This paper focuses on unravelling the 1st millennium AD in the present-day Netherlands and the applicability of modelling when studying the past. By presenting the results of several studies analysing changes (or persistence) in connectivity and habitation patterns, the significance of these findings for (spatial) modelling is derived. The transition between the Roman and early-medieval periods is particularly interesting in this respect as it is characterised by severe pan-European political, socio-economic and demographic changes. Additionally, recent studies in geosciences increasingly point to marked climatic and landscape changes, such as river avulsions and floods, occurring at the same time. The extent to which these environmental and cultural dynamics were entwined and mutually influential is generally unknown, especially on larger-scale levels. Lowlands, such as the Netherlands, are especially suited to study these complex interactions since boundary conditions, i.e. the set of conditions required for maintaining the existing equilibrium in a region, in such areas are particularly sensitive to change.

In this paper the combined results of several recently developed landscape-archaeological models are presented. These models spatially analyse natural and cultural dynamics in five manifestations: route networks, long-distance transport, settlement patterns, palaeodemographics and land-use systems. Combined, these manifestations provide information on connectivity, persistence and habitation, key concepts for the cultural landscape as a whole. Results show that only by integrating these modelling outcomes is it possible to reconstruct boundary conditions and high-resolution spatio-temporal frameworks for cultural-landscape change. Equally, these models invite reflection on their applicability and, as such, point to the need for new theoretical framing and the development of more multi-proxy, evidence-based and transdisciplinary research approaches in archaeology. The evident interrelationship between cultural and natural-landscape dynamics necessitates a more integrated and transparent research attitude, covering multiple scales and studying the cultural landscape as a whole. Only then can models reflect historical reality as closely as possible.

Spatial models are increasingly being used to target the most suitable areas for biodiversity conservation. This study investigates how the spatial tool Marxan with Zones (MARZONE) can be used to support the design of cost-effective biodiversity conservation policy. New in this study is the spatial analysis of the costs and effectiveness of different agro-environmental measures (AEMs) for habitat and biodiversity conservation in the Montado ecosystem in Portugal. A distinction is made between the financial costs paid to participating landowners and farmers for adopting AEMs and the broader economic opportunity costs of the corresponding land-use changes. Habitat and species conservation targets are furthermore defined interactively with the local government agency responsible for the management of protected areas, while the costs of agro-forestry activities and alternative land uses are estimated in direct consultation with local landowners. MARZONE identifies the spatial distribution of priority areas for conservation and the associated costs, some of which overlap with existing protected areas. These results provide useful insights into the trade-offs between nature conservation and the opportunity costs of protecting ecologically vulnerable areas, helping to improve current and future conservation policy design.

In both historical and modern conflicts, space plays a critical role in how interactions occur over time. Despite its importance, the spatial distribution of adversaries has often been neglected in mathematical models of conflict. In this paper, we propose an entropy-maximising spatial interaction method for disaggregating the impact of space, employing a general notion of ‘threat’ between two adversaries. This approach addresses a number of limitations that are associated with partial differential equation approaches to spatial disaggregation. We use this method to spatially disaggregate the Richardson model of conflict escalation, and then explore the resulting model with both analytical and numerical treatments. A bifurcation is identified that dramatically influences the resulting spatial distribution of conflict and is shown to persist under a range of model specifications. Implications of this finding for real-world conflicts are discussed.

The increase in pertussis cases in Minnesota in the last decade has been mainly attributed to the switch from whole cell to acellular pertussis [as part of the diphtheria, tetanus and acellular pertussis vaccine (DTaP)]. It is unclear, however, to what degree community-level risk factors also contribute. Understanding these factors can help inform public health policy-makers about where else to target resources. We performed an ecological analysis within Minnesota to identify risk factors at the county level using a Bayesian Poisson generalized linear areal model to account for spatial dependence. Univariate analyses suggested an association between increased pertussis rates at the county level and white maternal ethnicity, being US born, urban counties and average household size. In the multivariable analysis, the rate of pertussis was 1·79 times greater for urban vs. rural counties and 4·75 times greater for counties with a one-person larger average household size. Pertussis rates in counties with higher (i.e. 4+DTaP) receipt in children were 0·97 times lower. Examining county-level factors associated with varying levels of pertussis may help identify those counties that would most benefit from targeted interventions and increased resource allocation.

There is interest in the potential of companion animal surveillance to provide data to improve pet health and to provide early warning of environmental hazards to people. We implemented a companion animal surveillance system in Calgary, Alberta and the surrounding communities. Informatics technologies automatically extracted electronic medical records from participating veterinary practices and identified cases of enteric syndrome in the warehoused records. The data were analysed using time-series analyses and a retrospective space–time permutation scan statistic. We identified a seasonal pattern of reports of occurrences of enteric syndromes in companion animals and four statistically significant clusters of enteric syndrome cases. The cases within each cluster were examined and information about the animals involved (species, age, sex), their vaccination history, possible exposure or risk behaviour history, information about disease severity, and the aetiological diagnosis was collected. We then assessed whether the cases within the cluster were unusual and if they represented an animal or public health threat. There was often insufficient information recorded in the medical record to characterize the clusters by aetiology or exposures. Space–time analysis of companion animal enteric syndrome cases found evidence of clustering. Collection of more epidemiologically relevant data would enhance the utility of practice-based companion animal surveillance.

The objective of this study was to examine temporal and regional variations of sporadic Creutzfeldt–Jakob disease (sCJD) in a retrospective study using Japanese national surveillance data from 2001 to 2010. We calculated the incidence of sCJD by age and sex, derived the standardized incidence in each of the 47 prefectures, and performed spatial disease clustering analysis. The average annual incidence of sCJD was 1·026 per million in men (637 patients) and 1·132 per million in women (733 patients), a significant sex difference after adjustment for age (P = 0·001). The ratios of familial CJD to sCJD apparently increased between 2001–2005 and 2006–2010, possibly as a result of the nationwide introduction of genetic testing after 2006. Based on the data of 2006–2010, certain geographical clusters of sCJD were identified. The incidence of sCJD was higher in several specific prefectures compared to the national average. Thus, sCJD appears to have regional variations, suggesting the existence of genetic or region-specific factors affecting the incidence of the disease.

China has recently experienced a marked increase in the incidence of hand, foot and mouth disease (HFMD). Effective spatio-temporal monitoring of HFMD incidence is important for successful implementation of control and prevention measures. This study monitored county-level HFMD reported incidence rates for Sichuan province, China by examining spatio-temporal patterns. County-level data on HFMD daily cases between January 2008 and December 2013 were obtained from the China Information System for Disease Control and Prevention. We first conducted purely temporal and purely spatial descriptive analyses to characterize the distribution patterns of HFMD. Then, the global Moran's I statistic and space–time scan statistic were used to detect the spatial autocorrelation and identify the high-risk clusters in each year, respectively. A total of 212267 HFMD cases were reported in Sichuan province during the study period (annual average incidence 43·65/100000), and the incidence seasonal peak was between April and July. Relatively high incidence rates appeared in the northeastern–southwestern belt. HFMD had positive spatial autocorrelation at the county level with global Moran's I increasing from 0·27 to 0·52 (P < 0·001). Spatio-temporal cluster analysis detected six most-likely clusters and several secondary clusters from 2008 to 2013. The centres of the six most-likely clusters were all located in the provincial capital city Chengdu. Chengdu and its neighbouring cities had always been spatio-temporal clusters, which indicated the need for further intensive space–time surveillance. Allocating more resources to these areas at suitable times might help to reduce HFMD incidence more effectively.

Poaching with snares has been identified as the main cause of decline of the endemic roan antelope Hippotragus equinuslangheldi in Ruma National Park, Kenya, from > 200 in 1979 to 37 in 2009. However, the spatial snaring patterns in the Park are not clearly understood. The focus of our study was to map the spatial distribution of snares in the Park and to identify the factors influencing this distribution, to develop effective methods of wildlife protection. Using data collected from 56 sample plots during 2006–2008, coupled with geographical information system techniques, we investigated the association between the occurrence of snares and the distribution of geographical features (slope, elevation), infrastructure (roads, fences), essential resources for wildlife (water, salt licks, forage), roan locations and wildlife density. Ripley's L function for assessing complete spatial randomness indicated that snares occurred in clumps (hotspots) up to 4 km apart. Negative binomial regression indicated that these hotspots occurred (1) near water resources, salt licks and the Park boundary, (2) far from roan locations and Park roads, (3) in areas with low gradients and low wildlife density, and (4) in areas with burned vegetation. We recommend concentrating routine security patrol efforts and resources on snare hotspots to reduce snaring and to protect the roan antelope and other threatened wildlife.

Understanding the spatial distribution of disease is critical for effective disease control. Where formal address networks do not exist, tracking spatial patterns of clinical disease is difficult. Geolocation strategies were tested at rural health facilities in western Kenya. Methods included geocoding residence by head of compound, participatory mapping and recording the self-reported nearest landmark. Geocoding was able to locate 72·9% [95% confidence interval (CI) 67·7–77·6] of individuals to within 250 m of the true compound location. The participatory mapping exercise was able to correctly locate 82·0% of compounds (95% CI 78·9–84·8) to a 2 × 2·5 km area with a 500 m buffer. The self-reported nearest landmark was able to locate 78·1% (95% CI 73·8–82·1) of compounds to the correct catchment area. These strategies tested provide options for quickly obtaining spatial information on individuals presenting at health facilities.

Water supply-associated cryptosporidiosis outbreaks have decreased in England since the application of risk reduction measures to public water supplies. We hypothesized that smaller outbreaks were occurring which could be better detected by enhanced surveillance. Rolling analysis of detailed questionnaire data was applied prospectively in a population of 2·2 million in the south of England in 2009 and 2010. Detection of spatiotemporal clusters using SaTScan was later undertaken retrospectively. Together these approaches identified eight outbreaks, compared to an expectation of less than one based on national surveillance data. These outbreaks were small and associated with swimming pool use or, less commonly, direct (e.g. petting-farm) contact with animals. These findings suggest that frequent small-scale transmission in swimming pools is an important contributor to disease burden. Identification of swimming pool-level risk factors may inform preventative measures. These findings and the approaches described may be applicable to many other populations and to some other diseases.

This paper examines the importance of environmental factors (mosquito pools and home foreclosures) in human West Nile virus (WNV) transmission in California and Colorado. The role of environmental factors is investigated by applying an instrumental variable technique to a spatial filtering random-effects negative binomial model to correct for both spatial autocorrelation and endogeneity. The results suggest that mosquito pools and home foreclosures are significant in explaining the prevalence of human WNV. An innovative aspect of this research is that it emphasizes the role of home foreclosures in WNV transmission and in the allocation of resources. Knowledge of the factors associated with WNV prevalence is crucial for abatement of future outbreaks. The results suggest that more resources should be allocated to areas that have a high number of home foreclosures and mosquito pools for surveillance and mitigation of the disease.

The aim of this study was to monitor the spatio-temporal spread of influenza incidence in Spain during the 2009 pandemic and the following two influenza seasons 2010–2011 and 2011–2012 using a Bayesian Poisson mixed regression model; and implement this model of geographical analysis in the Spanish Influenza Surveillance System to obtain maps of influenza incidence for every week. In the pandemic wave the maps showed influenza activity spreading from west to east. The 2010–2011 influenza epidemic wave plotted a north-west/south-east pattern of spread. During the 2011–2012 season the spread of influenza was geographically heterogeneous. The most important source of variability in the model is the temporal term. The model of spatio-temporal spread of influenza incidence is a supplementary tool of influenza surveillance in Spain.

This study investigated the extent to which proximity to cattle and weather events in Alberta predispose human populations to E. coli O157 disease. Cases of human E. coli O157 infection in Alberta between 2004 and 2011 were obtained from the province's Communicable Disease Reporting System and Discharge Abstract Database. Regression models based on spatial area incorporated human infection data with livestock and weather covariates. A variety of regression models were applied (i.e. least squares, spatial lag/error, Poisson, negative binomial) to test the most appropriate approach. Ratios for the total number of calves, bulls and beef cows to human population were highlighted as significant cattle density variables in all final best-fitting models. Weather variables were not significant in final regression models averaged over the full study period. Our results provide evidence of a significant association between measures of cattle density and human E. coli O157 disease in Alberta.

Killer whales have been described in the Gulf of Cadiz, southern Spain, in spring and in the Strait of Gibraltar in summer. A total of 11,276 cetaceans sightings coming from different sources (dedicated research surveys, whale watching companies and opportunistic observations) were used to create two presence–‘pseudo-absence’ predictive generalized additive models (GAM), where presence data were defined as sightings of killer whales and ‘pseudo-absence’ data as sightings of other cetacean species. One model was created using spring data when killer whales’ main prey, Atlantic bluefin tuna, enter the Mediterranean Sea, and the other model used summer data when Atlantic bluefin tuna return to the Atlantic Ocean. Both model predictions show that killer whales are highly associated with a probable distribution of bluefin tuna during their migration throughout the study area, constraining their distribution to the Gulf of Cadiz in spring and the Strait of Gibraltar in spring and summer. Knowledge of the distribution of killer whales in the study area is essential to establish conservation measures for this population.

We present a stochastic dynamical model for the transmission of dengue that considers the co-evolution of the spatial dynamics of the vectors (Aedes aegypti) and hosts (human population), allowing the simulation of control strategies adapted to the actual evolution of an epidemic outbreak. We observed that imposing restrictions on the movement of infected humans is not a highly effective strategy. In contrast, isolating infected individuals with high levels of compliance by the human population is efficient even when implemented with delays during an ongoing outbreak. We also studied insecticide-spraying strategies assuming different (hypothetical) efficiencies. We observed that highly efficient fumigation strategies seem to be effective during an outbreak. Nevertheless, taking into account the controversial results on the use of spraying as a single control strategy, we suggest that carrying out combined strategies of fumigation and isolation during an epidemic outbreak should account for a suitable strategy for the attenuation of epidemic outbreaks.

Tuberculosis (TB) in livestock, caused by Mycobacterium bovis, persists in many countries. In the UK and Ireland, efforts to control TB through culling of badgers (Meles meles), the principal wildlife host, have failed and there is significant interest in vaccination of badgers as an alternative or complementary strategy. Using a simulation model, we show that where TB is self-contained within the badger population and there are no external sources of infection, limited-duration vaccination at a high level of efficacy can reduce or even eradicate TB from the badger population. However, where sources of external infection persist, benefits in TB reduction in badgers can only be achieved by ongoing, annual vaccination. Vaccination is likely to be most effective as part of an integrated disease management strategy incorporating a number of different approaches across the entire host community.

Campylobacteriosis is the commonest cause of bacterial enteritis in England yet the epidemiology of apparently sporadic cases is not well understood. Here we evaluated the feasibility of applying a space-time cluster detection method to routine laboratory surveillance data in the North East of England by simulating prospective weekly space-time cluster detection using SaTScan as if it had been performed for 2008–2011. From the 209 simulated weekly runs using a circular window, 20 distinct clusters were found which contained a median of 30 cases (interquartile range 15–66) from a median population of ∼134 000 persons. This corresponds to detection of a new cluster every 10 weeks. We found significant differences in age, sex and deprivation score distributions between areas within clusters compared to those without. The results of this study suggest that space-time detection of Campylobacter clusters could be used to find groups of cases amenable to epidemiological investigation.

Salmonella is the second most commonly reported human foodborne pathogen in England and Wales, and antimicrobial-resistant strains of Salmonella are an increasing problem in both human and veterinary medicine. In this work we used a generalized linear spatial model to estimate the spatial and temporal patterns of antimicrobial resistance in Salmonella Typhimurium in England and Wales. Of the antimicrobials considered we found a common peak in the probability that an S. Typhimurium incident will show resistance to a given antimicrobial in late spring and in mid to late autumn; however, for one of the antimicrobials (streptomycin) there was a sharp drop, over the last 18 months of the period of investigation, in the probability of resistance. We also found a higher probability of resistance in North Wales which is consistent across the antimicrobials considered. This information contributes to our understanding of the epidemiology of antimicrobial resistance in Salmonella.

Despite recent improvements, New Zealand still has one of the highest per-capita incidence rates of campylobacteriosis in the world. To reduce the incidence, a thorough understanding of the epidemiology of infection is needed. This retrospective analysis of 36 000 notified human cases during a high-risk period between 2001 and 2007 explored the spatial and temporal determinants of Campylobacter notifications at a fine spatial scale in order to improve understanding of the complex epidemiology. Social deprivation was associated with a decreased risk of notification, whereas urban residence was associated with an increased risk. However, for young children rural residence was a risk factor. High dairy cattle density was associated with an increased risk of notification in two of the three regions investigated. Campylobacter notification patterns exhibit large temporal variations; however, few factors were associated with periods of increased risk, in particular temperature did not appear to drive the seasonality in campylobacteriosis.