In April 2009, the most recent pandemic of influenza A began. We present the first estimates of pandemic mortality based on the newly-released final data on deaths in 2009 and 2010 in the United States.
We obtained data on influenza and pneumonia deaths from the National Center for Health Statistics (NCHS). Age- and sex-specific death rates, and age-standardized death rates, were calculated. Using negative binomial Serfling-type methods, excess mortality was calculated separately by sex and age groups.
In many age groups, observed pneumonia and influenza cause-specific mortality rates in October and November 2009 broke month-specific records since 1959 when the current series of detailed US mortality data began. Compared to the typical pattern of seasonal flu deaths, the 2009 pandemic age-specific mortality, as well as influenza-attributable (excess) mortality, skewed much younger. We estimate 2,634 excess pneumonia and influenza deaths in 2009–10; the excess death rate in 2009 was 0.79 per 100,000.
Pandemic influenza mortality skews younger than seasonal influenza. This can be explained by a protective effect due to antigenic cycling. When older cohorts have been previously exposed to a similar antigen, immune memory results in lower death rates at older ages. Age-targeted vaccination of younger people should be considered in future pandemics.
Nguyen AM, Noymer A (2013)
Influenza Mortality in the United States, 2009 Pandemic: Burden, Timing and Age Distribution
PLoS ONE 8(5): e64198. doi:10.1371/journal.pone.0064198
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We construct a stochastic SIR model for influenza spreading on a D-dimensional lattice, which represents the dynamic contact network of individuals. An age distributed population is placed on the lattice and moves on it. The displacement from a site to a nearest neighbor empty site, allows individuals to change the number and identities of their contacts. The dynamics on the lattice is governed by an attractive interaction between individuals belonging to the same age-class. The parameters, which regulate the pattern dynamics, are fixed fitting the data on the age-dependent daily contact numbers, furnished by the Polymod survey. A simple SIR transmission model with a nearest neighbors interaction and some very basic adaptive mobility restrictions complete the model. The model is validated against the age-distributed Italian epidemiological data for the influenza A(H1N1) during the season, with sensible predictions for the epidemiological parameters. For an appropriate topology of the lattice, we find that, whenever the accordance between the contact patterns of the model and the Polymod data is satisfactory, there is a good agreement between the numerical and the experimental epidemiological data. This result shows how rich is the information encoded in the average contact patterns of individuals, with respect to the analysis of the epidemic spreading of an infectious disease.
Liccardo A, Fierro A (2013)
A Lattice Model for Influenza Spreading
PLoS ONE 8(5): e63935. doi:10.1371/journal.pone.0063935
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Reducing health inequalities is a key objective for many governments and public health organizations. Whether inequalities are measured on the absolute (difference) or relative (ratio) scale can have a significant impact on judgments about whether health inequalities are increasing or decreasing, but both of these measures are not often presented in empirical studies. In this study we investigated the impact of selective presentation of health inequality measures on judgments of health inequality trends among 40 university undergraduates. We randomized participants to see either a difference or ratio measure of health inequality alongside raw mortality rates in 5 different scenarios. At baseline there were no differences between treatment groups in assessments of inequality trends, but selective exposure to the same raw data augmented with ratio versus difference inequality graphs altered participants’ assessments of inequality change. When absolute inequality decreased and relative inequality increased, exposure to ratio measures increased the probability of concluding that inequality had increased from 32.5% to 70%, but exposure to difference measures did not (35% vs. 25%). Selective exposure to ratio versus difference inequality graphs thus increased the difference between groups in concluding that inequality had increased from 2.5% (95% CI −9.5% to 14.5%) to 45% (95% CI 29.4 to 60.6). A similar pattern was evident for other scenarios where absolute and relative inequality trends gave conflicting results. In cases where measures of absolute and relative inequality both increased or both decreased, we did not find any evidence that assignment to ratio vs. difference graphs had an impact on assessments of inequality change. Selective reporting of measures of health inequality has the potential to create biased judgments of progress in ameliorating health inequalities.
Harper S, King NB, Young ME (2013)
Impact of Selective Evidence Presentation on Judgments of Health Inequality Trends: An Experimental Study
PLoS ONE 8(5): e63362. doi:10.1371/journal.pone.0063362
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A criminal career can be either general, with the criminal committing different types of crimes, or specialized, with the criminal committing a specific type of crime. A central problem in the study of crime specialization is to determine, from the perspective of the criminal, which crimes should be considered similar and which crimes should be considered distinct. We study a large set of Swedish suspects to empirically investigate generalist and specialist behavior in crime. We show that there is a large group of suspects who can be described as generalists. At the same time, we observe a non-trivial pattern of specialization across age and gender of suspects. Women are less prone to commit crimes of certain types, and, for instance, are more prone to specialize in crimes related to fraud. We also find evidence of temporal specialization of suspects. Older persons are more specialized than younger ones, and some crime types are preferentially committed by suspects of different ages.
Tumminello M, Edling C, Liljeros F, Mantegna RN, Sarnecki J (2013)
The Phenomenology of Specialization of Criminal Suspects
PLoS ONE 8(5): e64703. doi:10.1371/journal.pone.0064703
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The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air).
Citation: Huang Z, Wu X, Garcia AJ, Fik TJ, Tatem AJ (2013)
An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010
PLoS ONE 8(5): e64317. doi:10.1371/journal.pone.0064317
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There is believed to be a ‘beauty premium’ in key life outcomes: it is thought that people perceived to be more physically attractive have better educational outcomes, higher-status jobs, higher wages, and are more likely to marry. Evidence for these beliefs, however, is generally based on photographs in hypothetical experiments or studies of very specific population subgroups (such as college students). The extent to which physical attractiveness might have a lasting effect on such outcomes in ‘real life’ situations across the whole population is less well known. Using longitudinal data from a general population cohort of people in the West of Scotland, this paper investigated the association between physical attractiveness at age 15 and key socioeconomic outcomes approximately 20 years later. People assessed as more physically attractive at age 15 had higher socioeconomic positions at age 36– in terms of their employment status, housing tenure and income - and they were more likely to be married; even after adjusting for parental socioeconomic background, their own intelligence, health and self esteem, education and other adult socioeconomic outcomes. For education the association was significant for women but not for men. Understanding why attractiveness is strongly associated with long-term socioeconomic outcomes, after such extensive confounders have been considered, is important.
Benzeval M, Green MJ, Macintyre S (2013)
Does Perceived Physical Attractiveness in Adolescence Predict Better Socioeconomic Position in Adulthood? Evidence from 20 Years of Follow Up in a Population Cohort Study
PLoS ONE 8(5): e63975. doi:10.1371/journal.pone.0063975
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Can online behaviour be used as a proxy for studying urban mobility? The increasing availability of digital mobility traces has provided new insights into collective human behaviour. Mobility datasets have been shown to be an accurate proxy for daily behaviour and social patterns, and behavioural data from Twitter has been used to predict real world phenomena such as cinema ticket sale volumes, stock prices, and disease outbreaks. In this paper we correlate city-scale urban traffic patterns with online search trends to uncover keywords describing the pedestrian traffic location. By analysing a 3-year mobility dataset we show that our approach, called Location Archetype Keyword Extraction (LAKE), is capable of uncovering semantically relevant keywords for describing a location. Our findings demonstrate an overarching relationship between online and offline collective behaviour, and allow for advancing analysis of community-level behaviour by using online search keywords as a practical behaviour proxy.
Kostakos V, Juntunen T, Goncalves J, Hosio S, Ojala T (2013)
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