SAfE transport: wearing face masks significantly reduces the spread of COVID-19 on trains.

COVID-19 has had a substantial impact globally. It spreads readily, particularly in enclosed and crowded spaces, such as public transport carriages, yet there are limited studies on how this risk can be reduced. We developed a tool for exploring the potential impacts of mitigation strategies on public transport networks, called the Systems Analytics for Epidemiology in Transport (SAfE Transport). SAfE Transport combines an agent-based transit assignment model, a community-wide transmission model, and a transit disease spread model to support strategic and operational decision-making. For this simulated COVID-19 case study, the transit disease spread model incorporates both direct (person-to-person) and fomite (person-to-surface-to-person) transmission modes. We determine the probable impact of wearing face masks on trains over a seven day simulation horizon, showing substantial and statistically significant reductions in new cases when passenger mask wearing proportions are greater than 80%. The higher the level of mask coverage, the greater the reduction in the number of new infections. Also, the higher levels of mask coverage result in an earlier reduction in disease spread risk. These results can be used by decision makers to guide policy on face mask use for public transport networks.

Modeling stochastic processes 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 underlying diffusion mechanisms, which is challenging due to invisible infection pathways and time-evolving intensity of infection cases. Here, we propose a new diffusion framework for stochastic processes, which models disease spread across metapopulations by incorporating human mobility as topological pathways in a heterogeneous social system. We apply Bayesian inference with the stochastic Expectation-Maximization algorithm to quantify underlying diffusion dynamics in terms of exogeneity and endogeneity and estimate cross-regional infection flow based on Granger causality. The effectiveness of our proposed model is shown by using comprehensive simulation procedures (robustness tests with noisy data considering missing or delayed human case reporting in real situations) and by applying the model to real data from 15-y dengue outbreaks in Australia.

A general trait-based modelling framework for revealing patterns of airborne fungal dispersal threats to agriculture and native flora.

Fungal plant pathogens are of economic and ecological importance to global agriculture and natural ecosystems. Long-distance atmospheric dispersal of fungal spores (LAD) can pose threats to agricultural and native vegetation lands. An understanding of such patterns of fungal spore dispersal and invasion pathways can provide valuable insights into plant protection. Spore traits affect their dispersal abilities. We propose a general trait-based framework for modelling LAD to reveal dispersal patterns and pathways, and assess subsequent threats of arrival (TOA) quantitatively in the context of biosecurity. To illustrate the framework, we present a study of Australia and its surrounding land masses. The overall dispersal pattern covered almost the entire continent of Australia. Fungal spores in the size class of 10 and 20 µm (aerodynamic diameter) posed the greatest TOA. Our study shows the effects of morphological traits on these potential TOA, and how they varied between source regions, size classes, and seasons. Our framework revealed spore dispersal patterns and pathways. It also facilitates comparisons of spatio-temporal dispersal dynamics among fungal classes, gaining insights into atmospheric long-distance dispersal of fungi as a whole, and provides a basis for assessing fungal pest threats in potential source regions based on easily measured spore characteristics.

Should international borders re-open? The impact of travel restrictions on COVID-19 importation risk.

BACKGROUND: Novel coronavirus disease (COVID-19) has spread across the world at an unprecedented pace, reaching over 200 countries and territories in less than three months. In response, many governments denied entry to travellers arriving from various countries affected by the virus. While several industries continue to experience economic losses due to the imposed interventions, it is unclear whether the different travel restrictions were successful in reducing COVID-19 importations. METHODS: Here we develop a comprehensive probabilistic framework to model daily COVID-19 importations, considering different travel bans. We quantify the temporal effects of the restrictions and elucidate the relationship between incidence rates in other countries, travel flows and the expected number of importations into the country under investigation. RESULTS: As a cases study, we evaluate the travel bans enforced by the Australian government. We find that international travel bans in Australia lowered COVID-19 importations by 87.68% (83.39 - 91.35) between January and June 2020. The presented framework can further be used to gain insights into how many importations to expect should borders re-open. CONCLUSIONS: While travel bans lowered the number of COVID-19 importations overall, the effectiveness of bans on individual countries varies widely and directly depends on the change in behaviour in returning residents and citizens. Authorities may consider the presented information when planning a phased re-opening of international borders.

Parameter estimation for functional-structural plant models when data are scarce: using multiple patterns for rejecting unsuitable parameter sets.

BACKGROUND AND AIMS: Functional-structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible. METHODS: We used an existing FSP model of avocado (Persea americana 'Hass') and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization. KEY RESULTS: POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns. CONCLUSIONS: Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.

Global threat to agriculture from invasive species.

Invasive species present significant threats to global agriculture, although how the magnitude and distribution of the threats vary between countries and regions remains unclear. Here, we present an analysis of almost 1,300 known invasive insect pests and pathogens, calculating the total potential cost of these species invading each of 124 countries of the world, as well as determining which countries present the greatest threat to the rest of the world given their trading partners and incumbent pool of invasive species. We find that countries vary in terms of potential threat from invasive species and also their role as potential sources, with apparently similar countries sometimes varying markedly depending on specifics of agricultural commodities and trade patterns. Overall, the biggest agricultural producers (China and the United States) could experience the greatest absolute cost from further species invasions. However, developing countries, in particular, Sub-Saharan African countries, appear most vulnerable in relative terms. Furthermore, China and the United States represent the greatest potential sources of invasive species for the rest of the world. The analysis reveals considerable scope for ongoing redistribution of known invasive pests and highlights the need for international cooperation to slow their spread.

Native turncoats and indirect facilitation of species invasions.

At local scales, native species can resist invasion by feeding on and competing with would-be invasive species. However, this relationship tends to break down or reverse at larger scales. Here, we consider the role of native species as indirect facilitators of invasion and their potential role in this diversity-driven 'invasion paradox'. We coin the term 'native turncoats' to describe native facilitators of non-native species and identify eight ways they may indirectly facilitate species invasion. Some are commonly documented, while others, such as indirect interactions within competitive communities, are largely undocumented in an invasion context. Therefore, we use models to evaluate the likelihood that these competitive interactions influence invasions. We find that native turncoat effects increase with the number of resources and native species. Furthermore, our findings suggest the existence, abundance and effectiveness of native turncoats in a community could greatly influence invasion success at large scales.

Birds of a feather: using species assemblages to assess vulnerability to extinction.

Estimating extinction vulnerability for a large number of species presents significant challenges for researchers, but is of high importance considering the large number of species that are currently unassessed. We present a method using a type of artificial neural network (self organizing map; SOM), which utilizes the co-occurrence patterns of species to estimate each species' vulnerability to extinction. We use this method on Australian bird assemblages and compare the SOM-generated rankings for vulnerability with assessments from the IUCN Red List for those species in which populations have actually been estimated. For species that have had their populations estimated, the SOM performed well in distinguishing those species ranked of least concern by IUCN from those species in one of the other IUCN categories. Further, 19 species that were identified as highly vulnerable by the SOM analysis have never had their populations estimated and have been ranked by the IUCN of least concern. We show how the SOM can identify spatial variation in vulnerability for a species, and identify those regions in Australia in which the resident species have the greatest levels of vulnerability (central Australia). We conclude that the SOM provides a useful tool for researchers and agencies dealing with conservation strategies focused on large numbers of species and we urge a more detailed assessment of the 19 bird species identified by this analysis as vulnerable to extinction.

Quantifying the Establishment Likelihood of Invasive Alien Species Introductions Through Ports with Application to Honeybees in Australia.

The cost of an uncontrolled incursion of invasive alien species (IAS) arising from undetected entry through ports can be substantial, and knowledge of port-specific risks is needed to help allocate limited surveillance resources. Quantifying the establishment likelihood of such an incursion requires quantifying the ability of a species to enter, establish, and spread. Estimation of the approach rate of IAS into ports provides a measure of likelihood of entry. Data on the approach rate of IAS are typically sparse, and the combinations of risk factors relating to country of origin and port of arrival diverse. This presents challenges to making formal statistical inference on establishment likelihood. Here we demonstrate how these challenges can be overcome with judicious use of mixed-effects models when estimating the incursion likelihood into Australia of the European (Apis mellifera) and Asian (A. cerana) honeybees, along with the invasive parasites of biosecurity concern they host (e.g., Varroa destructor). Our results demonstrate how skewed the establishment likelihood is, with one-tenth of the ports accounting for 80% or more of the likelihood for both species. These results have been utilized by biosecurity agencies in the allocation of resources to the surveillance of maritime ports.

The role of global trade and transport network topology in the human-mediated dispersal of alien species.

More people and goods are moving further and more frequently via many different trade and transport networks under current trends of globalisation. These networks can play a major role in the unintended introduction of exotic species to new locations. With the continuing rise in global trade, more research attention is being focused on the role of networks in the spread of invasive species. This represents an emerging field of research in invasion science and the substantial knowledge being generated within other disciplines can provide ecologists with new tools with which to study invasions. For the first time, we synthesise studies from several perspectives, approaches and disciplines to derive the fundamental characteristics of network topology determining the likelihood of spread of organisms via trade and transport networks. These characteristics can be used to identify critical points of vulnerability within these networks and enable the development of more effective strategies to prevent invasions.

Prediction of viral spillover risk based on the mass action principle.

Infectious zoonotic disease emergence, through spillover events, is of global concern and has the potential to cause significant harm to society, as recently demonstrated by COVID-19. More than 70% of the 400 infectious diseases that emerged in the past five decades have a zoonotic origin, including all recent pandemics. There have been several approaches used to predict the risk of spillover through some of the known or suspected infectious disease emergence drivers, largely using correlative approaches. Here, we predict the spatial distribution of spillover risk by approximating general transmission through animal and human interactions. These mass action interactions are approximated through the multiplication of the spatial distribution of zoonotic virus diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both virus diversity and human population density are high, it should be noted that this is primarily a conceptual exercise. We compared our spillover risk map to key factors, including the model inputs of zoonotic virus diversity estimate map, human population density map, and the spatial distribution of species richness. Despite the limitations of this approach, this viral spillover map is a step towards developing a more comprehensive spillover risk prediction system to inform global monitoring.

Forecasting the probability of local dengue outbreaks in Queensland, Australia.

The global incidence of dengue is increasing, and many previously unaffected areas have reported local cases of the vector-borne disease in recent years. For the effective containment of local outbreaks health authorities rely on the prompt notification of new cases. However, due to severe under-reporting and misdiagnosis, non-endemic countries face difficulties in containing local outbreaks, and the possibility of dengue becoming endemic. Outbreak control measures in non-endemic countries are largely reactive and health authorities would benefit from a universal early warning system that forecasts the probability of dengue outbreaks for given times and locations. We develop a model that establishes a link between pre- and post-border risk of dengue outbreaks. Specifically, we predict the probability of travellers importing dengue from other countries as well as the probability of those travellers causing local outbreaks. Our model can act as an early warning system, forecasting likely times and places of dengue outbreaks. We run our model for the Australian state of Queensland over a period of twelve years. Our results reveal the airports where dengue infected travellers are most likely to arrive and geographic locations associated with high outbreak probabilities. Our results can be used by health authorities to better utilise prevention and control resources and lead to the development of new prevention measures.

Timely surveillance and temporal calibration of disease response against human infectious diseases.

BACKGROUND: Disease surveillance and response are critical components of epidemic preparedness. The disease response, in most cases, is a set of reactive measures that follow the outcomes of the disease surveillance. Hence, timely surveillance is a prerequisite for an effective response. METHODOLOGY/PRINCIPAL FINDINGS: We apply epidemiological soundness criteria in combination with the Latent Influence Point Process and time-to-event models to construct a disease spread network. The network implicitly quantifies the fertility (whether a case leads to secondary cases) and reproduction (number of secondary cases per infectious case) of the cases as well as the size and generations (of the infection chain) of the outbreaks. We test our approach by applying it to historic dengue case data from Australia. Using the data, we empirically confirm that high morbidity relates positively with delay in disease response. Moreover, we identify what constitutes timely surveillance by applying various thresholds of disease response delay to the network and report their impact on case fertility, reproduction, number of generations and ultimately, outbreak size. We observe that enforcing a response delay threshold of 5 days leads to a large average reduction across all parameters (occurrence 87%, reproduction 83%, outbreak size 80% and outbreak generations 47%), whereas extending the threshold to 10 days, in comparison, significantly limits the effectiveness of the response actions. Lastly, we identify the components of the disease surveillance system that can be calibrated to achieve the identified thresholds. CONCLUSION: We identify practically achievable, timely surveillance thresholds (on temporal scale) that lead to an effective response and identify how they can be satisfied. Our approach can be utilized to provide guidelines on spatially and demographically targeted resource allocation for public awareness campaigns as well as to improve diagnostic abilities and turn-around times for the doctors and laboratories involved.

Vaccination strategies on dynamic networks with indirect transmission links and limited contact information.

Infectious diseases are still a major global burden for modern society causing 13 million deaths annually. One way to reduce the morbidity and mortality rates from infectious diseases is through pre-emptive or targeted vaccinations. Current theoretical vaccination strategies based on contact networks, however, rely on highly specific individual contact information which is difficult and costly to obtain, in order to identify influential spreading individuals. Current approaches also focus only on direct contacts between individuals for spreading, and disregard indirect transmission where a pathogen can spread between one infected individual and one susceptible individual who visit the same location within a short time-frame without meeting. This paper presents a novel vaccination strategy which relies on coarse-grained contact information, both direct and indirect, that can be easily and efficiently collected. Rather than tracking exact contact degrees of individuals, our strategy uses the types of places people visit to estimate a range of contact degrees for individuals, considering both direct and indirect contacts. We conduct extensive computer simulations to evaluate the performance of our strategy in comparison to state-of-the-art vaccination strategies. Results show that, when considering indirect links, our lower cost vaccination strategy achieves comparable performance to the contact-degree based approach and outperforms other existing strategies without requiring over-detailed information.

Dispersal, patch leaving, and distribution of Homalodisca vitripennis (Hemiptera: Cicadellidae).

Homalodisca vitripennis (Germar) and related species have caused millions of dollars in damage to southern California vineyards in recent years through the vectoring of Pierce's disease. However, the effects of surrounding vegetation on the dispersal and distribution of H. vitripennis are poorly understood. Therefore, the relationship between dispersal rates and patch quality was tested, as well as the basic predictions of the marginal value theorem. Additional experiments were conducted to compare the H. vitripennis distribution in an isolated crape myrtle (Lagerstroemia indica) patch and a L. indica patch bordering two alternative host patches. In mark-release-recapture tests, H. vitripennis dispersed farther from the release point in a patch of low-quality host plants (Prunus persica) than in patches of high-quality host plants (L. indica). In addition, H. vitripennis remained in L. indica patches longer than in P. persica patches and adjusted patch residence times in P. persica in correlation with known changes in plant physiology. These data suggest that H. vitripennis follows the basic predictions of marginal value theorem. In distribution tests, H. vitripennis were more abundant in the patch center than patch edges in the isolated L. indica patch, but in a patch bordering cottonwood (Populus sp.) and peach (P. persica), H. vitripennis numbers were generally higher along the edges of the patch. These data suggest that alternate hosts bordering cropping systems may be important to the spatial dynamics of H. vitripennis. Implications of these spatial observations on the biology of H. vitripennis and potential control methods are discussed.

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.

Competitive exclusion of a worldwide invasive pest by a native. Quantifying competition between two phytophagous insects on two host plant species.

1. High competitive ability is believed to be an important characteristic of invasive species. Many animal studies have compared the competitive ability of invasive species with a native species that is being displaced, but few have looked at systems where an invasive species has failed to establish itself. These types of studies are important to determine if competition is relevant not only to invading species but also to the biotic resistance of a community. 2. The thrips species F. occidentalis is a highly invasive pest that has spread from its original range (the western states of the USA) to a worldwide distribution. Despite this, F. occidentalis is largely absent or occurs in low numbers in the eastern states of the USA, where the native F. tritici dominates. It is possible that F. tritici is competitively excluding F. occidentalis from this region. 3. Larval competition between these two thrips species was tested on two known plant hosts, Capsicum annuum (a crop plant), and Raphanus raphanistrum (an invasive weed), using a response surface design with number of larvae surviving as the response variable. The response surface design allowed competition models to be fit to data using maximum likelihood estimation, thus generating quantitative values for interspecific competition. 4. On both plant hosts, the native F. tritici did not experience significant interspecific competition from the invasive F. occidentalis. In contrast, F. occidentalis did experience significant interspecific competition from F. tritici. Competition from F. tritici larvae on F. occidentalis larvae was estimated to be 1.72 times (on C. annuum) and 1.76 times (on R. raphanistrum) the effect of intraspecific competition. The invasive F. occidentalis appears to be competitively excluded by the native F. tritici. 5. This study confirms the importance of competition in the biotic resistance of a community and is one of the few animal studies to not only test for competition in an apparently resistant ecosystem but also to quantify the level of interspecific competition between two animal species.