The postmortem fate of osteometric measurements: taphonomic alteration of landmarks and its implication for biological profiling

The aim of this research was to identify the measurements that are most frequently retrieved from buried skeletal remains to create the most applicable discriminant functions for osteometric assessments. The study sample was composed of 644 adult skeletons from IV-III BC Italy (Pontecagnano). All burials were in the supine position and individuals were buried either directly in the ground or in large stone coffins. The burial context did not present any environmental conditions that could have a detrimental effect on bonepreservation either in general or differentially between anatomical regions. Due to its complexity, the skull was divided in different regions (two macro areas and five regions, plus the mandible), while the long bones and scapula were examined as anatomical units. The results show that cranial measurements cannot be reliably used for anthropological analyses, as main landmarks are frequently affected by taphonomic factors. Given the higher frequency of their preservation, postcranial measurements are more appropriate for the creation of usable discriminant functions for the construction of biological profiles

How big is an outbreak likely to be? Methods for epidemic final-size calculation

Epidemic models have become a routinely used tool to inform policy on infectious disease. A particular interest at the moment is the use of computationally intensive inference to parametrize these models. In this context, numerical efficiency is critically important. We consider methods for evaluating the probability mass function of the total number of infections over the course of a stochastic epidemic, with a focus on homogeneous finite populations, but also considering heterogeneous and large populations. Relevant methods are reviewed critically, with existing and novel extensions also presented. We provide code in Matlab and a systematic comparison of numerical efficiency.Thomas House, Joshua V. Ross and David Sir

Drug Use Changes at the Individual Level: Results from a Longitudinal, Multisite Survey in Young Europeans Frequenting the Nightlife Scene

Background: Monitoring emerging trends in the increasingly dynamic European drug market is vital; however, information on change at the individual level is scarce. In the current study, we investigated changes in drug use over 12 months in European nightlife attendees. / Method: In this longitudinal online survey, changes in substances used, use frequency in continued users, and relative initiation of use at follow-up were assessed for 20 different substances. To take part, participants had to be aged 18–34 years; be from Belgium, Italy, the Netherlands, Sweden, or the UK; and have attended at least 6 electronic music events in the past 12 months at baseline. Of 8,045 volunteers at baseline, 2,897 completed the survey at both time points (36% follow-up rate), in 2017 and 2018. / Results: The number of people using ketamine increased by 21% (p < 0.001), and logarithmized frequency of use in those continuing use increased by 15% (p < 0.001; 95% CI: 0.07–0.23). 4-Fluoroamphetamine use decreased by 27% (p < 0.001), and logarithmized frequency of use in continuing users decreased by 15% (p < 0.001, 95% CI: −0.48 to −0.23). The drugs with the greatest proportion of relative initiation at follow-up were synthetic cannabinoids (73%, N = 30), mephedrone (44%, N = 18), alkyl nitrites (42%, N = 147), synthetic dissociatives (41%, N = 15), and prescription opioids (40%, N = 48). / Conclusions: In this European nightlife sample, ketamine was found to have the biggest increase in the past 12 months, which occurred alongside an increase in frequency of use in continuing users. The patterns of uptake and discontinuation of alkyl nitrates, novel psychoactive substances, and prescription opioids provide new information that has not been captured by existing cross-sectional surveys. These findings demonstrate the importance of longitudinal assessments of drug use and highlight the dynamic nature of the European drug landscape

Effect of Vaccines and Antivirals during the Major 2009 A(H1N1) Pandemic Wave in Norway – And the Influence of Vaccination Timing

To evaluate the impact of mass vaccination with adjuvanted vaccines (eventually 40% population coverage) and antivirals during the 2009 influenza pandemic in Norway, we fitted an age-structured SEIR model using data on vaccinations and sales of antivirals in 2009/10 in Norway to Norwegian ILI surveillance data from 5 October 2009 to 4 January 2010. We estimate a clinical attack rate of approximately 30% (28.7–29.8%), with highest disease rates among children 0–14 years (43–44%). Vaccination started in week 43 and came too late to have a strong influence on the pandemic in Norway. Our results indicate that the countermeasures prevented approximately 11–12% of potential cases relative to an unmitigated pandemic. Vaccination was found responsible for roughly 3 in 4 of the avoided infections. An estimated 50% reduction in the clinical attack rate would have resulted from vaccination alone, had the campaign started 6 weeks earlier. Had vaccination been prioritized for children first, the intervention should have commenced approximately 5 weeks earlier in order to achieve the same 50% reduction. In comparison, we estimate that a non-adjuvanted vaccination program should have started 8 weeks earlier to lower the clinical attack rate by 50%

Evaluation of vaccination strategies for SIR epidemics on random networks incorporating household structure

This paper is concerned with the analysis of vaccination strategies in a stochastic SIR (susceptible → infected → removed) model for the spread of an epidemic amongst a population of individuals with a random network of social contacts that is also partitioned into households. Under various vaccine action models, we consider both household-based vaccination schemes, in which the way in which individuals are chosen for vaccination depends on the size of the households in which they reside, and acquaintance vaccination, which targets individuals of high degree in the social network. For both types of vaccination scheme, assuming a large population with few initial infectives, we derive a threshold parameter which determines whether or not a large outbreak can occur and also the probability and fraction of the population infected by such an outbreak. The performance of these schemes is studied numerically, focusing on the influence of the household size distribution and the degree distribution of the social network. We find that acquaintance vaccination can significantly outperform the best household-based scheme if the degree distribution of the social network is heavy-tailed. For household-based schemes, when the vaccine coverage is insufficient to prevent a major outbreak and the vaccine is imperfect, we find situations in which both the probability and size of a major outbreak under the scheme which minimises the threshold parameter are \emph{larger} than in the scheme which maximises the threshold parameter

Controlling infectious disease outbreaks: Lessons from mathematical modelling.

Accepted versio

Modeling infectious disease dynamics in the complex landscape of global health.

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health

Quarantine for pandemic influenza control at the borders of small island nations

Background: Although border quarantine is included in many influenza pandemic plans, detailed guidelines have yet to be formulated, including considerations for the optimal quarantine length. Motivated by the situation of small island nations, which will probably experience the introduction of pandemic influenza via just one airport, we examined the potential effectiveness of quarantine as a border control measure. Methods: Analysing the detailed epidemiologic characteristics of influenza, the effectiveness of quarantine at the borders of islands was modelled as the relative reduction of the risk of releasing infectious individuals into the community, explicitly accounting for the presence of asymptomatic infected individuals. The potential benefit of adding the use of rapid diagnostic testing to the quarantine process was also considered. Results: We predict that 95% and 99% effectiveness in preventing the release of infectious individuals into the community could be achieved with quarantine periods of longer than 4.7 and 8.6 days, respectively. If rapid diagnostic testing is combined with quarantine, the lengths of quarantine to achieve 95% and 99% effectiveness could be shortened to 2.6 and 5.7 days, respectively. Sensitivity analysis revealed that quarantine alone for 8.7 days or quarantine for 5.7 days combined with using rapid diagnostic testing could prevent secondary transmissions caused by the released infectious individuals for a plausible range of prevalence at the source country (up to 10%) and for a modest number of incoming travellers (up to 8000 individuals). Conclusion: Quarantine atthe borders of island nations could contribute substantially to preventing the arrival of pandemic influenza (or at least delaying the arrival date). For small island nations we recommend consideration of quarantine alone for 9 days or quarantine for 6 days combined with using rapid diagnostic testing (if available). © 2009 Nishiura et al; licensee BioMed Central Ltd.published_or_final_versio

Theoretical basis to measure the impact of short-lasting control of an infectious disease on the epidemic peak

Background. While many pandemic preparedness plans have promoted disease control effort to lower and delay an epidemic peak, analytical methods for determining the required control effort and making statistical inferences have yet to be sought. As a first step to address this issue, we present a theoretical basis on which to assess the impact of an early intervention on the epidemic peak, employing a simple epidemic model. Methods. We focus on estimating the impact of an early control effort (e.g. unsuccessful containment), assuming that the transmission rate abruptly increases when control is discontinued. We provide analytical expressions for magnitude and time of the epidemic peak, employing approximate logistic and logarithmic-form solutions for the latter. Empirical influenza data (H1N1-2009) in Japan are analyzed to estimate the effect of the summer holiday period in lowering and delaying the peak in 2009. Results. Our model estimates that the epidemic peak of the 2009 pandemic was delayed for 21 days due to summer holiday. Decline in peak appears to be a nonlinear function of control-associated reduction in the reproduction number. Peak delay is shown to critically depend on the fraction of initially immune individuals. Conclusions. The proposed modeling approaches offer methodological avenues to assess empirical data and to objectively estimate required control effort to lower and delay an epidemic peak. Analytical findings support a critical need to conduct population-wide serological survey as a prior requirement for estimating the time of peak. © 2011 Omori and Nishiura; licensee BioMed Central Ltd.published_or_final_versio

Estimating a Markovian epidemic model using household serial interval data from the early phase of an epidemic

The clinical serial interval of an infectious disease is the time between date of symptom onset in an index case and the date of symptom onset in one of its secondary cases. It is a quantity which is commonly collected during a pandemic and is of fundamental importance to public health policy and mathematical modelling. In this paper we present a novel method for calculating the serial interval distribution for a Markovian model of household transmission dynamics. This allows the use of Bayesian MCMC methods, with explicit evaluation of the likelihood, to fit to serial interval data and infer parameters of the underlying model. We use simulated and real data to verify the accuracy of our methodology and illustrate the importance of accounting for household size. The output of our approach can be used to produce posterior distributions of population level epidemic characteristics.Andrew J. Black, Joshua V. Ros