Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections.

Scenario-based modeling frameworks have been widely used to support policy-making at state and federal levels in the United States during the COVID-19 response. While custom-built models can be used to support one-off studies, sustained updates to projections under changing pandemic conditions requires a robust, integrated, and adaptive framework. In this paper, we describe one such framework, UVA-adaptive, that was built to support the CDC-aligned Scenario Modeling Hub (SMH) across multiple rounds, as well as weekly/biweekly projections to Virginia Department of Health (VDH) and US Department of Defense during the COVID-19 response. Building upon an existing metapopulation framework, PatchSim, UVA-adaptive uses a calibration mechanism relying on adjustable effective transmissibility as a basis for scenario definition while also incorporating real-time datasets on case incidence, seroprevalence, variant characteristics, and vaccine uptake. Through the pandemic, our framework evolved by incorporating available data sources and was extended to capture complexities of multiple strains and heterogeneous immunity of the population. Here we present the version of the model that was used for the recent projections for SMH and VDH, describe the calibration and projection framework, and demonstrate that the calibrated transmissibility correlates with the evolution of the pathogen as well as associated societal dynamics.

Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

Sexually transmissible infections (STI) and HIV testing and diagnosis among Aboriginal and non-Aboriginal adolescents in contact with the Australian justice system: a cross-sectional study.

BACKGROUND: It is unclear what factors are associated with sexually transmissible infections (STI) and HIV testing and diagnosis among justice-involved adolescents, and if these differ for Aboriginal or Torres Strait Islander peoples. METHODS: A cross-sectional survey of 465 justice-involved adolescents (aged 14-17years) from Australia was conducted between 2016 and 2018. Participants were asked about sexual behaviours, STI/HIV knowledge, and prior STI diagnoses and testing. RESULTS: Approximately 38% (n =130) of those sexually active had ever been screened for STI/HIV and 17.8% (n =23) had been diagnosed with an STI. No participant reported living with HIV. For Aboriginal participants, being male (aOR 3.6, 95% CI 1.3-10.1) and having under three sexual partners in the past 12months (aOR 3.1, 95% CI 1.2-8.0) was associated with never having had an STI/HIV test. For non-Aboriginal participants, being male (aOR 2.7, 95%CI 1.2-5.7), single (aOR 2.4, 95% CI 1.2-4.9), attending school (aOR 2.4, 95% CI 1.1-5.1), not having sought sexual health information (aOR 2.8, 95% CI 1.4-5.8), and having a lower STI/HIV knowledge score (aOR 2.3, 95% CI 1.1-5.0) were associated with never having had an STI/HIV test. Factors associated with STI diagnosis were non-heterosexual sexual orientation (aOR 5.6, 95% CI 1.1-28.2), transactional sex (aOR 11.2, 95% CI 3.0-41.3), and having sought sexual health information (aOR 3.5, 95% CI 1.0-12.5). CONCLUSIONS: Males, particularly Aboriginal male adolescents, should be engaged with sexual health promotion and testing services as soon as they come into contact with the justice system. Approaches should consider different cultural, gender and sexual orientations.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support.

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

What patient assessment skills do pharmacist independent prescribers require to prescribe immunomodulators in myeloma?

AIM: To gain consensus on the patient assessment skills required by pharmacist independent prescribers prescribing immunomodulators in myeloma across National Health Service Scotland. METHODS: This was a two-phase study which used nominal group technique to gain local consensus followed by a two-round eDelphi questionnaire to gain national consensus across all cancer networks. SETTING: This project was conducted across the three cancer networks within NHS Scotland: South East Scotland Cancer Network; West of Scotland Cancer Network and North Cancer Alliance. SUBJECTS: Participants were invited from each cancer network (South East Scotland Cancer Network, West of Scotland Cancer Network and North Cancer Alliance) and included haematology consultants, haematology specialist registrars, haematology advanced nurse practitioners and haematology pharmacists. RESULTS: There were five participants in the nominal group technique. Twenty-two out of 31 patient assessment skills gained local consensus, seven patient assessment skills did not gain consensus and two patient assessment skills were deemed irrelevant. There were 12 and 14 participants in round one and two of the eDelphi questionnaire, respectively. Twenty-nine patient assessment skills were included in the first-round questionnaire and 21 gained consensus. The remaining eight patient assessment skills were included in round two where seven did not achieve consensus and one achieved disagreement consensus. CONCLUSION: This research outlines 21 patient assessment skills required for pharmacist independent prescribers to prescribe immunomodulators for myeloma patients according to haematology specialists in Scotland. Discussion on patient assessment skills without consensus showed that the pharmacist independent prescribers would have a shared responsibility with the consultant. This work should inform the development of a competency framework to allow training of pharmacist independent prescribers in Scotland. Some patient assessment skills could be transferrable for pharmacist independent prescribers prescribing systemic anti-cancer therapy for other haematological malignancies.

Nonfatal Strangulation During Domestic Violence Events in New South Wales: Prevalence and Characteristics Using Text Mining Study of Police Narratives.

Nonfatal strangulation (NFS) is a common form of domestic violence (DV) that frequently leaves no visible signs of injury and can be a portent for future fatality. A validated text mining approach was used to analyze a police dataset of 182,949 DV events for the presence of NFS. Results confirmed NFS within intimate partner relationships is a gendered form of violence. The presence of injury and/or other (non-NFS) forms of physical abuse, emotional/verbal/social abuse, and the perpetrator threatening to kill the victim, were associated with significantly higher odds of NFS perpetration. Police data contain rich information that can be accessed using automated methodologies such as text mining to add to our understanding of this pressing public health issue.

Scalable Epidemiological Workflows to Support COVID-19 Planning and Response.

The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counter-factual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6-9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.

Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness.

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatiotemporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56-110k infections, 3.2- 5.4k hospitalizations, and 700-900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3-6 million fewer infections, 181-306k fewer hospitalizations, and 51-62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.

From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance.

The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper, we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid-19/dashboard/, which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling - an important emerging area in data science.

Evaluating the impact of international airline suspensions on the early global spread of COVID-19.

Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information on air traffic between international airports has been demonstrated to be useful in retrospectively validating and prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as effective distance on the global air traffic data from IATA to quantify risk of emergence for different countries as a consequence of direct importation from China, and compare it against arrival times for the first 24 countries. Using this model trained on official first reports from WHO, we estimate time of arrival (ToA) for all other countries. We then incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in delaying the estimated arrival time for all other countries. Finally we use the infectious disease vulnerability indices to explain some of the estimated reporting delays.

"That's the Lowest Place on Earth!" Experiences of the Carceral Spaces of Australian Police Custody for Marginalized Young Men.

Young men are overrepresented among people detained in police custody in Victoria, Australia, a closed institution that has mostly escaped public scrutiny. Our study sheds light on this underexamined place from the perspective of 28 marginalized young men (aged 19-24) detained there prior to adult prison. Drawing on Bacchi's "What's the problem represented to be?" approach and the subdiscipline of carceral geography, we disrupt the assumed purpose of police custody as a place to simply detain people while awaiting court and/or transfer to prison. We illustrate how police custody, although ostensibly for ensuring the safety and protection of the community, privileges that of some over others, with detrimental effects for marginalized groups. We highlight how harsh, degrading, hostile environments intersected with lived experiences and exacerbated psychological, social, and physical health harms; made possible young men's constitution as dirty, violent, and subhuman; and worked to legitimize breaches of human rights.

"A spray bottle and a lollipop stick": An examination of policy prohibiting sterile injecting equipment in prison and effects on young men with injecting drug use histories.

BACKGROUND: Australian young male prisoners with histories of injecting drug use are more likely to report injecting in prison, to do so more frequently, and to be involved in more un-safe injecting-related practices than their older counterparts. Despite international evidence that prison needle and syringe programs are both feasible and effective in reducing the harms associated with injecting drug use in prison, these young men do not have access to such equipment. METHODS: We critically analyse the interview transcripts of 28 young men with histories of injecting drug use who were recently released from adult prisons in Victoria, Australia, and prison drug policy text. We use Bacchi's 'What's the problem represented to be?' approach to examine how the 'problem' of injecting drug use in prison is represented in prison drug policy, including the assumptions that underpin these problematisations, and the subjectification and lived effects that are produced for the young men in our study. RESULTS: Our analysis reveals how prison drug policy enables the creation and re-use of homemade injecting equipment crafted from unsterile items found in prison, and that in doing so the policy produces a range of stigmatising subjectification effects and other harmful material effects (such as hepatitis C virus transmission and injecting related injury and harms). Findings highlight, how injecting drug use is represented in policy silences other ways of understanding the 'problem' that may have less harmful effects for incarcerated young men who inject drugs. CONCLUSION: We argue that somewhat paradoxically, the approach of prohibiting access to sterile injecting equipment in prison-which is constituted as a solution for addressing such harms-in fact helps to produce them.

Aboriginal mothers in prison in Australia: a study of social, emotional and physical wellbeing.

OBJECTIVE: To describe the social, emotional and physical wellbeing of Aboriginal mothers in prison. METHODS: Cross-sectional survey, including a Short Form Health Survey (SF-12) and Kessler Psychological Distress Scale (5-item version) administered to Aboriginal women who self-identified as mothers. RESULTS: Seventy-seven Aboriginal mothers in New South Wales (NSW) and 84 in Western Australia (WA) participated in the study. Eighty-three per cent (n=59) of mothers in NSW were in prison for drug-related offences, 64.8% (n=46) of mothers in WA were in prison for offences committed under the influence of alcohol. Sixty-eight per cent (n=52) of mothers in NSW and 35% (n=28) of mothers in WA reported mental health problems. Physical (PCS) and Mental (MCS) component scores of SF-12 varied for mothers in NSW and WA. Mothers in NSW experienced poorer health and functioning than mothers in WA (NSW: PCS 49.5, MCS 40.6; WA: PCS 54.4, MCS 48.3) and high levels of psychological distress (NSW: 13.1; WA 10.1). CONCLUSIONS: Aboriginal mothers in prison have significant health needs associated with physical and mental health, and psychological distress. Implications for public health: Adoption of social and emotional wellbeing as an explanatory framework for culturally secure healthcare in prison is essential to improving health outcomes of Aboriginal mothers in prison in Australia.

Australian Aboriginal women prisoners' experiences of being a mother: a review.

PURPOSE: The rise in the incarceration of Aboriginal and Torres Strait Islander mothers is a major public health issue with multiple sequelae for Aboriginal children and the cohesiveness of Aboriginal communities. The purpose of this paper is to review the available literature relating to Australian Aboriginal women prisoners' experiences of being a mother. DESIGN/METHODOLOGY/APPROACH: The literature search covered bibliographic databases from criminology, sociology and anthropology, and Australian history. The authors review the literature on: traditional and contemporary Aboriginal mothering roles, values and practices; historical accounts of the impacts of white settlement of Australia and subsequent Aboriginal affairs policies and practices; and women's and mothers' experiences of imprisonment. FINDINGS: The review found that the cultural experiences of mothering are unique to Aboriginal mothers and contrasted to non-Aboriginal concepts. The ways that incarceration of Aboriginal mothers disrupts child rearing practices within the cultural kinship system are identified. PRACTICAL IMPLICATIONS: Aboriginal women have unique circumstances relevant to the concept of motherhood that need to be understood to develop culturally relevant policy and programs. The burden of disease and cycle of incarceration within Aboriginal families can be addressed by improving health outcomes for incarcerated Aboriginal mothers and female carers. ORIGINALITY/VALUE: To the authors' knowledge, this is the first literature review on Australian Aboriginal women prisoners' experiences of being a mother.

EpiViewer: an epidemiological application for exploring time series data.

BACKGROUND: Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher's recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources - data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. RESULTS: In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves. CONCLUSION: EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data.

Disparities in spread and control of influenza in slums of Delhi: findings from an agent-based modelling study.

OBJECTIVES: This research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its residents. METHODS: We use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza. RESULTS: Simulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%-55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums. CONCLUSIONS: Unique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.

Narratives of Young Men With Injecting Drug Use Histories Leaving Adult Prison.

This research focuses on an under-examined aspect of the post-release prison trajectory for a seldom-researched cohort. Narratives of the immediate days/weeks surrounding release were gathered from young men with histories of injecting drug use (IDU). Twenty-eight participants (aged 19-24) released from adult prisons in Victoria, Australia, participated in face-to-face in-depth qualitative interviews after release. Analysis of findings through the lens of a "risk environment" framework reveals how their experiences were compromised by risk factors embedded in the physical spaces and social situations they inhabited, as well as the multi-sectoral policy environments under which they were governed. A complex interplay between these factors, young men's drug use and broader issues of structural vulnerability, including institutionalization and social disadvantage, combined to limit young men's chances of "success"1 on the outside. Narratives provide evidence for interventions that transform risk environments into enabling environments, thereby promoting a more successful transition from prison to community for young men with IDU histories.

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.

BACKGROUND: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. OBJECTIVE: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. METHODS: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). RESULTS: WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. CONCLUSIONS: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world.