brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

How to Choose Target Facilities in a Region to Implement Carbapenem-resistant Enterobacteriaceae Control Measures.

BACKGROUND: When trying to control regional spread of antibiotic-resistant pathogens such as carbapenem-resistant Enterobacteriaceae (CRE), decision makers must choose the highest-yield facilities to target for interventions. The question is, with limited resources, how best to choose these facilities. METHODS: Using our Regional Healthcare Ecosystem Analyst-generated agent-based model of all Chicago metropolitan area inpatient facilities, we simulated the spread of CRE and different ways of choosing facilities to apply a prevention bundle (screening, chlorhexidine gluconate bathing, hand hygiene, geographic separation, and patient registry) to a resource-limited 1686 inpatient beds. RESULTS: Randomly selecting facilities did not impact prevalence, but averted 620 new carriers and 175 infections, saving $6.3 million in total costs compared to no intervention. Selecting facilities by type (eg, long-term acute care hospitals) yielded a 16.1% relative prevalence decrease, preventing 1960 cases and 558 infections, saving $62.4 million more than random selection. Choosing the largest facilities was better than random selection, but not better than by type. Selecting by considering connections to other facilities (ie, highest volume of discharge patients) yielded a 9.5% relative prevalence decrease, preventing 1580 cases and 470 infections, and saving $51.6 million more than random selection. Selecting facilities using a combination of these metrics yielded the greatest reduction (19.0% relative prevalence decrease, preventing 1840 cases and 554 infections, saving $59.6 million compared with random selection). CONCLUSIONS: While choosing target facilities based on single metrics (eg, most inpatient beds, most connections to other facilities) achieved better control than randomly choosing facilities, more effective targeting occurred when considering how these and other factors (eg, patient length of stay, care for higher-risk patients) interacted as a system.

How Long-Term Acute Care Hospitals Can Play an Important Role in Controlling Carbapenem-Resistant Enterobacteriaceae in a Region: A Simulation Modeling Study.

Typically, long-term acute care hospitals (LTACHs) have less experience in and incentives to implementing aggressive infection control for drug-resistant organisms such as carbapenem-resistant Enterobacteriaceae (CRE) than acute care hospitals. Decision makers need to understand how implementing control measures in LTACHs can impact CRE spread regionwide. Using our Chicago metropolitan region agent-based model to simulate CRE spread and control, we estimated that a prevention bundle in only LTACHs decreased prevalence by a relative 4.6%-17.1%, averted 1,090-2,795 new carriers, 273-722 infections and 37-87 deaths over 3 years and saved $30.5-$69.1 million, compared with no CRE control measures. When LTACHs and intensive care units intervened, prevalence decreased by a relative 21.2%. Adding LTACHs averted an additional 1,995 carriers, 513 infections, and 62 deaths, and saved $47.6 million beyond implementation in intensive care units alone. Thus, LTACHs may be more important than other acute care settings for controlling CRE, and regional efforts to control drug-resistant organisms should start with LTACHs as a centerpiece.

How Introducing a Registry With Automated Alerts for Carbapenem-resistant Enterobacteriaceae (CRE) May Help Control CRE Spread in a Region.

BACKGROUND: Regions are considering the use of electronic registries to track patients who carry antibiotic-resistant bacteria, including carbapenem-resistant Enterobacteriaceae (CRE). Implementing such a registry can be challenging and requires time, effort, and resources; therefore, there is a need to better understand the potential impact. METHODS: We developed an agent-based model of all inpatient healthcare facilities (90 acute care hospitals, 9 long-term acute care hospitals, 351 skilled nursing facilities, and 12 ventilator-capable skilled nursing facilities) in the Chicago metropolitan area, surrounding communities, and patient flow using our Regional Healthcare Ecosystem Analyst software platform. Scenarios explored the impact of a registry that tracked patients carrying CRE to help guide infection prevention and control. RESULTS: When all Illinois facilities participated (n = 402), the registry reduced the number of new carriers by 11.7% and CRE prevalence by 7.6% over a 3-year period. When 75% of the largest Illinois facilities participated (n = 304), registry use resulted in a 11.6% relative reduction in new carriers (16.9% and 1.2% in participating and nonparticipating facilities, respectively) and 5.0% relative reduction in prevalence. When 50% participated (n = 201), there were 10.7% and 5.6% relative reductions in incident carriers and prevalence, respectively. When 25% participated (n = 101), there was a 9.1% relative reduction in incident carriers (20.4% and 1.6% in participating and nonparticipating facilities, respectively) and 2.8% relative reduction in prevalence. CONCLUSIONS: Implementing an extensively drug-resistant organism registry reduced CRE spread, even when only 25% of the largest Illinois facilities participated due to patient sharing. Nonparticipating facilities garnered benefits, with reductions in new carriers.

Comparing perturbation models for evaluating stability of neuroimaging pipelines.

With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.

Does Choice of Influenza Vaccine Type Change Disease Burden and Cost-Effectiveness in the United States? An Agent-Based Modeling Study.

Offering a choice of influenza vaccine type may increase vaccine coverage and reduce disease burden, but it is more costly. This study calculated the public health impact and cost-effectiveness of 4 strategies: no choice, pediatric choice, adult choice, or choice for both age groups. Using agent-based modeling, individuals were simulated as they interacted with others, and influenza was tracked as it spread through a population in Washington, DC. Influenza vaccination coverage derived from data from the Centers for Disease Control and Prevention was increased by 6.5% (range, 3.25%-11.25%), reflecting changes due to vaccine choice. With moderate influenza infectivity, the number of cases averaged 1,117,285 for no choice, 1,083,126 for pediatric choice, 1,009,026 for adult choice, and 975,818 for choice for both age groups. Averted cases increased with increased coverage and were highest for the choice-for-both-age-groups strategy; adult choice also reduced cases in children. In cost-effectiveness analysis, choice for both age groups was dominant when choice increased vaccine coverage by ≥3.25%. Offering a choice of influenza vaccines, with reasonable resultant increases in coverage, decreased influenza cases by >100,000 with a favorable cost-effectiveness profile. Clinical trials testing the predictions made based on these simulation results and deliberation of policies and procedures to facilitate choice should be considered.

Geospatial Planning and the Resulting Economic Impact of Human Papillomavirus Vaccine Introduction in Mozambique.

BACKGROUND: Research has shown that the distance to the nearest immunization location can ultimately prevent someone from getting immunized. With the introduction of human papillomavirus (HPV) vaccine throughout the world, a major question is whether the target populations can readily access immunization. METHODS: In anticipation of HPV vaccine introduction in Mozambique, a country with a 2015 population of 25,727,911, our team developed Strategic Integrated Geo-temporal Mapping Application) to determine the potential economic impact of HPV immunization. We quantified how many people in the target population are reachable by the 1377 existing immunization locations, how many cannot access these locations, and the potential costs and disease burden averted by immunization. RESULTS: If the entire 2015 cohort of 10-year-old girls goes without HPV immunization, approximately 125 (111-139) new cases of HPV 16,18-related cervical cancer are expected in the future. If each health center covers a catchment area with a 5-km radius (ie, if people travel up to 5 km to obtain vaccines), then 40% of the target population could be reached to prevent 50 (44-55) cases, 178 (159-198) disability-adjusted life years, and US $202,854 (US $140,758-323,693) in health care costs and lost productivity. At higher catchment area radii, additional increases in catchment area radius raise population coverage with diminishing returns. CONCLUSIONS: Much of the population in Mozambique is unable to reach any existing immunization location, thereby reducing the potential impact of HPV vaccine. The geospatial information system analysis can assist in planning vaccine introduction strategies to maximize access and help the population reap the maximum benefits from an immunization program.

Quantifying the economic value and quality of life impact of earlier influenza vaccination.

BACKGROUND: Influenza vaccination is administered throughout the influenza disease season, even as late as March. Given such timing, what is the value of vaccinating the population earlier than currently being practiced? METHODS: We used real data on when individuals were vaccinated in Allegheny County, Pennsylvania, and the following 2 models to determine the value of vaccinating individuals earlier (by the end of September, October, and November): Framework for Reconstructing Epidemiological Dynamics (FRED), an agent-based model (ABM), and FluEcon, our influenza economic model that translates cases from the ABM to outcomes and costs [health care and lost productivity costs and quality-adjusted life-years (QALYs)]. We varied the reproductive number (R0) from 1.2 to 1.6. RESULTS: Applying the current timing of vaccinations averted 223,761 influenza cases, $16.3 million in direct health care costs, $50.0 million in productivity losses, and 804 in QALYs, compared with no vaccination (February peak, R0 1.2). When the population does not have preexisting immunity and the influenza season peaks in February (R0 1.2-1.6), moving individuals who currently received the vaccine after September to the end of September could avert an additional 9634-17,794 influenza cases, $0.6-$1.4 million in direct costs, $2.1-$4.0 million in productivity losses, and 35-64 QALYs. Moving the vaccination of just children to September (R0 1.2-1.6) averted 11,366-1660 influenza cases, $0.6-$0.03 million in direct costs, $2.3-$0.2 million in productivity losses, and 42-8 QALYs. Moving the season peak to December increased these benefits, whereas increasing preexisting immunity reduced these benefits. CONCLUSION: Even though many people are vaccinated well after September/October, they likely are still vaccinated early enough to provide substantial cost-savings.

A large-scale immuno-epidemiological simulation of influenza A epidemics.

BACKGROUND: Agent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic dynamics. METHODS: Using an equation-based, host-level mathematical model of influenza A virus infection, we develop a function that expresses the dependence of infectivity and symptoms of an infected individual on initial viral load, age, and viral strain phenotype. We incorporate this response function in a population-scale agent-based model of influenza A epidemic to create a hybrid multiscale modeling framework that reflects both population dynamics and individualized host response to infection. RESULTS: At the host level, we estimate parameter ranges using experimental data of H1N1 viral titers and symptoms measured in humans. By linearization of symptoms responses of the host-level model we obtain a map of the parameters of the model that characterizes clinical phenotypes of influenza infection and immune response variability over the population. At the population-level model, we analyze the effect of individualizing viral response in agent-based model by simulating epidemics across Allegheny County, Pennsylvania under both age-specific and age-independent severity assumptions. CONCLUSIONS: We present a framework for multi-scale simulations of influenza epidemics that enables the study of population-level effects of individual differences in infections and symptoms, with minimal additional computational cost compared to the existing population-level simulations.

The importance of nursing homes in the spread of methicillin-resistant Staphylococcus aureus (MRSA) among hospitals.

BACKGROUND: Hospital infection control strategies and programs may not consider control of methicillin-resistant Staphylococcus aureus (MRSA) in nursing homes in a county. METHODS: Using our Regional Healthcare Ecosystem Analyst, we augmented our existing agent-based model of all hospitals in Orange County (OC), California, by adding all nursing homes and then simulated MRSA outbreaks in various health care facilities. RESULTS: The addition of nursing homes substantially changed MRSA transmission dynamics throughout the county. The presence of nursing homes substantially potentiated the effects of hospital outbreaks on other hospitals, leading to an average 46.2% (range, 3.3%-156.1%) relative increase above and beyond the impact when only hospitals are included for an outbreak in OC's largest hospital. An outbreak in the largest hospital affected all other hospitals (average 2.1% relative prevalence increase) and the majority (~90%) of nursing homes (average 3.2% relative increase) after 6 months. An outbreak in the largest nursing home had effects on multiple OC hospitals, increasing MRSA prevalence in directly connected hospitals by an average 0.3% and in hospitals not directly connected through patient transfers by an average 0.1% after 6 months. A nursing home outbreak also had some effect on MRSA prevalence in other nursing homes. CONCLUSIONS: Nursing homes, even those not connected by direct patient transfers, may be a vital component of a hospital's infection control strategy. To achieve effective control, a hospital may want to better understand how regional nursing homes and hospitals are connected through both direct and indirect (with intervening stays at home) patient sharing.

FRED (a Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations.

BACKGROUND: Mathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within the population, and these mixing patterns depend in part on local factors including the spatial distribution and age structure of the population, the distribution of size and composition of households, employment status and commuting patterns of adults, and the size and age structure of schools. Finally, public health planners must take into account the health behavior patterns of the population, patterns that often vary according to socioeconomic factors such as race, household income, and education levels. RESULTS: FRED (a Framework for Reconstructing Epidemic Dynamics) is a freely available open-source agent-based modeling system based closely on models used in previously published studies of pandemic influenza. This version of FRED uses open-access census-based synthetic populations that capture the demographic and geographic heterogeneities of the population, including realistic household, school, and workplace social networks. FRED epidemic models are currently available for every state and county in the United States, and for selected international locations. CONCLUSIONS: State and county public health planners can use FRED to explore the effects of possible influenza epidemics in specific geographic regions of interest and to help evaluate the effect of interventions such as vaccination programs and school closure policies. FRED is available under a free open source license in order to contribute to the development of better modeling tools and to encourage open discussion of modeling tools being used to evaluate public health policies. We also welcome participation by other researchers in the further development of FRED.

Geospatial analytics to evaluate point-of-dispensing sites for mass immunizations in Allegheny County, Pennsylvania.

CONTEXT: Public health agencies use mass immunization locations to quickly administer vaccines to protect a population against an epidemic. The selection of such locations is frequently determined by available staffing levels and in some places, not all potential sites can be opened, often because of a lack of resources. Public health agencies need assistance in determining which n sites are the prime ones to open given available staff to minimize travel time and travel distance for those in the population who need to get to a site to receive treatment. OBJECTIVE: Employ geospatial analytical methods to identify the prime n locations from a predetermined set of potential locations (eg, schools) and determine which locations may not be able to achieve the throughput necessary to reach the herd immunity threshold based on varying R0 values. DESIGN: Spatial location-allocation algorithms were used to select the ideal n mass vaccination locations. SETTING: Allegheny County, Pennsylvania, served as the study area. MAIN OUTCOME MEASURES: The most favorable sites were selected and the number of individuals required to be vaccinated to achieve the herd immunity threshold for a given R0, ranging from 1.5 to 7, was determined. Locations that did not meet the Centers for Disease Control and Prevention throughput recommendation for smallpox were identified. RESULTS: At R0 = 1.5, all mass immunization locations met the required throughput to achieve the herd immunity threshold within 5 days. As R0s increased from 2 to 7, an increasing number of sites were inadequate to meet throughput requirements. CONCLUSIONS: Identifying the top n sites and categorizing those with throughput challenges allows health departments to adjust staffing, shift length, or the number of sites. This method has the potential to be expanded to select immunization locations under a number of additional scenarios.

Only adding stationary storage to vaccine supply chains may create and worsen transport bottlenecks.

Although vaccine supply chains in many countries require additional stationary storage and transport capacity to meet current and future needs, international donors tend to donate stationary storage devices far more often than transport equipment. To investigate the impact of only adding stationary storage equipment on the capacity requirements of transport devices and vehicles, we used HERMES (Highly Extensible Resource for Modeling Supply Chains) to construct a discrete event simulation model of the Niger vaccine supply chain. We measured the transport capacity requirement for each mode of transport used in the Niger vaccine cold chain, both before and after adding cold rooms and refrigerators to relieve all stationary storage constraints in the system. With the addition of necessary stationary storage, the average transport capacity requirement increased from 88% to 144% for cold trucks, from 101% to 197% for pickup trucks, and from 366% to 420% for vaccine carriers. Therefore, adding stationary storage alone may worsen or create new transport bottlenecks as more vaccines flow through the system, preventing many vaccines from reaching their target populations. Dynamic modeling can reveal such relationships between stationary storage capacity and transport constraints.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Data and Tools Integration in the Canadian Open Neuroscience Platform.

We present the Canadian Open Neuroscience Platform (CONP) portal to answer the research community's need for flexible data sharing resources and provide advanced tools for search and processing infrastructure capacity. This portal differs from previous data sharing projects as it integrates datasets originating from a number of already existing platforms or databases through DataLad, a file level data integrity and access layer. The portal is also an entry point for searching and accessing a large number of standardized and containerized software and links to a computing infrastructure. It leverages community standards to help document and facilitate reuse of both datasets and tools, and already shows a growing community adoption giving access to more than 60 neuroscience datasets and over 70 tools. The CONP portal demonstrates the feasibility and offers a model of a distributed data and tool management system across 17 institutions throughout Canada.

Recent performance improvements to the DFT and TDDFT in GAMESS.

The general atomic and molecular electronic structure system (GAMESS) is a quantum chemistry package used in the first-principles modeling of complex molecular systems using density functional theory (DFT) as well as a number of other post-Hartree-Fock methods. Both DFT and time-dependent DFT (TDDFT) are of particular interest to the materials modeling community. Millions of CPU hours per year are expended by GAMESS calculations on high-performance computing systems; any substantial reduction in the time-to-solution for these calculations represents a significant saving in CPU hours. As part of this work, three areas for improvement were identified: (1) the exchange-correlation (XC) integration grid, (2) profiling and optimization of the DFT code, and (3) TDDFT parallelization. We summarize the work performed in these task areas and present the resulting performance improvement. These software enhancements are available in 12JAN2009R3 or later versions of GAMESS.

Impact of introducing the pneumococcal and rotavirus vaccines into the routine immunization program in Niger.

OBJECTIVES: We investigated whether introducing the rotavirus and pneumococcal vaccines, which are greatly needed in West Africa, would overwhelm existing supply chains (i.e., the series of steps required to get a vaccine from the manufacturers to the target population) in Niger. METHODS: As part of the Bill and Melinda Gates Foundation-funded Vaccine Modeling Initiative, we developed a computational model to determine the impact of introducing these new vaccines to Niger's Expanded Program on Immunization vaccine supply chain. RESULTS: Introducing either the rotavirus vaccine or the 7-valent pneumococcal conjugate vaccine could overwhelm available storage and transport refrigerator space, creating bottlenecks that would prevent the flow of vaccines down to the clinics. As a result, the availability of all World Health Organization Expanded Program on Immunization vaccines to patients might decrease from an average of 69% to 28.2% (range = 10%-51%). Addition of refrigerator and transport capacity could alleviate this bottleneck. CONCLUSIONS: Our results suggest that the effects on the vaccine supply chain should be considered when introducing a new vaccine and that computational models can help assess evolving needs and prevent problems with vaccine delivery.

School closure as an influenza mitigation strategy: how variations in legal authority and plan criteria can alter the impact.

BACKGROUND: States' pandemic influenza plans and school closure statutes are intended to guide state and local officials, but most faced a great deal of uncertainty during the 2009 influenza H1N1 epidemic. Questions remained about whether, when, and for how long to close schools and about which agencies and officials had legal authority over school closures. METHODS: This study began with analysis of states' school-closure statutes and pandemic influenza plans to identify the variations among them. An agent-based model of one state was used to represent as constants a population's demographics, commuting patterns, work and school attendance, and community mixing patterns while repeated simulations explored the effects of variations in school closure authority, duration, closure thresholds, and reopening criteria. RESULTS: The results show no basis on which to justify statewide rather than school-specific or community-specific authority for school closures. Nor do these simulations offer evidence to require school closures promptly at the earliest stage of an epidemic. More important are criteria based on monitoring of local case incidence and on authority to sustain closure periods sufficiently to achieve epidemic mitigation. CONCLUSIONS: This agent-based simulation suggests several ways to improve statutes and influenza plans. First, school closure should remain available to state and local authorities as an influenza mitigation strategy. Second, influenza plans need not necessarily specify the threshold for school closures but should clearly define provisions for early and ongoing local monitoring. Finally, school closure authority may be exercised at the statewide or local level, so long as decisions are informed by monitoring incidence in local communities and schools.

Preparedness for pandemics: does variation among states affect the nation as a whole?

OBJECTIVE: Since states' public health systems differ as to pandemic preparedness, this study explored whether such heterogeneity among states could affect the nation's overall influenza rate. DESIGN: The Centers for Disease Control and Prevention produced a uniform set of scores on a 100-point scale from its 2008 national evaluation of state preparedness to distribute materiel from the Strategic National Stockpile (SNS). This study used these SNS scores to represent each state's relative preparedness to distribute influenza vaccine in a timely manner and assumed that "optimal" vaccine distribution would reach at least 35% of the state's population within 4 weeks. The scores were used to determine the timing of vaccine distribution for each state: each 10-point decrement of score below 90 added an additional delay increment to the distribution time. SETTING AND PARTICIPANTS: A large-scale agent-based computational model simulated an influenza pandemic in the US population. In this synthetic population each individual or agent had an assigned household, age, workplace or school destination, daily commute, and domestic intercity air travel patterns. MAIN OUTCOME MEASURES: Simulations compared influenza case rates both nationally and at the state level under 3 scenarios: no vaccine distribution (baseline), optimal vaccine distribution in all states, and vaccine distribution time modified according to state-specific SNS score. RESULTS: Between optimal and SNS-modified scenarios, attack rates rose not only in low-scoring states but also in high-scoring states, demonstrating an interstate spread of infections. Influenza rates were sensitive to variation of the SNS-modified scenario (delay increments of 1 day versus 5 days), but the interstate effect remained. CONCLUSIONS: The effectiveness of a response activity such as vaccine distribution could benefit from national standards and preparedness funding allocated in part to minimize interstate disparities.