The interplay of social constraints and individual variation in risk tolerance in the emergence of superspreaders.

Individual host behaviours can drastically impact the spread of infection through a population. Differences in the value individuals place on both socializing with others and avoiding infection have been shown to yield emergent homophily in social networks and thereby shape epidemic outcomes. We build on this understanding to explore how individuals who do not conform to their social surroundings contribute to the propagation of infection during outbreaks. We show how non-conforming individuals, even if they do not directly expose a disproportionate number of other individuals themselves, can become functional superspreaders through an emergent social structure that positions them as the functional links by which infection jumps between otherwise separate communities. Our results can help estimate the potential success of real-world interventions that may be compromised by a small number of non-conformists if their impact is not anticipated, and plan for how best to mitigate their effects on intervention success.

Age and Gender Trends in the Prevalence of Markers for Hepatitis E Virus Exposure in the Heterogeneous Bulgarian Population.

The prevalence of hepatitis E virus (HEV) in the Bulgarian population remains underestimated. The aim of the present study was to evaluate age and gender trends in HEV prevalence in the heterogeneous Bulgarian population. Stored serum samples from blood donors and different patient sub-populations-kidney recipients (KR), patients with Guillain-Barre syndrome (GBS), Lyme disease (LD), patients with liver involvement and a clinical diagnosis other than viral hepatitis A and E (non-AE), hemodialysis (HD) and HIV-positive patients (HIV)-were retrospectively investigated for markers of past and recent/ongoing HEV infection. The estimated overall seroprevalence of past infection was 10.6%, ranging from 5.9% to 24.5% for the sub-populations evaluated, while the seroprevalence of recent/ongoing HEV infection was 7.5%, ranging from 2.1% to 20.4%. The analysis of the individual sub-populations showed a different prevalence with respect to sex. In regard to age, the cohort effect was preserved, as a multimodal pattern was observed only for the GBS sub-population. Molecular analysis revealed HEV 3f and 3e. The type of the population is one of the main factors on which the anti-HEV prevalence depends, highlighting the need for the development of guidelines related to the detection and diagnosis of HEV infection with regard to specific patient populations.

Genetically Homogeneous or Heterogeneous Population - Distribution of the Recessive Allele in Selected Qualitative Characteristics of Selected Working-Age Population Sample.

Background: The genetic structure of each population can be explained according to the frequency of genes and their allelic variants, genotypes, and phenotypes. Objective: To analyze the genetic heterogeneity of the working-age population from the area of Sarajevo Canton based on classic genetic markers. The studied parameters of genetic heterogeneity were assessed by the relative frequency of the recessive allele for static-morphological traits (earlobe shape, chin shape, hairiness of the middle digital phalanx, bending of the distal phalanx of the little finger and digital index) and dynamic-morphological traits (rolling of the tongue into a groove, extensibility of the proximal thumb knuckle, extensibility of the distal thumb knuckle, the way the forearms are crossed, and the way the fists are made). Results: The results of the t-test showed a significant difference in the manifestation of the recessive homozygote for the observed parameters of qualitative variation in the subsamples of men and women. Only for two traits (attached earlobe and hyperextensibility of the distal knuckle of the thumb). The selected sample represent a relatively genetically homogenous population. Conclusion: This study serves as a valuable source of data for future research and the formation of a genetic database in Bosnia and Herzegovina.

Considering the lip print patterns of Ibo and Hausa Ethnic groups of Nigeria: checking the wave of ethnically driven terrorism.

Introduction: Lip print of an individual is distinct and could be a useful form of evidence to identify the ethnicity of a terrorist. Objectives: The study analyzed the distribution of lip print patterns of two major ethnic groups in Nigeria; Ibo and Hausa, to develop a strategic plan to check the wave of ethnically driven terrorism in Nigeria, carried out by groups such as Boko Haram and Indigeneous People of Biafra (IPOB). Materials and methods: The study comprised 800 participants of Ibo and Hausa ethnic groups (400 males and 400 females). The study adopted a digital method of lip print analysis and followed the guidelines outlined by the Institute of Medicine (IOM) for anthropometric measurements. The lip was classified, using Tsuchihashi and Suzuki method of classification. Results: The predominant lip print patterns of Ibo were Type I with complete vertical groove and Type III with intersect of groove for male and Type III for female. Type I' with partial length groove was the predominant pattern for both male and female Hausa. The lip width and height of female Ibo were longer than that of the Hausa counterpart (P < 0.05), but none of the anthropometric variables could predict the lip print pattern. Conclusion: The lip size and print could aid forensic investigation, though genetic diversity and heterogeneity of ethnic groups in Nigeria, especially that of Ibo, could setback use of lip print pattern to identify the ethnicity of an unknown individual in Nigeria to help determine the terrorist group to which they may belong.

A new method for interpolation of missing air quality data at monitor stations.

Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods.

Discrete Models in Epidemiology: New Contagion Probability Functions Based on Real Data Behavior.

Mathematical modeling is a tool used for understanding diseases dynamics. The discrete-time model is an especial case in modeling that satisfactorily describes the epidemiological dynamics because of the discrete nature of the real data. However, discrete models reduce their descriptive and fitting potential because of assuming a homogeneous population. Thus, in this paper, we proposed contagion probability functions according to two infection paradigms that consider factors associated with transmission dynamics. For example, we introduced probabilities of establishing an infectious interaction, the number of contacts with infectious and the level of connectivity or social distance within populations. Through the probabilities design, we overcame the homogeneity assumption. Also, we evaluated the proposed probabilities through their introduction into discrete-time models for two diseases and different study zones with real data, COVID-19 for Germany and South Korea, and dengue for Colombia. Also, we described the oscillatory dynamics for the last one using the contagion probabilities alongside parameters with a biological sense. Finally, we highlight the implementation of the proposed probabilities would improve the simulation of the public policy effect of control strategies over an infectious disease outbreak.

Harnessing the Potential of Wheat-Pea Species Mixtures: Evaluation of Multifunctional Performance and Wheat Diversity.

Species mixtures and heterogeneous crop populations are two promising approaches for diversified ecological cropping systems with increased resilience and reduced dependency on external inputs. Inter- and intraspecific diversity were evaluated in combination using 15 wheat (Triticum aestivum) entries, including line cultivars and heterogeneous populations (HPs), from central Europe and Hungary and one winter pea cultivar under organic conditions. Monocultures and wheat mixtures were evaluated multi-functionally for yield, quality, land use efficiency, crop protection, and wheat entry traits. Mixtures increased cereal grain quality, weed suppression, resource use efficiency, yield gain, and reduced lodging. Effects were stronger in 2018/19, which were characterized by dry and nutrient-poor conditions than in 2019/20 when nutrient levels were higher. Wheat entries varied considerably in protein content and yield in both mixtures and monocultures. Under higher nutrient availability, entry-based variation was reduced in both systems, and peas were suppressed. Because of low disease pressure, the wheat entries varied little in terms of disease protection services, and mixture effects on the disease were low. The multi-criteria framework identified stability of yield, yield gains, and quality under high environmental variability of mixtures as clear agronomic advantages with HPs being considerably more stable than line cultivars. Some line cultivars outperformed the HPs in either protein content or yield across environments but not both simultaneously. Trait analysis revealed a possible link between harvest index and reduced competition in mixtures, which can increase yield performance in specific line cultivars. System cultivar interactions were generally very low and highly dependent on environmental conditions. We conclude that while cultivar breeding for mixtures can be successful in monocultures, high environmental variation highlights the necessity of evaluating cultivars in mixtures. In addition, use of intraspecific diversity within interspecific mixed cropping systems can be a valuable addition to further improve mixture performance and its stability under increasing environmental stresses due to climate change.

Misinformation can prevent the suppression of epidemics.

The effectiveness of non-pharmaceutical interventions, such as mask-wearing and social distancing, as control measures for pandemic disease relies upon a conscientious and well-informed public who are aware of and prepared to follow advice. Unfortunately, public health messages can be undermined by competing misinformation and conspiracy theories, spread virally through communities that are already distrustful of expert opinion. In this article, we propose and analyse a simple model of the interaction between disease spread and awareness dynamics in a heterogeneous population composed of both trusting individuals who seek better quality information and will take precautionary measures, and distrusting individuals who reject better quality information and have overall riskier behaviour. We show that, as the density of the distrusting population increases, the model passes through a phase transition to a state in which major outbreaks cannot be suppressed. Our work highlights the urgent need for effective interventions to increase trust and inform the public.

The importance of using ordinal scores for patient classification based on health-related quality of life trajectories.

Changes in health-related quality of life (HRQoL) over time are not necessarily homogeneous within a population of interest. Our study aim was twofold: to determine homogeneous patient subpopulations distinguished by HRQoL trajectories, and to identify the particular patient profile associated with each subpopulation. To classify patients according to HRQoL dimension scores, we compared mixtures of linear mixed models (LMMs) classically applied to scores defined by the EORTC procedure, and mixtures of random effect cumulative models (CMs) applied to scores treated as ordinal variables. A simulation study showed that the mixture of LMMs overestimated the number of subpopulations and was less able to correctly classify patients than the mixture of CMs. Considering HRQoL scores as ordinal rather than continuous variables is relevant when classifying patients. The mixture of CMs for ordinal scores is able to identify homogeneous subpopulations and their associated trajectories. The application focused on changes over time in HRQoL data (collected using the EORTC QLQ-C30 questionnaire) from 132 breast cancer patients from the Moral study. Once the classification is obtained only from HRQoL scores, class membership was then explained through a logistic regression model, given a large panel of variables collected at baseline. Analysis of data revealed that deterioration over time of role functioning and insomnia was closely related to patient anxiety: anxiety at baseline is a prognostic factor for a poor level and/or a deterioration over time of HRQoL. For functional dimensions, large tumor size and high education level were associated with worse HRQoL scores.

Mapping short tandem repeats for liver gene expression traits helps prioritize potential causal variants for complex traits in pigs.

BACKGROUND: Short tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds. RESULTS: We identified 5203 and 5868 significantly expression STRs (eSTRs, FDR < 1%) in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively. CONCLUSIONS: This study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.

Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources, with application to COVID-19.

Testing provides essential information for managing infectious disease outbreaks, such as the COVID-19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease-positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false-negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data-driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID-19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID-19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.

Immunization using a heterogeneous geo-spatial population model: A qualitative perspective on COVID-19 vaccination strategies.

Epidemic modeling has been a key tool for understanding the impact of global viral outbreaks for over two decades. Recent developments of the COVID-19 pandemic have accelerated research using compartmental models, like SI, SIR, SEIR, with their appropriate modifications. However, there is a large body of recent research consolidated on homogeneous population mixing models, which are known to offer reduced tractability, and render conclusions hard to quantify. As such, based on our recent work, introducing the heterogeneous geo-spatial mobility population model (GPM), we adapt a modified SIR-V (susceptible-infected-recovered-vaccinated) epidemic model which embodies the idea of patient relapse from R back to S, vaccination of R and S patients (reducing their infectiousness), thus altering the infectiousness of V patients (from λn to λr). Simulation results spanning over a period of t = 2000 days (6 years, the period « 2020-2025) compare the impact of an epidemic outbreak with variable vaccination strategies, starting after 1 year (as is the case of COVID-19). The infected proportion in the remaining 5-year period is analyzed using vaccination rates from rv = 0 (no vaccination) to rv = 1. While rv < 0.4 is less effective during the earlier stages, all strategies with rv > 0.4 show a similar downward convergence reducing the number of infected by more than half, compared to no vaccination. Given the complexity of epidemic processes, we conclude that higher vaccination rates yield similar results, but a minimal rv = 0.4 (40% of population over five years) should be targeted.

Vaccine escape in a heterogeneous population: insights for SARS-CoV-2 from a simple model.

As a countermeasure to the SARS-CoV-2 pandemic, there has been swift development and clinical trial assessment of candidate vaccines, with subsequent deployment as part of mass vaccination campaigns. However, the SARS-CoV-2 virus has demonstrated the ability to mutate and develop variants, which can modify epidemiological properties and potentially also the effectiveness of vaccines. The widespread deployment of highly effective vaccines may rapidly exert selection pressure on the SARS-CoV-2 virus directed towards mutations that escape the vaccine-induced immune response. This is particularly concerning while infection is widespread. By developing and analysing a mathematical model of two population groupings with differing vulnerability and contact rates, we explore the impact of the deployment of vaccines among the population on the reproduction ratio, cases, disease abundance and vaccine escape pressure. The results from this model illustrate two insights: (i) vaccination aimed at reducing prevalence could be more effective at reducing disease than directly vaccinating the vulnerable; (ii) the highest risk for vaccine escape can occur at intermediate levels of vaccination. This work demonstrates a key principle: the careful targeting of vaccines towards particular population groups could reduce disease as much as possible while limiting the risk of vaccine escape.

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.

PURPOSE: Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design and evaluate a deep learning (DL) approach to automatic diagnosis of myocardial perfusion abnormalities from stress-only MPI. METHODS: The new DL approach developed for this study was compared to a conventional quantitative perfusion defect size (DS) method. A total of 37,243 patients (51.5% males) undergone stress 99mTc-Tetrofosmin or 99mTc-Sestamibi MPI were selected retrospectively from Yale New Haven Hospital. Patients were dichotomized as studies with normal (75.4%) or abnormal (24.6%) myocardial perfusion based on final diagnoses of clinical nuclear cardiologists. Stress myocardial perfusion defect size was calculated using Yale quantitative analytic software. A deep CNN was trained using the circumferential count profile maps derived from SPECT MPI and was evaluated for the diagnosis of perfusion abnormality with a 5-fold cross-validation approach. In each fold, 27,933, 1862 and 7448 patients were used as training, validation and testing datasets, respectively. The area under the receiver-operating characteristic curve (AUC) was calculated and analyzed for all patients as well as for the eight sub-groups classified based on patient genders, quantitative algorithms, radioactive tracers and SPECT cameras. RESULTS: The AUC value resulted from the DL method was significantly higher than that from the DS method (0.872 ± 0.002 vs. 0.838 ± 0.003, p < 0.01). Across the eight sub-groups, the DL method provided more consistent AUC values in terms of smaller standard deviation and higher diagnostic accuracy and specificity, but slightly lower sensitivity than the DS method (AUC: 0.865 ± 0.010 vs. 0.838 ± 0.019, Accuracy: 82.7% ± 2.5% vs. 78.5% ± 3.6%, Specificity: 84.9% ± 3.7% vs. 77.5% ± 6.5%, Sensitivity: 74.4% ± 4.2% vs. 79.8% ± 5.8%). CONCLUSIONS: The incorporation of deep learning for stress-only MPI has a considerable potential to improve the diagnostic accuracy and consistency in the detection of myocardial perfusion abnormalities.

Analyzing pre-post designs using the analysis of covariance models with and without the interaction term in a heterogeneous study population.

Pre-post parallel group randomized designs have been frequently used to compare the effectiveness of competing treatment strategies and the ordinary least squares (OLS)-based analysis of covariance model (ANCOVA) is a routine analytic approach. In many scenarios, the associations between the baseline and the post-randomization scores could differ between the treatment and control arms, which justifies the inclusion of the treatment by baseline score interaction in ANCOVA. This heterogeneity may also cause heteroscedastic errors in ANCOVA. In this study, we compared the performances of the ANCOVA models with and without the interaction term in estimating the marginal treatment effect in a heterogeneous two-arm pre-post design. We explored the relationship between the two nested ANCOVA models from the perspective of an omitted variable bias problem and further revealed the reasons why the usual ANCOVA may fail in heterogeneous scenario through the discussion of the three types of variances associated with the ANCOVA estimators of the marginal treatment effect: the target unconditional variance, the conditional variance allowing unequal error variances, and the OLS conditional variance derived under the assumption of constant error variance. We demonstrated analytically and with simulations that the proposed heteroscadastic-consistent variance estimators provide valid unconditional inference for ANCOVA, and the ANCOVA interaction model is more powerful than the ANCOVA main effect model when a design is unbalanced.

Adjusting Simon's optimal two-stage design for heterogeneous populations based on stratification or using historical controls.

In many cancer studies, the population under consideration is highly heterogeneous in terms of clinical, demographical, and biological covariates. As the covariates substantially impact the individual prognosis, the response probabilities of patients entering the study may strongly vary. In this case, the operating characteristics of classical clinical trial designs heavily depend on the covariates of patients entering the study. Notably, both type I and type II errors can be much higher than specified. In this paper, two modifications of Simon's optimal two-stage design correcting for heterogeneous populations are derived. The first modification assumes that the patient population is divided into a finite number of subgroups, where each subgroup has a different response probability. The second approach uses a logistic regression model based on historical controls to estimate the response probabilities of patients entering the study. The performance of both approaches is demonstrated using simulation examples.

Sexual Homicide: a Criminological Perspective.

PURPOSE OF REVIEW: This review presents recent findings of research focusing on sexual homicide. Firstly, we examine international comparative studies of victim, offender, and crime characteristics. Secondly, we review an important body of literature that compares developmental and psychological as well as the decision-making process of sexual murderers with other violent offenders. Thirdly, recent and major findings on links between sexual homicide and psychopathy as well as sadism are presented. Fourthly, findings on spatial behavior and factors that can explain it are discussed. Finally, the last section reviews characteristics of sexual homicide involving children and elderly victims. RECENT FINDINGS: International comparisons suggest that sexual homicide presents more similarities than differences across countries implying that most of knowledge can be generalized and applied to other countries. Comparisons with other types of violent offenders highlight that sexual murderers are a specific type of offenders with particular characteristics (criminal careers, developmental factors, personality disorders, lifestyle characteristics) and crime commission process (modus operandi, spatial behavior). As to the etiological factors of sexual homicide, psychopathy and sadism are important characteristics of sexual murderers leading their criminal life as well as their everyday life. Finally, studies focusing on specific types of vulnerable victims showed a high level of heterogeneity within the population of sexual murderers. Sexual murderers are specific offenders with peculiar characteristics and further research should develop proper knowledge on it. Creation of new international databases allows research to confirm and increase knowledge by considering sexual homicides as a complex heterogenous crime.

JOINT MEAN AND COVARIANCE MODELING OF MULTIPLE HEALTH OUTCOME MEASURES.

Health exams determine a patient's health status by comparing the patient's measurement with a population reference range, a 95% interval derived from a homogeneous reference population. Similarly, most of the established relation among health problems are assumed to hold for the entire population. We use data from the 2009-2010 National Health and Nutrition Examination Survey (NHANES) on four major health problems in the U.S. and apply a joint mean and covariance model to study how the reference ranges and associations of those health outcomes could vary among subpopulations. We discuss guidelines for model selection and evaluation, using standard criteria such as AIC in conjunction with posterior predictive checks. The results from the proposed model can help identify subpopulations in which more data need to be collected to refine the reference range and to study the specific associations among those health problems.

Set-membership estimations for the evolution of infectious diseases in heterogeneous populations.

The paper presents an approach for set-membership estimation of the state of a heterogeneous population in which an infectious disease is spreading. The population state may consist of susceptible, infected, recovered, etc. groups, where the individuals are heterogeneous with respect to traits, relevant to the particular disease. Set-membership estimations in this context are reasonable, since only vague information about the distribution of the population along the space of heterogeneity is available in practice. The presented approach comprises adapted versions of methods which are known in estimation and control theory, and involve solving parametrized families of optimization problems. Since the models of disease spreading in heterogeneous populations involve distributed systems (with non-local dynamics and endogenous boundary conditions), these problems are non-standard. The paper develops the needed theoretical instruments and a solution scheme. SI and SIR models of epidemic diseases are considered as case studies and the results reveal qualitative properties that may be of interest.

Epidemics on networks with heterogeneous population and stochastic infection rates.

In this paper we study the diffusion of an SIS-type epidemics on a network under the presence of a random environment, that enters in the definition of the infection rates of the nodes. Accordingly, we model the infection rates in the form of independent stochastic processes. To analyze the problem, we apply a mean field approximation, which allows to get a stochastic differential equations for the probability of infection in each node, and classical tools about stability, which require to find suitable Lyapunov's functions. Here, we find conditions which guarantee, respectively, extinction and stochastic persistence of the epidemics. We show that there exists two regions, given in terms of the coefficients of the model, one where the system goes to extinction almost surely, and the other where it is stochastic permanent. These two regions are, unfortunately, not adjacent, as there is a gap between them, whose extension depends on the specific level of noise. In this last region, we perform numerical analysis to suggest the true behavior of the solution.