scholarly journals Assessing the Factors of Dengue Transmission in Urban Environments of Pakistan

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 773
Author(s):  
Bushra Khalid ◽  
Cholaw Bueh ◽  
Abdul Ghaffar
Keyword(s):  
Neural Network ◽  
Population Density ◽  
Random Effects ◽  
Minimum Temperature ◽  
Human Population ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Human Population Density ◽  
Societal Factors ◽  
Dengue Transmission

The rationale of this study is to highlight the significance of relationships of dengue transmission with climate and societal factors for four major cities in Pakistan (i.e., Islamabad, Rawalpindi, Lahore, and Karachi). These cities have been observed to report higher numbers of dengue cases in the last few years, with the highest number of cases reported during 2011. With careful consideration, the relationships of dengue transmission with climate factors, human population density, and traveling in the study areas have been taken into account. Regression model and generalized linear mixed model (GLM) with Markov chain Monte Carlo (MCMC) algorithm are computed to determine the relationships and random effects of different social (human population density, traveling) and climate (minimum-maximum temperatures, and rainfall) factors on dengue transmission. Neural network (NN) with multilayer perceptron algorithm is used to analyze the normalized importance of different covariates relative to dengue transmission. The results show that minimum temperature and rainfall, together with societal factors, significantly affecting the transmission of dengue in the study areas. The magnitude of these relationships is also shown by the results of the neural network. GLM also shows the significant random effects of minimum temperature, rainfall, human population density, and traveling on dengue transmission during the studied years (2009–2018).

scholarly journals The generalizability crisis

2020 ◽  
pp. 1-37
Author(s):  
Tal Yarkoni
Keyword(s):  
Quantitative Analysis ◽  
Statistical Inference ◽  
Random Effects ◽  
False Positive ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Procedures ◽  
Subject Variability ◽  
Replication Crisis

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same set of hypothetical observations. Here I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology—the linear mixed model—I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that whereas the "random effect" formalism is used pervasively in psychology to model inter-subject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

scholarly journals Mountain definitions and their consequences

Alpine Botany ◽  
2021 ◽  
Author(s):  
Christian Körner ◽  
Davnah Urbach ◽  
Jens Paulsen
Keyword(s):  
Species Richness ◽  
Population Density ◽  
Climatic Change ◽  
Plant Species ◽  
Human Population ◽  
Short Communication ◽  
Plant Species Richness ◽  
Mountainous Terrain ◽  
Human Population Density

AbstractMountains are rugged structures in the landscape that are difficult to delineate. Given that they host an overproportional fraction of biodiversity of high ecological and conservational value, conventions on what is mountainous and what not are in need. This short communication aims at explaining the differences among various popular mountain definitions. Defining mountainous terrain is key for global assessments of plant species richness in mountains and their likely responses to climatic change, as well as for assessing the human population density in and around mountainous terrain.

scholarly journals Human population density correlates with phylogenetic diversity and plant vulnerability in southern Africa

2013 ◽  
Vol 86 ◽  
pp. 166 ◽  
Author(s):  
O. Maurin ◽  
T.J. Davies ◽  
K. Yessoufou ◽  
B.H. Daru ◽  
B.S. Bezeng ◽  
...  
Keyword(s):  
Population Density ◽  
Southern Africa ◽  
Human Population ◽  
Phylogenetic Diversity ◽  
Human Population Density

scholarly journals Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

2018 ◽  
Vol 147 ◽  
Author(s):  
A. Aswi ◽  
S. M. Cramb ◽  
P. Moraga ◽  
K. Mengersen
Keyword(s):  
Dengue Fever ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Random Effect ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Spatio Temporal

AbstractDengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

scholarly journals Reptile diversity of Sinos River Basin

2018 ◽  
Vol 18 (3) ◽  
Author(s):  
Camila Fernanda Moser ◽  
Fernanda Rodrigues de Avila ◽  
Roberto Baptista de Oliveira ◽  
Juliano Morales de Oliveira ◽  
Márcio Borges-Martins ◽  
...  
Keyword(s):  
Population Density ◽  
River Basin ◽  
Human Population ◽  
Urban Region ◽  
Research Centers ◽  
Human Population Density ◽  
In The Wild ◽  
Medical Importance ◽  
Scientific Collections

Abstract This work aimed to catalog the species of reptiles of the Sinos River Basin based on records from scientific collections and data collected in the field. We recorded 65 species, including 46 snakes, nine lizards, five turtles, four amphisbaenians and one caiman. Snakes composed most of the recorded specimens (91.3%), and the three most representative are venomous and of medical importance. The most urban region of the basin (Lowland) has the highest number of records. This fact may be a reflection of the high human population density in this region, which would have favored the encounter of specimens and their sending to scientific collections and research centers. It is worth highlighting that most species with few specimens in the collections are also rarely observed in the wild, such as Clelia hussani and Urostrophus vautieri. This observation makes it feasible that these populations are small or that they are declining.

Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks

Parasitology ◽  
2001 ◽  
Vol 122 (5) ◽  
pp. 563-569 ◽  
Author(s):  
D. A. ELSTON ◽  
R. MOSS ◽  
T. BOULINIER ◽  
C. ARROWSMITH ◽  
X. LAMBIN
Keyword(s):  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Negative Binomial ◽  
Generalized Linear Mixed Model ◽  
Red Grouse ◽  
Worked Example ◽  
Fixed And Random Effects ◽  
Individual Effects ◽  
Lagopus Lagopus Scoticus

The statistical aggregation of parasites among hosts is often described empirically by the negative binomial (Poisson-gamma) distribution. Alternatively, the Poisson-lognormal model can be used. This has the advantage that it can be fitted as a generalized linear mixed model, thereby quantifying the sources of aggregation in terms of both fixed and random effects. We give a worked example, assigning aggregation in the distribution of sheep ticksIxodes ricinuson red grouseLagopus lagopus scoticuschicks to temporal (year), spatial (altitude and location), brood and individual effects. Apparent aggregation among random individuals in random broods fell 8-fold when spatial and temporal effects had been accounted for.

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