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While agent-based models (ABMs) provide an effective means for investigating complex interactions between heterogeneous agents and their environment, they may hinder an improved understanding of phenomena being modeled due to inherent challenges associated with uncertainty in model parameters. This study uses uncertainty analysis and global sensitivity analysis (UA-GSA) to examine the effects of such uncertainty on model outputs. The statistics used in UA-GSA, however, are likely to be affected by the modifiable areal unit problem (MAUP). Therefore, to examine the scale varying-effects of model inputs, UA-GSA needs to be performed at multiple spatiotemporal scales. Unfortunately, performing comprehensive UA-GSA comes with considerable computational cost. In this paper, our cyberGIS-enabled spatiotemporally explicit UA-GSA approach helps to not only resolve the computational burden, but also to measure dynamic associations between model inputs and outputs. A set of computational and modeling experiments shows that input factors have scale-dependent impacts on modeling output variability. In other words, most of the input factors have relatively large impacts in a certain region, but may not influence outcomes in other regions. Furthermore, our spatiotemporally explicit UA-GSA approach sheds light on the effects of input factors on modeling outcomes that are particularly spatially and temporally clustered, such as the occurrence of communicable disease transmission.

(1) Background: The stochastic nature of agent-based models (ABMs) may be responsible for the variability of simulated outputs. Multiple simulation runs (i.e., replicates) need to be performed to have enough sample size for hypothesis testing and validating simulations. The simulation outputs in the early-stage of simulations from non-terminating ABMs may be underestimated (or overestimated). To avoid this initialization bias, the simulations need to be run for a burn-in period. This study proposes to use multiple scale space-time patterns to determine the number of required replicates and burn-in periods in spatially explicit ABMs, and develop an indicator for these purposes. (2) Methods: ABMs of vector-borne disease transmission were used as the case study. Particularly, we developed an index, D, which enables to take into consideration a successive coefficient of variance (CV) over replicates and simulation years. The comparison between the number of replicates and the burn-in periods determined by D and those chosen by CV was performed. (3) Results: When only a single pattern was used to determine the number of replicates and the burn-in periods, the results varied depending on the pattern. (4) Conclusions: As multiple scale space-time patterns were used for the purposes, the simulated outputs after the burn-in periods with a proper number of replicates would well reproduce multiple patterns of phenomena. The outputs may also be more useful for hypothesis testing and validation.

Topographic effects on Cs-137 concentrations in a forested area were quantitatively examined using 58 soil core samples collected in a village in Fukushima, Japan, which was directly impacted by the radioactive plume emitted during the 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. In this study, five topographic parameters and two soil properties were evaluated as controls on the soil Cs-137 concentration using generalized additive models (GAMs), a flexible statistical method for evaluating the functional dependencies of multiple parameters. GAMs employing soil dry bulk density, mass water content, and elevation explained 54 % of the observed concentrations of Cs-137 within this landscape, whereas GAMs employing elevation, slope, and upslope distance explained 47 % of the observed concentrations, which provide strong evidence of topographic effects on Cs-137 concentrations in soils. The model fit analysis confirmed that the topographic effects are strongest when multiple topographic parameters and soil properties are included. The ability of each topographic feature to predict Cs-137 concentrations was influenced by the resolution of the digital elevation models. The movement of Cs-137 into the subsurface in this area near Fukushima was faster in comparison to regions affected by the Chernobyl Nuclear Power Plant accident. These results suggest that the effects of topographic parameters should be considered carefully in the use of anthropogenic radionuclides as environmental tracers and in the assessment of current and future environmental risks due to nuclear power plant accidents.

Sensitivity analysis (SA) in spatially explicit agent-based models (ABMs) has emerged to address some of the challenges associated with model specification and parameterization. For spatially explicit ABMs, the comparison of spatial or spatio-temporal patterns has been advocated to evaluate models. Nevertheless, less attention has been paid to understanding the extent to which parameter values in ABMs are responsible for mismatch between model outcomes and observations. In this paper, we propose the use of multiple scale space-time patterns in variance-based global sensitivity analysis (GSA). A vector-borne disease transmission model was used as the case study. Input factors used in GSA include one related to the environment (introduction rates), two related to interactions between agents and environment (level of herd immunity, mosquito population density), and one that defines agent state transition (mosquito extrinsic incubation period). The results show parameters related to interactions between agents and the environment have great impact on the ability of a model to reproduce observed patterns, although the magnitudes of such impacts vary by space-time scales. Additionally, the results highlight the time-dependent sensitivity to parameter values in spatially explicit ABMs. The GSA performed in this study helps in identifying the input factors that need to be carefully parameterized in the model to implement ABMs that well reproduce observed patterns at multiple space-time scales.

Alors que divers organismes internationaux ont récemment proposé des plans pour la création de systèmes de surveillance de la résistance antimicrobienne (RAM), la plupart des PRMF ne disposent pas des infrastructures et des ressources pour le faire efficacement. La RAM est donc un problème majeur d’équité en matière de santé, mais aussi un exemple de la connexion dans notre monde unique: les populations les plus à risque disposent des mécanismes de surveillance et de confinement les plus médiocres; dans le même temps, la migration mondiale, les voyages et le commerce nous rendent tous vulnérables. Les mesures que nous utilisons actuellement ne capturent pas de manière significative l'impact potentiel de la RAM aux niveaux sanitaire, économique et sociétal plus large. La surveillance mondiale de la RAM devrait inclure une surveillance étroite de l'utilisation d'antibiotiques chez les animaux, ainsi que les concentrations de divers produits chimiques libérés dans l'environnement. En fin de compte, les efforts coordonnés de divers scientifiques et parties prenantes, dans diverses disciplines et dans le monde, sont essentiels pour relever les défis pressants de la RAM mondiale croissante.

Researchers have measured aerial gamma-rays using remote measurement devices to estimate the radionuclide concentrations in soils. However, an issue arises when measuring air dose rates in forested areas, where canopies exist between the remote sensing device and the ground. When researchers do the reverse conversion from soil contamination levels to the air dose rates, the conversion formula is derived by assuming a flat ground surface (Jacob et al., 1994; Saito and Jacob, 1998); however, topography in forested areas is often not flat. This study examines the relationships among the air dose rates under the canopies, topography, and the soil contamination levels on forested hillslopes in Fukushima, Japan. Gamma-ray air dose rates were measured using a hand-held scintillator during the summer of 2018 (two days), and in the winter of 2019 using KURAMA, a portable scintillator carried in a backpack (one day). This study employed numerical methods, including semivariogram and R's CAR (Companion to Applied Regression) package, to find aerial gamma-ray activities' spatial structure and the optimal combinations of topographic predictors. The survey routes and soil sampling points did not coincide completely. Thus, spatial and temporal reorganizations and re-definitions of the spatial boundary to incorporate the air (boundaryless mass), topography (three-dimensional structure), and soil samples (point measurements) were needed to overcome data analysis challenges. Some preliminary results show that the median air dose rate on a summer day with stronger winds (wind speed 7.1 m/s) was closer to the one on a winter day than to the median on the other summer day with calm air. Distance dependency (semivariogram range) on the windy summer day and the winter day was 30 to 60 m. Distance dependency on a summer day with calm air was much longer, > 500 m. The aerial gamma-ray levels were not in a linear relationship with the elevation. Meanwhile, the areas under evergreen trees showed higher air dose rates (3-8 %) than the areas under deciduous trees in absolute measurements. However, the differences were not statistically significant. The combination of slope degrees, hillslope aspects, and curvature or upslope distance best described the air dose rates, depending on the survey routes, although their predictabilities (R2) were low, 0.35 at the most. The air dose rates, which were estimated from soil samples' effective relaxation mass depths, did not correlate with the actual air dose measurements, but this result is under further investigation. This study's complete results will provide additional consideration points to the gamma-ray air dose vs. soil contamination assessment in the forested areas.

This research collected forest soil samples from Fukushima, Japan, where the 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident contaminated the land. The purpose of this study was to examine how the local topography influenced the radiocesium (Cs-137) accumulation patterns in soils over the years since the accident. As an analytical method, the general additive model (GAM) was used to determine at what percentages the topographic parameters explain Cs-137 contamination levels down to a depth of 30 cm. For comparison, topographic parameters were extracted from both 1 m and 10 m digital evaluation models (DEMs). The effects of topography were compared with the effects of the soil water content and dry soil bulk density. An additional Tukey’s honestly significant difference (HSD) test was conducted to determine the significance of the hillslope aspect and vegetation cover differences on concentration predictions. The results showed that, at this study site, topographic parameters extracted from the 10 m DEM better predicted Cs-137 levels. The models with a single topographic parameter did not explain Cs-137 levels higher than 30 %. However, combining the parameters improved the explanation percentages. The relative influences of topographic parameters and soil properties were similar throughout the soil depth, showing their subsurface co-functionalities for Cs-137 concentration levels. Tukey’s HSD test results showed the inter-effects of topography and vegetation cover differences. The results of this study indicate that the selection of topographic parameters, as well as the chosen methods of their extractions, have implications for physical models assessing radionuclide contamination levels.

Antimicrobials are perhaps the greatest public health gain ever of humankind. As 2000 approached, many claimed penicillin to be the greatest discovery of the millennium; commercially available only from the early 1940s, the drug had already saved an estimated 200 million lives. Currently, however, the emergence and spread of multidrug-resistant pathogens threatens to reverse these benefits, and experts believe we are in the dawn of a post-antibiotic era. The global projections are alarming: AR infections may cause ten million deaths/year and lead all causes of mortality by 2050. Similar to other pressing global issues, the burden of AR is inequitably distributed, with 90% of AR infections occurring in Asia and Africa. This chapter discusses various forces, natural and anthropogenic, that contribute to AR, and how successful containment demands coordinated efforts from environmental, behavioral, and medical scientists, to public health educators and leaders in agricultural and international policy.

Forests provide valuable water and nutrient resources to farming activities as well as places for various leisure activities. However, decontaminating forests in the aftermath of a massive radionuclide contamination event presents challenges because of the topography and the difficulty of collecting a large number of field samples. Achieving accurate remote measurements also can be hindered by the canopy cover. In Fukushima, Japan, where elevated radioactive fallout occurred following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident in March 2011, about 70 percent of the land is covered with forests. Soil samples collected in a forest in Iitate Village, Fukushima, beginning in 2016 through 2018 still contained an average 98200 Bq/kg of Cs-137 in the top 4 cm depth (std. dev. 114100 Bq/kg). Thus, decontamination and identification of Cs-137 distribution patterns in those forests is still a pressing issue. However, soil types in that region, 1000 mm annual precipitation with intense rain during typhoons, and microtopography have presented challenges to understanding how Cs-137 behaves in those forests. In this study, six topographic parameters were computed from 1-m and 10-m resolution DEMs and the relationships between those parameters and soil water content and bulk density were systematically analyzed for their effects on Cs-137 concentration levels. As the first analytical step, correlation indices and the generalized additive models (GAM) analysis were conducted on those parameters. The results show that not all topographic effects are apparent in the correlation analysis, yet the results can be improved when mixed with other parameters in GAM models. Overall the effect of topographic parameters on Cs-137 levels is DEM resolution-dependent while individual soil properties indicate a strong relationship. Also, it was found that depending on the analysis depth, correlation levels and significance of those parameters in GAM models fluctuate. As the second step, Cs-137 levels were extrapolated to a larger area in the study site to understand further the connections between topography and soil properties. The results, including the limitations and proposals for future forest decontamination, will be presented in the session. Understanding how Cs-137 moves and accumulates in forests, especially immediately after contamination, is critical to avoiding the negative impacts on the environment by decontamination measures and to protecting lowlands from harmful radioactivity levels. This study contributes to the radionuclide research field by presenting an example of data analysis processes using field sampled data.

Understanding bat habitat use and how bat activity changes in response to differing habitats across time and space is critical in developing and implementing effective bat conservation actions. To investigate the utility of geographic information systems (GIS) in studying bat habitat interactions, habitat delineations and bioacoustic sampling were conducted along two transects in Chautauqua County, NY (USA) from mid-May until the end of August 2013. Surveys were vehicular, and driven between 29 and 32 kmph in order to match bats' flying speed. They were conducted starting 30 min after sunset on nights where the temperature was greater than 13 °C. In total, twenty surveys were completed, and 1248 bat calls were identified to species. Mixed models regression analysis revealed significant interactions among all of the species of bat analyzed in the model. The model was supported with a secondary analysis comparing bat call density with land cover. This study supports the hypothesis that bats forage in different habitats at the species level and indicates the importance of forested areas to bats. Additionally, the methodology for this study has the potential to gather large data sets in a short period of time, while collecting data on several species of bat at once and has been shown to be useful in identifying important habitat features for bats using bioacoustics and geospatial analysis. Since the data has been collected following state guidelines, the dataset and its analysis establish a baseline for future data collection campaigns and in performing a similar analysis for other regions within the state of New York or areas worldwide.

Spatially explicit agent-based models (ABMs) have been widely utilized to simulate the dynamics of spatial processes that involve the interactions of individual agents. The assumptions embedded in the ABMs may be responsible for uncertainty in the model outcomes. To ensure the reliability of the outcomes in terms of their space-time patterns, model validation should be performed. In this paper, we propose the use of multiple scale spatio-temporal patterns for validating spatially explicit ABMs. We evaluated several specifications of vector-borne disease transmission models by comparing space-time patterns of model outcomes to observations at multiple scales via the sum of root mean square error (RMSE) measurement. The results indicate that specifications of the spatial configurations of residential area and immunity status of individual humans are of importance to reproduce observed patterns of dengue outbreaks at multiple space-time scales. Our approach to using multiple scale spatio-temporal patterns can help not only to understand the dynamic associations between model specifications and model outcomes, but also to validate spatially explicit ABMs.

Sedentary behavior and lack of physical activity are key modifiable behavioral risk factors for chronic health problems, such as obesity and diabetes. Little is known about how sedentary behavior and physical activity among adolescents spatially cluster. The objective was to detect spatial clustering of sedentary behavior and physical activity among Boston adolescents. Data were used from the 2008 Boston Youth Survey Geospatial Dataset, a sample of public high school students who responded to a sedentary behavior and physical activity questionnaire. Four binary variables were created: 1) TV watching (>2 hours/day), 2) video games (>2 hours/day), 3) total screen time (>2 hours/day); and 4) 20 minutes/day of physical activity (≥5 days/week). A spatial scan statistic was utilized to detect clustering of sedentary behavior and physical activity. One statistically significant cluster of TV watching emerged among Boston adolescents in the unadjusted model. Students inside the cluster were more than twice as likely to report > 2 hours/day of TV watching compared to respondents outside the cluster. No significant clusters of sedentary behavior and physical activity emerged. Findings suggest that TV watching is spatially clustered among Boston adolescents. Such findings may serve to inform public health policymakers by identifying specific locations in Boston that could provide opportunities for policy intervention. Future research should examine what is linked to the clusters, such as neighborhood environments and network effects.

Community-acquired multidrug resistant Enterobacteriaceae (MDR-Ent) infections continue to increase in the United States. In prior studies, we identified neighboring regions in Chicago, Illinois, where children have 5 to 6 times greater odds of MDR-Ent infections. To prevent community spread of MDR-Ent, we need to identify the MDR-Ent reservoirs. A pilot study of 4 Chicago waterways for MDR-Ent and associated antibiotic resistance genes (ARGs) was conducted. Three waterways (A1 to A3) are labeled safe for “incidental contact recreation” (e.g., kayaking), and A4 is a nonrecreational waterway that carries nondisinfected water. Surface water samples were collected and processed for standard bacterial culture and shotgun metagenomic sequencing. Generally, A3 and A4 (neighboring waterways which are not hydraulically connected) were strikingly similar in bacterial taxa, ARG profiles, and abundances of corresponding clades and genera within the Enterobacteriaceae. Additionally, total ARG abundances recovered from the full microbial community were strongly correlated between A3 and A4 (R(2) = 0.97). Escherichia coli numbers (per 100 ml water) were highest in A4 (783 most probable number [MPN]) and A3 (200 MPN) relative to A2 (84 MPN) and A1 (32 MPN). We found concerning ARGs in Enterobacteriaceae such as MCR-1 (colistin), Qnr and OqxA/B (quinolones), CTX-M, OXA and ACT/MIR (beta-lactams), and AAC (aminoglycosides). We found significant correlations in microbial community composition between nearby waterways that are not hydraulically connected, suggesting cross-seeding and the potential for mobility of ARGs. Enterobacteriaceae and ARG profiles support the hypothesized concerns that recreational waterways are a potential source of community-acquired MDR-Ent.

Despite extensive efforts on achieving reproducible agent-based models (ABMs) to improve the capability of this widely adopted methodology, it remains challenging to reproduce and replicate pre-existing ABMs, due to a number of factors such as diverse computing resources and ABMs platforms. In this study, we propose to employ CyberGIS-Jupyter for spatially explicit ABMs. CyberGIS-Jupyter is a cyberGIS framework to achieve data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. Influenza transmission in the city of Miami, Florida, USA was used as a case study. In the model, Influenza is transmitted through the contact networks of individual human agents, which are constructed based on commuting behaviors. CyberGIS-Jupyter can support one not only to conduct collaborative and transparent modeling, but also to perform modeling simulation on advanced cyberinfrastructure resources. It may contribute to boosting the reproducibility and replicability of ABMs.

The effects of water, sanitation, and hygiene (WASH) interventions have been well acknowledged to reduce the risk from diarrheal disease-causing pathogens. In spite of the recognized importance of WASH interventions on the reduction of diarrheal disease, there are still gaps in the understanding of the time-varying effects of interventions. To bridge this research gap, we developed agent-based models (ABMs) of diarrheal disease transmission in a community context. In the model, infections occur via two pathways: (i) between household members within the household environment and (ii) from the community environment outside the household. To measure the effectiveness of WASH interventions, we performed global sensitivity analysis (GSA) at the macro and micro temporal scales, varying the level of intervention coverage in the community. We simulated three intervention strategies, implemented separately in the experiments. The clean drinking water intervention, sanitation intervention, and hand washing intervention had similar success rates in the long-term. The handwashing intervention had the largest immediate effect. This highlights that proper short- and long-term intervention strategies need to be considered for disease control and the effective management of limited resources.Les effets des interventions sur l'eau, les sanitaires et l'hygiène (WASH) ont été bien reconnus pour réduire le risque d'agents pathogènes causant des maladies diarrhéiques. En dépit de l'importance reconnue des interventions WASH sur la réduction des maladies diarrhéiques, il reste des lacunes dans la compréhension des variations en fonction du temps des effets des interventions. Pour combler cette lacune en matière de recherche, nous avons développé des modèles à base d'agents (MBA) de la transmission des maladies diarrhéiques dans un contexte communautaire. Dans le modèle, les infections se produisent via deux voies: (1) entre les membres du ménage dans l'environnement du ménage et (2) depuis l'environnement de la communauté en dehors du ménage. Pour mesurer l'efficacité des interventions WASH, nous avons effectué une analyse de sensibilité globale (ASG) aux échelles macro et micro-temporelles, en faisant varier le niveau de couverture des interventions dans la communauté. Nous avons simulé trois stratégies d'intervention, mises en œuvre séparément dans les expériences. Les interventions sur l'eau potable, sur les sanitaires et sur le lavage des mains ont eu des taux de réussite similaires à long terme. L'intervention sur le lavage des mains a eu l'effet immédiat le plus important. Cela montre qu'il faut envisager des stratégies d'intervention appropriées à court et à long terme pour lutter contre la maladie et pour la gestion efficace des ressources limitées.

BackgroundThe purpose of this cross-sectional study was to determine the percentage of the US population with 60 min ground or air access to accredited or state-designated endovascular-capable stroke centers (ECCs) and non-endovascular capable stroke centers (NECCs) and the percentage of NECCs with an ECC within a 30 min drive.MethodsStroke centers were identified and classified broadly as ECCs or NECCs. Geographic mapping of stroke centers was performed. The population was divided into census blocks, and their centroids were calculated. Fastest air and ground travel times from centroid to nearest ECC and NECC were estimated.ResultsOverall, 49.6% of US residents had 60 min ground access to ECCs. Approximately 37.7% (113 million) lack 60 min ground or air access to ECCs. Approximately 84.4% have 60 min access to NECCs. Ground-only access was available to 77.9%. Approximately 738 NECCs (45.4%) had an ECC within a 30 min drive.ConclusionNearly one-third of the US population lacks 60 min access to endovascular stroke care, but this is highly variable. Transport models and planning of additional centers should be tailored to each state depending on location and proximity of existing facilities.

Background: Understanding geographic variation in youth drug use is important for both identifying etiologic factors and planning prevention interventions. However, little research has examined spatial clustering of drug use among youths by using rigorous statistical methods. Objectives: The purpose of this study was to examine spatial clustering of youth use of tobacco, alcohol, and marijuana. Methods: Responses on tobacco, alcohol, and marijuana use from 1,292 high school students ages 13–19 who provided complete residential addresses were drawn from the 2008 Boston Youth Survey Geospatial Dataset. Response options on past month use included “none,” “1–2,” “3–9,” and “10 or more.” The response rate for each substance was approximately 94%. Spatial clustering of youth drug use was assessed using the spatial Bernoulli model in the SatScan™ software package. Results: Approximately 12%, 36%, and 18% of youth reported any past-month use of tobacco, alcohol, and/or marijuana, respectively. Two cluster...

Densely populated countries in Asia, such as Bangladesh, are considered to be major contributors to the increased occurrence of global antimicrobial resistance (AMR). Several factors make low-and middle-income countries vulnerable to increased emergence and spread of AMR in the environment including limited regulations on antimicrobial drug use, high volume of antimicrobials used in human medicine and agricultural production, and poor wastewater management. Previous monitoring campaigns to investigate the presence of antibiotics in the aquatic environment have employed targeted analysis in which selected antibiotics are measured using liquid chromatography with tandem mass spectrometry (LC/MS/MS). However, this approach can miss several important contaminants that can contribute to the selective pressure that promotes maintenance and dissemination of antibiotic resistance genes (ARGs) in the environment. Nontarget analysis by suspect screening and reanalysis of stored digital data of previously ran samples can provide information on analytes that were formerly uncharacterized and may be chemicals of emerging concern (CECs). In this study, surface waters in both urban and rural sites in Bangladesh were collected and analyzed for the presence of antibiotic residues and other pharmaceuticals. Utilizing targeted analysis, the antibiotics with the highest concentrations detected were ciprofloxacin (1407 ng/L) and clarithromycin (909 ng/L). In addition, using high-resolution LC/MS/MS in the first ever application of retrospective analysis in samples from Bangladesh, additional antibiotics clindamycin, lincomycin, linezolid, metronidazole, moxifloxacin, nalidixic acid, and sulfapyridine were detected. Prevalence of amoxicillin transformation products in surface waters was also confirmed. In addition, medicinal and agricultural antifungal compounds were frequently found in Bangladeshi surface waters. This later finding – the near ubiquity of antifungal agents in environmental samples – is of particular concern, as it may be contributing to the alarming rise of multi-drug resistant fungal (e.g. Candida auris) disease recently seen in humans throughout the world.

International audience; Dengue is of public health importance in tropical and sub-tropical regions. Dengue virus (DENV) transmission dynamics was studied in Kamphaeng Phet Province, Thailand, using an enhanced spatiotemporal surveillance of 93 hospitalized subjects with confirmed dengue (initiates) and associated cluster individuals (associates) with entomologic sampling. A total of 438 associates were enrolled from 208 houses with household members with a history of fever, located within a 200-m radius of an initiate case. Of 409 associates, 86 (21%) had laboratory-confirmed DENV infection. A total of 63 (1.8%) of the 3,565 mosquitoes collected were dengue polymerase chain reaction positive (PCR+). There was a significant relationship between spatial proximity to the initiate case and likelihood of detecting DENV from associate cases and Aedes mosquitoes. The viral detection rate from human hosts and mosquito vectors in this study was higher than previously observed by the study team in the same geographic area using different methodologies. We propose that the sampling strategy used in this study could support surveillance of DENV transmission and vector interactions.

This study examined the spatio-temporal relationship between on-premises alcohol outlet locations and crime events in Buffalo, New York. Specifically, this research examined whether crime became clustered around on-premises alcohol outlet locations after those locations were licensed. Data from licensed alcohol outlets and violent crime events that occurred between 2005 and 2011 were analyzed using global and local bivariate space-time k-function analyses. The global bivariate space-time K-function analyses indicated that there was dispersion between bars and crime over space and time. Personal crimes showed both dispersion and clustering. Local analyses revealed clustering between alcohol outlets and crimes at discrete space-time intervals. Spatio-temporal analysis of the association between bars and crime gives an indication of the direction of the relationship between them. If this dynamic is better understood, it could be better regulated.

The objective was to detect geospatial clustering of sugar-sweetened beverage (SSB) intake in Boston adolescents (age = 16.3 ± 1.3 years [range: 13-19]; female = 56.1%; White = 10.4%, Black = 42.6%, Hispanics = 32.4%, and others = 14.6%) using spatial scan statistics. We used data on self-reported SSB intake from the 2008 Boston Youth Survey Geospatial Dataset (n = 1292). Two binary variables were created: consumption of SSB (never versus any) on (1) soda and (2) other sugary drinks (e.g., lemonade). A Bernoulli spatial scan statistic was used to identify geospatial clusters of soda and other sugary drinks in unadjusted models and models adjusted for age, gender, and race/ethnicity. There was no statistically significant clustering of soda consumption in the unadjusted model. In contrast, a cluster of non-soda SSB consumption emerged in the middle of Boston (relative risk = 1.20, p = .005), indicating that adolescents within the cluster had a 20% higher probability of reporting non-soda SSB intake than outside the cluster. The cluster was no longer significant in the adjusted model, suggesting spatial variation in non-soda SSB drink intake correlates with the geographic distribution of students by race/ethnicity, age, and gender.

As geospatial researchers' access to high-performance computing clusters continues to increase alongside the availability of high-resolution spatial data, it is imperative that techniques are devised to exploit these clusters' ability to quickly process and analyze large amounts of information. This research concentrates on the parallel computation of A Multidirectional Optimal Ecotope-Based Algorithm AMOEBA. AMOEBA is used to derive spatial weight matrices for spatial autoregressive models and as a method for identifying irregularly shaped spatial clusters. While improvements have been made to the original ‘exhaustive’ algorithm, the resulting ‘constructive’ algorithm can still take a significant amount of time to complete with large datasets. This article outlines a parallel implementation of AMOEBA the P-AMOEBA written in Java utilizing the message passing library MPJ Express. In order to account for differing types of spatial grid data, two decomposition methods are developed and tested. The benefits of using the new parallel algorithm are demonstrated on an example dataset. Results show that different decompositions of spatial data affect the computational load balance across multiple processors and that the parallel version of AMOEBA achieves substantially faster runtimes than those reported in related publications.

Objective To determine the temporal intervals at which spatial clustering of dengue hospitalisations occurs. Methods Space-time analysis of 262 people hospitalised and serologically confirmed with dengue virus infections in Kamphaeng Phet, Thailand was performed. The cases were observed between 1 January 2009 and 6 May 2011. Spatial coordinates of each patient’s home were captured using the Global Positioning System. A novel method based on the Knox test was used to determine the temporal intervals between cases at which spatial clustering occurred. These intervals are indicative of the length of time between successive illnesses in the chain of dengue virus transmission. Results The strongest spatial clustering occurred at the 15–17-day interval. There was also significant spatial clustering over short intervals (2–5 days). The highest excess risk was observed within 200 m of a previous hospitalised case and significantly elevated risk persisted within this distance for 32–34 days. Conclusions Fifteen to seventeen days are the most likely serial interval between successive dengue illnesses. This novel method relies only on passively detected, hospitalised case data with household locations and provides a useful tool for understanding region-specific and outbreak-specific dengue virus transmission dynamics. Objectif: Determiner les intervalles temporels au cours desquels survient le regroupement spatial des hospitalisations pour dengue. Methodes: L’’analyse spatio-temporelle de 262 personnes hospitalisees et confirmees par la serologie pour des infections au virus de la dengue a Kamphaeng Phet, en Thailande a ete realisee. Les cas ont ete observes entre le 1er janvier 2009 et le 6 mai 2011. Les coordonnees spatiales du domicile de chaque patient ont ete capturees a l’aide du Systeme de Positionnement Global. Une nouvelle methode basee sur le test de Knox a ete utilisee pour determiner les intervalles temporels entre les cas au cours desquels le regroupement spatial a eu lieu. Ces intervalles sont des indicateurs de la duree de temps entre les maladies successives dans la chaine de transmission de virus de la dengue. Resultats: Le plus important regroupement spatial a eu lieu dans l’intervalle de 15 a 17 jours. Il y avait egalement un regroupement spatial significatif sur des intervalles courts (2 a 5 jours). Le risque en exces le plus eleve a ete observea endeans 200 m d’un cas precedent hospitalise et un risque significativement eleve a persiste dans cette zone durant 32 a 34 jours. Conclusions: L’intervalle de series de 15 a 17 jours est le plus probable entre les maladies successives de la dengue. Cette nouvelle methode repose uniquement sur les donnees de cas hospitalises, detectes passivement avec les emplacements des menages et constitue un outil utile pour comprendre la dynamique de la transmission du virus de la dengue specifique a la region et specifique a l’epidemie. Objetivo: Determinar los intervalos temporales en los que ocurren conglomerados espaciales de hospitalizacion por dengue. Metodos: Se realizo un analisis espacio temporal a 262 personas hospitalizadas y con confirmacion serologica de infeccion por el virus del dengue en Kamphaeng Phet, Tailandia. Los casos se observaron entre el 1 de Enero del 2009 y el 6 de Mayo del 2011. Las coordenadas espaciales del hogar de cada paciente fueron capturadas utilizando un Sistema de Posicionamiento Global. Se utilizo un nuevo metodo, basado en la prueba de Knox, para determinar los intervalos temporales entre los casos en los que habia conglomerados espaciales. Estos intervalos son indicativos del lapso de tiempo entre enfermedades sucesivas en la cadena de transmision del virus del dengue. Resultados: El mayor conglomerado espacial ocurrio en un intervalo de 15 a 17 dias. Tambien habia un conglomerado espacial significativo en intervalos cortos (2–5 dias). El mayor exceso de riesgo se observo dentro de un radio de 200 m de un caso previamente hospitalizado y un riesgo significativamente elevado persistia, dentro de esta distancia, durante 32–34 dias. Conclusiones: Los 15–17 dias son el intervalo mas probable entre dos casos sucesivos de dengue. Este nuevo metodo depende solamente de la disponibilidad de datos de casos hospitalizados, detectados de forma pasiva y con hogares localizables, y brinda una herramienta util para entender la dinamica de transmision del virus del dengue, region-especifica y especifica de brotes.

We investigated the validity of Walk Scores and Transit Scores from the Walk Score website using several objective geographic information systems (GIS) measures of neighborhood walkabiltiy and transit availability based on 400- and 800-m street network buffers. Address data come from the 2008 Boston Youth Survey Geospatial Dataset, a school-based sample of public high school students in Boston, MA with complete residential address information (n = 1,292). GIS data were used to create multiple objective measures of neighborhood walkability and transit availability. We also obtained Walk Scores and Transit Scores. We calculated Spearman correlations of Walk Scores and Transit Scores with the GIS neighborhood walkability/transit availability measures as well as Spearman correlations accounting for spatial autocorrelation. Several significant correlations were observed between Walk Score and 400-m buffer GIS measures of neighborhood walkability; all significant correlations were found for the 800-m buffer. All correlations between Transit Scores and GIS measures of neighborhood transit availability were also significant (all p < 0.0001). However, the magnitude of correlations varied by the GIS measure and neighborhood definition. Relative to the 400-m buffer, correlations for the 800-m buffer were higher. This study suggests that Walk Score is a good, convenient tool to measure certain aspects of neighborhood walkability and transit availability (such as density of retail destinations, density of recreational open space, intersection density, residential density and density of subway stops). However, Walk Score works best at larger spatial scales.

Neighborhood walkability can influence physical activity. We evaluated the validity of Walk Score(®) for assessing neighborhood walkability based on GIS (objective) indicators of neighborhood walkability with addresses from four US metropolitan areas with several street network buffer distances (i.e., 400-, 800-, and 1,600-meters). Address data come from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5-11 years and their families participating in YMCA-administered, after-school programs located in four geographically diverse metropolitan areas in the US (n = 733). GIS data were used to measure multiple objective indicators of neighborhood walkability. Walk Scores were also obtained for the participant's residential addresses. Spearman correlations between Walk Scores and the GIS neighborhood walkability indicators were calculated as well as Spearman correlations accounting for spatial autocorrelation. There were many significant moderate correlations between Walk Scores and the GIS neighborhood walkability indicators such as density of retail destinations and intersection density (p0.05). The magnitude varied by the GIS indicator of neighborhood walkability. Correlations generally became stronger with a larger spatial scale, and there were some geographic differences. Walk Score(®) is free and publicly available for public health researchers and practitioners. Results from our study suggest that Walk Score(®) is a valid measure of estimating certain aspects of neighborhood walkability, particularly at the 1600-meter buffer. As such, our study confirms and extends the generalizability of previous findings demonstrating that Walk Score is a valid measure of estimating neighborhood walkability in multiple geographic locations and at multiple spatial scales.

In this paper, we present an efficiency improvement for the algorithm called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, devised by Aldstadt and Getis (Geogr Anal 38(4):327–343, 2006). AMOEBA embeds a local spatial autocorrelation statistic in an iterative procedure in order to identify spatial clusters (ecotopes) of related spatial units. We provide an analysis of the computational complexity of the original AMOEBA and develop an alternative formulation that reduces computational time without losing optimality. Empirical evidence is provided using georeferenced socio-demographic data in Accra, Ghana.

We investigated how temporal and spatial effects confound the functional relationship between pupal and adult populations of Aedes aegypti and thus the value of pupal numbers as predictors of dengue transmission risk in Kamphaeng Phet, Thailand. We found considerable seasonal shifts in productivity of key containers. Tires contained much less pupae in the dry season than in the wet season. Earthenware jars and cement tanks for washing purposes were consistent producers over the entire study period. Houses in the two villages, with approximately twice as many houses per unit area, were significantly more likely to have adults and pupae. No significant annual, seasonal, or spatial effects on the strength of correlations between pupal and adult populations were found. Except for 2 (of 16) occasions, pupal, and adult populations were correlated strongly in time and space. Our results are consistent with application of the pupal survey technique for assessing dengue transmission risk.

Given that tools for dengue emergency control are limited, continuous evaluation of the effectiveness of insecticide applications in the field is of utmost importance. Such studies will provide a sound basis for defining spraying schemes for public health authorities in dengue-affected countries. In this article, we address the following research questions: How do different space spraying strategies affect Aedes aegypti (L.) (Diptera: Culicidae) populations in both space and time? More specifically, how well are these mosquitoes killed, and how quickly do their populations recover and from where? Field trials were carried out with ultralow volume sprayers in Kamphaeng Phet province, Thailand, with a pyrethrin mixture that was applied 1) indoors only, 2) indoors plus outdoors, 3) indoors with a doubled spraying time, and 4) indoors with doubled spraying time plus outdoors. We found that within 7 d, Ae. aegypti populations recovered to ≈50% of their original numbers. Spraying the outdoor area and doubling the time sprayed per room only had a significant impact on mosquito numbers 1 d after spraying. Two and 7 d after spraying, these effects were no longer detected. By investigating the spatial arrangement of Ae. aegypti numbers, we found that during the first 2 d after spraying immigration from untreated areas extended ≈15 m into the sprayed area, whereas after 7 d this effect extended up to 50 m. Results are discussed in relation to ongoing dengue control efforts in Thailand.

Cardiac remodeling and failure are regulated by a myriad of cardiac protein phosphorylations. In the present study, cardiac phosphoprotein patterns were examined in rodent and human hearts Left ventricular tissue samples were obtained from human systolic failing (n = 5) and control (n = 5) hearts and from two rat models of hypertensive heart failure, i.e., spontaneously hypertensive heart failure and Dahl salt-sensitive rats and corresponding controls. Phosphoproteins were separated by 2D-DIGE with Cydye staining, phosphoprotein patterns were analyzed using pixel intensity in rectified images. Specific phosphoproteins which were different in human versus rodent hearts were identified by MALDI-TOF/TOF Mass Spectrometry. Targeted pair-wise analyses showed differences (p

Background: Rodent models are commonly used to study hypertensive heart disease. Several recent studies have probed the level of correlation between specific signaling pathways and proteins in human and rodents. Current evidence is overwhelming that protein phosphorylations play a key role in cardiac remodeling. Methods: Left ventricular tissue samples were obtained from human systolic failing (n=5) and control (n=5) hearts and 3 rat models of hypertensive heart failure (aortic banding, Dahl salt-sensitive, and spontaneously hypertensive rats (SHR)) and corresponding controls. Total proteins were extracted and and phosphoenrichment performed. Phosphoproteins were separated by 2D-DIGE with Cydye staining. Gel images were registered and rectified for composite analysis and statistical comparisons using pixel intensity. Phosphoproteins were identified by MALDI-TOF/TOF Mass Spectrometry. Results: The patterns of overall protein abundance from normal and failing hearts were not statistically different. However, when the composite of human hearts were compared with composite patterns of phosphoproteins in normal and failing rodent hearts, there were profound differences in the phosphoprotein patterns in 26% of pixels in registered images (P < 0.05). Targeted pair wise analyses showed differences (P < 0.05) between human and rodent hearts for troponin T, myosin light chain, peroxiredoxin, and haptoglobin phosphorylations. Conclusions: Together, the present results indicate significant differences in cardiac phosphoproteins in human versus rodent heart and the importance of confirming findings from rodent studies in humans for translational studies of kinases, phosphatases, and phosphoproteins. This may specifically relate to studies of phosphorylation of myosin light chain and troponin.

The creation of a spatial weights matrix by a procedure called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, is dependent on the use of a local spatial autocorrelation statistic. The result is (1) a vector that identifies those spatial units that are related and unrelated to contiguous spatial units and (2) a matrix of weights whose values are a function of the relationship of the ith spatial unit with all other nearby spatial units for which there is a spatial association. In addition, the AMOEBA procedure aids in the demarcation of clusters, called ecotopes, of related spatial units. Experimentation reveals that AMOEBA is an effective tool for the identification of clusters. A comparison with a scan statistic procedure (SaTScan) gives evidence of the value of AMOEBA. Total fertility rates in enumeration districts in Amman, Jordan, are used to show a real-world example of the use of AMOEBA for the construction of a spatial weights matrix and for the identification of clusters. Again, comparisons reveal the effectiveness of the AMOEBA procedure.

Dengue is a mosquito-borne infectious disease that is endemic in tropical and subtropical countries. Many individual-level simulation models have been developed to test hypotheses about dengue virus transmission. Often these efforts assume that human host and mosquito vector populations are randomly or uniformly distributed in the environment. Although, the movement of mosquitoes is affected by spatial configuration of buildings and mosquito populations are highly clustered in key buildings, little research has focused on the influence of the local built environment in dengue transmission models. We developed an agent-based model of dengue transmission in a village setting to test the importance of using realistic environments in individual-level models of dengue transmission. The results from one-way ANOVA analysis of simulations indicated that the differences between scenarios in terms of infection rates as well as serotype-specific dominance are statistically significant. Specifically, the infection rates in scenarios of a realistic environment are more variable than those of a synthetic spatial configuration. With respect to dengue serotype-specific cases, we found that a single dengue serotype is more often dominant in realistic environments than in synthetic environments. An agent-based approach allows a fine-scaled analysis of simulated dengue incidence patterns. The results provide a better understanding of the influence of spatial heterogeneity on dengue transmission at a local scale.

Background Long-term homologous and temporary heterologous protection from dengue virus (DENV) infection may be mediated by neutralizing antibodies. However, neutralizing antibody titers (NTs) have not been clearly associated with protection from infection. Methodology/Principal Findings Data from two geographic cluster studies conducted in Kamphaeng Phet, Thailand were used for this analysis. In the first study (2004–2007), cluster investigations of 100-meter radius were triggered by DENV-infected index cases from a concurrent prospective cohort. Subjects between 6 months and 15 years old were evaluated for DENV infection at days 0 and 15 by DENV PCR and IgM ELISA. In the second study (2009–2012), clusters of 200-meter radius were triggered by DENV-infected index cases admitted to the provincial hospital. Subjects of any age ≥6 months were evaluated for DENV infection at days 0 and 14. In both studies, subjects who were DENV PCR positive at day 14/15 were considered to have been “susceptible” on day 0. Comparison subjects from houses in which someone had documented DENV infection, but the subject remained DENV negative at days 0 and 14/15, were considered “non-susceptible.” Day 0 samples were presumed to be from just before virus exposure, and underwent plaque reduction neutralization testing (PRNT). Seventeen “susceptible” (six DENV-1, five DENV-2, and six DENV-4), and 32 “non-susceptible” (13 exposed to DENV-1, 10 DENV-2, and 9 DENV-4) subjects were evaluated. Comparing subjects exposed to the same serotype, receiver operating characteristic (ROC) curves identified homotypic PRNT titers of 11, 323 and 16 for DENV-1, -2 and -4, respectively, to differentiate “susceptible” from “non-susceptible” subjects. Conclusions/Significance PRNT titers were associated with protection from infection by DENV-1, -2 and -4. Protective NTs appeared to be serotype-dependent and may be higher for DENV-2 than other serotypes. These findings are relevant for both dengue epidemiology studies and vaccine development efforts., Author Summary Dengue is caused by four different dengue virus serotypes (DENV-1, -2, -3, -4). Infection induces long-term protection against the same serotype, but only short-term protection, and possible enhancement, from different serotypes. DENV neutralizing antibody titers (NTs) are thought to mediate protection or modify disease. Association of NTs with protection from infection has not, however, been clearly demonstrated. We analyzed data from two geographic clusters studies conducted in Kamphaeng Phet, Thailand, in which DENV NTs just before virus exposure were compared between DENV-infected “susceptible” and non-infected “non-susceptible” subjects. NTs appeared to be associated with protection against DENV-1, -2, and -4, but at different NT cutoff levels, with the cutoff for DENV-2 appearing to be the highest. These findings are relevant for ongoing efforts to investigate dengue epidemiology and develop dengue vaccine candidates.

A four-year longitudinal cohort and geographic cluster study in rural Thailand was conducted to characterize the clinical spectrum of dengue virus (DENV) infection. Symptomatic DENV infections in the cohort were detected by active school absence-based surveillance that triggered cluster investigations around ill cohort children. Data from 189 cohort children with symptomatic DENV infection and 126 contact children in the clusters with DENV infection were analyzed. Of infected contacts, only 19% were asymptomatic; 81% were symptomatic, but only 65.9% reported fever. Symptom-based case definitions were unreliable for diagnosis. Symptomatic infections in contacts were milder with lower DENV RNA levels than the cohort. Infections in contacts with fever history were more likely to have detectable DENV RNA than infections without fever history. Mild infections identified by cluster investigations account for a major proportion of all DENV infections. These findings are relevant for disease burden assessments, transmission modeling, and determination of vaccine impact.

Measurements of neighborhood exposures likely vary depending on the definition of “neighborhood” selected. This study examined the extent to which neighborhood definition influences findings regarding spatial accessibility to tobacco retailers among youth. We defined spatial accessibility to tobacco retailers (i.e., tobacco retail density, closest tobacco retailer, and average distance to the closest 5 tobacco retailers) on the basis of circular and network buffers of 400 m and 800 m, census block groups, and census tracts by using residential addresses from the 2008 Boston Youth Survey Geospatial Dataset (n = 1,292). Friedman tests (to compare overall differences in neighborhood definitions) were applied. There were differences in measurements of youths' access to tobacco retailers according to the selected neighborhood definitions, and these were marked for the 2 spatial proximity measures (both P < 0.01 for all differences). For example, the median average distance to the closest 5 tobacco retailers was 381.50 m when using specific home addresses, 414.00 m when using census block groups, and 482.50 m when using census tracts, illustrating how neighborhood definition influences the measurement of spatial accessibility to tobacco retailers. These analyses suggest that, whenever possible, egocentric neighborhood definitions should be used. The use of larger administrative neighborhood definitions can bias exposure estimates for proximity measures.

Background Multiple and varied benefits have been suggested for increased neighborhood walkability. However, spatial inequalities in neighborhood walkability likely exist and may be attributable, in part, to residential segregation. Objective Utilizing a spatial demographic perspective, we evaluated potential spatial inequalities in walkable neighborhood amenities across census tracts in Boston, MA (US). Methods The independent variables included minority racial/ethnic population percentages and percent of families in poverty. Walkable neighborhood amenities were assessed with a composite measure. Spatial autocorrelation in key study variables were first calculated with the Global Moran's I statistic. Then, Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were calculated as well as Spearman correlations accounting for spatial autocorrelation. We fit ordinary least squares (OLS) regression and spatial autoregressive models, when appropriate, as a final step. Results Significant positive spatial autocorrelation was found in neighborhood socio-demographic characteristics (e.g. census tract percent Black), but not walkable neighborhood amenities or in the OLS regression residuals. Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were not statistically significant, nor were neighborhood socio-demographic characteristics significantly associated with walkable neighborhood amenities in OLS regression models. Conclusions Our results suggest that there is residential segregation in Boston and that spatial inequalities do not necessarily show up using a composite measure. Comments Future research in other geographic areas (including international contexts) and using different definitions of neighborhoods (including small-area definitions) should evaluate if spatial inequalities are found using composite measures but also should use measures of specific neighborhood amenities.

Background Built environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers). Methods Data come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran’s I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools. Results We found significant positive spatial autocorrelation in the built environment features examined (Global Moran’s I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran’s I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p p Conclusion Some relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents.

Background Based on spatiotemporal clustering of human dengue virus (DENV) infections, transmission is thought to occur at fine spatiotemporal scales by horizontal transfer of virus between humans and mosquito vectors. To define the dimensions of local transmission and quantify the factors that support it, we examined relationships between infected humans and Aedes aegypti in Thai villages. Methodology/Principal Findings Geographic cluster investigations of 100-meter radius were conducted around DENV-positive and DENV-negative febrile “index” cases (positive and negative clusters, respectively) from a longitudinal cohort study in rural Thailand. Child contacts and Ae. aegypti from cluster houses were assessed for DENV infection. Spatiotemporal, demographic, and entomological parameters were evaluated. In positive clusters, the DENV infection rate among child contacts was 35.3% in index houses, 29.9% in houses within 20 meters, and decreased with distance from the index house to 6.2% in houses 80–100 meters away (p, Author Summary Dengue is the leading cause of mosquito-borne viral infections globally. An improved understanding of the spatial and temporal distribution of dengue virus (DENV) transmission between humans and the principal vector, Aedes aegypti, can enhance prevention programs. Human DENV infection is known to occur at very fine spatiotemporal scales. We sought to link and quantify human DENV infections with infectious mosquitoes at these fine scales by conducting geographic cluster investigations around febrile children with and without DENV infection. We found that DENV infection in children was positively associated with houses in which infectious mosquitoes were captured. These houses also had more Ae. aegypti pupae and adult female mosquitoes than neighboring houses. However, the neighboring houses still had elevated rates of human DENV infection. Our results indicate that certain houses with high risk of DENV transmission contribute disproportionately to DENV amplification and spread to surrounding houses. At a given point in time, people and mosquitoes from a small portion of houses are responsible for the majority of DENV transmission.

Dengue virus (DENV) causes more human morbidity and mortality than any other arboviral disease. Each year, an estimated 3.6 billion people are at risk, 36 million develop dengue fever (DF), 2.1 million develop severe dengue hemorrhagic fever (DHF)/dengue shock syndrome, and 21 000 die [1]. Much of the available epidemiologic and clinical data on dengue are based on passive reporting. An improved understanding of both the patterns of DENV transmission and clinical spectrum of infection would improve public health surveillance and inform mitigation strategies. Prospective cohort studies in Thailand [2–6], Indonesia [7, 8], Nicaragua [9], Vietnam [10], and Peru [11, 12] are addressing knowledge gaps by providing a more comprehensive picture of the spectrum of DENV infection from clinically inapparent infection to severe disease. These increasingly detailed datasets have revealed marked spatial and temporal heterogeneity in the intensity of DENV transmission and the ratio of inapparent-to-symptomatic infections [13]. To further explore the range of DENV infection outcomes, especially with regard to inapparent infections that may contribute to transmission dynamics, we and others have initiated studies incorporating geographic cluster designs [6, 14, 15] in which symptomatic DENV infections trigger sampling of people who live in close proximity to an individual with a documented DENV infection. Geographic cluster studies can elucidate transmission dynamics on a finer spatial and temporal scale than is possible with longitudinal cohort studies, thus allowing increasingly detailed analysis of the factors that affect transmission dynamics. We previously published the findings from the first 2 years of a 4-year combined longitudinal cohort and geographic cluster study in Kamphaeng Phet, Thailand [6]. Those data indicated that DENV transmission in rural locations occurs in a remarkably focal manner and suggested that environmental factors may influence transmission risk. In the current report, we present a more detailed analysis of the full 4-year prospective study, which includes a transition in the predominant circulating DENV serotype. Our results indicate that many DENV infections that were considered clinically inapparent in the longitudinal cohort were, in fact, viremic and therefore potentially contributed to DENV transmission.

Background Transmission of dengue viruses (DENV), the leading cause of arboviral disease worldwide, is known to vary through time and space, likely owing to a combination of factors related to the human host, virus, mosquito vector, and environment. An improved understanding of variation in transmission patterns is fundamental to conducting surveillance and implementing disease prevention strategies. To test the hypothesis that DENV transmission is spatially and temporally focal, we compared geographic and temporal characteristics within Thai villages where DENV are and are not being actively transmitted. Methods and Findings Cluster investigations were conducted within 100 m of homes where febrile index children with (positive clusters) and without (negative clusters) acute dengue lived during two seasons of peak DENV transmission. Data on human infection and mosquito infection/density were examined to precisely (1) define the spatial and temporal dimensions of DENV transmission, (2) correlate these factors with variation in DENV transmission, and (3) determine the burden of inapparent and symptomatic infections. Among 556 village children enrolled as neighbors of 12 dengue-positive and 22 dengue-negative index cases, all 27 DENV infections (4.9% of enrollees) occurred in positive clusters (p < 0.01; attributable risk [AR] = 10.4 per 100; 95% confidence interval 1–19.8 per 100]. In positive clusters, 12.4% of enrollees became infected in a 15-d period and DENV infections were aggregated centrally near homes of index cases. As only 1 of 217 pairs of serologic specimens tested in positive clusters revealed a recent DENV infection that occurred prior to cluster initiation, we attribute the observed DENV transmission subsequent to cluster investigation to recent DENV transmission activity. Of the 1,022 female adult Ae. aegypti collected, all eight (0.8%) dengue-infected mosquitoes came from houses in positive clusters; none from control clusters or schools. Distinguishing features between positive and negative clusters were greater availability of piped water in negative clusters (p < 0.01) and greater number of Ae. aegypti pupae per person in positive clusters (p = 0.04). During primarily DENV-4 transmission seasons, the ratio of inapparent to symptomatic infections was nearly 1:1 among child enrollees. Study limitations included inability to sample all children and mosquitoes within each cluster and our reliance on serologic rather than virologic evidence of interval infections in enrollees given restrictions on the frequency of blood collections in children. Conclusions Our data reveal the remarkably focal nature of DENV transmission within a hyperendemic rural area of Thailand. These data suggest that active school-based dengue case detection prompting local spraying could contain recent virus introductions and reduce the longitudinal risk of virus spread within rural areas. Our results should prompt future cluster studies to explore how host immune and behavioral aspects may impact DENV transmission and prevention strategies. Cluster methodology could serve as a useful research tool for investigation of other temporally and spatially clustered infectious diseases.

We investigated how temporal and spatial effects confound the functional relationship between pupal and adult populations of Aedes aegypti and thus the value of pupal numbers as predictors of dengue transmission risk in Kamphaeng Phet, Thailand. We found considerable seasonal shifts in productivity of key containers. Tires contained much less pupae in the dry season than in the wet season. Earthenware jars and cement tanks for washing purposes were consistent producers over the entire study period. Houses in the two villages, with approximately twice as many houses per unit area, were significantly more likely to have adults and pupae. No significant annual, seasonal, or spatial effects on the strength of correlations between pupal and adult populations were found. Except for 2 (of 16) occasions, pupal, and adult populations were correlated strongly in time and space. Our results are consistent with application of the pupal survey technique for assessing dengue transmission risk.