Biosecurity and post-arrival pathways in New Zealand: relating alien organism detections to tourism indicators

Between-country tourism is established as a facilitator of the spread of invasive alien species; however, little attention has been paid to the question of whether tourism contributes to the arrival and subsequent dispersal of exotic organisms within national borders. To assess the strength of evidence that tourism is a driver for the accidental introducing and dispersal of exotic organisms, we sourced three national databases covering the years 2011 to 2017, namely international and domestic hotel guest nights and national population counts, along with records of exotic organism detections collected by the Ministry for Primary Industries, New Zealand’s government agency that oversees biosecurity. We fitted statistical models to assess the strength of the relationship between monthly exotic organism interception rate, guest nights and population, the latter as a baseline. The analysis showed that levels of incursion detection were significantly related to tourism records reflecting the travel of both international and domestic tourists, even when population was taken into account. There was also a significant positive statistical correlation between the levels of detection of exotic organisms and human population. The core take-home message is that a key indicator of within-country human population movement, namely the number of nights duration spent in specific accommodation, is statistically significantly correlated to the contemporaneous detection of exotic pests. We were unable to distinguish between the effects of international as opposed to domestic tourists. We conclude that this study provides evidence of impact of within-country movement upon the internal spread of exotic species, although important caveats need to be considered.

Analysing human population movement data for malaria control and elimination

Background Human population movement poses a major obstacle to malaria control and elimination. With recent technological advances, a wide variety of data sources and analytical methods have been used to quantify human population movement (HPM) relevant to control and elimination of malaria. Methods The relevant literature and selected studies that had policy implications that could help to design or target malaria control and elimination interventions were reviewed. These studies were categorized according to spatiotemporal scales of human mobility and the main method of analysis. Results Evidence gaps exist for tracking routine cross-border HPM and HPM at a regional scale. Few studies accounted for seasonality. Out of twenty included studies, two studies which tracked daily neighbourhood HPM used descriptive analyses as the main method, while the remaining studies used statistical analyses or mathematical modelling. Conclusion Although studies quantified varying types of human population movement covering different spatial and temporal scales, methodological gaps remain that warrant further studies related to malaria control and elimination.

Comparing metapopulation dynamics of infectious diseases under different models of human movement

Newly available datasets present exciting opportunities to investigate how human population movement contributes to the spread of infectious diseases across large geographical distances. It is now possible to construct realistic models of infectious disease dynamics for the purposes of understanding global-scale epidemics. Nevertheless, a remaining unanswered question is how best to leverage the new data to parameterize models of movement, and whether one’s choice of movement model impacts modeled disease outcomes. We adapt three well-studied models of infectious disease dynamics, the susceptible–infected–recovered model, the susceptible–infected–susceptible model, and the Ross–Macdonald model, to incorporate either of two candidate movement models. We describe the effect that the choice of movement model has on each disease model’s results, finding that in all cases, there are parameter regimes where choosing one movement model instead of another has a profound impact on epidemiological outcomes. We further demonstrate the importance of choosing an appropriate movement model using the applied case of malaria transmission and importation on Bioko Island, Equatorial Guinea, finding that one model produces intelligible predictions of R0, whereas the other produces nonsensical results.

Human population movement can impede the elimination of soil-transmitted helminth transmission in regions with heterogeneity in mass drug administration coverage and transmission potential between villages: a metapopulation analysis

Background Soil-transmitted helminth (STH) infections affect predominantly socio-economically disadvantaged populations in sub-Saharan Africa, East Asia and the Americas. Previous mathematical modelling studies have evaluated optimal intervention strategies to break STH transmission in clusters of villages. These studies assumed that villages are closed independent units with no movement of people in or out of communities. Here we examine how human population movement, for example, of seasonal migrant labourers, affect the outcome of mass drug administration (MDA) programmes. Results We used a stochastic individual-based metapopulation model to analyse the impact of human population movement at varying rates on STH elimination efforts. Specifically, we looked at seasonal clumped movement events of infected individuals into a village. We showed that even if on average 75% of the entire resident population within a village are treated, an annual rate of 2–3% of the population arriving from an untreated source village can reduce the probability of STH elimination to less than 50% in high-prevalence settings. If a village is infection-free, an annual movement rate of 2–3% from an infected source village imposes a risk of re-introduction of STH of 75% or higher, unless the prevalence in the source village is less than 20%. Even a single arrival of 2–3% of the population can impose a risk of re-introducing STH of 50% or greater depending on the prevalence in the source village. The risk of re-introduction also depends on both the age group of moving individuals and STH species, since the pattern of cross-sectional age-prevalence and age-intensity profiles of infection in the human host are species-specific. Conclusions Planning for STH elimination programmes should account for human mobility patterns in defined regions. We recommend that individuals arriving from areas with ongoing STH transmission should receive preventive chemotherapy for STHs. This can most easily be implemented if migration is seasonal and overlaps with treatment rounds, e.g. seasonal migrant labour. Moreover, transmission hotspots in or near treatment clusters should be eliminated, for example, by implementing appropriate water, sanitation and hygiene (WASH) measures and targeting treatment to individuals living in hotspots.

Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment

Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.

Revisiting Key Entry Routes of Human Epidemic Arboviruses into the Mainland Americas through Large-Scale Phylogenomics

The rapid worldwide spread of chikungunya (CHIKV), dengue (DENV), and Zika (ZIKV) viruses have raised great international concern. Knowledge about the entry routes and geographic expansion of these arboviruses to the mainland Americas remain incomplete and controversial. Epidemics caused by arboviruses continue to cause socioeconomic burden globally, particularly in countries where vector control is difficult due to climatic or infrastructure factors. Understanding how the virus circulates and moves from one country to another is of paramount importance to assist government and health officials in anticipating future epidemics, as well as to take steps to help control or mitigate the spread of the virus. Through the analyses of the sequences of arbovirus genomes collected at different locations over time, we identified patterns of accumulated mutations, being able to trace routes of dispersion of these viruses. Here, we applied robust phylogenomic methods to trace the evolutionary dynamics of these arboviruses with special focus on Brazil, the epicenter of these triple epidemics. Our results show that CHIKV, DENV-1–4, and ZIKV followed a similar path prior to their first introductions into the mainland Americas, underscoring the need for systematic arboviral surveillance at major entry points of human population movement between countries such as airports and seaports.

The contemporary distribution of Trypanosoma cruzi infection in humans, alternative hosts and vectors

Chagas is a potentially fatal chronic disease affecting large numbers of people across the Americas and exported throughout the world through human population movement. It is caused by the Trypanosoma cruzi parasite, which is transmitted by triatomine vectors to humans and a wide range of alternative host species. The database described here was compiled to allow the risk of vectorial transmission to humans to be mapped using geospatial models. The database collates all available records, published since 2003, for prevalence and occurrence of infection in humans, vectors and alternative hosts, and links each record to a defined time and location. A total of 16,802 records of infection have been extracted from the published literature and unpublished sources. The resulting database can be used to improve our understanding of the geographic variation in vector infection prevalence and to estimate the risk of vectorial transmission of T. cruzi to humans.