| 1. |
- Harding, Karin C., 1968, et al.
(författare)
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Population Wide Decline in Somatic Growth in Harbor Seals—Early Signs of Density Dependence
- 2018
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Ingår i: Frontiers in Ecology and the Environment. - : Frontiers Media SA. - 1540-9295 .- 1540-9309 .- 2296-701X. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- The harbor seal populations of Danish and Swedish waters have had turbulent population dynamics during the last century. They were severely depleted by hunting in the beginning of the twentieth century, followed by rapid recovery due to protective measures. They were victims to two mass mortalities caused by Phocine Distemper Virus (PDV) epidemics. Long term monitoring and intensive sampling during the last decades now allow analysis of population level phenomena in response to shifting population size. We compare somatic growth curves from several seal populations including 2,041 specimens with known age, length and population size at birth. Asymptotic body lengths of female harbor seals were 148 cm in all four regions in 1988, when seal abundances had been kept low by hunting for decades. Males were 158 cm, being 10 cm longer. However, in 2002 the asymptotic lengths of seals differed among regions. While seals in the Kattegat showed similar asymptotic lengths as in 1988, seals in the Skagerrak were significantly shorter, where both male and female asymptotic lengths declined by 7 cm. We estimate the area of available feeding grounds in the two sea regions and find the density of seals per square kilometer feeding ground to be three times greater in the Skagerrak compared to the Kattegat. Thus, the shorter body length of seals in the Skagerrak can be an early signal of density dependence. Hampered body growth is known to trigger a suite of changes in life history traits, including delayed age at sexual maturity, higher juvenile mortality and lowered fecundity. These mechanisms all point at a possible “smooth route toward carrying capacity” with gradually reduced population growth rate as the main response to high population density. Recent aerial surveys confirm declining rates of population increase in the Skagerrak.
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| 2. |
- Salmon, Maelle, et al.
(författare)
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Bayesian outbreak detection in the presence of reporting delays
- 2015
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Ingår i: Biometrical Journal. - : Wiley. - 0323-3847 .- 1521-4036. ; 57:6, s. 1051-1067
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Tidskriftsartikel (refereegranskat)abstract
- One use of infectious disease surveillance systems is the statistical aberration detection performed on time series of counts resulting from the aggregation of individual case reports. However, inherent reporting delays in such surveillance systems make the considered time series incomplete, which can be an impediment to the timely detection and thus to the containment of emerging outbreaks. In this work, we synthesize the outbreak detection algorithms of Noufaily etal.(2013) and Manitz and Hohle(2013) while additionally addressing right truncation caused by reporting delays. We do so by considering the resulting time series as an incomplete two-way contingency table which we model using negative binomial regression. Our approach is defined in a Bayesian setting allowing a direct inclusion of all sources of uncertainty in the derivation of whether an observed case count is to be considered an aberration. The proposed algorithm is evaluated both on simulated data and on the time series of Salmonella Newport cases in Germany in 2011. Altogether, our method aims at allowing timely aberration detection in the presence of reporting delays and hence underlines the need for statistical modeling to address complications of reporting systems. An implementation of the proposed method is made available in the R package surveillance as the function bodaDelay.
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| 3. |
- Salmon, Maelle, et al.
(författare)
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Monitoring Count Time Series in R : Aberration Detection in Public Health Surveillance
- 2016
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Ingår i: Journal of Statistical Software. - : Foundation for Open Access Statistic. - 1548-7660. ; 70:10, s. 1-35
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Tidskriftsartikel (refereegranskat)abstract
- Public health surveillance aims at lessening disease burden by, e.g., timely recognizing emerging outbreaks in case of infectious diseases. Seen from a statistical perspective, this implies the use of appropriate methods for monitoring time series of aggregated case reports. This paper presents the tools for such automatic aberration detection offered by the R package surveillance. We introduce the functionalities for the visualization, modeling and monitoring of surveillance time series. With respect to modeling we focus on univariate time series modeling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. Applications of such modeling include illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g., by spline-modeling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdispersion using beta-binomial or Dirichlet-multinomial modeling. With respect to monitoring we consider detectors based on either a Shewhart-like single timepoint comparison between the observed count and the predictive distribution or by likelihood-ratio based cumulative sum methods. Finally, we illustrate how surveillance can support aberration detection in practice by integrating it into the monitoring workflow of a public health institution. Altogether, the present article shows how well surveillance can support automatic aberration detection in a public health surveillance context.
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