A diffusion model for the adoption of agricultural innovations in structured adopting populations

We introduce a new model for examining the dynamics of uptake of technological innovations in agricultural systems, using the adoption of zero-till wheat in the rice-wheat system in Haryana state, India, as a case study. A new equation is derived which describes the dynamics of adoption over time and takes into account the effect of aggregation (e.g. on a spatial and/or cultural basis) in the adopting population on the rate of adoption. The model extends previous phenomenological models by removing the assumption of homogeneity in the non-adopting fraction of the population. We show how factors affecting the per capita rate of adoption can be captured using cognitive mapping and simulate the dynamics of the adoption process.Bass curve, adoption, innovation, Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Mathematical modeling tendencies in plant pathology

Nowadays plant diseases represent one of the major threats for crops around the world, because they carry healthy, economical, environmental and social problems. Considering this, it is necessary to have a description of the dynamics of plant disease in order to have sustainable strategies to prevent and diminish the impact of the diseases in crops. Mathematical tools have been employed to create models which give a description of epidemic dynamics; the commonly mathematical tools used are: Diseaseprogress curves, Linked Differential Equation (LDE), Area Under disease Progress Curve (AUDPC) and computer simulation. Nevertheless, there are other tools that have been employed in epidemiology of plant disease like: statistical tools, visual evaluations and pictorial assessment. Each tool has its own advantages and disadvantages. The nature of the problem and the epidemiologist necessities determine the mathematical tool to be used and the variables to be included into the model. This paperpresents review of the tools used in epidemiology of plant disease remarking their advantages and disadvantages and mathematical modeling tendencies in plant pathology

How social learning shapes the efficacy of preventative health behaviors in an outbreak.

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior

The Effects of Statistical Multiplicity of Infection on Virus Quantification and Infectivity Assays

Many biological assays are employed in virology to quantify parameters of interest. Two such classes of assays, virus quantification assays (VQA) and infectivity assays (IA), aim to estimate the number of viruses present in a solution, and the ability of a viral strain to successfully infect a host cell, respectively. VQAs operate at extremely dilute concentrations and results can be subject to stochastic variability in virus-cell interactions. At the other extreme, high viral particle concentrations are used in IAs, resulting in large numbers of viruses infecting each cell, enough for measurable change in total transcription activity. Furthermore, host cells can be infected at any concentration regime by multiple particles, resulting in a statistical multiplicity of infection (SMOI) and yielding potentially significant variability in the assay signal and parameter estimates. We develop probabilistic models for SMOI at low and high viral particle concentration limits and apply them to the plaque (VQA), endpoint dilution (VQA), and luciferase reporter (IA) assays. A web-based tool implementing our models and analysis is also developed and presented. We test our proposed new methods for inferring experimental parameters from data using numerical simulations and show improvement on existing procedures in all limits.Comment: 19 pages, 11 figures, 1 tabl

Modelling the effects of the infectious environment on pig growth and intake

Sub-clinical disease can have large effects on animal production with significant economic consequences. Animal health and welfare are increasingly important criteria in animal production, and the removal of antibiotic growth promoters has added pressure on production systems. No general model has yet been proposed for predicting the growth and performance of animals exposed to pathogens. A robust framework for predicting growth during health and disease may assist in the design of nutritional, environmental and genetic management strategies. A core part of any animal growth model is how it predicts the partitioning of dietary protein and energy to protein and lipid retention for different genotypes at different degrees of maturity. Solutions proposed in the literature to the partitioning problem were described in detail and criticised in relation to their scope, generality and economy of parameters (Chapter 1). They all raised the issue, often implicitly, of the factors that affect the net marginal efficiency of using absorbed dietary protein for protein retention. Partitioning rules that withstood qualitative criticisms were then tested against literature data and a general quantitative partitioning rule was concluded for that had two key parameters: the maximum marginal efficiency of protein retention and the energy to protein ratio at which the maximum efficiency is achieved (Chapter 2). A general rule was identified which was able to predict protein retention for both protein and energy limiting diets in healthy animals. In Chapter 3 a general model was developed for predicting effects of sub-clinical pathogen challenges of different doses and virulence on the intake of animals. Pathogen induced anorexia is the major consequence on hosts during the course of infection. The model was for the period from recognition of a pathogen through to acquisition and subsequent expression of immunity. It is crucial to define the pathogen challenge (in terms of dose and virulence) and the degree of resistance of different hosts, when comparing their responses in RFI. There is no general agreement on the consequences of pathogen challenges, other than anorexia, that need to be included in a predictive framework of growth. In Chapter 4 literature data was reviewed for different kinds of pathogen challenges of a range of hosts to identify reductions in growth beyond that caused by anorexia: these were host, dose and time dependent. In only some instances did anorexia fully explain the reductions in growth. Solutions were needed for describing the protein costs of innate and acquired immune responses and repair of damaged tissues. Increased energy requirements depended on immune responses, repair of damage and fever. In Chapter 5 a framework was proposed that predicts the performance of different genotypes (in terms of growth potential and disease resistance) when challenged by different doses of pathogens and given access to different foods. The model predicts amino acid and energy requirements due to growth and immune responses, and a partitioning rule was developed for partitioning scarce resources between growth and immune responses. Predictions can be made on the performance of different animal genotypes when they are given access to different quality foods and exposed to pathogens. The development of the model and its predictions, together with future testing, may contribute significantly towards our understanding of nutrition and genotype interactions during exposure to pathogens

An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing

The authors would like to thank the support on this research by the CRISP Project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD