Risk science offers an integrated approach to resilience

Why do we hear calls to separate and independently manage aspects of risk and resilience that are inherently related? These arguments are inconsistent with more holistic and integrated responses to wicked challenges—such as climate change—that are necessary if we are to find balances and synergies. The justification of such views is based on misconceptions of risk science that are no longer accurate. Rather than being irrelevant, the risk concept and related literature provide a wealth of resilience analysis resources that are potentially being overlooked. In this Perspective, we discuss how the modern view of risk can provide an integrated framework for the key aspects of resilience

Integrating Equity Considerations into Agent-Based Modeling: A Conceptual Framework and Practical Guidance

Advancing equity is a complex challenge for society, science, and policy. Agent-based models are increasingly used as scientific tools to advance understanding of systems, inform decision-making, and share knowledge. Yet, equity has not received due attention within the agent-based modeling (ABM) literature. In this paper, we develop a conceptual framework and provide guidance for integrating equity considerations into ABM research and modeling practice. The framework conceptualizes ABM as interfacing with equity outcomes at two levels (the science-society interface and within the model itself) and the modeler as a filter and lens that projects knowledge between the target system and the model. Within the framework, we outline three complementary, equity-advancing action pathways: (1) engage stakeholders, (2) acknowledge positionality and bias, and (3) assess equity with agent-based models. For Pathway 1, we summarize existing guidance within the participatory modeling literature. For Pathway 2, we introduce the positionality and bias document as a tool to promote modeler and stakeholder reflexivity throughout the modeling process. For Pathway 3, we synthesize a typology of approaches for modeling equity and ffer a set of preliminary suggestions for best practice. By engaging with these action pathways, modelers both reduce the risks of inadvertently perpetuating inequity and harness the opportunities for ABM to play a larger role in creating a more equitable future

Efficient Bayesian inference for COM-Poisson regression models

COM-Poisson regression is an increasingly popular model for count data. Its main advantage is that it permits to model separately the mean and the variance of the counts, thus allowing the same covariate to affect in different ways the average level and the variability of the response variable. A key limiting factor to the use of the COM-Poisson distribution is the calculation of the normalisation constant: its accurate evaluation can be time-consuming and is not always feasible. We circumvent this problem, in the context of estimating a Bayesian COM-Poisson regression, by resorting to the exchange algorithm, an MCMC method applicable to situations where the sampling model (likelihood) can only be computed up to a normalisation constant. The algorithm requires to draw from the sampling model, which in the case of the COM-Poisson distribution can be done efficiently using rejection sampling. We illustrate the method and the benefits of using a Bayesian COM-Poisson regression model, through a simulation and two real-world data sets with different levels of dispersion

Genetic polymorphisms associated with the inflammatory response in bacterial meningitis

BACKGROUND Bacterial meningitis (BM) is an infectious disease that results in high mortality and morbidity. Despite efficacious antibiotic therapy, neurological sequelae are often observed in patients after disease. Currently, the main challenge in BM treatment is to develop adjuvant therapies that reduce the occurrence of sequelae. In recent papers published by our group, we described the associations between the single nucleotide polymorphisms (SNPs) AADAT +401C > T, APEX1 Asn148Glu, OGG1 Ser326Cys and PARP1 Val762Ala and BM. In this study, we analyzed the associations between the SNPs TNF -308G > A, TNF -857C > T, IL-8 -251A > T and BM and investigated gene-gene interactions, including the SNPs that we published previously. METHODS The study was conducted with 54 BM patients and 110 healthy volunteers (as the control group). The genotypes were investigated via primer-introduced restriction analysis-polymerase chain reaction (PIRA-PCR) or polymerase chain reaction-based restriction fragment length polymorphism (PCR-RFLP) analysis. Allelic and genotypic frequencies were also associated with cytokine and chemokine levels, as measured with the x-MAP method, and cell counts. We analyzed gene-gene interactions among SNPs using the generalized multifactor dimensionality reduction (GMDR) method. RESULTS We did not find significant association between the SNPs TNF -857C > T and IL-8 -251A > T and the disease. However, a higher frequency of the variant allele TNF -308A was observed in the control group, associated with changes in cytokine levels compared to individuals with wild type genotypes, suggesting a possible protective role. In addition, combined inter-gene interaction analysis indicated a significant association between certain genotypes and BM, mainly involving the alleles APEX1 148Glu, IL8 -251 T and AADAT +401 T. These genotypic combinations were shown to affect cyto/chemokine levels and cell counts in CSF samples from BM patients. CONCLUSIONS In conclusion, this study revealed a significant association between genetic variability and altered inflammatory responses, involving important pathways that are activated during BM. This knowledge may be useful for a better understanding of BM pathogenesis and the development of new therapeutic approaches

Emergency logistics for wildfire suppression based on forecasted disaster evolution

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). © 2017 The Author(s

Efficient simulation-based discrete optimization

In many practical applications of simulation it is desirable to optimize the levels of integer or binary variables that are inputs for the simulation model. In these cases, the objective function must often be estimated through an expensive simulation process, and the optimization problem is NP-hard, leading to a computationally difficult problem. We investigate efficient solution methods for this problem, and we propose an approach that reduces the number of runs of the simulation by using ridge regression to approximate some of the simulation calls. This approach is shown to significantly decrease the computational cost but at a cost of slightly worse solution values