Impacts of observation frequency on proximity contact data and modeled transmission dynamics.

Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.

Dynamics of an Excess Electron in Molten LiF, KF, MgF2, and BeF2.

Ab initio molecular dynamics simulations suggest that the dynamics of an excess electron in different types of molten salts are not always the same. In molten LiF, KF, and MgF2, the excess electron localizes in the cavity as a solvated electron for 10 ps, which agrees with the widely accepted theory of Pikaev. In molten BeF2, the excess electron shows a different localization pattern: it mostly exists in localized states but also occurs in many delocalized states. This "localize-delocalize" pattern originates from the high viscosity of BeF2 (16 000 cP at 900 °C), which will lead to slow ionic motion and finally result in slow solvent relaxation. Besides, the species formed by the localization of the excess electron in these four melts are also different. The spectral feature (broad peak in the vis-IR region) of the localized electron in molten alkaline halides was also observed in LiF, KF, MgF2, and BeF2. Both an excess electron and electrons in the bulk liquid could contribute to the spectra, but the excitation of the excess electron makes a bigger contribution to the broad vis-IR peak. Our predicted spectrum of molten LiF/KF qualitatively reproduces the major feature of the experimental spectrum, which partially validates our simulations.

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach.

BACKGROUND: Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. OBJECTIVE: We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases. METHODS: We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. RESULTS: Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). CONCLUSIONS: We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

Lignin self-assembly and auto-adhesion for hydrophobic cellulose/lignin composite film fabrication.

Large amounts of lignin are produced as a by-product of paper pulping, resulting in a tremendous waste of natural resources with potential uses across various areas. To achieve the value-added utilization of agricultural waste and lignin, we developed a method for the fabrication of a lignin structure-designed hydrophobic film (LSHF) directly through solvent/anti-solvent self-assembly (acetic acid aqueous solution/n-hexane) and auto-adhesion of acetic acid lignin (AL) on the surface of a lignocellulose film (LCF). As the morphology structure revealed, the LSHF had a rough surface composed of lignin colloidal spheres, which significantly improved the water contact angle (WCA) from ~80° to ~130°. Furthermore, benefiting from the auto-adhesion of lignin, the WCA was more stable in 240 s, demonstrating that the LSHF had a lower WCA decrease (15.53 % - 25.55 % decrease) than the LCF (41.97 % - 61.11 % decrease) and the sample without auto-adhesion (100 % decrease). Simultaneously, auto-adhesion endowed the LSHF with a ~50 % increase in tensile strength. This work provides a novel strategy for the fabrication of hydrophobic cellulose/lignin composite films via lignin self-assembly and auto-adhesion.

Temporal aggregation impacts on epidemiological simulations employing microcontact data.

BACKGROUND: Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population's contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes. METHODS: We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks. RESULTS: No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs - which extrapolate from data collected over a brief period - exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation. CONCLUSIONS: The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.

Seeking the optimal schedule for chickenpox vaccination in Canada: Using an agent-based model to explore the impact of dose timing, coverage and waning of immunity on disease outcomes.

Many countries continue to consider implementing a universal chickenpox vaccine program; however, there is no consensus on the most appropriate and effective timing between vaccine doses. The chickenpox vaccine schedule debate is highlighted in Canada, where there are currently eight different vaccine schedules across the country. The objective of this study was to test the overall effectiveness of chickenpox vaccination, as well as the specific impact of two different vaccine schedules, on chickenpox disease outcomes in Alberta over 75 years. Using an agent-based model of chickenpox disease, we tested the impact of three vaccination scenarios including: baseline (no vaccination), a long dosing interval-Schedule LDI (1st dose - 12 months; 2nd dose -  4-6 years) and a short dosing interval-Schedule SDI (1st dose - 12 months; 2nd dose - 18 months) on chickenpox and shingles disease outcomes. Chickenpox vaccination led to a substantial decrease in chickenpox incidence over 75 years post-vaccine implementation. Compared to Schedule LDI, Schedule SDI resulted in a significantly lower chickenpox incidence, a higher age of chickenpox infection, a lower chickenpox breakthrough rate and a higher shingles incidence rate. Our model findings suggest that the chickenpox vaccine is effective over a long period of time and the dose timing of the vaccine may impact disease outcomes and vaccine effectiveness. However, the effectiveness of the vaccine dose timing is only one consideration for policy-makers who are implementing a chickenpox vaccine program, with others including risk of adverse events, the impact of the schedule on other antigens in a combination vaccine, parental acceptance and the cost associated with different schedules.

'Turning the tide' on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling.

INTRODUCTION: Hyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to manage and prevent this condition are contested. Dynamic simulation models (DSM) can test policy and program scenarios before implementation in the real world. This paper reports the development and use of an advanced DSM exploring the impact of maternal weight status interventions on incidence of HIP. METHODS: A consortium of experts collaboratively developed a hybrid DSM of HIP, comprising system dynamics, agent-based and discrete event model components. The structure and parameterization drew on a range of evidence and data sources. Scenarios comparing population-level and targeted prevention interventions were simulated from 2018 to identify the intervention combination that would deliver the greatest impact. RESULTS: Population interventions promoting weight loss in early adulthood were found to be effective, reducing the population incidence of HIP by 17.3% by 2030 (baseline ('business as usual' scenario)=16.1%, 95% CI 15.8 to 16.4; population intervention=13.3%, 95% CI 13.0 to 13.6), more than targeted prepregnancy (5.2% reduction; incidence=15.3%, 95% CI 15.0 to 15.6) and interpregnancy (4.2% reduction; incidence=15.5%, 95% CI 15.2 to 15.8) interventions. Combining targeted interventions for high-risk groups with population interventions promoting healthy weight was most effective in reducing HIP incidence (28.8% reduction by 2030; incidence=11.5, 95% CI 11.2 to 11.8). Scenarios exploring the effect of childhood weight status on entry to adulthood demonstrated significant impact in the selected outcome measure for glycemic regulation, insulin sensitivity in the short term and HIP in the long term. DISCUSSION: Population-level weight reduction interventions will be necessary to 'turn the tide' on HIP. Weight reduction interventions targeting high-risk individuals, while beneficial for those individuals, did not significantly impact forecasted HIP incidence rates. The importance of maintaining interventions promoting healthy weight in childhood was demonstrated.

Evaluation of the effect of chickenpox vaccination on shingles epidemiology using agent-based modeling.

BACKGROUND: Biological interactions between varicella (chickenpox) and herpes zoster (shingles), two diseases caused by the varicella zoster virus (VZV), continue to be debated including the potential effect on shingles cases following the introduction of universal childhood chickenpox vaccination programs. We investigated how chickenpox vaccination in Alberta impacts the incidence and age-distribution of shingles over 75 years post-vaccination, taking into consideration a variety of plausible theories of waning and boosting of immunity. METHODS: We developed an agent-based model representing VZV disease, transmission, vaccination states and coverage, waning and boosting of immunity in a stylized geographic area, utilizing a distance-based network. We derived parameters from literature, including modeling, epidemiological, and immunology studies. We calibrated our model to the age-specific incidence of shingles and chickenpox prior to vaccination to derive optimal combinations of duration of boosting (DoB) and waning of immunity. We conducted paired simulations with and without implementing chickenpox vaccination. We computed the count and cumulative incidence rate of shingles cases at 10, 25, 50, and 75 years intervals, following introduction of vaccination, and compared the difference between runs with vaccination and without vaccination using the Mann-Whitney U-test to determine statistical significance. We carried out sensitivity analyses by increasing and lowering vaccination coverage and removing biological effect of boosting. RESULTS: Chickenpox vaccination led to a decrease in chickenpox cases. The cumulative incidence of chickenpox had dropped from 1,254 cases per 100,000 person-years pre chickenpox vaccination to 193 cases per 100,000 person-years 10 years after the vaccine implementation. We observed an increase in the all-ages shingles cumulative incidence at 10 and 25 years post chickenpox vaccination and mixed cumulative incidence change at 50 and 75 years post-vaccination. The magnitude of change was sensitive to DoB and ranged from an increase of 22-100 per 100,000 person-years at 10 years post-vaccination for two and seven years of boosting respectively (p < 0.001). At 75 years post-vaccination, cumulative incidence ranged from a decline of 70 to an increase of 71 per 100,000 person-years for two and seven years of boosting respectively (p < 0.001). Sensitivity analyses had a minimal impact on our inferences except for removing the effect of boosting. DISCUSSION: Our model demonstrates that over the longer time period, there will be a reduction in shingles incidence driven by the depletion of the source of shingles reactivation; however in the short to medium term some age cohorts may experience an increase in shingles incidence. Our model offers a platform to further explore the relationship between chickenpox and shingles, including analyzing the impact of different chickenpox vaccination schedules and cost-effectiveness studies.

Building Research Capacity: Results of a Feasibility Study Using a Novel mHealth Epidemiological Data Collection System Within a Gestational Diabetes Population.

Public health researchers have traditionally relied on individual self-reporting when collecting much epidemiological surveillance data. Data acquisition can be costly, difficult to acquire, and the data often notoriously unreliable. An interesting option for the collection of individual health (or indicators of individual health) data is the personal smartphone. Smartphones are ubiquitous, and the required infrastructure is well-developed across Canada, including many remote areas. Researchers and health professionals are asking themselves how they might exploit increasing smartphone uptake for the purposes of data collection, hopefully leading to improved individual and public health. A novel smartphone-based epidemiological data collection and analysis system has been developed by faculty and students from the CEPHIL (Computational Epidemiology and Public Health Informatics) Lab in the Department of Computer Science at the University of Saskatchewan. A pilot feasibility study was then designed to examine possible relationships between smartphone sensor data, surveys and individual clinical data within a population of pregnant women. The study focused on the development of Gestational Diabetes (GDM), a transient condition during pregnancy, but with serious potential post-birth complications for both mother and child. The researchers questioned whether real-time smartphone data could improve the clinical management and outcomes of women at risk for developing GDM, enabling earlier treatment. The initial results from this small study did not show improved prediction of GDM, but did demonstrate that real-time individual health and sensor data may be readily collected and analyzed efficiently while maintaining confidentiality. Because the original version of the data collection software could only run on Android phones, this often meant the study participants were required to carry two phones, and this often meant the study phone was not carried, and therefore data not collected. The lessons learned will greatly inform future research.

Evaluation of outbreak response immunization in the control of pertussis using agent-based modeling.

BACKGROUND: Pertussis control remains a challenge due to recently observed effects of waning immunity to acellular vaccine and suboptimal vaccine coverage. Multiple outbreaks have been reported in different ages worldwide. For certain outbreaks, public health authorities can launch an outbreak response immunization (ORI) campaign to control pertussis spread. We investigated effects of an outbreak response immunization targeting young adolescents in averting pertussis cases. METHODS: We developed an agent-based model for pertussis transmission representing disease mechanism, waning immunity, vaccination schedule and pathogen transmission in a spatially-explicit 500,000-person contact network representing a typical Canadian Public Health district. Parameters were derived from literature and calibration. We used published cumulative incidence and dose-specific vaccine coverage to calibrate the model's epidemiological curves. We endogenized outbreak response by defining thresholds to trigger simulated immunization campaigns in the 10-14 age group offering 80% coverage. We ran paired simulations with and without outbreak response immunization and included those resulting in a single ORI within a 10-year span. We calculated the number of cases averted attributable to outbreak immunization campaign in all ages, in the 10-14 age group and in infants. The count of cases averted were tested using Mann-Whitney U test to determine statistical significance. Numbers needed to vaccinate during immunization campaign to prevent a single case in respective age groups were derived from the model. We varied adult vaccine coverage, waning immunity parameters, immunization campaign eligibility and tested stronger vaccination boosting effect in sensitivity analyses. RESULTS: 189 qualified paired-runs were analyzed. On average, ORI was triggered every 26 years. On a per-run basis, there were an average of 124, 243 and 429 pertussis cases averted across all age groups within 1, 3 and 10 years of a campaign, respectively. During the same time periods, 53, 96, and 163 cases were averted in the 10-14 age group, and 6, 11, 20 in infants under 1 (p < 0.001, all groups). Numbers needed to vaccinate ranged from 49 to 221, from 130 to 519 and from 1,031 to 4,903 for all ages, the 10-14 age group and for infants, respectively. Most sensitivity analyses resulted in minimal impact on a number of cases averted. DISCUSSION: Our model generated 30 years of longitudinal data to evaluate effects of outbreak response immunization in a controlled study. Immunization campaign implemented as an outbreak response measure among adolescents may confer benefits across all ages accruing over a 10-year period. Our inference is dependent on having an outbreak of significant magnitude affecting predominantly the selected age and achieving a comprehensive vaccine coverage during the campaign. Economic evaluations and comparisons with other control measures can add to conclusions generated by our work.

A Theoretical Basis for Entropy-Scaling Effects in Human Mobility Patterns.

Characterizing how people move through space has been an important component of many disciplines. With the advent of automated data collection through GPS and other location sensing systems, researchers have the opportunity to examine human mobility at spatio-temporal resolution heretofore impossible. However, the copious and complex data collected through these logging systems can be difficult for humans to fully exploit, leading many researchers to propose novel metrics for encapsulating movement patterns in succinct and useful ways. A particularly salient proposed metric is the mobility entropy rate of the string representing the sequence of locations visited by an individual. However, mobility entropy rate is not scale invariant: entropy rate calculations based on measurements of the same trajectory at varying spatial or temporal granularity do not yield the same value, limiting the utility of mobility entropy rate as a metric by confounding inter-experimental comparisons. In this paper, we derive a scaling relationship for mobility entropy rate of non-repeating straight line paths from the definition of Lempel-Ziv compression. We show that the resulting formulation predicts the scaling behavior of simulated mobility traces, and provides an upper bound on mobility entropy rate under certain assumptions. We further show that this formulation has a maximum value for a particular sampling rate, implying that optimal sampling rates for particular movement patterns exist.