matteo Convertino; Antonio Annis; Fernando Nardi. Information-theoretic portfolio decision model for optimal flood management. Environmental Modelling & Software 2019, 119, 258 -274.
AMA StyleMatteo Convertino, Antonio Annis, Fernando Nardi. Information-theoretic portfolio decision model for optimal flood management. Environmental Modelling & Software. 2019; 119 ():258-274.
Chicago/Turabian StyleMatteo Convertino; Antonio Annis; Fernando Nardi. 2019. "Information-theoretic portfolio decision model for optimal flood management." Environmental Modelling & Software 119, no. : 258-274.
The concept of resilience occupies an increasingly prominent position within contemporary efforts to confront many of modernity's most pressing challenges, including global environmental change, famine, infrastructure, poverty, and terrorism, to name but a few. Received views of resilience span a broad conceptual and theoretical terrain, with a diverse range of application domains and settings. In this paper, we identify several foundational tenets --- dealing primarily with intent/intentionality and uncertainty --- that are seen to underlie a number of recent accounts of resilience, and we explore the implications of these tenets for ongoing attempts to articulate the rudiments of an overarching resilience paradigm. Firstly, we explore the complemental nature of risk and resilience, looking, initially, at the role that linearity assumptions play in numerous resilience frameworks found in the literature. We then explore the limitations of these assumptions for efforts directed at modeling risk and resilience in complex domains. These discussions are then used to motivate a pluralistic conception of resilience, drawing inspiration and content from a broad range of sources and empirical domains, including information, network, and decision theories. Secondly, we sketch the rudiments of a framework for engineered resilience, the primary focus of which is the exploration of the fundamental challenges that system design and system performance pose for resilience managers. The conception of engineered resilience set forth here also considers how challenges concerning time and predictability should factor explicitly into the formal schemes that are used to represent and model resilience. Finally, we conclude with a summary of our findings, and we provide a brief sketch of possible future research directions.
Matteo Convertino; James Valverde. Toward a Pluralistic Conception of Resilience. 2019, 1 .
AMA StyleMatteo Convertino, James Valverde. Toward a Pluralistic Conception of Resilience. . 2019; ():1.
Chicago/Turabian StyleMatteo Convertino; James Valverde. 2019. "Toward a Pluralistic Conception of Resilience." , no. : 1.
Matteo Convertino; Antonio Annis; Fernando Nardi. Information-theoretic Portfolio Decision Model for Optimal Flood Management. 2019, 1 .
AMA StyleMatteo Convertino, Antonio Annis, Fernando Nardi. Information-theoretic Portfolio Decision Model for Optimal Flood Management. . 2019; ():1.
Chicago/Turabian StyleMatteo Convertino; Antonio Annis; Fernando Nardi. 2019. "Information-theoretic Portfolio Decision Model for Optimal Flood Management." , no. : 1.
The human microbiome is an extremely complex ecosystem considering the number of bacterial species, their interactions, and its variability over space and time. Here, we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder in human populations. Based on a novel information theoretic network inference model, we detected potential species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interactions and scale-free topology versus random unhealthy networks. We detected an inverse scaling relationship between species total outgoing information flow, meaningful of node interactivity, and relative species abundance (RSA). The top ten interacting species are also the least relatively abundant for the healthy microbiome and the most detrimental. These findings support the idea about the diminishing role of network hubs and how these should be defined considering the total outgoing information flow rather than the node degree. Macroecologically, the healthy microbiome is characterized by the highest Pareto total species diversity growth rate, the lowest species turnover, and the smallest variability of RSA for all species. This result challenges current views that posit a universal association between healthy states and the highest absolute species diversity in ecosystems. Additionally, we show how the transitory microbiome is unstable and microbiome criticality is not necessarily at the phase transition between healthy and unhealthy states. We stress the importance of considering portfolios of interacting pairs versus single node dynamics when characterizing the microbiome and of ranking these pairs in terms of their interactions (i.e., species collective behavior) that shape transition from healthy to unhealthy states. The macroecological characterization of the microbiome is useful for public health and disease diagnosis and etiognosis, while species-specific analyses can detect beneficial species leading to personalized design of pre- and probiotic treatments and microbiome engineering.
Jie Li; Matteo Convertino. Optimal Microbiome Networks: Macroecology and Criticality. Entropy 2019, 21, 506 .
AMA StyleJie Li, Matteo Convertino. Optimal Microbiome Networks: Macroecology and Criticality. Entropy. 2019; 21 (5):506.
Chicago/Turabian StyleJie Li; Matteo Convertino. 2019. "Optimal Microbiome Networks: Macroecology and Criticality." Entropy 21, no. 5: 506.
Suboptimal ambient temperature exposure significantly affects public health. Previous studies have primarily focused on risk assessment, with few examining the health outcomes from an economic perspective. To inform environmental health policies, we estimated the economic costs of health outcomes associated with suboptimal temperature in the Minneapolis/St. Paul Twin Cities Metropolitan Area. We used a distributed lag nonlinear model to estimate attributable fractions/cases for mortality, emergency department visits, and emergency hospitalizations at various suboptimal temperature levels. The analyses were stratified by age group (i.e., youth (0–19 years), adult (20–64 years), and senior (65+ years)). We considered both direct medical costs and loss of productivity during economic cost assessment. Results show that youth have a large number of temperature-related emergency department visits, while seniors have large numbers of temperature-related mortality and emergency hospitalizations. Exposures to extremely low and high temperatures lead to $2.70 billion [95% empirical confidence interval (eCI): $1.91 billion, $3.48 billion] (costs are all based on 2016 USD value) economic costs annually. Moderately and extremely low and high temperature leads to $9.40 billion [eCI: $6.05 billion, $12.57 billion] economic costs. The majority of the economic costs are consistently attributed to cold (>75%), rather than heat exposures and to mortality (>95%), rather than morbidity. Our findings support prioritizing temperature-related health interventions designed to minimize the economic costs by targeting seniors and to reduce attributable cases by targeting youth.
Yang Liu; Shubhayu Saha; Brendalynn O. Hoppe; Matteo Convertino. Degrees and dollars – Health costs associated with suboptimal ambient temperature exposure. Science of The Total Environment 2019, 678, 702 -711.
AMA StyleYang Liu, Shubhayu Saha, Brendalynn O. Hoppe, Matteo Convertino. Degrees and dollars – Health costs associated with suboptimal ambient temperature exposure. Science of The Total Environment. 2019; 678 ():702-711.
Chicago/Turabian StyleYang Liu; Shubhayu Saha; Brendalynn O. Hoppe; Matteo Convertino. 2019. "Degrees and dollars – Health costs associated with suboptimal ambient temperature exposure." Science of The Total Environment 678, no. : 702-711.
Having sustained, over the course of more than two decades, record-breaking natural catastrophe losses, American insurers and reinsurers are justifiably questioning the potential linkage between anthropogenic climate change and extreme weather. Here, we explore issues pertaining to this linkage, looking at both the likely short-term implications for the insurance industry, as well as potential longer-term impacts on financial performance and corporate resilience. We begin our discussion with an overview of the implications that climate change is likely to have on the industry, especially as it relates to how catastrophic risks are construed, assessed, and managed. We then present the rudiments of an econometric analysis that explores the financial resilience of the property/casualty (P/C) industry in the face of both natural and man-made catastrophes. In this analysis, we explore the profitability consequences of several illustrative scenarios involving large-scale losses from extreme weather—specifically, a sequence of storms like those striking the U.S. in 2004—and a scenario that explores the prospect of a Katrina-scale storm in combination with a mass terror attack on the scale of 9/11. At systemic levels of aggregation, our analysis suggests a high degree of macro-resilience for the P/C industry. Moreover, we find that insurer resilience is higher for larger impacts, considering both the speed of recovery, as well as the inverse of the area under the unaffected system profile. We conclude with a summary of our findings and a closing commentary that explores the potential implications of these results for P/C insurers moving forward.
L. James Valverde; Matteo Convertino. Insurer Resilience in an Era of Climate Change and Extreme Weather: An Econometric Analysis. Climate 2019, 7, 55 .
AMA StyleL. James Valverde, Matteo Convertino. Insurer Resilience in an Era of Climate Change and Extreme Weather: An Econometric Analysis. Climate. 2019; 7 (4):55.
Chicago/Turabian StyleL. James Valverde; Matteo Convertino. 2019. "Insurer Resilience in an Era of Climate Change and Extreme Weather: An Econometric Analysis." Climate 7, no. 4: 55.
The human microbiome is extremely complex considering the amount of species, their interactions, and its variability over time as a function of environmental drivers. Here we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder linked to many causes. Based on a novel information theoretic network inference model (that considers conditional entropy reduction till the maximum entropy is not reduced further) we detect species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interaction and small-world features for functional node degree and distance versus random unhealthy networks. We detect an inverse scaling relationship between species total outgoing information flow (''active flow'') and abundance. The top 10 interacting species are also the least abundant for the healthy microbiome and the most detrimental; however these species are controlled by other species (via negative feedbacks) and the microbiome is self-organized into a healthy state. On the contrary, the most abundant species for the unhealthy microbiome are the least interactive and the most detrimental. These findings support the idea about a diminishing role of network hubs and hubs should be defined considering total outgoing information flow. The healthy microbiome is characterized by high diversity growth rate, small species similarity decay over time (i.e. low species turnover), and small variability in the abundance of all species. This result challenges current views that posit an association between health states and the highest diversity in ecosystems rather than the highest biodiversity growth as in this study. In a network perspective the healthy microbiome is configured as a small-world network with a tendency toward a critical scale-free network while the unhealthy one is organized as a random network with many more interacting species. We show how the transitory microbiome at the edge of the healthy and unhealthy ones is unstable and criticality of the healthy microbiome is not at the phase transition (or second order) between order and chaos but in a meta-stable state (on the contrary of other critical systems where energy and entropy grow in the same direction and criticality is at the transition). We stress out the importance of considering interacting pairs versus single node dynamics when characterizing the microbiome nexus and of ranking these pairs in terms of their dynamics; interactions (i.e. species collective behavior) shape transition from healthy to unhealthy states. The macroecological characterization of the microbiome is useful for diagnostic purposes and disease etiognosis while species-specific analyses can detect species that are more beneficial to humans leading to personalized design of pre- and pro-biotic...
Jie Li; Matteo Convertino. Optimal Microbiome Networks: Macroecological Characterization and Criticality. 2018, 1 .
AMA StyleJie Li, Matteo Convertino. Optimal Microbiome Networks: Macroecological Characterization and Criticality. . 2018; ():1.
Chicago/Turabian StyleJie Li; Matteo Convertino. 2018. "Optimal Microbiome Networks: Macroecological Characterization and Criticality." , no. : 1.
Environmental burdens such as air pollution are inequitably distributed with groups of lower socioeconomic statuses, which tend to comprise of large proportions of racial minorities, typically bearing greater exposure. Such groups have also been shown to present more severe health outcomes which can be related to adverse pollution exposure. Air pollution exposure, especially in urban areas, is usually impacted by the built environment, such as major roadways, which can be a significant source of air pollution. This study aims to examine inequities in prevalence of cardiovascular and respiratory diseases in the Atlanta metropolitan region as they relate to exposure to air pollution and characteristics of the built environment. Census tract level data were obtained from multiple sources to model health outcomes (asthma, chronic obstructive pulmonary disease, coronary heart disease, and stroke), pollution exposure (particulate matter and nitrogen oxides), demographics (ethnicity and proportion of elderly residents), and infrastructure characteristics (tree canopy cover, access to green space, and road intersection density). Conditional autoregressive models were fit to the data to account for spatial autocorrelation among census tracts. The statistical model showed areas with majority African-American populations had significantly higher exposure to both air pollutants and higher prevalence of each disease. When considering univariate associations between pollution and health outcomes, the only significant association existed between nitrogen oxides and COPD being negatively correlated. Greater percent tree canopy cover and green space access were associated with higher prevalence of COPD, CHD, and stroke. Overall, in considering health outcomes in connection with pollution exposure infrastructure and ethnic demographics, demographics remained the most significant explanatory variable.
Joseph L. Servadio; Abiola S. Lawal; Tate Davis; Josephine Bates; Armistead G. Russell; Anu Ramaswami; Matteo Convertino; Nisha Botchwey. Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure. Journal of Urban Health 2018, 96, 219 -234.
AMA StyleJoseph L. Servadio, Abiola S. Lawal, Tate Davis, Josephine Bates, Armistead G. Russell, Anu Ramaswami, Matteo Convertino, Nisha Botchwey. Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure. Journal of Urban Health. 2018; 96 (2):219-234.
Chicago/Turabian StyleJoseph L. Servadio; Abiola S. Lawal; Tate Davis; Josephine Bates; Armistead G. Russell; Anu Ramaswami; Matteo Convertino; Nisha Botchwey. 2018. "Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure." Journal of Urban Health 96, no. 2: 219-234.
Emergency risk communication (ERC) programs that activate when the ambient temperature is expected to cross certain extreme thresholds are widely used to manage relevant public health risks. In practice, however, the effectiveness of these thresholds has rarely been examined. The goal of this study is to test if the activation criteria based on extreme temperature thresholds, both cold and heat, capture elevated health risks for all-cause and cause-specific mortality and morbidity in the Minneapolis-St. Paul Metropolitan Area. A distributed lag nonlinear model (DLNM) combined with a quasi-Poisson generalized linear model is used to derive the exposure-response functions between daily maximum heat index and mortality (1998-2014) and morbidity (emergency department visits; 2007-2014). Specific causes considered include cardiovascular, respiratory, renal diseases, and diabetes. Six extreme temperature thresholds, corresponding to 1st-3rd and 97th-99th percentiles of local exposure history, are examined. All six extreme temperature thresholds capture significantly increased relative risks for all-cause mortality and morbidity. However, the cause-specific analyses reveal heterogeneity. Extreme cold thresholds capture increased mortality and morbidity risks for cardiovascular and respiratory diseases and extreme heat thresholds for renal disease. Percentile-based extreme temperature thresholds are appropriate for initiating ERC targeting the general population. Tailoring ERC by specific causes may protect some but not all individuals with health conditions exacerbated by hazardous ambient temperature exposure.
Yang Liu; Brenda O. Hoppe; Matteo Convertino. Threshold Evaluation of Emergency Risk Communication for Health Risks Related to Hazardous Ambient Temperature. Risk Analysis 2018, 38, 2208 -2221.
AMA StyleYang Liu, Brenda O. Hoppe, Matteo Convertino. Threshold Evaluation of Emergency Risk Communication for Health Risks Related to Hazardous Ambient Temperature. Risk Analysis. 2018; 38 (10):2208-2221.
Chicago/Turabian StyleYang Liu; Brenda O. Hoppe; Matteo Convertino. 2018. "Threshold Evaluation of Emergency Risk Communication for Health Risks Related to Hazardous Ambient Temperature." Risk Analysis 38, no. 10: 2208-2221.