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C17H16I2N4OZn, monoclinic, P21/n, a = 7.4827(5) Å, b = 14.9521(10) Å, c = 18.2838(13) Å, β = 99.251(1)°, V = 2019.0(2) Å3, Z = 4, R gt (F) = 0.0404, wR ref (F 2) = 0.787, T = 298 K.

BACKGROUND The COVID-19 pandemic has highlighted the need for accurate and responsive epidemiological simulations of epidemic outbreaks. These simulations must be grounded in quantities derived from the measurements, for example of the period over which a person is infectious or the mortality rate of the disease. Particularly difficult parameters to estimate are those associated with contacts between individuals. Digital contact tracing data, such as that provided by Bluetooth beaconing or GPS co-locating, 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 In this paper, we compare GPS co-located versus Bluetooth beacon derived proximity contact data for their impacts on transmission models’ results, under community and types of diseases. METHODS We examine the contact patterns derived from three previously collected datasets employing both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data app. We compared the structure of contact networks inferred from proximity contact data collected with modalities of GPS co-locating and Bluetooth beaconing. We assessed the impact of sensing modalities on 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, Recovered (SEIR) transmission models of four different contagious diseases. We demonstrate their differences with violin plots, t-tests, and Kullback-Leibler (KL) divergence. RESULTS Both the network structure analyses show visually salient differences between GPS co-locating and Bluetooth beaconing collected proximity contact data, regardless of the underlying population. The violin plots of attack rate and KL-divergence of individual infection risks demonstrate discernible differences for different sensing modalities regardless of the underlying population and diseases. The results of t-tests on attack rate between different sensing modalities are mostly significant, with P-value less than 0.001. CONCLUSIONS We show that the contact networks generated from these two measurement modalities are different and generate significantly different attack rates across multiple datasets 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 simulation outcome cannot be ignored.

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.

Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.

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.

Smoking is one of the foremost public health threats listed by the World Health Organization, and surveillance is a key to informing effective policies. High smartphone penetration and mature smartphone sensor data collecting techniques make smartphone sensor data based smoking monitoring viable, yet an effective classification algorithm remains elusive. In this paper, we sought to classify smoking using multivariate Hidden Markov models (HMMs) informed by binned time-series of transformed sensor data collected with smartphone-based Wi-Fi, GPS, and accelerometer sensors. Our model is trained on smartphone sensor time series data labeled with self-reported smoking periods. Two-fold cross-validation shows \(A_{z}\) (area under receiver operating characteristic curve) for HMMs using five features = (0.52, 0.84). Comparison of univariate HMMs and multivariate HMMs, suggests a high accuracy of multivariate HMMs for smoking periods classification.

BackgroundPertussis 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.MethodsWe 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–WhitneyUtest 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.Results189 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.DiscussionOur 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.

BackgroundBiological 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.MethodsWe 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.ResultsChickenpox 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.DiscussionOur 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.

Infectious disease spread is difficult to accurately measure and model. Even for well-studied pathogens, uncertainties remain regarding dynamics of mixing behavior and how to balance simulation-generated estimates with empirical data. While Markov Chain Monte Carlo approaches sample posteriors given empirical data, health applications of such methods have not considered dynamics associated with model error. We present here an Extended Kalman Filter (EKF) approach for recurrent simulation regrounding as empirical data arrives throughout outbreaks. The approach simultaneously considers empirical data accuracy, growing simulation error between measurements, and supports estimation of changing model parameters. We evaluate our approach using a two-level system, with “ground truth” generated by an agent-based model simulating epidemics over empirical microcontact networks, and noisy measurements fed into an EKF corrected aggregate model. We find that the EKF solution improves outbreak peak estimation and can compensate for inaccuracies in model structure and parameter estimates.

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.

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.

Smartphones have the potential to revolutionize health monitoring and delivery. Significant attention has been given to personal health devices and systems to help individuals and medical practitioners monitor health and treatment compliance. The data collected from these systems also has significant value to public health workers and epidemiologists. However, requirements for backend analysis and supplemental data differ between personal and public health applications. In this paper we describe iEpi, an end-to-end system for collecting, analyzing, and utilizing contextual microdata through smartphones for epidemiological and public health applications.