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.

A novel role for the pineal gland: Regulating seasonal shifts in the gut microbiota of Siberian hamsters.

The gut microbiota plays a significant role in a variety of host behavioral and physiological processes. The mechanisms by which the gut microbiota and the host communicate are not fully resolved but include both humoral and direct neural signals. The composition of the microbiota is affected by internal (host) factors and external (environmental) factors. One such signal is photoperiod, which is represented endogenously by nocturnal pineal melatonin (MEL) secretion. Removal of the MEL signal via pinealectomy abolishes many seasonal responses to photoperiod. In Siberian hamsters (Phodopus sungorus), MEL drives robust seasonal shifts in physiology and behavior, such as immunity, stress, body mass, and aggression. While the profile of the gut microbiota also changes by season, it is unclear whether these changes are driven by pineal signals. We hypothesized that the pineal gland mediates seasonal alterations in the composition of the gut microbiota. To test this, we placed pinealectomized and intact hamsters into long or short photoperiods for 8 weeks, collected weekly fecal samples, and measured weekly food intake, testis volume, and body mass. We determined microbiota composition using 16S rRNA sequencing (Illumina MiSeq). We found significant effects of treatment and time on the abundances of numerous bacterial genera. We also found significant associations between individual OTU abundances and body mass, testis mass, and food intake, respectively. Finally, results indicate a relationship between overall community structure, and body and testis masses. These results firmly establish a role for the pineal gland in mediating seasonal alterations in the gut microbiota. Further, these results identify a novel neuroendocrine pathway by which a host regulates seasonal shifts in gut community composition, and indicates a relationship between seasonal changes in the gut microbiota and seasonal physiological adjustments.

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.

Microbial Ecology of Snow Reveals Taxa-Specific Biogeographical Structure.

Snows that persist late into the growing season become colonized with numerous metabolically active microorganisms, yet underlying mechanisms of community assembly and dispersal remain poorly known. We investigated (Illumina MiSeq) snow-borne bacterial, fungal, and algal communities across a latitudinal gradient in Fennoscandia and inter-continental distribution between northern Europe and North America. Our data indicate that bacterial communities are ubiquitous regionally (across Fennoscandia), whereas fungal communities are regionally heterogeneous. Both fungi and bacteria are biogeographically heterogeneous inter-continentally. Snow algae, generally thought to occur in colorful algae blooms (red, green, or yellow) on the snow surface, are molecularly described here as an important component of snows even in absence of visible algal growth. This suggests that snow algae are a previously underestimated major biological component of visually uncolonized snows. In contrast to fungi and bacteria, algae exhibit no discernible inter-continental or regional community structure and exhibit little endemism. These results indicate that global and regional snow microbial communities and their distributions may be dictated by a combination of size-limited propagule dispersal potential and restrictions (bacteria and fungi) and homogenization of ecologically specialized taxa (snow algae) across the globe. These results are among the first to compare inter-continental snow microbial communities and highlight how poorly understood microbial communities in these threatened ephemeral ecosystems are.

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.

Spatial and successional dynamics of microbial biofilm communities in a grassland stream ecosystem.

Biofilms represent a metabolically active and structurally complex component of freshwater ecosystems. Ephemeral prairie streams are hydrologically harsh and prone to frequent perturbation. Elucidating both functional and structural community changes over time within prairie streams provides a general understanding of microbial responses to environmental disturbance. We examined microbial succession of biofilm communities at three sites in a third-order stream at Konza Prairie over a 2- to 64-day period. Microbial abundance (bacterial abundance, chlorophyll a concentrations) increased and never plateaued during the experiment. Net primary productivity (net balance of oxygen consumption and production) of the developing biofilms did not differ statistically from zero until 64 days suggesting a balance of the use of autochthonous and allochthonous energy sources until late succession. Bacterial communities (MiSeq analyses of the V4 region of 16S rRNA) established quickly. Bacterial richness, diversity and evenness were high after 2 days and increased over time. Several dominant bacterial phyla (Beta-, Alphaproteobacteria, Bacteroidetes, Gemmatimonadetes, Acidobacteria, Chloroflexi) and genera (Luteolibacter, Flavobacterium, Gemmatimonas, Hydrogenophaga) differed in relative abundance over space and time. Bacterial community composition differed across both space and successional time. Pairwise comparisons of phylogenetic turnover in bacterial community composition indicated that early-stage succession (≤16 days) was driven by stochastic processes, whereas later stages were driven by deterministic selection regardless of site. Our data suggest that microbial biofilms predictably develop both functionally and structurally indicating distinct successional trajectories of bacterial communities in this ecosystem.

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.

Moth outbreaks alter root-associated fungal communities in subarctic mountain birch forests.

Climate change has important implications on the abundance and range of insect pests in forest ecosystems. We studied responses of root-associated fungal communities to defoliation of mountain birch hosts by a massive geometrid moth outbreak through 454 pyrosequencing of tagged amplicons of the ITS2 rDNA region. We compared fungal diversity and community composition at three levels of moth defoliation (intact control, full defoliation in one season, full defoliation in two or more seasons), replicated in three localities. Defoliation caused dramatic shifts in functional and taxonomic community composition of root-associated fungi. Differentially defoliated mountain birch roots harbored distinct fungal communities, which correlated with increasing soil nutrients and decreasing amount of host trees with green foliar mass. Ectomycorrhizal fungi (EMF) abundance and richness declined by 70-80 % with increasing defoliation intensity, while saprotrophic and endophytic fungi seemed to benefit from defoliation. Moth herbivory also reduced dominance of Basidiomycota in the roots due to loss of basidiomycete EMF and increases in functionally unknown Ascomycota. Our results demonstrate the top-down control of belowground fungal communities by aboveground herbivory and suggest a marked reduction in the carbon flow from plants to soil fungi following defoliation. These results are among the first to provide evidence on cascading effects of natural herbivory on tree root-associated fungi at an ecosystem scale.

The rich and the sensitive: diverse fungal communities change functionally with the warming Arctic.

Fungi are very abundant and functionally pivotal in Arctic terrestrial ecosystems. Yet, our understanding of their community composition, diversity and particularly their environmental drivers is superficial at the very best. In this issue of Molecular Ecology, Timling et al. (2014) describe perhaps one of the most comprehensive and geographically ambitious molecular studies on Arctic fungal communities to date. The results highlight the potential sensitivity of the fungal communities to plant communities, environmental conditions and therefore to environmental change. Thus, these studies lay a foundation to educated speculation on the fungal community migration northwards as a result of predicted climate change.

Contrasting primary successional trajectories of fungi and bacteria in retreating glacier soils.

Early community assembly of soil microbial communities is essential for pedogenesis and development of organic legacies. We examined fungal and bacterial successions along a well-established temperate glacier forefront chronosequence representing ~70 years of deglaciation to determine community assembly. As microbial communities may be heavily structured by establishing vegetation, we included nonvegetated soils as well as soils from underneath four plant species with differing mycorrhizal ecologies (Abies lasiocarpa, ectomycorrhizal; Luetkea pectinata, arbuscular mycorrhizal; Phyllodoce empetriformis, ericoid mycorrhizal; Saxifraga ferruginea, nonmycorrhizal). Our main objectives were to contrast fungal and bacterial successional dynamics and community assembly as well as to decouple the effects of plant establishment and time since deglaciation on microbial trajectories using high-throughput sequencing. Our data indicate that distance from glacier terminus has large effects on biomass accumulation, community membership, and distribution for both fungi and bacteria. Surprisingly, presence of plants rather than their identity was more important in structuring bacterial communities along the chronosequence and played only a very minor role in structuring the fungal communities. Further, our analyses suggest that bacterial communities may converge during assembly supporting determinism, whereas fungal communities show no such patterns. Although fungal communities provided little evidence of convergence in community structure, many taxa were nonrandomly distributed across the glacier foreland; similar taxon-level responses were observed in bacterial communities. Overall, our data highlight differing drivers for fungal and bacterial trajectories during early primary succession in recently deglaciated soils.

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.

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread.

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide.

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.