scholarly journals Percolation models of pathogen spillover

2019 ◽  
Vol 374 (1782) ◽  
pp. 20180331 ◽  
Author(s):  
Alex D. Washburne ◽  
Daniel E. Crowley ◽  
Daniel J. Becker ◽  
Kezia R. Manlove ◽  
Marissa L. Childs ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Linear Models ◽  
Mechanistic Model ◽  
Piecewise Linear ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Management Intervention ◽  
Management Concepts ◽  
The Impact ◽  
Reservoir Hosts

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

scholarly journals The Evolution of Data Sharing Practices in the Psychological Literature

2021 ◽  
Author(s):  
Judith Neve ◽  
Guillaume A Rousselet
Keyword(s):  
Data Sharing ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Future Research ◽  
Science Data ◽  
Editorial Policies ◽  
Psychological Science ◽  
The Impact

Sharing data has many benefits. However, data sharing rates remain low, for the most part well below 50%. A variety of interventions encouraging data sharing have been proposed. We focus here on editorial policies. Kidwell et al. (2016) assessed the impact of the introduction of badges in Psychological Science; Hardwicke et al. (2018) assessed the impact of Cognition’s mandatory data sharing policy. Both studies found policies to improve data sharing practices, but only assessed the impact of the policy for up to 25 months after its implementation. We examined the effect of these policies over a longer term by reusing their data and collecting a follow-up sample including articles published up until December 31st, 2019. We fit generalized additive models as these allow for a flexible assessment of the effect of time, in particular to identify non-linear changes in the trend. These models were compared to generalized linear models to examine whether the non-linearity is needed. Descriptive results and the outputs from generalized additive and linear models were coherent with previous findings: following the policies in Cognition and Psychological Science, data sharing statement rates increased immediately and continued to increase beyond the timeframes examined previously, until reaching close to 100%. In Clinical Psychological Science, data sharing statement rates started to increase only two years following the implementation of badges. Reusability rates jumped from close to 0% to around 50% but did not show changes within the pre-policy nor the post-policy timeframes. Journals that did not implement a policy showed no change in data sharing rates or reusability over time. There was variability across journals in the levels of increase, so we suggest future research should examine a larger number of policies to draw conclusions about their efficacy. We also encourage future research to investigate the barriers to data sharing specific to psychology subfields to identify the best interventions to tackle them.

scholarly journals An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets

2011 ◽  
Vol 68 (10) ◽  
pp. 2252-2263 ◽  
Author(s):  
Stéphanie Mahévas ◽  
Youen Vermard ◽  
Trevor Hutton ◽  
Ane Iriondo ◽  
Angélique Jadaud ◽  
...  
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Technical Information ◽  
Fishing Effort ◽  
Additive Models ◽  
Induced Variation ◽  
Fishing Fleets ◽  
The Impact ◽  
Selection Of ◽  
Experience Effect

Abstract Mahévas, S., Vermard, Y., Hutton, T., Iriondo, A., Jadaud, A., Maravelias, C. D., Punzón, A., Sacchi, J., Tidd, A., Tsitsika, E., Marchal, P., Goascoz, N., Mortreux, S., and Roos, D. 2011. An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets. – ICES Journal of Marine Science, 68: 2252–2263. The impact of the fishing effort exerted by a vessel on a population depends on catchability, which depends on population accessibility and fishing power. The work investigated whether the variation in fishing power could be the result of the technical characteristics of a vessel and/or its gear or whether it is a reflection of inter-vessel differences not accounted for by the technical attributes. These inter-vessel differences could be indicative of a skipper/crew experience effect. To improve understanding of the relationships, landings per unit effort (lpue) from logbooks and technical information on vessels and gears (collected during interviews) were used to identify variables that explained variations in fishing power. The analysis was undertaken by applying a combination of generalized additive models and generalized linear models to data from several European fleets. The study highlights the fact that taking into account information that is not routinely collected, e.g. length of headline, weight of otter boards, or type of groundrope, will significantly improve the modelled relationships between lpue and the variables that measure relative fishing power. The magnitude of the skipper/crew experience effect was weaker than the technical effect of the vessel and/or its gear.

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

scholarly journals Prevalence of internalizing disorders, symptoms, and traits across age using advanced nonlinear models

2021 ◽  
pp. 1-10
Author(s):  
Hanna M. van Loo ◽  
Lian Beijers ◽  
Martijn Wieling ◽  
Trynke R. de Jong ◽  
Robert A. Schoevers ◽  
...  
Keyword(s):  
Internalizing Symptoms ◽  
Nonlinear Models ◽  
Generalized Additive Models ◽  
Population Sample ◽  
Internalizing Disorders ◽  
Relative Difference ◽  
Additive Models ◽  
Point Prevalence ◽  
Cross Sectional ◽  
The Impact

Abstract Background Most epidemiological studies show a decrease of internalizing disorders at older ages, but it is unclear how the prevalence exactly changes with age, and whether there are different patterns for internalizing symptoms and traits, and for men and women. This study investigates the impact of age and sex on the point prevalence across different mood and anxiety disorders, internalizing symptoms, and neuroticism. Methods We used cross-sectional data on 146 315 subjects, aged 18–80 years, from the Lifelines Cohort Study, a Dutch general population sample. Between 2012 and 2016, five current internalizing disorders – major depression, dysthymia, generalized anxiety disorder, social phobia, and panic disorder – were assessed according to DSM-IV criteria. Depressive symptoms, anxiety symptoms, neuroticism, and negative affect (NA) were also measured. Generalized additive models were used to identify nonlinear patterns across age, and to investigate sex differences. Results The point prevalence of internalizing disorders generally increased between the ages of 18 and 30 years, stabilized between 30 and 50, and decreased after age 50. The patterns of internalizing symptoms and traits were different. NA and neuroticism gradually decreased after age 18. Women reported more internalizing disorders than men, but the relative difference remained stable across age (relative risk ~1.7). Conclusions The point prevalence of internalizing disorders was typically highest between age 30 and 50, but there were differences between the disorders, which could indicate differences in etiology. The relative gap between the sexes remained similar across age, suggesting that changes in sex hormones around the menopause do not significantly influence women's risk of internalizing disorders.

scholarly journals A Longitudinal Analysis of the Impact of Distance Driven on the Probability of Car Accidents

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 91
Author(s):  
Jean-Philippe Boucher ◽  
Roxane Turcotte
Keyword(s):  
Poisson Distribution ◽  
Fixed Effects ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Panel Count Data ◽  
Insurance Company ◽  
Smooth Functions ◽  
Claim Frequency ◽  
The Impact ◽  
The Relationship

Using telematics data, we study the relationship between claim frequency and distance driven through different models by observing smooth functions. We used Generalized Additive Models (GAM) for a Poisson distribution, and Generalized Additive Models for Location, Scale, and Shape (GAMLSS) that we generalize for panel count data. To correctly observe the relationship between distance driven and claim frequency, we show that a Poisson distribution with fixed effects should be used because it removes residual heterogeneity that was incorrectly captured by previous models based on GAM and GAMLSS theory. We show that an approximately linear relationship between distance driven and claim frequency can be derived. We argue that this approach can be used to compute the premium surcharge for additional kilometers the insured wants to drive, or as the basis to construct Pay-as-you-drive (PAYD) insurance for self-service vehicles. All models are illustrated using data from a major Canadian insurance company.

Climate Influences on Meningitis Incidence in Northwest Nigeria

2014 ◽  
Vol 6 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Auwal F. Abdussalam ◽  
Andrew J. Monaghan ◽  
Vanja M. Dukić ◽  
Mary H. Hayden ◽  
Thomas M. Hopson ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Skill Score ◽  
Additive Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Explanatory Variables ◽  
Score Statistics

Abstract Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

scholarly journals Estimating the impact of vaccination using age–time-dependent incidence rates of hepatitis B

2007 ◽  
Vol 136 (3) ◽  
pp. 341-351 ◽  
Author(s):  
N. HENS ◽  
M. AERTS ◽  
Z. SHKEDY ◽  
P. KUNG'U KIMANI ◽  
M. KOJOUHOROVA ◽  
...  
Keyword(s):  
Hepatitis B ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Penalized Regression ◽  
Incidence Rates ◽  
Additive Models ◽  
Time Dependent ◽  
Model Framework ◽  
Penalized Regression Splines ◽  
The Impact

SUMMARYThe objective of this study was to model the age–time-dependent incidence of hepatitis B while estimating the impact of vaccination. While stochastic models/time-series have been used before to model hepatitis B cases in the absence of knowledge on the number of susceptibles, this paper proposed using a method that fits into the generalized additive model framework. Generalized additive models with penalized regression splines are used to exploit the underlying continuity of both age and time in a flexible non-parametric way. Based on a unique case notification dataset, we have shown that the implemented immunization programme in Bulgaria resulted in a significant decrease in incidence for infants in their first year of life with 82% (79–84%). Moreover, we have shown that conditional on an assumed baseline susceptibility percentage, a smooth force-of-infection profile can be obtained from which two local maxima were observed at ages 9 and 24 years.

scholarly journals Modeling tropospheric ozone and particulate matter in Tunis, Tunisia using generalized additive model

2021 ◽  
Vol 31 (2) ◽  
Author(s):  
Zouhour Hammouda ◽  
Leila Hedhili Zaier ◽  
Nadege Blond
Keyword(s):  
Particulate Matter ◽  
Tropospheric Ozone ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Meteorological Condition ◽  
Additive Models ◽  
Linear Functions ◽  
Meteorological Variables ◽  
Time Variables ◽  
The Impact

The main purpose of this paper is to analyze the sensitivity of tropospheric ozone and particulate matter concentrations to changes in local scale meteorology with the aid of meteorological variables (wind speed, wind direction, relative humidity, solar radiation and temperature) and intensity of traffic using hourly concentration of NOX, which are measured in three different locations in Tunis, (i.e. Gazela, Mannouba and Bab Aliwa). In order to quantify the impact of meteorological conditions and precursor concentrations on air pollution, a general model was developed where the logarithm of the hourly concentrations of O3 and PM10 were modeled as a sum of non-linear functions using the framework of Generalized Additive Models (GAMs). Partial effects of each predictor are presented. We obtain a good fit with R² = 85% for the response variable O3 at Bab Aliwa station. Results show the aggregate impact of meteorological variables in the models explained 29% of the variance in PM10 and 41% in O3. This indicates that local meteorological condition is an active driver of air quality in Tunis. The time variables (hour of the day, day of the week and month) also have an effect. This is especially true for the time variable “month” that contributes significantly to the description of the study area.

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