Inference of epidemiological parameters from household stratified data

We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters---governing within-household transmission, recovery, and between-household transmission---from data of the day upon which each individual became infectious and the household in which each infection occurred, as would be available from first few hundred studies. Each method is a form of Bayesian Markov Chain Monte Carlo that allows us to calculate a joint posterior distribution for all parameters and hence the household reproduction number and the early growth rate of the epidemic. The first method performs exact Bayesian inference using a standard data-augmentation approach; the second performs approximate Bayesian inference based on a likelihood approximation derived from branching processes. These methods are compared for computational efficiency and posteriors from each are compared. The branching process is shown to be an excellent approximation and remains computationally efficient as the amount of data is increased

An Induced Natural Selection Heuristic for Finding Optimal Bayesian Experimental Designs

Bayesian optimal experimental design has immense potential to inform the collection of data so as to subsequently enhance our understanding of a variety of processes. However, a major impediment is the difficulty in evaluating optimal designs for problems with large, or high-dimensional, design spaces. We propose an efficient search heuristic suitable for general optimisation problems, with a particular focus on optimal Bayesian experimental design problems. The heuristic evaluates the objective (utility) function at an initial, randomly generated set of input values. At each generation of the algorithm, input values are "accepted" if their corresponding objective (utility) function satisfies some acceptance criteria, and new inputs are sampled about these accepted points. We demonstrate the new algorithm by evaluating the optimal Bayesian experimental designs for the previously considered death, pharmacokinetic and logistic regression models. Comparisons to the current "gold-standard" method are given to demonstrate the proposed algorithm as a computationally-efficient alternative for moderately-large design problems (i.e., up to approximately 40-dimensions)

Calculation of disease dynamics in a population of households

Early mathematical representations of infectious disease dynamics assumed a single, large, homogeneously mixing population. Over the past decade there has been growing interest in models consisting of multiple smaller subpopulations (households, workplaces, schools, communities), with the natural assumption of strong homogeneous mixing within each subpopulation, and weaker transmission between subpopulations. Here we consider a model of SIRS (susceptible-infectious-recovered-suscep​tible) infection dynamics in a very large (assumed infinite) population of households, with the simplifying assumption that each household is of the same size (although all methods may be extended to a population with a heterogeneous distribution of household sizes). For this households model we present efficient methods for studying several quantities of epidemiological interest: (i) the threshold for invasion; (ii) the early growth rate; (iii) the household offspring distribution; (iv) the endemic prevalence of infection; and (v) the transient dynamics of the process. We utilize these methods to explore a wide region of parameter space appropriate for human infectious diseases. We then extend these results to consider the effects of more realistic gamma-distributed infectious periods. We discuss how all these results differ from standard homogeneous-mixing models and assess the implications for the invasion, transmission and persistence of infection. The computational efficiency of the methodology presented here will hopefully aid in the parameterisation of structured models and in the evaluation of appropriate responses for future disease outbreaks

Networks and the epidemiology of infectious disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues

Drivers of the Australian native pet trade : the role of species traits, socioeconomic attributes and regulatory systems

Acknowledgements We acknowledge that the land on which we conducted our research is the traditional land of the Kaurna people of the Adelaide Plains. We pay our respects to Kaurna elders past, present and emerging. We thank the South Australian Department for the Environment and Water for recording and facilitating access to all permit data used in our analysis. This research was funded by the Centre for Invasive Species Solutions (Project P01-I-002). PC was supported by an Australian Research Council Discovery grant (DP210103050). PG-D was supported by the NERC grant NE/S011641/1 under the Newton LATAM funding programme.Peer reviewedPublisher PD

Modelling the impact of antimalarial quality on the transmission of sulfadoxine-pyrimethamine resistance in Plasmodium falciparum

Background: The use of poor quality antimalarial medicines, including the use of non-recommended medicines for treatment such as sulfadoxine-pyrimethamine (SP) monotherapy, undermines malaria control and elimination efforts. Furthermore, the use of subtherapeutic doses of the active ingredient(s) can theoretically promote the emergence and transmission of drug resistant parasites. Methods: We developed a deterministic compartmental model to quantify the impact of antimalarial medicine quality on the transmission of SP resistance, and validated it using sensitivity analysis and a comparison with data from Kenya collected in 2006. We modelled human and mosquito population dynamics, incorporating two Plasmodium falciparum subtypes (SP-sensitive and SP-resistant) and both poor quality and good quality (artemether-lumefantrine) antimalarial use. Findings: The model predicted that an increase in human malaria cases, and among these, an increase in the proportion of SP-resistant infections, resulted from an increase in poor quality SP antimalarial use, whether it was full- or half-dose SP monotherapy. Interpretation: Our findings suggest that an increase in poor quality antimalarial use predicts an increase in the transmission of resistance. This highlights the need for stricter control and regulation on the availability and use of poor quality antimalarial medicines, in order to offer safe and effective treatments, and work towards the eradication of malaria

Modulation of Hepatic Amyloid Precursor Protein and Lipoprotein Receptor-Related Protein 1 by Chronic Alcohol Intake: Potential Link Between Liver Steatosis and Amyloid-β

Heavy alcohol consumption is a known risk factor for various forms of dementia and the development of Alzheimer’s disease (AD). In this work, we investigated how intragastric alcohol feeding may alter the liver-to-brain axis to induce and/or promote AD pathology. Four weeks of intragastric alcohol feeding to mice, which causes significant fatty liver (steatosis) and liver injury, caused no changes in AD pathology markers in the brain [amyloid precursor protein (APP), presenilin], except for a decrease in microglial cell number in the cortex of the brain. Interestingly, the decline in microglial numbers correlated with serum alanine transaminase (ALT) levels, suggesting a potential link between liver injury and microglial loss in the brain. Intragastric alcohol feeding significantly affected two hepatic proteins important in amyloid-beta (Aβ) processing by the liver: 1) alcohol feeding downregulated lipoprotein receptor-related protein 1 (LRP1, ∼46%), the major receptor in the liver that removes Aβ from blood and peripheral organs, and 2) alcohol significantly upregulated APP (∼2-fold), a potentially important source of Aβ in the periphery and brain. The decrease in hepatic LRP1 and increase in hepatic APP likely switches the liver from being a remover or low producer of Aβ to an important source of Aβ in the periphery, which can impact the brain. The downregulation of LRP1 and upregulation of APP in the liver was observed in the first week of intragastric alcohol feeding, and also occurred in other alcohol feeding models (NIAAA binge alcohol model and intragastric alcohol feeding to rats). Modulation of hepatic LRP1 and APP does not seem alcohol-specific, as ob/ob mice with significant steatosis also had declines in LRP1 and increases in APP expression in the liver. These findings suggest that liver steatosis rather than alcohol-induced liver injury is likely responsible for regulation of hepatic LRP1 and APP. Both obesity and alcohol intake have been linked to AD and our data suggests that liver steatosis associated with these two conditions modulates hepatic LRP1 and APP to disrupt Aβ processing by the liver to promote AD

Bone-Induced Expression of Integrin β3 Enables Targeted Nanotherapy of Breast Cancer Metastases

Bone metastases occur in approximately 70% of metastatic breast cancer patients, often leading to skeletal injuries. Current treatments are mainly palliative and underscore the unmet clinical need for improved therapies. In this study, we provide preclinical evidence for an antimetastatic therapy based on targeting integrin β3 (β3), which is selectively induced on breast cancer cells in bone by the local bone microenvironment. In a preclinical model of breast cancer, β3 was strongly expressed on bone metastatic cancer cells, but not primary mammary tumors or visceral metastases. In tumor tissue from breast cancer patients, β3 was significantly elevated on bone metastases relative to primary tumors from the same patient (n = 42). Mechanistic investigations revealed that TGFβ signaling through SMAD2/SMAD3 was necessary for breast cancer induction of β3 within the bone. Using a micelle-based nanoparticle therapy that recognizes integrin αvβ3 (αvβ3-MPs of ∼12.5 nm), we demonstrated specific localization to breast cancer bone metastases in mice. Using this system for targeted delivery of the chemotherapeutic docetaxel, we showed that bone tumor burden could be reduced significantly with less bone destruction and less hepatotoxicity compared with equimolar doses of free docetaxel. Furthermore, mice treated with αvβ3-MP-docetaxel exhibited a significant decrease in bone-residing tumor cell proliferation compared with free docetaxel. Taken together, our results offer preclinical proof of concept for a method to enhance delivery of chemotherapeutics to breast cancer cells within the bone by exploiting their selective expression of integrin αvβ3 at that metastatic site