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An impressive number of COVID-19 data catalogs exist. None, however, are optimized for data science applications, e.g., inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 case data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, and key demographic characteristics.

The coronavirus pandemic and in-person contact restrictions necessitated rapid implementation of telehealth, specifically videoconferencing, to provide essential care to patients. This study surveyed 25 pediatric behavioral health providers at a single center during their first month of utilizing telehealth during coronavirus disease 2019 (COVID-19). Twenty-one participants completed a pre-questionnaire distributed prior to telehealth service delivery, and 23 providers completed a post-questionnaire approximately three weeks later. Results indicate the majority of behavioral health providers had no experience providing telehealth services prior to COVID-19. The majority of participating behavioral health providers utilized telehealth to provide pediatric patient care within the first month of access to telehealth. Participants’ confidence in their ability to provide telehealth services significantly increased within the first month of implementation, regardless of previous training in telehealth. This study identified differences between anticipated and actual barriers to treatment, with technological issues identified as the largest actual barrier to service delivery. Participants indicated a preference for in-person service delivery, which they reported allows for better rapport-building, behavioral observations, reduced technological barriers, and fewer distractions. However, most participants reported they intend to continue utilizing telehealth for certain types of behavioral health services (e.g., diagnostic interviews and outpatient therapy) after the pandemic has subsided.

In response to the COVID-19 public health emergency, we developed an , first released publicly on January 22, 2020, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The dashboard visualizes and tracks the number of reported confirmed cases, deaths and recoveries for all countries affected by COVID-19. Further, all the data collected and displayed on the dashboard is made freely available in a GitHub repository, along with the live feature layers of the dashboard. The motivation behind the development of the dashboard was to provide researchers, public health authorities and the general public with a user-friendly tool to track the outbreak situation as it unfolds, critically, with access to the data underlying it. The demand for such a service became evident in the first weeks the dashboard was online, and by the end of February we were receiving over one billion requests for the dashboard feature layers every day, which since increased to between three and 4.5 billion requests every day. The dashboard has been featured on most major national and international media outlets (NYT, Washington Post, CNN, NPR, etc), and is either directly embedded in their websites, or used as the data source for in-house mapping efforts. Further, members of the public health community, including local and national governmental organizations, emergency response teams, public health agencies, and infectious disease researchers around the world rely on the dashboard and its data for informing and planning COVID-19 response. In this talk I will give a brief overview of the evolution of the dashboard, discuss some of the challenges we faced along the way, and suggest some methods by which disease tracking could be done better in the future.

The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states, and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics., Genomic and epidemiological analyses provide a clearer picture of one of the earliest SARS-CoV-2 superspreader events in the United States in accelerating transmission, with a single introduction of the virus being responsible for most cases during this period.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

BACKGROUND The link between chronic diseases and the Big 6 lifestyle risk behaviors (ie, poor diet, physical inactivity, smoking, alcohol use, sedentary recreational screen time, and poor sleep) is well established. It is critical to target these lifestyle risk behaviors, as they often co-occur and emerge in adolescence. Smartphones have become an integral part of everyday life, and many adolescents already use mobile apps to monitor their lifestyle behaviors and improve their health. Smartphones may be a valuable platform for engaging adolescents with interventions to prevent key chronic disease risk behaviors. OBJECTIVE The aim of this paper is to describe the development, usability, and acceptability of the Health4Life app, a self-monitoring smartphone app for adolescents that concurrently targets the Big 6 lifestyle behaviors. METHODS The development of the Health4Life app was an iterative process conducted in collaboration with adolescents and experts. The development process consisted of three stages: scoping the literature; end user consultations, which included a web-based survey (N=815; mean age 13.89, SD 0.89 years) and a focus group (N=12) among adolescents; and app development and beta testing. Following this development work, 232 adolescents were asked to rate the usability and acceptability of the app. RESULTS The process resulted in a self-monitoring smartphone app that allows adolescent users to track and set goals for the Big 6 health behaviors, using in-app rewards and notifications to enhance engagement. The overall adolescent feedback was positive in terms of user-friendly design, content, relevance, and helpfulness. Commonly identified areas for improvement were to increase interactive features and display recorded health behaviors differently to improve interpretability. CONCLUSIONS The Health4Life app is a co-designed, self-monitoring smartphone app for adolescents that concurrently targets the Big 6 lifestyle behaviors. Adolescents rated the app as highly acceptable and usable. The app has the potential to efficiently and effectively modify important risk factors for chronic disease among young people and is currently being evaluated in a world-first trial of 6640 secondary school students in 71 schools across Australia. CLINICALTRIAL

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the physical encounters of individuals on public transit systems is thus critical to understand the drivers of infectious disease outbreaks. For instance previous work has explored the impact of recurring patterns inherent in human mobility on disease spread, but has not considered other dimensions such as the distance travelled or the number of encounters. Here, we consider multiple mobility dimensions simultaneously to uncover critical information for the design of effective intervention strategies. We use one month of citywide smart card travel data collected in Sydney, Australia to classify bus passengers along three dimensions, namely the degree of exploration, the distance travelled and the number of encounters. Additionally, we simulate disease spread on the transport network and trace the infection paths. We investigate in detail the transmissions between the classified groups while varying the infection probability and the suspension time of pathogens. Our results show that characterizing individuals along multiple dimensions simultaneously uncovers a complex infection interplay between the different groups of passengers, that would remain hidden when considering only a single dimension. We also identify groups that are more influential than others given specific disease characteristics, which can guide containment and vaccination efforts., 10 pages, 10 figures and 1 table. To be published in the 2020 21st IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (IEEE WOWMOM 2020) conference program and the proceedings

In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015–2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector–human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.

Objective Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors. Materials and Methods We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors’ collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics’ relationship with success. Results We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric (P Conclusions Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.

COVID-19 is present in every state and over 90 percent of all counties in the United States. Decentralized government efforts to reduce spread, combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the U.S.a challenge. We generate a novel metric to represent social distancing behavior derived from mobile phone data and examine its relationship with COVID-19 case reports at the county level. Our analysis reveals that social distancing is strongly correlated with decreased COVID-19 case growth rates for the 25 most affected counties in the United States, with a lag period consistent with the incubation time of SARS-CoV-2. We also demonstrate evidence that social distancing was already under way in many U.S. counties before state or local-level policies were implemented. This study strongly supports social distancing as an effective way to mitigate COVID-19 transmission in the United States.

Summary Background Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge. Methods We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zurich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county. Findings Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35–63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9–12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message. Interpretation This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future. Funding None.

BACKGROUND Chronic diseases are the leading cause of death worldwide. Addressing key lifestyle risk factors during adolescence is critical for improving physical and mental health outcomes and reducing chronic disease risk. Schools are ideal intervention settings, and electronic health (eHealth) interventions afford several advantages, including increased student engagement, scalability, and sustainability. Although lifestyle risk behaviors tend to co-occur, few school-based eHealth interventions have targeted multiple behaviors concurrently. OBJECTIVE This study aims to summarize the co-design and user testing of the Health4Life school-based program, a web-based cartoon intervention developed to concurrently prevent 6 key lifestyle risk factors for chronic disease among secondary school students: alcohol use, smoking, poor diet, physical inactivity, sedentary recreational screen time, and poor sleep (the Big 6). METHODS The development of the Health4Life program was conducted over 18 months in collaboration with students, teachers, and researchers with expertise relevant to the Big 6. The iterative process involved (1) scoping of evidence and systematic literature review; (2) consultation with adolescents (N=815) via a cross-sectional web-based survey to identify knowledge gaps, attitudes, barriers, and facilitators in relation to the Big 6; (3) content and web development; and (4) user testing of the web-based program with students (n=41) and teachers (n=8) to evaluate its acceptability, relevance, and appeal to the target audience. RESULTS The co-design process resulted in a six-module, evidence-informed program that uses interactive cartoon storylines and web-based delivery to engage students. Student and teacher feedback collected during user testing was positive in terms of acceptability and relevance. Commonly identified areas for improvement concerned the length of modules, age appropriateness of language and alcohol storyline, the need for character backstories and links to syllabus information, and feasibility of implementation. Modifications were made to address these issues. CONCLUSIONS The Health4Life school-based program is the first universal, web-based program to concurrently address 6 important chronic disease risk factors among secondary school students. By adopting a multiple health behavior change approach, it has the potential to efficiently modify the Big 6 risk factors within one program and to equip young people with the skills and knowledge needed to achieve and maintain good physical and mental health throughout adolescence and into adulthood.

Outbreaks of infectious diseases present a global threat to human health and are considered a major health-care challenge. One major driver for the rapid spatial spread of diseases is human mobility. In particular, the travel patterns of individuals determine their spreading potential to a great extent. These travel behaviors can be captured and modelled using novel location-based data sources, e.g., smart travel cards, social media, etc. Previous studies have shown that individuals who cannot be characterized by their most frequently visited locations spread diseases farther and faster; however, these studies are based on GPS data and mobile call records which have position uncertainty and do not capture explicit contacts. It is unclear if the same conclusions hold for large scale real-world transport networks. In this paper, we investigate how mobility patterns impact disease spread in a large-scale public transit network of empirical data traces. In contrast to previous findings, our results reveal that individuals with mobility patterns characterized by their most frequently visited locations and who typically travel large distances pose the highest spreading risk., 6 Pages, 8 figures and 2 tables. Published in: 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

Individuals with Autism Spectrum Disorder (ASD) may interact with law enforcement officers (LEOs) as victims of crime, witnesses to crime, or suspects of crime. Interactions between LEOs and those with ASD may go awry which raises questions about levels of training, experiences, and knowledge acquired by LEOs. Seventy-two LEOs reported on their experiences and training related to ASD and completed a survey of autism knowledge. The majority (72.2%) of LEOs reported no formal training for interacting with individuals with ASD. For LEOs responding to calls involving ASD, officers with prior training reported better preparation. Officers' responses to the knowledge survey varied considerably. Results support the need for formalized training in ASD for LEOs.

A critical issue in origin-destination (O-D) demand estimation is under-determination: the number of O-D pairs to be estimated is often much greater than the number of monitored links. In real world, some centroids tend to be more popular than others, and only few trips are made for intro-zonal travel. Consequently, a large portion of trips will be made for a small portion of O-D pairs, meaning many O-D pairs have only a few or even zero trips. Mathematically, this implies that the O-D matrix is sparse. Also, the correlation between link flows is often neglected in the O-D estimation problem, which can be obtained from day-to-day loop detector count data. Thus, sparsity regularisation is combined with link flow correlation to provide additional inputs for the O-D estimation process to mitigate the issue of under-determination and thereby improve estimation quality. In addition, a novel strategic user equilibrium model is implemented to provide route choice of users for the O-D estimation problem, which explicitly accounts for demand and link flow volatility. The model is formulated as a convex generalised least squares problem with regularisation, the usefulness of sparsity assumption, and link flow correlation is presented in the numerical analysis.

Purpose An innovative collaborative care model to improve transitions of care (TOC) for patients with heart failure (HF) is described. Summary As part of a broad effort by New Hanover Regional Medical Center (NHRMC) to reduce avoidable 30-day hospital readmissions and decrease associated healthcare costs through a team-centered, value-based approach to patient care, an interprofessional team was formed to help reduce hospital readmissions among discharged patients with HF. The team consists of 5 TOC pharmacists, 4 community paramedics, and 4 advanced care practitioners (ACPs) who collaborate to coordinate care and prevent 30-day readmissions among patients with HF transitioning from the hospital to the community setting. Each team member plays an integral role in providing high-quality postdischarge care. The TOC pharmacist ensures that patients have access to all needed medications, provides in-home medication reconciliation services, makes medication recommendations, and alerts the team of potential medication-related issues. Community paramedics conduct home visits consisting of physical and mental health assessments, diet and disease state education, reviews of medication bottles and education on proper medication use, and administration of i.v. diuretics to correct volume status under provider orders. The ACPs offer close clinic follow-up (typically initiated within 7 days of discharge) as well as long-term HF management and education. Conclusion At NHRMC, collaboration among healthcare professionals, including a TOC pharmacist, community paramedics, and ACPs, has assisted in the growth and expansion of services provided to patients with HF.

Modern public transport networks provide an efficient medium for the spread of infectious diseases within a region. The ability to identify components of the public transit system most likely to be carrying infected individuals during an outbreak is critical for public health authorities to be able to plan for outbreaks, and control their spread. In this study we propose a novel network structure, denoted as the vehicle trip network, to capture the dynamic public transit ridership patterns in a compact form, and illustrate how it can be used for efficient detection of the high risk network components. We evaluate a range of network-based statistics for the vehicle trip network, and validate their ability to identify the routes and individual vehicles most likely to spread infection using simulated epidemic scenarios. A variety of outbreak scenarios are simulated, which vary by their set of initially infected individuals and disease parameters. Results from a case study using the public transit network from Twin Cities, MN are presented. The results indicate that the set of transit vehicle trips at highest risk of infection can be efficiently identified, and are relatively robust to the initial conditions of the outbreak. Furthermore, the methods are illustrated to be robust to two types of data uncertainty, those being passenger infection levels and travel patterns of the passengers.

A palliative approach to care focuses on what matters most to patients with life-limiting illness, including chronic kidney disease (CKD). Despite recent publication of related clinical practice guidelines in nephrology, there is limited information about how to practically implement these recommendations. In this Perspective, we describe our experience integrating a palliative approach within routine care of patients with CKD glomerular filtration rate categories 4 and 5 (G4-G5) across a provincial kidney care network during the past 15 years. The effort was led by a multidisciplinary group, tasked with building capacity and developing tools and resources for practical integration within a provincial network structure. We used an evidence-based framework that includes recommendations for 4 pillars of palliative care to guide our work: (1) patient identification, (2) advance care planning, (3) symptom assessment and management, and (4) caring of the dying patient and bereavement. Activities within each pillar have been iteratively implemented across all kidney care programs using existing committees and organizational structures. Key quality indicators were used to guide strategic planning and improvement. We supported culture change through the use of multiple strategies simultaneously. Altogether, we established and integrated palliative care activities into routine CKD G4-G5 care across the continuum from nondialysis to dialysis populations.

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate \b{eta} and congestion dissipation rate {\mu}. We describe the dynamics of congestion propagation and dissipation using these new parameters, \b{eta}, and {\mu}, embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time., Comment: 10 pages, 8 figures

We contrasted Behavior Assessment System for Children, Second Edition–Parent Rating Scales–Preschool Form (BASC-2 PRS-P) parent ratings for 242 (52% African American; 39% White) preschool-aged children from three diagnostic groups: autism spectrum disorder (ASD), intellectual disability (ID), and ASD and ID (ASD/ID). Across diagnostic groups, atypical behavior, attention, and adaptive functioning were rated as problematic; Developmental Social Disorders (DSD) scores were elevated across diagnostic groups and did not differ. Several BASC-2 clinical and adaptive scales differed across diagnostic groups, with parent-rated aggression higher for the ID group, parent-rated social withdrawal higher for the ASD group, and parent-rated social skills lower for the ASD/ID group. No differences in BASC-2 scores were found across African American and White preschoolers. The BASC-2 identified problem areas in a preschool clinical sample that were consistent with those of preschoolers with developmental disabilities. However, findings did not support use of the DSD as an ASD-specific screening measure.

High volumes of passenger air travel increase the risk of infectious disease epidemics and pandemics. Regional preparedness planning for large-scale outbreaks requires models that are able to capture outbreak dynamics within a control policy evaluation framework. Previous studies focused on either modeling outbreak dynamics or optimizing outbreak control decisions; this paper proposes an integrated approach that combines both aspects. A multiscale epidemic outbreak model is introduced that is designed to capture the infection dynamics at both the local (city) scale and the global (air travel) scale. A bilevel decision-making framework is then proposed to identify the optimal set of outbreak control policies, while accounting for local and global outbreak dynamics. The model is implemented for a case study in which a hypothetical epidemic outbreak is assumed to emerge from within the United States, and different control resource allocation strategies are explored and evaluated. The results highlight the importance of accounting for outbreak dynamics within the decision-making process and provide insight into the design and efficiency of a range of control strategies. This research is an initial effort to be followed by further research on the design of outbreak control strategies by using optimization algorithms under this framework.

To exploit the potential of electric vehicles (EVs) as a sustainable form of transport, the technology must be integrated into the traditional transport planning process. EV energy consumption will also become an essential issue for regional energy providers who will need to adapt to the additional electricity demand created by EVs. This study presents research to facilitate planning for EVs by incorporating travelers' behavior and energy consumption into the evaluation process by introducing a novel framework for the network design problem (NDP) which employs a previously introduced constrained shortest path algorithm that accounts for the distance limitations imposed on EV drivers. For certain design scenarios, the total travel time and total energy consumption are shown to be conflicting objectives. In addition, a new equity issue that arises in networks comprised of mixed classes of vehicles is explored. Results illustrate that a given design scenario can impact vehicle user groups differently.

Travel demand modelling has a long history going back to 50s when the conventional four-step modelling structure was introduced and developed for Chicago and Detroit, USA. However, the first travel demand models for freight movements were not developed until 40 years after the first model for passenger cars. Freight models are yet limited to studies looking at goods movements by truck and rail. This paper explores the effectiveness of the conventional travel demand modelling techniques for maritime container movements. Two approaches for modelling the movement of trade in the global maritime container network are discussed. The conventional methods of trip generation and distribution are applied in a sequential model, and compared against an alternative joint model methodology. Results show that the sequential methodology achieves high accuracy, while the joint methodology reveals more detailed trade relationships. Significant relationships are revealed, such as the varying influence of airports, the negative impact of coastline length and the impact of being an island, on containerised trade volumes. The findings of this paper provide a basis for modelling the container network from a transportation discipline viewpoint.

SUMMARYPreliminary evidence suggests that direct poultry contact may play a lesser role in transmission of avian influenza A(H7N9) than A(H5N1) to humans. To better understand differences in risk factors, we quantified the degree of poultry contact reported by H5N1 and H7N9 World Health Organization-confirmed cases. We used publicly available data to classify cases by their degree of poultry contact, including direct and indirect. To account for potential data limitations, we used two methods: (1) case population method in which all cases were classified using a range of sources; and (2) case subset method in which only cases with detailed contact information from published research literature were classified. In the case population, detailed exposure information was unavailable for a large proportion of cases (H5N1, 54%; H7N9, 86%). In the case subset, direct contact proportions were higher in H5N1 cases (70·3%) than H7N9 cases (40·0%) (χ2 = 18·5, P < 0·001), and indirect contact proportions were higher in H7N9 cases (44·6%) than H5N1 cases (19·4%) (χ2 = 15·5, P < 0·001). Together with emerging evidence, our descriptive analysis suggests direct poultry contact is a clearer risk factor for H5N1 than for H7N9, and that other risk factors should also be considered for H7N9.

This study combines novel multibeam electron microscopy with a geographical information system approach to create a first, seamless, navigable anatomic map of the human hip and its cellular inhabitants. Using spatial information acquired by localizing relevant map landmarks (e.g. cells, blood vessels), network modeling will enable disease epidemiology studies in populations of cells inhabiting tissues and organs.

The ongoing Zika epidemic in the Americas has challenged public health surveillance, response, and control systems. Even as the epidemic appears to be near its end in the Americas, it is unclear whether substantial Zika virus transmission may still be ongoing. This issue is exacerbated by large discrepancies in local case reporting and significant delays in detecting outbreaks due to surveillance gaps. To uncover locations with lingering outbreaks in the Americas, we investigated travel-associated Zika cases diagnosed in the United States and Europe to identify signatures of transmission dynamics that were not captured by local reporting. We found that a large and unreported Zika outbreak occurred in Cuba during 2017, a year after peak transmission in neighboring countries, with cases still appearing in 2018. By sequencing Zika virus from infected travelers, we show that the 2017 outbreak in Cuba was sparked by long-lived lineages of Zika virus introduced from multiple places in the Americas a year prior. Our data suggest that while aggressive mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, in the absence of vaccines, herd immunity, or strong international coordination, such control measures may need to be maintained to be effective. Our study highlights how Zika virus may still be ‘silently’ spreading in the Americas and provides a framework for more accurately understanding outbreak dynamics.

Background In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak’s expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner. Methods In this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future. Results The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks. Conclusions Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints. Electronic supplementary material The online version of this article (10.1186/s12916-019-1389-3) contains supplementary material, which is available to authorized users.

Background More than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010–2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission. Methods We present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016–2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution. Results Our results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions. Conclusions Our study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.

The introduction and spread of emerging infectious diseases is increasing in both prevalence and scale. Whether naturally, accidentally or maliciously introduced, the substantial uncertainty surrounding the emergence of novel viruses, specifically where they may come from and how they will spread, demands robust and quantifiably validated outbreak control policies that can be implemented in real time. This work presents a novel mathematical modeling framework that integrates both outbreak dynamics and outbreak control into a decision support tool for mitigating infectious disease pandemics that spread through passenger air travel. An ensemble of border control strategies that exploit properties of the air traffic network structure and expected outbreak behavior are proposed. A stochastic metapopulation epidemic model is developed to evaluate and rank the control strategies based on their effectiveness in reducing the spread of outbreaks. Sensitivity analyses are conducted to illustrate the robustness of the proposed control strategies across a range of outbreak scenarios, and a case study is presented for the 2009 H1N1 influenza pandemic. This study highlights the importance of strategically allocating outbreak control resources, and the results can be used to identify the most robust border control policy that can be implemented in the early stages of an outbreak.

The DSM-5 features level of support ratings for social communication (SC) and restrictive and repetitive behaviors (RRB) for individuals with autism spectrum disorder (ASD). We contrasted cognitive, adaptive, and autism severity scores across SC and RRB groups for 158 individuals with ASD diagnosed in a developmental disabilities clinic. Roughly 46% of individuals were identified by licensed psychologists’ clinical judgement as needing Level 2 SC support and 49% were identified as needing Level 2 RRB support. No individuals were rated as needing a combination of Level 1/Level 3 supports across domains. MANOVA and direct discriminant analysis revealed that both SC and RRB groups showed a graded pattern of higher adaptation/lower autism severity to lower adaptation/higher autism severity from Level 1 to Level 3.

Ramp metering is a control technology used to manage the flow of traffic entering motorways and freeways, with the primary aim of minimizing congestion on the main thoroughfare. This technique has been studied and implemented globally since the 1960s. It has been shown that ramp meters improve the overall efficiency of the system; however, the distribution of the benefits and costs across users has been questioned, and this is one of the main constraints on user acceptance of the ramp metering system. The typical methodology used in the literature is to assume that the most equitable condition is when all on-ramps have the same delay across space or time. This research developed a new definition of horizontal equity for ramp meters and a proposed method for calculating it. A hypothetical microsimulation model was developed on the basis of a motorway in Sydney, Australia, and used as the platform to demonstrate how the proposed equity definition can be evaluated. To assist in the interpretation, two configurations of a ramp metering algorithm were simulated and compared. Finally, the typical equality measure used in the literature was calculated for the same scenarios and compared with the proposed equity measure. The results showed that these two measures can favor scenarios. A qualitative discussion of the expected benefits of the proposed equity measure is offered. Those expected benefits are an easy-to-communicate means of justifying the metering rates for user acceptance (rates that are arguably fairer, compared with the typical equality measure); a measure that is complementary to integration with other intelligent transportation system technology such as tolled bypass lanes; and ease of incorporation in the long-term traffic management plan.

An understanding is needed of how epidemics spread to new regions via the global air traffic network so that effective strategies for outbreak control can be developed. Various studies have focused on predicting epidemic spread via the complex air traffic network. However, there is a gap in the literature demanding real-time predictive models that exploit the heterogeneous nature of the air travel pattern to optimize decision making among a set of potential control strategies. A bilevel optimization model is proposed to solve the resource allocation problem for an ongoing epidemic spreading via the air traffic network. The upper-level objective is to optimize the distribution of limited resources for epidemic control, and the lower-level simulation model computes the risk posed to the network under possible scenarios. Results from a demonstration network highlight the advantages of this model. A case study evaluates the risk posed by Ebola to the United States through the domestic air traffic network. The results demonstrate the ability of the model to develop real-time strategies that account for the heterogeneous nature of the air traffic network and the complex dynamics of epidemic spread.

Obesity and other chronic diseases are becoming more prevalent in affluent countries such as Australia. Researchers are trying to understand and combat this trend. One related growing stream of research explores the role of the built environment and transport system on an individual’s weight. However, results from many studies conducted have been contradictory. A primary cause of these contradictions is due to how neighborhood areas are defined, which directly affects how the built environment variables are calculated in geographic information systems. The potential impacts on regression analysis resulting from different data aggregation methods are well documented in spatial studies, geography, and regional planning fields, and the problem is primarily referred to as the modifiable aerial unit problem. In this paper, the focus is on reducing the error caused by the modifiable aerial unit problem by introducing a new data aggregation method. Individual health and lifestyle data are obtained from the survey of households, income, and labor dynamics in Australia, and the relationship between the built environment and obesity is evaluated by using a discrete choice model. The proposed aggregation method is evaluated across three spatial scales and compared against a conventional data aggregation method (i.e., using predefined administrative boundaries such as census tracts). The results reveal a stronger relationship between land use variables and obesity when the proposed aggregation method is implemented. This paper is relevant primarily to researchers because it provides an improved aggregation method to deal with some privacy restrictions of surveys. It is also relevant to practitioners and policy makers by its quantification of the association between specific built environment variables and obesity.

Real-time control of infectious disease outbreaks represents one of the greatest epidemiological challenges currently faced. In this paper we address the problem of identifying contagion patterns responsible for the spread of a disease in a network, which can be applied in real-time to evaluate an ongoing outbreak. We focus on the scenario where limited information, i.e. infection reports which may or may not include the actual source, is available during an ongoing outbreak and we seek the most likely infection tree that spans at least a set of known infected nodes. This problem can be represented using a maximum likelihood constrained Steiner tree model where the objective is to find a spanning tree with an assignment of integer nodes weights. We propose a novel formulation and solution method based on a two-step heuristic which (1) reduces the initial graph using a polynomial time algorithm designed to find feasible infection paths and (2) solves an exact mixed integer linear programming reformulation of the maximum likelihood model on the resulting subgraph. The proposed methodology can be applied to outbreaks which may evolve from multiple sources. Simulated contagion episodes are used to evaluate the performance of our solution method. Our results show that the approach is computationally efficient and is able to reconstruct a significant proportion of the outbreak, even in the context of low levels of information availability.

The aim of this work was to explore the comparative epidemiology of influenza viruses, H5N1 and H7N9, in both bird and human populations. Specifically, the article examines similarities and differences between the two viruses in their genetic characteristics, distribution patterns in human and bird populations and postulated mechanisms of global spread. In summary, H5N1 is pathogenic in birds, while H7N9 is not. Yet both have caused sporadic human cases, without evidence of sustained, human-to-human spread. The number of H7N9 human cases in the first year following its emergence far exceeded that of H5N1 over the same time frame. Despite the higher incidence of H7N9, the spatial distribution of H5N1 within a comparable time frame is considerably greater than that of H7N9, both within China and globally. The pattern of spread of H5N1 in humans and birds around the world is consistent with spread through wild bird migration and poultry trade activities. In contrast, human cases of H7N9 and isolations of H7N9 in birds and the environment have largely occurred in a number of contiguous provinces in south-eastern China. Although rates of contact with birds appear to be similar in H5N1 and H7N9 cases, there is a predominance of incidental contact reported for H7N9 as opposed to close, high-risk contact for H5N1. Despite the high number of human cases of H7N9 and the assumed transmission being from birds, the corresponding level of H7N9 virus in birds in surveillance studies has been low, particularly in poultry farms. H7N9 viruses are also diversifying at a much greater rate than H5N1 viruses. Analyses of certain H7N9 strains demonstrate similarities with engineered transmissible H5N1 viruses which make it more adaptable to the human respiratory tract. These differences in the human and bird epidemiology of H5N1 and H7N9 raise unanswered questions as to how H7N9 has spread, which should be investigated further.

Epidemics of dengue fever occur every 3 to 4 years in the Philippines. The risk of a dengue fever outbreak in the Philippines has been heightened since Typhoon Haiyan struck the region in November 2013 because recovery efforts have been slow to progress. The structural damage caused by the typhoon resulted in an abundance of standing water, which can serve as mosquito breeding grounds. Should an outbreak of dengue occur, infected travelers departing the Philippines pose a risk because they may introduce the dengue virus into local vector populations (of Aedes aegypti and Aedes albopictus) at their destinations. This study quantifies the risk posed by potentially infected travelers departing the Philippines by using a network model that incorporates predictive species distribution models, demographic features of destination areas, and air traffic volumes. The analysis reveals that a majority of travelers departing Philippine airports deplane in regions suitable for harboring Aedes aegypti or Aedes albopictus; this factor increases the likelihood of further spread of the disease if an outbreak were to occur. Because it is impractical to implement all possible monitoring and control measures at all locations connected to the Philippines, this analysis ranks the set of candidates and thus allows surveillance resources to be optimally deployed.

This paper presents an evaluation framework for high-occupancy/toll (HOT) lanes, extending previous frameworks by incorporat- ing departure time choice alongside stochasticity in demand. Drivers are divided into two classes, strategic and captive drivers. Strategic drivers time their departures to minimize expected generalized cost and can choose either the free lanes or HOT lane. Captive drivers have random departure times, and must choose the free lanes. Generalized cost for strategic drivers takes the form of the well-known schedule delay penalty function combined with travel time. The traffic flow model involves two parallel bot- tlenecks, one for each lane group. Four toll algorithms are compared: a fixed toll which is constant in time and unresponsive; a dynamic, but unresponsive toll based on expected demand; a dynamic and responsive toll which adapts based on congestion in the HOT lane; and a “full-information” toll which is aware of all captive driver inflows.

Infectious disease dynamic modeling can support outbreak emergency responses. We conducted a workshop to canvas the needs of stakeholders in Australia for practical, real-time modeling tools for infectious disease emergencies. The workshop was attended by 29 participants who represented government, defense, general practice, and academia stakeholders. We found that modeling is underused in Australia and its potential is poorly understood by practitioners involved in epidemic responses. The development of better modeling tools is desired. Ideal modeling tools for operational use would be easy to use, clearly indicate underlying parameterization and assumptions, and assist with policy and decision making.

We extend the existing toll pricing studies with fixed demand to stochastic demand. A new and practical second-best pricing problem with uncertain demand is proposed and formulated as a stochastic mathematical program with equilibrium constraints. In view of the problem structure, we develop a tailored global optimization algorithm. This algorithm incorporates a sample average approximation scheme, a relaxation-strengthening method, and a linearization approach. The proposed global optimization algorithm is applied to three networks: a two-link network, a seven-eleven network and the Sioux-Falls. The results demonstrate that using a single fixed estimation of future demand may overestimate the future system performance, which is consistent with previous studies. Moreover, the optimal toll obtained by using the mean demand value may not be optimal considering demand uncertainty. The proposed global optimization algorithm explicitly captures demand uncertainty and yields solutions that outperform those without considering demand uncertainty.

Between April 2012 and June 2014, 820 laboratory-confirmed cases of the Middle East respiratory syndrome coronavirus (MERS-CoV) have been reported in the Arabian Peninsula, Europe, North Africa, Southeast Asia, the Middle East, and the United States. The observed epidemiology is different to SARS, which showed a classic epidemic curve and was over in eight months. The much longer persistence of MERS-CoV in the population, with a lower reproductive number, some evidence of human-to-human transmission but an otherwise sporadic pattern, is difficult to explain. Using available epidemiological data, we implemented mathematical models to explore the transmission dynamics of MERS-CoV in the context of mass gatherings such as the Hajj pilgrimage, and found a discrepancy between the observed and expected epidemiology. The fact that no epidemic occurred in returning Hajj pilgrims in either 2012 or 2013 contradicts the long persistence of the virus in human populations. The explanations for this discrepancy include an ongoing, repeated nonhuman/sporadic source, a large proportion of undetected or unreported human-to-human cases, or a combination of the two. Furthermore, MERS-CoV is occurring in a region that is a major global transport hub and hosts significant mass gatherings, making it imperative to understand the source and means of the yet unexplained and puzzling ongoing persistence of the virus in the human population.

Summary The Zika epidemic in the Americas has challenged surveillance and control. As the epidemic appears to be waning, it is unclear whether transmission is still ongoing, which is exacerbated by discrepancies in reporting. To uncover locations with lingering outbreaks, we investigated travel-associated Zika cases to identify transmission not captured by reporting. We uncovered an unreported outbreak in Cuba during 2017, a year after peak transmission in neighboring islands. By sequencing Zika virus, we show that the establishment of the virus was delayed by a year and that the ensuing outbreak was sparked by long-lived lineages of Zika virus from other Caribbean islands. Our data suggest that, although mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, such measures need to be maintained to be effective. Our study highlights how Zika virus may still be “silently” spreading and provides a framework for understanding outbreak dynamics. Video Abstract, Graphical Abstract, Highlights • Travel surveillance and genomics uncovered hidden Zika transmission • An unreported and 1-year delayed Zika outbreak was detected in Cuba • Mosquito control may delay, not prevent, Zika virus establishment • A surveillance framework to detect hidden outbreaks was created, A combination of travel surveillance and clinical virus genomic sequencing of infected travelers provides a framework for detecting hidden outbreaks, such as an unreported Zika outbreak in Cuba during 2017.

Transport network pricing schemes are an integral traffic management strategy that can be implemented to reduce congestion, among other network impacts. However, the problem of determining tolls becomes much more complex when multiple sources of demand uncertainty are considered. This paper proposes a novel tolling model based on a particular variant of strategic user equilibrium in which users base their route choice decisions on a known demand distribution. The study showed that by using an average daily demand, a marginal social cost-based tolling approach could induce near optimal conditions in a strategic network. However, uncertainty was associated with the long-term future planning demand; inaccurate forecasts of future demand could result in poor realized tolling scheme performance. Therefore, this paper also proposes a method to test the robustness of a tolling scheme, which is the reliability of the link tolls under a range of future demand scenario realizations. Results demonstrated that evalua...

High-occupancy toll (HOT) lanes have become an increasingly prevalent form of congestion management pricing in the United States in the past few decades. The success of a HOT facility depends on the pricing scheme, which determines the utilization of the HOT lane, and the corresponding congestion relief on the parallel general purpose lanes. An additional complexity in determining HOT rates arises from the inevitable variability in travel demand inherent to transport networks. A successful tolling scheme must therefore be robust to changes in travel demand. Various tolling schemes for HOT facilities are examined in efforts to identify robust pricing policies. The expected performance and the corresponding variability of the facility are evaluated under each pricing scheme for different demand profiles. The focus is on noncorrelated demand uncertainties (i.e., number of arrivals during given time interval is independent of number of arrivals in preceding and following time intervals), which are modeled by ...