On "Sexual contacts and epidemic thresholds," models and inference for Sexual partnership distributions

Recent work has focused attention on statistical inference for the population distribution of the number of sexual partners based on survey data. The characteristics of these distributions are of interest as components of mathematical models for the transmission dynamics of sexually-transmitted diseases (STDs). Such information can be used both to calibrate theoretical models, to make predictions for real populations, and as a tool for guiding public health policy. Our previous work on this subject has developed likelihood-based statistical methods for inference that allow for low-dimensional, semi-parametric models. Inference has been based on several proposed stochastic process models for the formation of sexual partnership networks. We have also developed model selection criteria to choose between competing models, and assessed the fit of different models to three populations: Uganda, Sweden, and the USA. Throughout this work, we have emphasized the correct assessment of the uncertainty of the estimates based on the data analyzed. We have also widened the question of interest to the limitations of inferences from such data, and the utility of degree-based epidemiological models more generally. In this paper we address further statistical issues that are important in this area, and a number of confusions that have arisen in interpreting our work. In particular, we consider the use of cumulative lifetime partner distributions, heaping and other issues raised by Liljeros et al. in a recent working paper.Comment: 22 pages, 5 figures in linked working pape

demogR: A Package for the Construction and Analysis of Age-structured Demographic Models in R

The analysis of matrix population models has become a fundamental tool in ecology, conservation biology, and life history theory. In this paper, I present demogR, a package for analyzing age-structured population models in R. The package includes tools for the construction and analysis of matrix population models. In addition to the standard analyses commonly used in evolutionary demography and conservation biology, demogR contains a variety of tools from classical demography. This includes the construction of period life tables, and the generation of model mortality and fertility schedules for human populations. The tools in demogR are generally applicable to age-structured populations but are particularly useful for analyzing problems in human ecology. I illustrate some of the capabilities of the package by doing an evolutionary demographic analysis of several human populations.

demogR: A Package for the Construction and Analysis of Age-structured Demographic Models in R

The analysis of matrix population models has become a fundamental tool in ecology, conservation biology, and life history theory. In this paper, I present demogR, a package for analyzing age-structured population models in R. The package includes tools for the construction and analysis of matrix population models. In addition to the standard analyses commonly used in evolutionary demography and conservation biology, demogR contains a variety of tools from classical demography. This includes the construction of period life tables, and the generation of model mortality and fertility schedules for human populations. The tools in demogR are generally applicable to age-structured populations but are particularly useful for analyzing problems in human ecology. I illustrate some of the capabilities of the package by doing an evolutionary demographic analysis of several human populations

Early Assessment of Anxiety and Behavioral Response to Novel Swine-Origin Influenza A(H1N1)

Since late April, 2009, a novel influenza virus A (H1N1), generally referred to as the "swine flu," has spread around the globe and infected hundreds of thousands of people. During the first few days after the initial outbreak in Mexico, extensive media coverage together with a high degree of uncertainty about the transmissibility and mortality rate associated with the virus caused widespread concern in the population. The spread of an infectious disease can be strongly influenced by behavioral changes (e.g., social distancing) during the early phase of an epidemic, but data on risk perception and behavioral response to a novel virus is usually collected with a substantial delay or after an epidemic has run its course

Timing Is Everything: International Variations in Historical Sexual Partnership Concurrency and HIV Prevalence

Higher prevalence of concurrent partnerships is one hypothesis for the severity of the HIV epidemic in the countries of Southern Africa. But measures of the prevalence of concurrency alone do not adequately capture the impact concurrency will have on transmission dynamics. The importance of overlap duration and coital exposure are examined here.We conducted a comparison of data from three studies of sexual behavior carried out in the early 1990s in Uganda, Thailand and the US. Using cumulative concurrency measures, the three countries appeared somewhat similar. Over 50% of both Thai and Ugandan men reported a concurrency within the last three partnerships and over 20% reported a concurrency in the last year, the corresponding rates among US men were nearly 20% for Blacks and Hispanics, and about 10% for other racial/ethnic groups. Concurrency measures that were more sensitive to overlap duration, however, showed large differences. The point prevalence of concurrency on the day of interview was over 10% among Ugandan men compared to 1% for Thai men. Ugandan concurrencies were much longer duration – a median of about two years – than either the Thai (1 day) or US concurrencies (4–9 months across all groups), and involved 5–10 times more coital risk exposure with the less frequent partner. In the US, Blacks and Hispanics reported higher prevalence, longer duration and greater coital exposure than Whites, but were lower than Ugandans on nearly every measure. Together, the differences in the prevalence, duration and coital exposure of concurrent partnerships observed align with the HIV prevalence differentials seen in these populations at the time the data were collected.There were substantial variations in the patterns of concurrent partnerships within and between populations. More long-term overlapping partnerships, with regular coital exposure, were found in populations with greater HIV epidemic severity

Contribution of Company Affiliation and Social Contacts to Risk Estimates of Between-Farm Transmission of Avian Influenza

BACKGROUND: Models of between-farm transmission of pathogens have identified service vehicles and social groups as risk factors mediating the spread of infection. Because of high levels of economic organization in much of the poultry industry, we examined the importance of company affiliation, as distinct from social contacts, in a model of the potential spread of avian influenza among broiler poultry farms in a poultry-dense region in the United States. The contribution of company affiliation to risk of between-farm disease transmission has not been previously studied. METHODOLOGY/PRINCIPAL FINDINGS: We obtained data on the nature and frequency of business and social contacts through a national survey of broiler poultry growers in the United States. Daily rates of contact were estimated using Monte Carlo analysis. Stochastic modeling techniques were used to estimate the exposure risk posed by a single infectious farm to other farms in the region and relative risk of exposure for farms under different scenarios. The mean daily rate of vehicular contact was 0.82 vehicles/day. The magnitude of exposure risk ranged from <1% to 25% under varying parameters. Risk of between-farm transmission was largely driven by company affiliation, with farms in the same company group as the index farm facing as much as a 5-fold increase in risk compared to farms contracted with different companies. Employment of part-time workers contributed to significant increases in risk in most scenarios, notably for farms who hired day-laborers. Social visits were significantly less important in determining risk. CONCLUSIONS/SIGNIFICANCE: Biosecurity interventions should be based on information on industry structure and company affiliation, and include part-time workers as potentially unrecognized sources of viral transmission. Modeling efforts to understand pathogen transmission in the context of industrial food animal production should consider company affiliation in addition to geospatial factors and pathogen characteristics. Restriction of social contacts among farmers may be less useful in reducing between-farm transmission

Assessing Commitment and Reporting Fidelity to a Text Message-Based Participatory Surveillance in Rural Western Uganda.

Syndromic surveillance, the collection of symptom data from individuals prior to or in the absence of diagnosis, is used throughout the developed world to provide rapid indications of outbreaks and unusual patterns of disease. However, the low cost of syndromic surveillance also makes it highly attractive for the developing world. We present a case study of electronic participatory syndromic surveillance, using participant-mobile phones in a rural region of Western Uganda, which has a high infectious disease burden, and frequent local and regional outbreaks. Our platform uses text messages to encode a suite of symptoms, their associated durations, and household disease burden, and we explore the ability of participants to correctly encode their symptoms, with an average of 75.2% of symptom reports correctly formatted between the second and 11th reporting timeslots. Concomitantly we identify divisions between participants able to rapidly adjust to this unusually participatory style of data collection, and those few for whom the study proved more challenging. We then perform analyses of the resulting syndromic time series, examining the clustering of symptoms by time and household to identify patterns such as a tendency towards the within-household sharing of respiratory illness.National Institute of Health (Grant ID: TW009237)This is the final version of the article. It first appeared from the Public Library of Science via http://dx.doi.org/10.1371/journal.pone.015597

The form of uncertainty affects selection for social learning

Social learning is a critical adaptation for dealing with different forms of variability. Uncertainty is a severe form of variability where the space of possible decisions or probabilities of associated outcomes are unknown. We identified four theoretically important sources of uncertainty: temporal environmental variability; payoff ambiguity; selection-set size; and effective lifespan. When these combine, it is nearly impossible to fully learn about the environment. We develop an evolutionary agent-based model to test how each form of uncertainty affects the evolution of social learning. Agents perform one of several behaviours, modelled as a multi-armed bandit, to acquire payoffs. All agents learn about behavioural payoffs individually through an adaptive behaviour-choice model that uses a softmax decision rule. Use of vertical and oblique payoff-biased social learning evolved to serve as a scaffold for adaptive individual learning – they are not opposite strategies. Different types of uncertainty had varying effects. Temporal environmental variability suppressed social learning, whereas larger selection-set size promoted social learning, even when the environment changed frequently. Payoff ambiguity and lifespan interacted with other uncertainty parameters. This study begins to explain how social learning can predominate despite highly variable real-world environments when effective individual learning helps individuals recover from learning outdated social information

Evaluation of sensors for the detection of energy resolved very soft x-ray fluorescence

Energy-dispersive imaging spectroscopy of X-ray emission from the Earth’s aurorae promises to further knowledge in the field of aeronomy. Time- and spatially-resolved observations of fluorescence from the dominant atmospheric components require the detection of X-rays as soft as 390 eV with a resolution of no more than 100 eV at these energies. The Auroral X-ray Imaging Spectrometer (AXIS) instrument of the Disturbed and quiet time Ionosphere-thermosphere System at High Altitudes (DISHA) mission is expected to perform these observations. The baseline instrument design has suggested the use of an electron-multiplying charge-coupled device (EMCCD). The EMCCD’s electron-multiplying register can reduce the effective readout noise and enable the detection of signals as small as a single photoelectron. For the detection of soft X-rays, however, the noise penalty from the EM register’s stochastic process degrades energy resolution. Emerging CMOS image sensors (CIS), particularly the Teledyne e2v CIS221-X test device, with back illumination, full depletion (with 36 μm thickness), large pixel sizes (40 μm), and low readout noise (3 e- rms effective) are expected to achieve the required performance without the effects of the EM register. Simple models for X-ray event sensitivity, detectability, and resolution, indicate that candidate CIS equal or better EMCCD performance. Furthermore, CIS offer other advantages including lower power consumption, higher operating temperature, and increased radiation hardness. However, these sensors introduce other behaviors that may impact their apparent benefits, which initial experimental testing and analyses are working to understand