Maria del Carmen Fabrizio

Analysis of the transmission of Trypanosoma cruzi infection through hosts and vectors.

Calculating epidemiological measures of infection by Trypanosoma cruzi, the causative agent of Chagas disease, is complex, because it involves several species, different stages of infection in humans and multiple transmission routes. Using the next-generation matrix method, we analysed a model which considers the three stages of human infection, triatomines and dogs (the main domestic reservoirs of T. cruzi when triatomines are present) and the main transmission routes. We derived R 0 and type-reproduction numbers T. We deduced formulas for the number of new infections generated through each transmission route by each infected individual. We applied our findings in Argentine Gran Chaco. The expressions achieved allowed quantifying the high infectivity of dogs and emphasizing the epidemiological importance of the long and asymptomatic chronic indeterminate stage in humans in the spread of the infection. According to the model, it is expected that one infected human infects 21 triatomines, that 100 infected triatomines are necessary to infect one human and 34 to infect a dog, and that each dog infects on average one triatomine per day. Our results may allow quantifying the effect of control measures on infected humans, triatomines and dogs (or other highly infected vertebrate) or on a specific route of transmission, in other scenarios.

Modelling American trypanosomiasis in an endemic zone: application to the initial spread of household infection in the Argentine Chaco.

The complex dynamics of Trypanosoma cruzi infection (Chagas disease) involves different actors and multiple transmission routes. Based on the information currently available, here, we propose a new and more comprehensive model to better understand the dynamics of the infection. This mathematical deterministic model was formulated considering: (i) the three clinical forms in humans: acute, chronic indeterminate and chronic with determinate pathology, (ii) the three main modes of transmission in the human population: vector‐borne, congenital and transfusional, (iii) populations of triatomines and dogs as the main domestic reservoirs of T. cruzi and (iv) open populations. A numerical simulation was also performed to estimate the initial spread of the infection in a typical rural household in the endemic zone of the Argentine Gran Chaco. We also analysed the incidence of infected individuals corresponding to each of the three species (humans/triatomines/dogs) over times until the appearance of the first case in the other species. The model predicts that, in the absence of control measures, a few infected individuals are sufficient for the establishment and dispersion of the infection in all the inhabitants of the household. The model proposed and the results obtained allow describing the consequences of the presence of infected individuals in any of the three species considered in the dynamics and the output of the infection.

Modelling inter-human transmission dynamics of Chagas disease: analysis and application

Transmission of Trypanosoma cruzi, the causal agent of Chagas disease, has expanded from rural endemic to urban areas due to migration. This so-called urban Chagas is an emerging health problem in American, European, Australian and Japanese cities. We present a mathematical model to analyse the dynamics of urban Chagas to better understand its epidemiology. The model considers the three clinical stages of the disease and the main routes of inter-human transmission. To overcome the complexities of the infection dynamics, the next-generation matrix method was developed. We deduced expressions which allowed estimating the number of new infections generated by an infected individual through each transmission route at each disease stage, the basic reproduction number and the number of individuals at each disease stage at the outbreak of the infection. The analysis was applied to Buenos Aires city (Argentina). We estimated that 94% of the new infections are generated by individuals in the chronic indeterminate stage. When migration was not considered, the infection disappeared slowly and R0 = 0.079, whereas when migration was considered, the number of individuals in each stage of the infection tended to stabilize. The expressions can be used to estimate different numbers of infected individuals in any place where only inter-human transmission is possible.

Measuring University Students’ Approaches to Learning Statistics: An Invariance Study

The aim of the current study was to provide evidence that an abbreviated version of the Approaches and Study Skills Inventory for Students (ASSIST) was invariant across different languages and educational contexts in measuring university students’ learning approaches to statistics. Data were collected on samples of university students attending undergraduate introductory statistics courses in five countries (Argentina, Italy, Australia, Turkey, and Vietnam). Using factor analysis, we confirmed the three-factor (Deep, Surface, and Strategic approach) model holds across the five samples, and we provided evidence of configural and measurement invariance. The current version of the ASSIST for statistics learners is a suitable scale for researchers and teachers working in the field of statistics education and represents promising tool for multinational studies on approaches to learning statistics.

A Comparison of first year statistics units’ content and contexts in a multinational study, with a case study for the validation of ASSIST in Australia

The study of statistics has become widespread throughout many degrees around the world in many universities, as the emphasis on evidence-based decision making has gained momentum in the business world. Students’ approaches to their learning bear significant weight over the skills and understanding that students acquire during their studies. Three distinct learning approaches have been identified by researchers over the last three decades: deep, surface (British Journal of Educational Psychology 46:115–127, 1976) and strategic (Educational Research Journal 5:18–28, 1990). The discrepancy between desired learning outcomes and the aptitude and skills that students of statistics acquire (e.g. International Statistical Review 63:25–34, 1995) is well documented but the underlying reasons for choosing different learning approaches in statistics has only been investigated in limited studies and only from the perspective of a student’s demographics. It is therefore important to understand how unit and student characteristics might encourage students to utilise certain approaches, especially students who do not major in statistics. The aims of the current chapter are therefore to provide a brief review of learning approaches, a detailed description of the multinational study and validation of the Approaches and Study Skills Inventory for Students (ASSIST) as a measure of the learning approaches utilised by a cohort of Australian students of statistics.

THE ANALYSIS OF THE TWO-PERIOD REPEATED MEASUREMENTS CROSSOVER DESIGN WITH APPLICATION TO A FORESTRY PROBLEM

The two-period repeated measurements crossover design is not often used in agricultural studies. It is, however, an attractive model, involving the confluence of two powerful statistical ideas, treatment crossover and repeated measurements on the same experimental unit. This paper presents one approach for the statistical analysis of such design based on the work of Wallenstein and Fisher (1977). It is shown how the data may be transformed so that it can be analyzed under the framework of a completely randomized repeated measurements design. We formalize the analysis in the context of a forestry experiment conducted on poplar trees (Populus SP,), to compare the efficacy of two treatments to prevent damage by the coleopteran insect Platypus sulcatus (ambrosia beetle). Two insecticides were applied in a crossover fashion to two groups of 8 poplar trees each. Each tree was treated with one insecticide and evaluated on three occasions during the first year, received no treatment during the following one-year washout phase, and then (in the third year) received the other treatment and was evaluated on three occasions. One of the parameters analyzed to test for treatment differences was the number of tree lesions attributed to the insect. We present the results of our work and discuss the potential usefulness as well as the limitations of this interesting design.

SEQUENTIAL ANALYSIS OF AGRICULTURAL EXPERIMENTS

Interim monitoring of accumulating data has been widely used in clinical trials, but it has not received the same attention in agricultural experimentation. The methodology, however, can be a useful tool in agronomic trials designed to find better production techniques or optimal animal treatments at low cost, plus the possible economic advantages resulting from correct early decisions. These sequential procedures for testing hypothesis with available data in successive periods of time dictate termination of the experiment when a significant difference is detected, or otherwise continuation of the experiment to the end of the stipulated time or until all the planned sample size is realized. The statistical cost of repeated testing of part of the same data is a reduction in the significance levels a to the time-related significance levels a j (aj<a). We apply three methods for this type of analysis, which we illustrate with two examples involving respectively, comparisons of two proportions and two means from normally distributed random variables with unknown variances. The examples show the usefulness and limitations of the proposed methods and also that there can be no absolute rule for choosing the best method of analysis in a particular case. The optimal strategy depends on the specifics of the trial and the investigators criterion to choose the ajO

TWO-FACTOR AGRICULTURAL EXPERIMENT WITH REPEATED MEASURES ON ONE FACTOR IN A COMPLETE RANDOMIZED DESIGN

A typical agricultural experiment involves comparisons of several treatments at different points in time. The ensuing lack of independence between observations of the same experimental unit may then impair the attainment of statistical significance by the standard analysis of variance, and calls for the application of more powerful methods. This paper addresses one such method, the so-called two-factor experiment with repeated measures on one factor. We discuss the adequacy of this model in the context of three concrete examples drawn from agricultural experimentation.