Risk maps: transmission and burden of vector-borne diseases

Bluetongue Risk Map for Vaccination and Surveillance Strategies in India

Bluetongue virus (BTV, Sedoreoviridae: Orbivirus) causes an economically important disease, namely, bluetongue (BT), in domestic and wild ruminants worldwide. BTV is endemic to South India and has occurred with varying severity every year since the virus was first reported in 1963. BT can cause high morbidity and mortality to sheep flocks in this region, resulting in serious economic losses to subsistence farmers, with impacts on food security. The epidemiology of BTV in South India is complex, characterized by an unusually wide diversity of susceptible ruminant hosts, multiple vector species biting midges (Culicoides spp., Diptera: Ceratopogonidae), which have been implicated in the transmission of BTV and numerous co-circulating virus serotypes and strains. BT presence data (1997-2011) for South India were obtained from multiple sources to develop a presence/absence model for the disease. A non-linear discriminant analysis (NLDA) was carried out using temporal Fourier transformed variables that were remotely sensed as potential predictors of BT distribution. Predictive performance was then characterized using a range of different accuracy statistics (sensitivity, specificity, and Kappa). The top ten variables selected to explain BT distribution were primarily thermal metrics (land surface temperature, i.e., LST, and middle infrared, i.e., MIR) and a measure of plant photosynthetic activity (the Normalized Difference Vegetation Index, i.e., NDVI). A model that used pseudo-absence points, with three presence and absence clusters each, outperformed the model that used only the recorded absence points and showed high correspondence with past BTV outbreaks. The resulting risk maps may be suitable for informing disease managers concerned with vaccination, prevention, and control of BT in high-risk areas and for planning future state-wide vector and virus surveillance activities.

Seasonality and the persistence of vector-borne pathogens

Many vector-borne diseases are affected by the seasonality of the environment. Yet, seasonality can act on distinct steps of the life cycle of the pathogen and it is often difficult to predict the influence of the periodic fluctuations of the environment on the basic reproduction ratio R0 of vector-borne pathogens. Here, we analyse a general vector-borne disease model and we account for periodic fluctuations of different components of the pathogen's life cycle. We develop a perturbation analysis framework to obtain useful approximations to evaluate the overall consequences of seasonality on the R0 of the pathogen. This analysis reveals when seasonality is expected to increase or to decrease pathogen persistence. We show that seasonality in vector density or in the biting rate of the vector can have opposite effects on persistence and we provide a useful biological explanation for this result based on the covariance between key compartments of the epidemiological model. This framework could be readily extended to explore the influence of seasonality on other components of the life cycle of vector-borne pathogens.

Elevation determines the spatial risk of Anthrax outbreaks in Karnataka, India

Anthrax is an economically important livestock disease affecting subsistence farmers and it is of zoonotic importance. Anthrax is endemic in many states of India including Karnataka. Identification of spatial risk factors for occurrence of anthrax and development of predictive risk maps are required for planning adequate vaccination in high-risk areas as well as targeted surveillance activities in animals, humans and environment. In this study, village level anthrax outbreak locations from Karnataka (1997-2016) were geo-referenced and predictive risk map was developed using temporally Fourier Processed remotely sensed variables. A non-linear discriminant analysis approach was used to develop the risk map for Karnataka. Elevation was identified as top predictor variable in the 10 variables selected. The predicted risk map showed good accuracy and validation statistics when evaluated using different metrics (Kappa, sensitivity, specificity, AUC). The predicted risk map also showed good correspondence with past outbreaks. Further, we used Bayesian Penalised spline method to estimate species response curves for top 10 variables selected. The validated risk map can be used in planning vaccination strategy and surveillance in high-risk areas.

Landscape epidemiology of neglected tick-borne pathogens in central Europe

Studies of tick-borne diseases (TBDs) in Europe focus on pathogens with principal medical importance (e.g. Lyme disease and tick-borne encephalitis), but we have limited epidemiological information on the neglected pathogens, such as the members of the genera Anaplasma, Rickettsia, Babesia and Candidatus Neoehrlichia mikurensis. Here, we integrated an extensive field sampling, laboratory analysis and GIS models to provide first publicly available information on pathogen diversity, prevalence and infection risk for four overlooked zoonotic TBDs in the Czech Republic. In addition, we assessed the effect of landscape variables on the abundance of questing ticks at different spatial scales and examined whether pathogen prevalence increased with tick density. Our data from 13,340 ticks collected in 142 municipalities showed that A. phagocytophilum (MIR = 3.5%) and Ca. Neoehrlichia mikurensis (MIR = 4.0%) pose geographically uneven risks with localized hotspots, while Rickettsia (MIR = 4.9%) and Babesia (MIR = 1.1%) had relatively homogeneous spatial distribution. Landscape variables had significant effect on tick abundance up to the scale of 1 km around the sampling sites. Questing ticks responded positively to landscape diversity and configuration, especially to forest patch density that strongly correlates with the amount of woodland-grassland ecotones. For all four pathogens, we found higher prevalence in places with higher densities of ticks, confirming the hypothesis that tick abundance amplifies the risk of TB infection. Our findings highlight the importance of landscape parameters for tick vectors, likely due to their effect on small vertebrates as reservoir hosts. Future studies should explicitly investigate the combined effect of landscape parameters and the composition and population dynamics of hosts on the host-vector-pathogen system.

Winter is coming: Pathogen emergence in seasonal environments

Many infectious diseases exhibit seasonal dynamics driven by periodic fluctuations of the environment. Predicting the risk of pathogen emergence at different points in time is key for the development of effective public health strategies. Here we study the impact of seasonality on the probability of emergence of directly transmitted pathogens under different epidemiological scenarios. We show that when the period of the fluctuation is large relative to the duration of the infection, the probability of emergence varies dramatically with the time at which the pathogen is introduced in the host population. In particular, we identify a new effect of seasonality (the winter is coming effect) where the probability of emergence is vanishingly small even though pathogen transmission is high. We use this theoretical framework to compare the impact of different preventive control strategies on the average probability of emergence. We show that, when pathogen eradication is not attainable, the optimal strategy is to act intensively in a narrow time interval. Interestingly, the optimal control strategy is not always the strategy minimizing R₀, the basic reproduction ratio of the pathogen. This theoretical framework is extended to study the probability of emergence of vector borne diseases in seasonal environments and we show how it can be used to improve risk maps of Zika virus emergence.

Seasonality drives fluctuations in the probability of pathogen emergence, with dramatic consequences for public health and agriculture. We show that this probability of pathogen emergence can be vanishingly small before the low transmission season. We derive the conditions for the existence of this winter is coming effect and identify optimal control strategies that minimize the risk of pathogen emergence. We generalize this framework to account for different forms of environmental variations, different modes of control and complex pathogen life cycles. We illustrate how this framework can be used to improve predictions of Zika emergence at different points in space and time.

Human Trypanosoma cruzi infection is driven by eco-social interactions in rural communities of the Argentine Chaco

The transmission of Trypanosoma cruzi to humans is determined by multiple ecological, socio-economic and cultural factors acting at different scales. Their effects on human infection with T. cruzi have often been examined separately or using a limited set of ecological and socio-demographic variables. Herein, we integrated the ecological and social dimensions of human infection risk with the spatial distribution patterns of human and vector (Triatoma infestans) infection in rural communities of the Argentine Chaco composed of indigenous people (90% Qom) and a creole minority. We conducted serosurveys in 470 households aiming at complete population enumeration over 2012–2015. The estimated seroprevalence of T. cruzi prior to the implementation of an insecticide spraying campaign (2008) was 29.0% (N = 1,373 in 301 households), and was twice as large in Qom than creoles. Using generalized linear mixed models, human seropositive cases significantly increased with infected triatomine abundance, having a seropositive household co-inhabitant and household social vulnerability (a multidimensional index of poverty), and significantly decreased with increasing host availability in sleeping quarters (an index summarizing the number of domestic hosts for T. infestans). Vulnerable household residents were exposed to a higher risk of infection even at low infected-vector abundances. The risk of being seropositive increased significantly with house infestation among children from stable households, whereas both variables were not significantly associated among children from households exhibiting high mobility within the communities, possibly owing to less consistent exposures. Human infection was clustered by household and at a larger spatial scale, with hotspots of human and vector infection matching areas of higher social vulnerability. These results were integrated in a risk map that shows high-priority areas for targeted interventions oriented to suppress house (re)infestations, detect and treat infected children, and thus reduce the burden of future disease.

Chagas disease is one of the main neglected tropical diseases (NTDs) affecting vulnerable communities in Latin America where transmission by triatomine vectors still occurs. Access to diagnosis and treatment is one of the remaining challenges for sustainable control of Chagas disease in endemic areas. In this study, we integrated the ecological and social determinants of human infection with the spatial component to identify individuals, households and geographic sectors at higher risk of infection. We found that human infection was more prevalent in indigenous people compared to creoles and increased with the abundance of infected vectors and with household social vulnerability (a multidimensional index of poverty). We also found that the social factors modulated the effect of the abundance of infected vectors: vulnerable-household residents were exposed to a higher risk of infection even at low infected-vector abundance, and human mobility within the area determined a lower and more variable exposure to the vector over time. These results were integrated in a risk map that showed high-priority areas, which can be used in designing cost-effective serological screening strategies adapted to resource-constrained areas.

Using imperfect data in predictive mapping of vectors: a regional example of Ixodes ricinus distribution

BACKGROUND

Knowledge of Ixodes ricinus tick distribution is critical for surveillance and risk management of transmissible tick-borne diseases such as Lyme borreliosis. However, as the ecology of I. ricinus is complex, and robust long-term geographically extensive distribution tick data are limited, mapping often relies on datasets collected for other purposes. We compared the modelled distributions derived from three datasets with information on I. ricinus distribution (quantitative I. ricinus count data from scientific surveys; I. ricinus presence-only data from public submissions; and a combined I. ricinus dataset from multiple sources) to assess which could be reliably used to inform Public Health strategy. The outputs also illustrate the strengths and limitations of these three types of data, which are commonly used in mapping tick distributions.

METHODS

Using the Integrated Nested Laplace algorithm we predicted I. ricinus abundance and presence-absence in Scotland and tested the robustness of the predictions, accounting for errors and uncertainty.

RESULTS

All models fitted the data well and the covariate predictors for I. ricinus distribution, i.e. deer presence, temperature, habitat, index of vegetation, were as expected. Differences in the spatial trend of I. ricinus distribution were evident between the three predictive maps. Uncertainties in the spatial models resulted from inherent characteristics of the datasets, particularly the number of data points, and coverage over the covariate range used in making the predictions.

CONCLUSIONS

Quantitative I. ricinus data from scientific surveys are usually considered to be gold standard data and we recommend their use where high data coverage can be achieved. However in this study their value was limited by poor data coverage. Combined datasets with I. ricinus distribution data from multiple sources are valuable in addressing issues of low coverage and this dataset produced the most appropriate map for national scale decision-making in Scotland. When mapping vector distributions for public-health decision making, model uncertainties and limitations of extrapolation need to be considered; these are often not included in published vector distribution maps. Further development of tools to better assess uncertainties in the models and predictions are necessary to allow more informed interpretation of distribution maps.

Model of Risk of Exposure to Lyme Borreliosis and Tick-Borne Encephalitis Virus-Infected Ticks in the Border Area of the Czech Republic (South Bohemia) and Germany (Lower Bavaria and Upper Palatinate)

In Europe, Lyme borreliosis (LB) and tick-borne encephalitis (TBE) are the two vector-borne diseases with the largest impact on human health. Based on data on the density of host-seeking Ixodes ricinus ticks and pathogen prevalence and using a variety of environmental data, we have created an acarological risk model for a region where both diseases are endemic (Czech Republic-South Bohemia and Germany-Lower Bavaria, Upper Palatinate). The data on tick density were acquired by flagging 50 sampling sites three times in a single season. Prevalence of the causative agents of LB and TBE was determined. Data on environmental variables (e.g., altitude, vegetation cover, NDVI, land surface temperature) were obtained from various sources and processed using geographical information systems. Generalized linear models were used to estimate tick density, probability of tick infection, and density of infected ticks for the whole area. A significantly higher incidence of human TBE cases was recorded in South Bohemia compared to Bavarian regions, which correlated with a lower tick density in Bavaria. However, the differences in pathogen prevalence rates were not significant. The model outputs were made available to the public in the form of risk maps, indicating the distribution of tick-borne disease risk in space.

Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016

Vector-borne diseases (VBDs) infect over one billion people and are responsible for over one million deaths each year, globally. Chikungunya (CHIK) and Dengue Fever (DENF) are emerging VBDs due to overpopulation, increases in urbanization, climate change, and other factors. Colombia has recently experienced severe outbreaks of CHIK AND DENF. Both viruses are transmitted by the Aedes mosquitoes and are preventable with a variety of surveillance and vector control measures (e.g. insecticides, reduction of open containers, etc.). Spatiotemporal statistics can facilitate the surveillance of VBD outbreaks by informing public health officials where to allocate resources to mitigate future outbreaks. We utilize the univariate Kulldorff space-time scan statistic (STSS) to identify and compare statistically significant space-time clusters of CHIK and DENF in Colombia during the outbreaks of 2015 and 2016. We also utilize the multivariate STSS to examine co-occurrences (simultaneous excess incidences) of DENF and CHIK, which is critical to identify regions that may have experienced the greatest burden of VBDs. The relative risk of CHIK and DENF for each Colombian municipality belonging to a univariate and multivariate cluster is reported to facilitate targeted interventions. Finally, we visualize the results in a three-dimensional environment to examine the size and duration of the clusters. Our approach is the first of its kind to examine multiple VBDs in Colombia simultaneously, while the 3D visualizations are a novel way of illustrating the dynamics of space-time clusters of disease.

Environmental Determinants of the Distribution of Chagas Disease Vector Triatoma dimidiata in Colombia

Triatoma dimidiata (Hemiptera: Reduviidae) is a secondary vector of Trypanosoma cruzi in Colombia and represents an important epidemiological risk mainly in the central and oriental regions of the country where it occupies sylvatic, peridomestic, and intradomestic ecotopes, and because of this complex distribution, its distribution and abundance could be conditioned by environmental factors. In this work, we explored the relationship between T. dimidiata distribution and environmental factors in the northwest, northeast, and central zones of Colombia and developed predictive models of infestation in the country. The associations between the presence ofT. dimidiata and environmental variables were studied using logistic regression models and ecological niche modeling for a sample of villages in Colombia. The analysis was based on the information collected in field about the presence ofT. dimidiata and the environmental data for each village extracted from remote sensing images. The presence of Triatoma dimidiata(Latreille, 1811) was found to be significantly associated with the maximum vegetation index, minimum land surface temperature (LST), and the digital elevation for the statistical model. Temperature seasonality, annual precipitation, and vegetation index were the variables that most influenced the ecological niche model ofT. dimidiata distribution. The logistic regression model showed a good fit and predicted suitable habitats in the Andean and Caribbean regions, which agrees with the known distribution of the species, but predicted suitable habitats in the Pacific and Orinoco regions proposing new areas of research. Improved models to predict suitable habitats forT. dimidiata hold promise for spatial targeting of integrated vector management.

The Fungus among Us: Cryptococcus neoformans and Cryptococcus gattii Ecological Modeling for Colombia

The environmental isolation of Cryptococcus spp. is typically a difficult undertaking. Collecting samples in the field is costly in terms of travel, personnel time and materials. Furthermore, the recovery rate of Cryptococcus spp. may be very low, thereby requiring a large number of samples to be taken without any guarantee of success. Ecological niche modeling is a tool that has traditionally been used to forecast the distribution of plant and animal of species for biodiversity and conservation purposes. Here, we use it in a public health application to produce risk area maps for cryptococcal disease in Colombia. The Genetic Algorithm for Ruleset Production (GARP) was used to create models for Cryptococcus neoformans (C. neoformans) and Cryptococcus gattii (C. gattii), based on environmental sampling and clinical records data recorded since 1987. These maps could be used to focus public health messaging related to cryptococcal disease, and it enables us to characterize the ecological niche for Cryptococcus in Colombia. We found that the OPEN ACCESS J. Fungi 2015, 1 333 ecological niche for C. gattii in Colombia is quite diverse, establishing itself in sub-tropical and temperate ecoregions within the country. This suggests that C. gattii is highly adaptive to different ecological conditions in Colombia and different regions of the world.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea

Background

Malaria is the world’s most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality.

Methods

We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as LOW, MEDIUM or HIGH, where these classes are defined as a total of 0–2, 3–16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak.

Results

Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7–8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the MEDIUM class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3.

Conclusions

A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict LOW, MEDIUM or HIGH cases 7–8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

Risk assessment of vector-borne diseases for public health governance

OBJECTIVES

In the context of public health, risk governance (or risk analysis) is a framework for the assessment and subsequent management and/or control of the danger posed by an identified disease threat. Generic frameworks in which to carry out risk assessment have been developed by various agencies. These include monitoring, data collection, statistical analysis and dissemination. Due to the inherent complexity of disease systems, however, the generic approach must be modified for individual, disease-specific risk assessment frameworks.

STUDY DESIGN

The analysis was based on the review of the current risk assessments of vector-borne diseases adopted by the main Public Health organisations (OIE, WHO, ECDC, FAO, CDC etc…).

METHODS

Literature, legislation and statistical assessment of the risk analysis frameworks.

RESULTS

This review outlines the need for the development of a general public health risk assessment method for vector-borne diseases, in order to guarantee that sufficient information is gathered to apply robust models of risk assessment. Stochastic (especially spatial) methods, often in Bayesian frameworks are now gaining prominence in standard risk assessment procedures because of their ability to assess accurately model uncertainties.

CONCLUSIONS

Risk assessment needs to be addressed quantitatively wherever possible, and submitted with its quality assessment in order to enable successful public health measures to be adopted. In terms of current practice, often a series of different models and analyses are applied to the same problem, with results and outcomes that are difficult to compare because of the unknown model and data uncertainties. Therefore, the risk assessment areas in need of further research are identified in this article.

Spatial and temporal patterns of locally-acquired dengue transmission in northern Queensland, Australia, 1993-2012

Background

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992–1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993–2012.

Methods

Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques.

Results

2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ² = 15.17, d.f. = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland.

Conclusions

Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas.

Exploring the spatio-temporal dynamics of reservoir hosts, vectors, and human hosts of West Nile virus: a review of the recent literature

Over the last two decades West Nile Virus (WNV) has been responsible for significant disease outbreaks in humans and animals in many parts of the World. Its extremely rapid global diffusion argues for a better understanding of its geographic extent. The purpose of this inquiry was to explore spatio-temporal patterns of WNV using geospatial technologies to study populations of the reservoir hosts, vectors, and human hosts, in addition to the spatio-temporal interactions among these populations. Review of the recent literature on spatial WNV disease risk modeling led to the conclusion that numerous environmental factors might be critical for its dissemination. New Geographic Information Systems (GIS)-based studies are monitoring occurrence at the macro-level, and helping pinpoint areas of occurrence at the micro-level, where geographically-targeted, species-specific control measures are sometimes taken and more sophisticated methods of surveillance have been used.

Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa

There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection.

Vectorborne diseases in West Africa: geographic distribution and geospatial characteristics

This paper provides an overview of the methods in which geographic information systems (GIS) and remote sensing (RS) technology have been used to visualise and analyse data related to vectorborne diseases (VBD) in West Africa and to discuss the potential for these approaches to be routinely included in future studies of VBDs. GIS/RS studies of diseases that are associated with a specific geographic landscape were reviewed, including malaria, human African trypanosomiasis, leishmaniasis, lymphatic filariasis, Loa loa filariasis, onchocerciasis, Rift Valley fever, dengue, yellow fever, borreliosis, rickettsioses, Buruli ulcer and Q fever. RS data and powerful spatial modelling methods improve our understanding of how environmental factors affect the vectors and transmission of VBDs. There is great potential for the use of GIS/RS technologies in the surveillance, prevention and control of vectorborne and other infectious diseases in West Africa.

Tracking and visualization of space-time activities for a micro-scale flu transmission study

Background

Infectious diseases pose increasing threats to public health with increasing population density and more and more sophisticated social networks. While efforts continue in studying the large scale dissemination of contagious diseases, individual-based activity and behaviour study benefits not only disease transmission modelling but also the control, containment, and prevention decision making at the local scale. The potential for using tracking technologies to capture detailed space-time trajectories and model individual behaviour is increasing rapidly, as technological advances enable the manufacture of small, lightweight, highly sensitive, and affordable receivers and the routine use of location-aware devices has become widespread (e.g., smart cellular phones). The use of low-cost tracking devices in medical research has also been proved effective by more and more studies. This study describes the use of tracking devices to collect data of space-time trajectories and the spatiotemporal processing of such data to facilitate micro-scale flu transmission study. We also reports preliminary findings on activity patterns related to chances of influenza infection in a pilot study.

Methods

Specifically, this study employed A-GPS tracking devices to collect data on a university campus. Spatiotemporal processing was conducted for data cleaning and segmentation. Processed data was validated with traditional activity diaries. The A-GPS data set was then used for visual explorations including density surface visualization and connection analysis to examine space-time activity patterns in relation to chances of influenza infection.

Results

When compared to diary data, the segmented tracking data demonstrated to be an effective alternative and showed greater accuracies in time as well as the details of routes taken by participants. A comparison of space-time activity patterns between participants who caught seasonal influenza and those who did not revealed interesting patterns.

Conclusions

This study proved that tracking technology an effective technique for obtaining data for micro-scale influenza transmission research. The findings revealed micro-scale transmission hotspots on a university campus and provided insights for local control and prevention strategies.

Risk mapping of Anopheles gambiae s.l. densities using remotely-sensed environmental and meteorological data in an urban area: Dakar, Senegal

Introduction

High malaria transmission heterogeneity in an urban environment is basically due to the complex distribution of Anopheles larval habitats, sources of vectors. Understanding 1) the meteorological and ecological factors associated with differential larvae spatio-temporal distribution and 2) the vectors dynamic, both may lead to improving malaria control measures with remote sensing and high resolution data as key components. In this study a robust operational methodology for entomological malaria predictive risk maps in urban settings is developed.

Methods

The Tele-epidemiology approach, i.e., 1) intensive ground measurements (Anopheles larval habitats and Human Biting Rate, or HBR), 2) selection of the most appropriate satellite data (for mapping and extracting environmental and meteorological information), and 3) use of statistical models taking into account the spatio-temporal data variability has been applied in Dakar, Senegal.

Results

First step was to detect all water bodies in Dakar. Secondly, environmental and meteorological conditions in the vicinity of water bodies favoring the presence of Anopheles gambiae s.l. larvae were added. Then relationship between the predicted larval production and the field measured HBR was identified, in order to generate An. gambiae s.l. HBR high resolution maps (daily, 10-m pixel in space).

Discussion and Conclusion

A robust operational methodology for dynamic entomological malaria predictive risk maps in an urban setting includes spatio-temporal variability of An. gambiae s.l. larval habitats and An. gambiae s.l. HBR. The resulting risk maps are first examples of high resolution products which can be included in an operational warning and targeting system for the implementation of vector control measures.

Wetlands, climate zones and Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is the second most common mosquito-borne disease in Australia, but the linkages of the wetlands and climate zones with BFV transmission remain unclear. We aimed to examine the relationship between the wetlands, climate zones and BFV risk in Queensland, Australia. Data on the wetlands, climate zones, population and BFV cases for the period 1992 to 2008 were obtained from relevant government agencies. BFV risk was grouped as low-, medium- and high-level based on BFV incidence percentiles. The buffer zones around each BFV case were made using 1, 5, 10, 15, 20, 25 and 50km distances. We performed a discriminant analysis to determine the differences between wetland classes and BFV risk within each climate zone. The discriminant analyses show that saline 1, riverine and saline tidal influence were the most significant contributors to BFV risk in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. These models had classification accuracies of 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV risk varies with wetland class and climate zone. The discriminant analysis is a useful tool to quantify the links between wetlands, climate zones and BFV risk.

Environmental factors underlying spatial patterns of sand flies (Diptera: Psychodidae) associated with leishmaniasis in southern Sinai, Egypt

Although Leishmania major is endemic in parts of the Sinai of Egypt, the ecology and distribution of Leishmania sand fly vectors in southern Sinai has not been well characterized. Accordingly, additional sand fly samples were obtained at 41 sites in the southern Sinai region during 1996-1997, and analyzed to improve the characterization of risk of sand fly-borne pathogens. Using a Geographic Information System (GIS), species-specific spatial distributions that might suggest zoonotic cutaneous leishmaniasis (ZCL) risk areas were determined in relation to contextual environmental factors, including geology, hydrogeology, climate variables and elevation. Southern Sinai was characterized by a diverse sand fly fauna (eight Phlebotomus species), probably attributable to highly variable landscape and environmental factors. Phlebotomus alexandri, Phlebotomus kazeruni and Phlebotomus sergenti were widespread and abundant, Phlebotomus papatasi and Phlebotomus bergeroti were less frequent, and Phlebotomus arabicus, Phlebotomus major and Phlebotomus orientalis had highly restricted distributions. Logistic regression models indicated that elevation and climatic conditions were limiting determinants for the distributions of sand flies in southern Sinai. Based on the predicted distribution of P. papatasi, a recognized vector of L. major, about one-quarter of southern Sinai may be at high risk of ZCL. Risk areas for the suspected ZCL vector P. bergeroti had a more patchy distribution. Results suggest that future studies should include other factors related to vector abundance, vector competence, human population, and parasite and reservoir host(s) to produce more comprehensive ZCL transmission risk maps, thus helping in planning effective prevention and control strategies.

Processes affecting Aedes aegypti (Diptera: Culicidae) infestation and abundance: inference through statistical modeling and risk maps in northern Argentina

Understanding the processes that affect Aedes aegypti (L.) (Diptera: Culicidae) may serve as a starting point to create and/or improve vector control strategies. For this purpose, we performed statistical modeling of three entomological surveys conducted in Clorinda City, northern Argentina. Previous 'basic' models of presence or absence of larvae and/or pupae (infestation) and the number of pupae in infested containers (productivity), mainly based on physical characteristics of containers, were expanded to include variables selected a priori reflecting water use practices, vector-related context factors, the history of chemical control, and climate. Model selection was performed using Akaike's Information Criterion. In total, 5,431 water-holding containers were inspected and 12,369 Ae. aegypti pupae collected from 963 positive containers. Large tanks were the most productive container type. Variables reflecting every putative process considered, except for history of chemical control, were selected in the best models obtained for infestation and productivity. The associations found were very strong, particularly in the case of infestation. Water use practices and vector-related context factors were the most important ones, as evidenced by their impact on Akaike's Information Criterion scores of the infestation model. Risk maps based on empirical data and model predictions showed a heterogeneous distribution of entomological risk. An integrated vector control strategy is recommended, aiming at community participation for healthier water use practices and targeting large tanks for key elements such as lid status, water addition frequency and water use.

Conditions of malaria transmission in Dakar from 2007 to 2010

BACKGROUND

Previous studies in Dakar have highlighted the spatial and temporal heterogeneity of Anopheles gambiae s.l. biting rates. In order to improve the knowledge of the determinants of malaria transmission in this city, the present study reports the results of an extensive entomological survey that was conducted in 45 areas in Dakar from 2007 to 2010.

METHODS

Water collections were monitored for the presence of anopheline larvae. Adult mosquitoes were sampled by human landing collection. Plasmodium falciparum circumsporozoïte (CSP) protein indexes were measured by ELISA (enzyme-linked immunosorbent assay), and the entomological inoculation rates were calculated.

RESULTS

The presence of anopheline larvae were recorded in 1,015 out of 2,683 observations made from 325 water collections. A water pH of equal to or above 8.0, a water temperature that was equal to or above 30°C, the absence of larvivorous fishes, the wet season, the presence of surface vegetation, the persistence of water and location in a slightly urbanised area were significantly associated with the presence of anopheline larvae and/or with a higher density of anopheline larvae. Most of the larval habitats were observed in public areas, i.e., freely accessible. A total of 496,310 adult mosquitoes were caught during 3096 person-nights, and 44967 of these specimens were identified as An.gambiae s.l. The mean An. gambiae s.l. human-biting rate ranged from 0.1 to 248.9 bites per person per night during the rainy season. Anopheles arabiensis (93.14%), Anopheles melas (6.83%) and An. gambiae s.s. M form (0.03%) were the three members of the An. gambiae complex. Fifty-two An. arabiensis and two An. melas specimens were CSP-positive, and the annual CSP index was 0.64% in 2007, 0.09% in 2008-2009 and 0.12% in 2009-2010. In the studied areas, the average EIR ranged from 0 to 17.6 infected bites per person during the entire transmission season.

CONCLUSION

The spatial and temporal heterogeneity of An. gambiae s.l. larval density, adult human-biting rate (HBR) and malaria transmission in Dakar has been confirmed, and the environmental factors associated with this heterogeneity have been identified. These results pave the way for the creation of malaria risk maps and for a focused anti-vectorial control strategy.

Role of the repartition of wetland breeding sites on the spatial distribution of Anopheles and Culex, human disease vectors in southern France

Background

In this study, carried out in the Camargue region (France), we combined entomological data with geomatic and modelling tools to assess whether the location of breeding sites may explain the spatial distribution of adult mosquitoes. The species studied are important and competent disease vectors in Europe: Culex modestus Ficalbi and Cx. pipiens Linnaeus (West Nile virus), Anopheles atroparvus Van Thiel, a former Plasmodium vector, and An. melanoon Hackett, competent to transmit Plasmodium.

Using a logistic regression model, we first evaluated which land cover variables determined the presence of Culex and Anopheles larva. The resulting probability map of larval presence then was used to project the average probability of finding adults in a buffer area. This was compared to the actual number of adults collected, providing a quantitative assessment of adult dispersal ability for each species.

Results

The distribution of Cx. modestus and An. melanoon is mainly driven by the repartition of irrigated farm fields and reed beds, their specific breeding habitats. The presence of breeding sites explained the distribution of adults of both species. The buffer size, reflecting the adult dispersal ability, was 700 m for Cx. modestus and 1000 m for An. melanoon. The comparatively stronger correlation observed for Cx. modestus suggested that other factors may affect the distribution of adult An. melanoon. We did not find any association between Cx. pipiens larval presence and the biotope due to the species' ubiquist character.

Conclusion

By applying the same method to different species, we highlighted different strengths of association between land cover (irrigated farm fields and reed beds), larval presence and adult population distribution.

This paper demonstrates the power of geomatic tools to quantify the spatial organization of mosquito populations, and allows a better understanding of links between landcover, breeding habitats, presence of immature mosquito populations and adult distributions for different species.

An integrated database on ticks and tick-borne zoonoses in the tropics and subtropics with special reference to developing and emerging countries

Tick-borne zoonoses (TBZ) are emerging diseases worldwide. A large amount of information (e.g. case reports, results of epidemiological surveillance, etc.) is dispersed through various reference sources (ISI and non-ISI journals, conference proceedings, technical reports, etc.). An integrated database-derived from the ICTTD-3 project ( http://www.icttd.nl )-was developed in order to gather TBZ records in the (sub-)tropics, collected both by the authors and collaborators worldwide. A dedicated website ( http://www.tickbornezoonoses.org ) was created to promote collaboration and circulate information. Data collected are made freely available to researchers for analysis by spatial methods, integrating mapped ecological factors for predicting TBZ risk. The authors present the assembly process of the TBZ database: the compilation of an updated list of TBZ relevant for (sub-)tropics, the database design and its structure, the method of bibliographic search, the assessment of spatial precision of geo-referenced records. At the time of writing, 725 records extracted from 337 publications related to 59 countries in the (sub-)tropics, have been entered in the database. TBZ distribution maps were also produced. Imported cases have been also accounted for. The most important datasets with geo-referenced records were those on Spotted Fever Group rickettsiosis in Latin-America and Crimean-Congo Haemorrhagic Fever in Africa. The authors stress the need for international collaboration in data collection to update and improve the database. Supervision of data entered remains always necessary. Means to foster collaboration are discussed. The paper is also intended to describe the challenges encountered to assemble spatial data from various sources and to help develop similar data collections.

Mapping the current distribution and predicted spread of the leishmaniosis sand fly vector in the madrid region (Spain) based on environmental variables and expected climate change

Leishmaniosis caused by Leishmania infantum is a widespread zoonotic disease that is endemic in the Mediterranean basin. Based on prior point abundance data for the two sand fly vectors of leishmaniosis in the Madrid region (Phlebotomus perniciosus and Phlebotomus ariasi), models were constructed to predict the spatial distribution patterns of these vectors. The models were obtained by negative binomial regression of several environmental variables and then used to map vector distributions. To validate the maps, we used serological prevalence data of Leishmania infection in dogs and incidence data were obtained through questionnaires completed by veterinarians in the region. Seropositive dogs and veterinary clinics registering a higher incidence of canine leishmaniosis appeared closer to our modeled vector foci. In the face of climate change, we simulated the future distributions of the sand flies for each third of the 21st century and predicted their spread in the region.

Using geographic information systems and decision support systems for the prediction, prevention, and control of vector-borne diseases

Emerging and resurging vector-borne diseases cause significant morbidity and mortality, especially in the developing world. We focus on how advances in mapping, Geographic Information System, and Decision Support System technologies, and progress in spatial and space-time modeling, can be harnessed to prevent and control these diseases. Major themes, which are addressed using examples from tick-borne Lyme borreliosis; flea-borne plague; and mosquito-borne dengue, malaria, and West Nile virus disease, include (a) selection of spatial and space-time modeling techniques, (b) importance of using high-quality and biologically or epidemiologically relevant data, (c) incorporation of new technologies into operational vector and disease control programs, (d) transfer of map-based information to stakeholders, and (e) adaptation of technology solutions for use in resource-poor environments. We see great potential for the use of new technologies and approaches to more effectively target limited surveillance, prevention, and control resources and to reduce vector-borne and other infectious diseases.

Predicting geographical human risk of West Nile virus--Saskatchewan, 2003 and 2007

OBJECTIVES

To detail the use of a model to predict areas of low, medium, and high risk of West Nile virus (WNV) in humans in both 2003 and 2007 in the province of Saskatchewan. To identify consistent high-risk areas from year to year as well as important environmental variables within those high-risk areas.

METHODS

The number of laboratory-confirmed WNV individuals was obtained from Saskatchewan Health by rural municipality. The population at risk was obtained from Statistics Canada by rural municipality. Climate and habitat variables were incorporated into a discriminant analysis model with the production of risk maps as an end product.

RESULTS

The discriminant analysis models had testing classification accuracies of 67% in 2003 and 44% in 2007. Climate and habitat variables remained important in all models while some habitat variables were less important in 2007. Risk maps from historically trained 2007 model revealed a southwest to northeast decreasing trend of risk.

CONCLUSION

The models could be useful for indicating areas of high risk on a year-to-year basis or based on historical data. High-risk regions are characterized by less rainfall in June and July followed by higher temperatures in July and August with less vegetation and water coverage than low-risk regions.

Mapping the basic reproduction number (R₀) for vector-borne diseases: a case study on bluetongue virus

Geographical maps indicating the value of the basic reproduction number, R₀, can be used to identify areas of higher risk for an outbreak after an introduction. We develop a methodology to create R₀ maps for vector-borne diseases, using bluetongue virus as a case study. This method provides a tool for gauging the extent of environmental effects on disease emergence. The method involves integrating vector-abundance data with statistical approaches to predict abundance from satellite imagery and with the biologically mechanistic modelling that underlies R₀. We illustrate the method with three applications for bluetongue virus in the Netherlands: 1) a simple R₀ map for the situation in September 2006, 2) species-specific R₀ maps based on satellite-data derived predictions, and 3) monthly R₀ maps throughout the year. These applications ought to be considered as a proof-of-principle and illustrations of the methods described, rather than as ready-to-use risk maps. Altogether, this is a first step towards an integrative method to predict risk of establishment of diseases based on mathematical modelling combined with a geographic information system that may comprise climatic variables, landscape features, land use, and other relevant factors determining the risk of establishment for bluetongue as well as of other emerging vector-borne diseases.

Socio-environmental predictors of Barmah forest virus transmission in coastal areas, Queensland, Australia

OBJECTIVE

To assess the socio-environmental predictors of Barmah forest virus (BFV) transmission in coastal areas, Queensland, Australia.

METHODS

Data on BFV notified cases, climate, tidal levels and socioeconomic index for area (SEIFA) in six coastal cities, Queensland, for the period 1992-2001 were obtained from the relevant government agencies. Negative binomial regression models were used to assess the socio-environmental predictors of BFV transmission.

RESULTS

The results show that maximum and minimum temperature, rainfall, relative humidity, high and low tide were statistically significantly associated with BFV incidence at lags 0-2 months. The fitted negative binomial regression models indicate a significant independent association of each of maximum temperature (beta = 0.139, P = 0.000), high tide (beta = 0.005, P = 0.000) and SEIFA index (beta = -0.010, P = 0.000) with BFV transmission after adjustment for confounding variables.

CONCLUSIONS

The transmission of BFV disease in Queensland coastal areas seemed to be determined by a combination of local social and environmental factors. The model developed in this study may have applications in the control and prevention of BFV disease in these areas.

British Columbia's fish health regulatory framework's contribution to sustainability goals related to salmon aquaculture

Salmon farming is a significant contribution to the global seafood market to which the goal of sustainability is often applied. Diseases related to farms are perhaps the most contentious issues associated with sustainable salmon farming. We reviewed literature and policies in British Columbia, Canada, as well as interviewed key informants to examine how fish health regulations do or could support sustainability goals. We found four main obstacles to the development and application of a sustainability-based health management system. First, salmon farming faced the same challenges as other industries when trying to establish an operational definition of sustainability that captures all stakeholders' interests. Second, there was no program responsible for integrating the various regulations, responsible departments, and monitoring efforts to develop a comprehensive view of sustainability. Third, there was inadequate research base and social consensus on the criteria that should be used to track health outcomes for sustainability purposes. Fourth, the regulatory and management paradigm for salmon farming has been focused on diseases and pathogens as opposed to embracing a more inclusive health promotion model that includes biotic, abiotic, and social determinants of health. A transparent and inclusive participatory process that effectively links expert views with community and industry concerns should serve as the foundation for the next generation of health management regulations for salmon farming.

Spatial modelling of the potential temperature-dependent transmission of vector-associated diseases in the face of climate change: main results and recommendations from a pilot study in Lower Saxony (Germany)

The sustained climate change is going to modify the geographic distribution, the seasonal transmission gate and the intensity of the transmission of vector-borne diseases such as malaria or the bluetongue disease. These diseases occur nowadays at higher latitudes or altitudes. A further rise in ambient temperature and rainfall will extend the duration of the season in which mosquito vectors are transmitting pathogens. The parasites transmitted by the vectors also benefit from increasing temperatures, as both their reproduction and development are then accelerated, too. Thus, it seemed prudent to examine potential effects on the seasonal transmission gate due to the ongoing and predicted climate changes. Lower Saxony (northwest Germany) is a former malaria region with highest incidences of Anopheles atroparvus and tertian malaria along the coastal zones before malaria had finally become extinct in the early 1950s. Nevertheless, the Anopheles mosquitoes which transmit the malaria pathogens have still been present in Lower Saxony up to now. This together with the climate change-related implications gave reason to investigate whether a new autochthonous transmission could take place if the malaria pathogen is introduced again in Lower Saxony. Thus, the potential spatial and temporal structure of temperature-driven malaria transmissions was mapped using the basic reproduction rate (R (0)) and measured and predicted air temperatures (1947-1960, 1961-1990, 1985-2004, 2020, 2060, 2100, each best case and worst case scenario). This paper focuses on both the summarizing of the results from this risk modelling approach and on the conclusions to be drawn. The recommendations highlight the need to link vector monitoring as one of the key elements of an epidemiological monitoring with the environmental monitoring.

Spatial and temporal variability of the Glossina palpalis palpalis population in the Mbini focus (Equatorial Guinea)

Background

Human African Trypanosomiasis is a vector-borne parasitic disease. The geographical distribution of the disease is linked to the spatial distribution of the tsetse fly. As part of a control campaign using traps, the spatial and temporal variability is analysed of the glossina populations present in the Mbini sleeping sickness foci (Equatorial Guinea).

Results

A significant drop in the annual mean of the G. p. palpalis apparent density was noted from 2004 to 2005, although seasonal differences were not observed. The apparent density (AD) of G. p. palpalis varies significantly from one biotope to another. The fish dryers turned out to be zones with the greatest vector density, although the AD of G. p. palpalis fell significantly in all locations from 2004 to 2005.

Conclusion

Despite the tsetse fly density being relatively low in fish dryers and jetties, the population working in those zones would be more exposed to infection. The mono-pyramidal traps in the Mbini focus have been proven to be a useful tool to control G. p. palpalis, even though the activity on the banks of the River Wele needs to be intensified. The application of spatial analysis techniques and geographical information systems are very useful tools to discriminate zones with high and low apparent density of G. p. palpalis, probably associated with different potential risk of sleeping sickness transmission.

A Rift Valley fever atlas for Africa

Rift Valley fever (RVF) epidemics have serious consequences for human and animal health and the livestock trade. Recent epidemics have occurred in previously unaffected regions, increasing concerns that the geographical range of RVF will continue to expand. We conducted an extensive, systematic review of the literature to obtain serological data for RVF in Africa, collected between 1970 and 2000 from human, livestock and wild ungulate populations. Aims were to calculate sub-national estimates of RVF infection prevalence and to define areas where no information was available. We presented the data (aggregated at the first administrative level of countries) using a geographical information system. Data from 71 publications were used to build a spatially explicit Bayesian logistic-regression model, with spatial and non-spatial random effects, allowing us to identify clusters of high and low RVF seroprevalence, and fixed effects that described the disparate nature of the survey subjects and methods. Significant high-prevalence clusters encompassed areas that had experienced epidemics during the late 20th century and significant low-prevalence clusters were located in contiguous areas of Western and Central Africa.

Application of portfolio theory to risk-based allocation of surveillance resources in animal populations

Distribution of finite levels of resources between multiple competing tasks can be a challenging problem. Resources need to be distributed across time periods and geographic locations to increase the probability of detection of a disease incursion or significant change in disease pattern. Efforts should focus primarily on areas and populations where risk factors for a given disease reach relatively high levels. In order to target resources into these areas, the overall risk level can be evaluated periodically across locations to create a dynamic national risk landscape. Methods are described to integrate the levels of various risk factors into an overall risk score for each area, to account for the certainty or variability around those measures and then to allocate surveillance resources across this risk landscape. In addition to targeting resources into high risk areas, surveillance continues in lower risk areas where there is a small yet positive chance of disease occurrence. In this paper we describe the application of portfolio theory concepts, routinely used in finance, to design surveillance portfolios for a series of examples. The appropriate level of resource investment is chosen for each disease or geographical area and time period given the degree of disease risk and uncertainty present.

Association of West Nile virus illness and urban landscapes in Chicago and Detroit

BACKGROUND

West Nile virus infection in humans in urban areas of the Midwestern United States has exhibited strong spatial clustering during epidemic years. We derived urban landscape classes from the physical and socio-economic factors hypothesized to be associated with West Nile Virus (WNV) transmission and compared those to human cases of illness in 2002 in Chicago and Detroit. The objectives were to improve understanding of human exposure to virus-infected mosquitoes in the urban context, and to assess the degree to which environmental factors found to be important in Chicago were also found in Detroit.

RESULTS

Five urban classes that partitioned the urban space were developed for each city region. The classes had many similarities in the two settings. In both regions, the WNV case rate was considerably higher in the urban class associated with the Inner Suburbs, where 1940-1960 era housing dominates, vegetation cover is moderate, and population density is moderate. The land cover mapping approach played an important role in the successful and consistent classification of the urban areas.

CONCLUSION

The analysis demonstrates how urban form and past land use decisions can influence transmission of a vector-borne virus. In addition, the results are helpful to develop hypotheses regarding urban landscape features and WNV transmission, they provide a structured method to stratify the urban areas to locate representative field study sites specifically for WNV, and this analysis contributes to the question of how the urban environment affects human health.

Hydrological modelling of snail dispersal patterns in Msambweni, Kenya and potential resurgence of Schistosoma haematobium transmission

Urinary schistosomiasis is an important source of human morbidity in Msambweni, Kenya, where the intermediate host snail, Bulinus nasutus is found in ponds and water pools. In the past, aquatic habitats in the area have been studied separately; however, recent collections of B. nasutus snails and shells indicated that many of these ponds are in fact connected during and following sufficient rains. Satellite imagery and a geographical information system (GIS) were used to survey the main water courses and potential drainage routes, to locate potential source populations of snails and to determine probable snail dispersal routes. The 2 water bodies implicated as being the most important Schistosoma haematobium transmission foci in the area were found to differ in their degree of connectivity to other B. nasutus source habitats. One pond becomes connected even after normal rains, while the other pond requires prolonged rains or flooding to become connected with source habitats. Consequently, the transmission foci differ in their susceptibility to snail population control measures. Spatially explicit dispersal models that consider the spatial and temporal patterns of connectivity between aquatic habitats will contribute to improved snail surveillance and more focused control for urinary schistosomiasis at a local level.

Spatial and temporal transmission risk of Dirofilaria immitis in Argentina

The aim of this work was to assess spatial and seasonal Dirofilaria immitis transmission risk throughout Argentina with models based on the temperature threshold below which filarial development will not proceed in the mosquito (i.e. 14 degrees C), the occurrence and the number of potential vector mosquito species, and the Heartworm Development Units derived from the degree-days concept. The four models showed a similar increasing southwest-northeast tendency and correlated significantly with canine prevalences used as external validation data. About one-third of Argentina would be suitable for heartworm transmission and the highest risk areas include the north-eastern provinces. According to our models, heartworm transmission is markedly seasonal with peaks in January and February; no region would support transmission throughout the year. To improve the present models, it is necessary to know which mosquito species are competent rather than potential vectors in the country. We believe the present study provides the first risk assessment maps for D. immitis transmission in the Southern Hemisphere and provides a useful guide for heartworm prevention during the transmission periods in different regions of Argentina.

Mapping malaria transmission in West and Central Africa

We have produced maps of Plasmodium falciparum malaria transmission in West and Central Africa using the Mapping Malaria Risk in Africa (MARA) database comprising all malaria prevalence surveys in these regions that could be geolocated. The 1846 malaria surveys analysed were carried out during different seasons, and were reported using different age groupings of the human population. To allow comparison between these, we used the Garki malaria transmission model to convert the malaria prevalence data at each of the 976 locations sampled to a single estimate of transmission intensity E, making use of a seasonality model based on Normalized Difference Vegetation Index (NDVI), temperature and rainfall data. We fitted a Bayesian geostatistical model to E using further environmental covariates and applied Bayesian kriging to obtain smooth maps of E and hence of age-specific prevalence. The product is the first detailed empirical map of variations in malaria transmission intensity that includes Central Africa. It has been validated by expert opinion and in general confirms known patterns of malaria transmission, providing a baseline against which interventions such as insecticide-treated nets programmes and trends in drug resistance can be evaluated. There is considerable geographical variation in the precision of the model estimates and, in some parts of West Africa, the predictions differ substantially from those of other risk maps. The consequent uncertainties indicate zones where further survey data are needed most urgently. Malaria risk maps based on compilations of heterogeneous survey data are highly sensitive to the analytical methodology.

Bayesian spatio-temporal modelling of national milk-recording data of seasonal-calving New Zealand dairy herds

A spatio-temporal analysis was undertaken with the aim of identifying the dynamics of herd mean individual cow SCCs (MICSCC) in seasonally calving New Zealand dairy herds. Two datasets were extracted from the Livestock Improvement Corporation's extensive national dairy recording database: (1) milk-recording data aggregated at the herd-level and (2) sales questionnaire data containing information on the size, location and infrastructure of each farm. A Bayesian spatio-temporal modelling approach was applied to the analysis. The data were aggregated by 10 km(2) grid cells and linear regression models were developed with spatially structured and unstructured random effects, a linear temporal trend random effect and spatial-temporal interactions for log-transformed median MISCC (ln(median MISCC)). Significant associations were found between ln(median MISCC) and milk yield, milk fat, milk protein, farm area and number of cups in the dairy. This led us to suggest that SCCs should be adjusted for volume and constituents prior to determining a threshold MISCC for identification of subclinical mastitis (SCM) problem herds. Part, or all, of the temporal trend in MISCC in the spatio-temporal model was accounted for by inclusion of yield and milk constituents as independent variables. This supports the hypothesis of a dilution effect with potential consequences for misdiagnosis of SCM, particularly in late lactation. Unmeasured covariates were similarly likely to be spatially structured and unstructured.

Spatiotemporal patterns of reinfestation by Triatoma guasayana (Hemiptera: Reduviidae) in a rural community of northwestern Argentina

Triatoma guasayana (Wygodzinsky & Abalos) is a secondary vector of Trypanosoma cruzi (Chagas), the etiologic agent of Chagas disease, in the Chaco region of Argentina, Bolivia, and Paraguay. The spatial distribution of T. guasayana in a rural community in northwestern Argentina is described and analyzed using very high spatial resolution satellite imagery, geographic information systems, and spatial statistics. Since a 1992 residual spraying with insecticides of all houses, site-specific domestic and peridomestic reinfestations by triatomine bugs were monitored using various methods semiannually from 1993 to 2002. The reinfestation by T. guasayana started with finding of only adult bugs in a few sites. Bug abundance was significantly clustered and predominantly peridomestic in the southern and northern extremes of the community. The identified source of reinfestation in the northern cluster was a colonized wood pile, whereas no potential peridomestic source was found for the southern cluster. The spatial distribution of T. guasayana was positively associated with the abundance and spatial distribution of goats. Active dispersal from the hypothesized source and the surrounding sylvatic environment, and passive transport of bugs in wood piles seems to be the most likely mechanisms underlying the observed spatial pattern of T. guasayana. The absence of domestic colonization indicates that, to date, there is no trend toward increased local domiciliation of T. guasayana. The clustering zones can be considered "hot spots" where bug invasion from other sources is expected to be higher and where eventually, introduction of sylvatic T. cruzi to suitable hosts may occur.

Spatial statistical analysis of adult mosquito (Diptera: Culicidae) counts: an example using light trap data, in Redland Shire, Southeastern Queensland, Australia

Many mosquito control agencies use carbon dioxide-baited traps as surveillance tools for adult vector populations. However, decisions regarding the number and location of trap sites and the frequency of collections are often based on logistical issues, and not on the bionomics or spatial distribution of the target species. Therefore, with the aim of providing practical information for adult mosquito surveillance programs, we used an array of 81 carbon dioxide- and octenol-baited lights traps to obtain weekly samples of adult mosquitoes in Redland Shire in southeastern Queensland, Australia. The spatial patterns of four different mosquito species were examined, and positive spatial autocorrelation in trap counts was evident for Ochlerotatus vigilax (Skuse), Coquillettidia linealis (Skuse), and Culex annulirostris Skuse, but not for the container species Ochlerotatus notoscriptus (Skuse). Of the three species that exhibited spatially correlated trap counts, the autocorrelation was greatest in Oc. vigilax at a lag distance of 0-1.5 km, with Moran's I values of 0.30-0.64. Moran's I indices were also positive and statistically significant (P < 0.05) at lag distances of 1.5-3.0 and 3.0-4.5 km on each of the 15 sampling occasions. However, at 3.0-4.5 km the Moran's I values were low, which indicated only weak spatial autocorrelation in trap counts. Universal kriging was used to estimate the numbers of each species at unsampled locations throughout the study area, and leave-one-out cross validation analyses indicated that this was a robust method for Cq. linealis and Oc. vigilax. In contrast, trap counts for the container-breeding species Oc. notoscriptus were randomly distributed and the interpolated counts were not reliable. Comparisons of weekly contour maps of adult mosquito counts indicated a consistent spatial pattern for Oc. vigilax and Cq. linealis. Particular geographic areas had consistently high or low numbers of vectors, and these patterns were stable from year to year. Definition of geographic areas with consistently high or low numbers of vectors may allow control activities to be focused in areas with the greatest risk of arbovirus transmission.

Predicting geographic variation in cutaneous leishmaniasis, Colombia

Predicting Geographic Variation in ACL, Colombia

Approximately 6,000 cases of cutaneous leishmaniasis are reported annually in Colombia, a greater than twofold increase since the 1980s. Such reports certainly underestimate true incidence, and their geographic distribution is likely biased by local health service effectiveness. We investigated how well freely available environmental data explain the distribution of cases among 1,079 municipalities. For each municipality, a unique predictive logistic regression model was derived from the association among remaining municipalities between elevation, land cover (preclassified maps derived from satellite images), or both, and the odds of at least one case being reported. Land cover had greater predictive power than elevation; using both datasets improved accuracy. Fitting separate models to different ecologic zones, reflecting transmission cycle diversity, enhanced the accuracy of predictions. We derived measures that can be directly related to disease control decisions and show how results can vary, depending on the threshold selected for predicting a disease-positive municipality. The results identify areas where disease is most likely to be underreported.

Geographic information systems and spatial analysis of adult Ixodes scapularis (Acari: Ixodidae) in the Middle Atlantic region of the U.S.A

In the Middle Atlantic region of the U.S.A., the vector of Lyme disease, human granulocytic ehrlichiosis, babesiosis, and other human and veterinary pathogens is the black-legged tick, Ixodes scapularis Say. In 1997 and 1998, 663 adult I. scapularis ticks were collected from 320 transects spanning 66,400 km2 in five states of the Middle Atlantic region. Tick abundance patterns were clustered, with relatively high numbers along the coastal plain of the Chesapeake Bay, decreasing to the west and south. There were significant associations between tick abundance and land cover, distance to water, distance to forest edge, elevation, and soil type.

A global livestock production and health atlas (GLiPHA) for interactive presentation, integration and analysis of livestock data

An interactive electronic atlas has been developed with the purpose of providing a scaleable overview of spatial and temporal variation in animal production and health-related information for decision and policy makers in national and international institutions. The information contained in the atlas is currently managed and presented using the Key Indicators Mapping System (KIMS), and will also be integrated using the Key Indicators Database System (KIDS). Both systems were developed by the World Agricultural Information Centre of the FAO (FAO-WAICENT), the former as a stand-alone application and the latter for access via the Internet. Components of the atlas include vector maps, livestock disease and production databases, rules for country-level disease risk classification and 'disease cards' containing basic background information on diseases included in the atlas. The disease data is currently based primarily on Office International des Epizooties (OIE) disease reports, and the livestock production data on the FAO-WAICENT database. The atlas is highly interactive and allows visual presentation of information using maps, tables and charts. It also contains links to relevant resource information on the Internet. Diseases covered in the animal health layer include most OIE List A diseases and a subset of OIE List B diseases. Extensive analyses have been conducted to develop a set of qualitative and semi-quantitative criteria that allow improved disease status classifications based on 5-years cumulative OIE disease reports, and official disease control declarations. Classification rules were determined depending on the epidemiological features of each disease and considering spatial heterogeneity of disease presence in local regions.