A Pilot Study of Chicago Waterways as Reservoirs of Multidrug-Resistant Enterobacteriaceae (MDR-Ent) in a High-Risk Region for Community-Acquired MDR-Ent Infection in Children.

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 (R2 = 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.

Examining time-dependent effects of water, sanitation, and hygiene (WASH) interventions using an agent-based model.

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.

Geospatial clustering in sugar-sweetened beverage consumption among Boston youth.

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.

Local spatial clustering in youths' use of tobacco, alcohol, and marijuana in Boston.

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 clusters of elevated past tobacco use among Boston youths were generated, one of which was statistically significant. This cluster, located in the South Boston neighborhood, had a relative risk of 5.37 with a p-value of 0.00014. There was no significant localized spatial clustering in youth past alcohol or marijuana use in either the unadjusted or adjusted models. CONCLUSION: Significant spatial clustering in youth tobacco use was found. Finding a significant cluster in the South Boston neighborhood provides reason for further investigation into neighborhood characteristics that may shape adolescents' substance use behaviors. This type of research can be used to evaluate the underlying reasons behind spatial clustering of youth substance and to target local drug abuse prevention interventions and use.

Examination of how neighborhood definition influences measurements of youths' access to tobacco retailers: a methodological note on spatial misclassification.

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.

Global antimicrobial resistance: a complex and dire threat with few definite answers.

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.

Assessing the impact of areal unit selection and the modifiable areal unit problem on associative statistics between cases of tick-borne disease and entomological indices.

The modifiable areal unit problem (MAUP) is a cause of statistical and visual bias when aggregating data according to spatial units, particularly when spatial units may be changed arbitrarily. The MAUP is a concern in vector-borne disease research when entomological metrics gathered from point-level sampling data are related to epidemiological data aggregated to administrative units like counties or ZIP Codes. Here, we assess the statistical impact of the MAUP when calculating correlations between randomly aggregated cases of anaplasmosis in New York State during 2017 and a geostatistical layer of an entomological risk index for Anaplasma phagocytophilum in blacklegged ticks (Ixodes scapularis Say, Acari: Ixodidae) collected during the fall of 2017. Correlations were also calculated using various administrative boundaries for comparison. We also demonstrate the impact of the MAUP on data visualization using choropleth maps and offer pycnophylactic interpolation as an alternative. Polygon simulations indicate that increasing the number of polygons decreases correlation coefficients and their variability. Correlation coefficients calculated using ZIP Code tabulation area and Census tract polygons were beyond 4 standard deviations from the mean of the simulated correlation coefficients. These results indicate that using smaller polygons may not best incorporate the geographical context of the tick-borne disease system, despite the tendency of researchers to strive for more granular spatial data and associations.

Underrecognized mildly symptomatic viremic dengue virus infections in rural Thai schools and villages.

BACKGROUND: The understanding of dengue virus (DENV) transmission dynamics and the clinical spectrum of infection are critical to informing surveillance and control measures. Geographic cluster studies can elucidate these features in greater detail than cohort studies alone. METHODS: A 4-year longitudinal cohort and geographic cluster study was undertaken in rural Thailand. Cohort children underwent pre-/postseason serology and active school absence-based surveillance to detect inapparent and symptomatic dengue. Cluster investigations were triggered by cohort dengue and non-dengue febrile illnesses (positive and negative clusters, respectively). RESULTS: The annual cohort incidence of symptomatic dengue ranged from 1.3% to 4.4%. DENV-4 predominated in the first 2 years, DENV-1 in the second 2 years. The inapparent-to-symptomatic infection ratio ranged from 1.1:1 to 2.9:1. Positive clusters had a 16.0% infection rate, negative clusters 1.1%. Of 119 infections in positive clusters, 59.7% were febrile, 20.2% were afebrile with other symptoms, and 20.2% were asymptomatic. Of 16 febrile children detected during cluster investigations who continued to attend school, 9 had detectable viremia. CONCLUSIONS: Dengue transmission risk was high near viremic children in both high- and low-incidence years. Inapparent infections in the cohort overestimated the rate of asymptomatic infections. Ambulatory children with mild febrile viremic infections could represent an important component of dengue transmission.

Habitat suitability mapping using logistic regression analysis of long-term bioacoustic bat survey dataset in the Cassadaga Creek watershed (USA).

Bat species show global ecological importance, yet their numbers are declining worldwide. Understanding bat-habitat interactions is crucial in terms of developing effective conservation plans. In an effort to model bat habitat suitability in the Cassadaga Creek watershed, long-term bioacoustic bat data (spanning 2009-2020) was compiled, georeferenced and statistically analyzed using logistic regression techniques. In total, 1600 bat occurrence records from five species of bat (559 Eptesicus fuscus, 560 Lasionycteris noctivagans, 143 Lasiurus borealis, 260 Lasiurus cinereus, and 78 Myotis lucifugus) were paired with pseudo-absence points to study the relationship between bat calling behavior and land cover. All bats but Myotis lucifugus had a statistically significant relationship with forested land cover, and all bats had negative interactions with agricultural habitats. Geospatial data was coupled with the statistical output to create maps of habitat suitability and echolocation calling density. This work provides a model that can be employed worldwide to evaluate bat habitat needs or patterns in echolocation behavior. Future research will incorporate a more recently collected dataset that is of greater geographic diversity with a larger number of environmental variables in the species distribution model.

Space-time analysis of hospitalised dengue patients in rural Thailand reveals important temporal intervals in the pattern of dengue virus transmission.

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.

Racial differences in the built environment--body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods.

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 < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer. 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.

Space, race, and poverty: Spatial inequalities in walkable neighborhood amenities?

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.

Chiropteran chatter in Chautauqua, NY (USA): Using acoustic sampling and geographic information systems to create a baseline bat habitat dataset.

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.

Mapping access to endovascular stroke care in the USA and implications for transport models.

BACKGROUND: The 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. METHODS: Stroke 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. RESULTS: Overall, 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. CONCLUSION: Nearly 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.

A cyberGIS approach to spatiotemporally explicit uncertainty and global sensitivity analysis for agent-based modeling of vector-borne disease transmission.

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.

Retrospective suspect screening reveals previously ignored antibiotics, antifungal compounds, and metabolites in Bangladesh surface waters.

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.

Using Multiple Scale Spatio-Temporal Patterns for Validating Spatially Explicit Agent-Based Models.

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.

Using Multiple Scale Space-Time Patterns in Variance-Based Global Sensitivity Analysis for Spatially Explicit Agent-Based Models.

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.

Spatial and temporal clustering of dengue virus transmission in Thai villages.

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.