67 research outputs found
Causal Inference in Disease Spread across a Heterogeneous Social System
Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635
Estimating the Social Welfare Effects of New Zealand Apple Imports
This paper provides a demonstration of how a comprehensive economic framework, which takes into account both the gains from trade and the costs of invasive species outbreaks, can inform decision-makers when making quarantine decisions. Using the theoretical framework developed in Cook and Fraser (2008) an empirical estimation is made of the economic welfare consequences for Australia of allowing quarantine-restricted trade in New Zealand apples to take place. The results suggest the returns to Australian society from importing New Zealand apples are likely to be negative. The price differential between the landed product with SPS measures in place and the autarkic price is insufficient to outweigh the increase in expected damage resulting from increased fire blight risk. As a consequence, this empirical analysis suggests the net benefits created by opening up this trade are marginal.International Relations/Trade,
A global model for predicting the arrival of imported dengue infections
With approximately half of the world's population at risk of contracting
dengue, this mosquito-borne disease is of global concern. International
travellers significantly contribute to dengue's rapid and large-scale spread by
importing the disease from endemic into non-endemic countries. To prevent
future outbreaks and dengue from establishing in non-endemic countries,
knowledge about the arrival time and location of infected travellers is
crucial. We propose a network model that predicts the monthly number of
dengue-infected air passengers arriving at any given airport. We consider
international air travel volumes to construct weighted networks, representing
passenger flows between airports. We further calculate the probability of
passengers, who travel through the international air transport network, being
infected with dengue. The probability of being infected depends on the
destination, duration and timing of travel. Our findings shed light onto dengue
importation routes and reveal country-specific reporting rates that have been
until now largely unknown. This paper provides important new knowledge about
the spreading dynamics of dengue that is highly beneficial for public health
authorities to strategically allocate the often limited resources to more
efficiently prevent the spread of dengue.Comment: 32 pages, 20 figure
Prioritizing the risk of plant pests by clustering methods; self-organising maps, k-means and hierarchical clustering
For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential
to establish and cause significant impact in an endangered area. Such prioritization is often qualitative,
subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years,
cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to
indicate the risk of new organism establishment. Such an approach is based on the premise that the cooccurrence
of well-known global invasive pest species in a region is not random, and that the pest species
profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other
words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational
intelligence method called a Kohonen self-organizing map (SOM), a type of artificial neural
network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a
well known dimension reduction and visualization method especially useful for high dimensional data
that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the
SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and
recipient regions. More important, however SOM connection weights that result from the analysis can
be used to rank the strength of association of each species within each regional assemblage. Species with
high weights that are not already established in the target region are identified as high risk. However, the
SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within
other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species
risk assessment, and discuss other clustering methods such as k-means, hierarchical clustering and the
incorporation of the SOM analysis into criteria based approaches to assess pest risk
Predicting Invasive Fungal Pathogens Using Invasive Pest Assemblages: Testing Model Predictions in a Virtual World
Predicting future species invasions presents significant challenges to researchers and government agencies. Simply considering the vast number of potential species that could invade an area can be insurmountable. One method, recently suggested, which can analyse large datasets of invasive species simultaneously is that of a self organising map (SOM), a form of artificial neural network which can rank species by establishment likelihood. We used this method to analyse the worldwide distribution of 486 fungal pathogens and then validated the method by creating a virtual world of invasive species in which to test the SOM. This novel validation method allowed us to test SOM's ability to rank those species that can establish above those that can't. Overall, we found the SOM highly effective, having on average, a 96–98% success rate (depending on the virtual world parameters). We also found that regions with fewer species present (i.e. 1–10 species) were more difficult for the SOM to generate an accurately ranked list, with success rates varying from 100% correct down to 0% correct. However, we were able to combine the numbers of species present in a region with clustering patterns in the SOM, to further refine confidence in lists generated from these sparsely populated regions. We then used the results from the virtual world to determine confidences for lists generated from the fungal pathogen dataset. Specifically, for lists generated for Australia and its states and territories, the reliability scores were between 84–98%. We conclude that a SOM analysis is a reliable method for analysing a large dataset of potential invasive species and could be used by biosecurity agencies around the world resulting in a better overall assessment of invasion risk
Biosecurity and Yield Improvement Technologies Are Strategic Complements in the Fight against Food Insecurity
The delivery of food security via continued crop yield improvement alone is not an effective food security strategy, and must be supported by pre- and post-border biosecurity policies to guard against perverse outcomes. In the wake of the green revolution, yield gains have been in steady decline, while post-harvest crop losses have increased as a result of insufficiently resourced and uncoordinated efforts to control spoilage throughout global transport and storage networks. This paper focuses on the role that biosecurity is set to play in future food security by preventing both pre- and post-harvest losses, thereby protecting crop yield. We model biosecurity as a food security technology that may complement conventional yield improvement policies if the gains in global farm profits are sufficient to offset the costs of implementation and maintenance. Using phytosanitary measures that slow global spread of the Ug99 strain of wheat stem rust as an example of pre-border biosecurity risk mitigation and combining it with post-border surveillance and invasive alien species control efforts, we estimate global farm profitability may be improved by over US$4.5 billion per annum
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