PastoralScape : an environment-driven model of vaccination decision making within pastoralist groups in East Africa

Economic and cultural resilience among pastoralists in East Africa is threatened by the interconnected forces of climate change and contagious diseases spread. A key factor in the resilience of livestock dependent communities is human decision making regarding vaccination against preventable diseases such as Rift Valley fever and Contagious Bovine Pleuropneumonia. The relationship between healthy and productive livestock and economic development of poor households and communities is mediated by human decision making. This paper describes a coupled human and natural systems agent-based model that focuses on One Health. Disease propagation and animal nutritional health are driven by historical GIS data that captures changes in foraging condition. The results of a series of experiments are presented that demonstrate the sensitivity of a transformed Random Field Ising Model of human decision making to changes in human memory and rationality parameters. Results presented communicate that convergence in the splitting of households between vaccinating or not is achieved for combinations of memory and rationality. The interaction of these cognition parameters with public information and social networks of opinions is detailed. This version of the PastoralScape model is intended to form the basis upon which richer economic and human factor models can be built. © 2021, University of Surrey. All rights reserved

Automated component creation for legacy C++ and fortran codes.

A significant amount of work has been spent creating component models and programming environments, but little support exists for automation in the process of creating components from existing codes. To entice users to adopt the component-based paradigm over traditional programming models, integration of legacy codes must be as simple and fast as possible, We present a system for automating the IDL generation stage of component development based on source code analysis of legacy C, C-t-4 and Fortran codes using the Program Database Toolkit. Together with IDL compilation tools such as Babel, we provide an alternative to hand-written IDL code for legacy applications and libraries. In addition to generating IDL, we propose an XML-based method for specifying meta-data related to type mapping and wrapper generation that can be shared between our tools and IDL compilers. The component model of choice for this work is the Common Component Architecture (CCA) using the Scientific Interface Definition Language (SIDL), though the concepts presented can be applied to other models

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study

Background: The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality. Research Questions: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA. Study Design and Methods: We conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. Results: We prospectively analyzed 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) included intensive care unit (ICU)-level care, 1,480 (5.4%) included invasive mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted overall mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted overall mortality with AUROC 0.94. In the subset of patients with COVID-19, we predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. Interpretation: We developed and validated an accurate, in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model, that improved upon SOFA. Study Question: Can we improve upon the SOFA score for real-time mortality prediction during the COVID-19 pandemic by leveraging electronic health record (EHR) data? Results: We rapidly developed and implemented a novel yet SOFA-anchored mortality model across 12 hospitals and conducted a prospective cohort study of 27,296 adult hospitalizations, 1,358 (5.0%) of which were positive for SARS-CoV-2. The Charlson Comorbidity Index and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. Interpretation: A novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic