Community-serving research addressing climate change impacts on vector-borne diseases.

The impacts of climate change on vector-borne diseases are uneven across human populations. This pattern reflects the effect of changing environments on the biology of transmission, which is also modulated by social and other inequities. These disparities are also linked to research outcomes that could be translated into tools for transmission reduction, but are not necessarily actionable in the communities where transmission occurs. The transmission of vector-borne diseases could be averted by developing research that is both hypothesis-driven and community-serving for populations affected by climate change, where local communities interact as equal partners with scientists, developing and implementing research projects with the aim of improving community health. In this Personal View, we share five principles that have guided our research practice to serve the needs of communities affected by vector-borne diseases.

Anopheles albimanus (Diptera: Culicidae) Ensemble Distribution Modeling: Applications for Malaria Elimination.

In the absence of entomological information, tools for predicting Anopheles spp. presence can help evaluate the entomological risk of malaria transmission. Here, we illustrate how species distribution models (SDM) could quantify potential dominant vector species presence in malaria elimination settings. We fitted a 250 m resolution ensemble SDM for Anopheles albimanus Wiedemann. The ensemble SDM included predictions based on seven different algorithms, 110 occurrence records and 70 model projections. SDM covariates included nine environmental variables that were selected based on their importance from an original set of 28 layers that included remotely and spatially interpolated locally measured variables for the land surface of Costa Rica. Goodness of fit for the ensemble SDM was very high, with a minimum AUC of 0.79. We used the resulting ensemble SDM to evaluate differences in habitat suitability (HS) between commercial plantations and surrounding landscapes, finding a higher HS in pineapple and oil palm plantations, suggestive of An. albimanus presence, than in surrounding landscapes. The ensemble SDM suggested a low HS for An. albimanus at the presumed epicenter of malaria transmission during 2018-2019 in Costa Rica, yet this vector was likely present at the two main towns also affected by the epidemic. Our results illustrate how ensemble SDMs in malaria elimination settings can provide information that could help to improve vector surveillance and control.

Trade, uneven development and people in motion: Used territories and the initial spread of COVID-19 in Mesoamerica and the Caribbean.

Mesoamerica and the Caribbean form a region comprised by middle- and low-income countries affected by the COVID-19 pandemic differently. Here, we ask whether the spread of COVID-19, measured using early epidemic growth rates (r), reproduction numbers (R t ), accumulated cases, and deaths, is influenced by how the 'used territories' across the regions have been differently shaped by uneven development, human movement and trade differences. Using an econometric approach, we found that trade openness increased cases and deaths, while the number of international cities connected at main airports increased r, cases and deaths. Similarly, increases in concentration of imports, a sign of uneven development, coincided with increases in early epidemic growth and deaths. These results suggest that countries whose used territory was defined by a less uneven development were less likely to show exacerbated COVID-19 patterns of transmission. Health outcomes were worst in more trade-dependent countries, even after controlling for the impact of transmission prevention and mitigation policies, highlighting how structural effects of economic integration in used territories were associated with the initial COVID-19 spread in Mesoamerica and the Caribbean.

Aedes albopictus and Aedes flavopictus (Diptera: Culicidae) pre-imaginal abundance patterns are associated with different environmental factors along an altitudinal gradient.

Aedes (Stegomyia) albopictus (Skuse) is a major global invasive mosquito species that, in Japan, co-occurs with Aedes (Stegomyia) flavopictus Yamada, a closely related species recently intercepted in Europe. Here, we present results of a detailed 25-month long study where we biweekly sampled pupae and fourth instar larvae of these two species from ovitraps set along Mt. Konpira, Nagasaki, Japan. This setting allowed us to ask whether these species had different responses to changes in environmental variables along the altitudinal gradient of an urban hill. We found that spatially Ae. albopictus abundance decreased, while Ae. flavopictus abundance increased, the further away from urban land. Ae. flavopictus also was more abundant than Ae. albopictus in locations with homogenous vegetation growth with a high mean Enhanced Vegetation Index (EVI), platykurtic EVI, and low SD in canopy cover, while Ae. albopictus was more abundant than Ae. flavopictus in areas with more variable (high SD) canopy cover. Moreover, Ae. flavopictus abundance negatively impacted the spatial abundance of Ae. albopictus. Temporally we found that Ae. flavopictus was more likely to be present in Mt. Konpira at lower temperatures than Ae. albopictus. Our results suggest that spatial and temporal abundance patterns of these two mosquito species are partially driven by their different response to environmental factors.

Synchrony of globally invasive Aedes spp. immature mosquitoes along an urban altitudinal gradient in their native range.

Mosquito-borne infections often have concerted peaks, or are synchronous, across landscapes. This phenomenon might be driven by vector responses to similar environmental conditions that synchronize their abundance. While adult mosquito populations can be synchronous over spatial scales ranging from a few meters to a few kilometers, little to nothing is known about immature mosquito synchrony, including its relationship with mosquito colonization and persistence in larval habitats. Here, we present results from a 2-yearlong synchrony study in co-occurring populations of Aedes (Stegomyia) albopictus (Skuse), Aedes (Stegomyia) flavopictus Yamada and Aedes (Finlaya) japonicus japonicus (Theobald), three invasive mosquito species, along an urban altitudinal gradient in Japan. We found that Ae. albopictus was asynchronous while Ae. flavopictus and Ae. j. japonicus had synchrony that, respectively, tracked geographic and altitudinal patterns of temperature correlation. Spatially, Ae. albopictus was more persistent at hotter locations near urban land use, while Ae. j. japonicus and Ae. flavopictus increasingly persisted farther away from urban land. Temporally, Ae. albopicus and Ae. flavopictus decreased the proportion of colonized habitats following variable rainfall, while Ae. j. japonicus increased with vegetation growth and leptokurtic temperatures. Our results support the hypothesis that immature mosquito synchrony is autonomous from dispersal and driven by common environmental conditions.

Landscape and Environmental Factors Influencing Stage Persistence and Abundance of the Bamboo Mosquito, Tripteroides bambusa (Diptera: Culicidae), across an Altitudinal Gradient.

The bamboo mosquito, Tripteroides bambusa (Yamada) (Diptera: Culicidae), is a common insect across East Asia. Several studies have looked at the ecology of Tr. bambusa developmental stages separately, but little is known about the factors associated with the persistence (how often) and abundance (how many individuals) of Tr. bambusa stages simultaneously studied across a heterogeneous landscape. Here, we ask what environmental and landscape factors are associated with the persistence and abundance of Tr. bambusa stages across the altitudinal gradient of Mt. Konpira, Nagasaki City, Japan. During a season-long study we counted 8065 (7297 4th instar larvae, 670 pupae and 98 adults) Tr. bambusa mosquitoes. We found that persistence and abundance patterns were not associated among stages, with the exception of large (4th instar) and small (1st to 3rd instars) larvae persistence, which were positively correlated. We also found that relative humidity was associated with the persistence of Tr. bambusa aquatic stages, being positively associated with large and small larvae, but negatively with pupae. Similarly, landscape aspect changed from positive to negative the sign of its association with Tr. bambusa pupae and adults, highlighting that environmental associations change with life stage. Meanwhile, Tr. bambusa abundance patterns were negatively impacted by more variable microenvironments, as measured by the negative impacts of kurtosis and standard deviation (SD) of environmental variables, indicating Tr. bambusa thrives in stable environments, suggesting this mosquito species has a finely grained response to environmental changes.

Spatial PM2.5 mobile source impacts using a calibrated indicator method.

Motor vehicles are major sources of fine particulate matter (PM2.5), and the PM2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NOx) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NOx estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects. Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.

Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley.

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R 2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3 -, 0.78 and 0.23 for SO4 2-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4 2- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach.

BACKGROUND: Ground-level ozone is a potent airway irritant and a determinant of respiratory morbidity. Susceptibility to the health effects of ambient ozone may be influenced by both intrinsic and extrinsic factors, such as neighborhood socioeconomic status (SES). Questions remain regarding the manner and extent that factors such as SES influence ozone-related health effects, particularly across different study areas. METHODS: Using a 2-stage modeling approach we evaluated neighborhood SES as a modifier of ozone-related pediatric respiratory morbidity in Atlanta, Dallas, & St. Louis. We acquired multi-year data on emergency department (ED) visits among 5-18 year olds with a primary diagnosis of respiratory disease in each city. Daily concentrations of 8-h maximum ambient ozone were estimated for all ZIP Code Tabulation Areas (ZCTA) in each city by fusing observed concentration data from available network monitors with simulations from an emissions-based chemical transport model. In the first stage, we used conditional logistic regression to estimate ZCTA-specific odds ratios (OR) between ozone and respiratory ED visits, controlling for temporal trends and meteorology. In the second stage, we combined ZCTA-level estimates in a Bayesian hierarchical model to assess overall associations and effect modification by neighborhood SES considering categorical and continuous SES indicators (e.g., ZCTA-specific levels of poverty). We estimated ORs and 95% posterior intervals (PI) for a 25 ppb increase in ozone. RESULTS: The hierarchical model combined effect estimates from 179 ZCTAs in Atlanta, 205 ZCTAs in Dallas, and 151 ZCTAs in St. Louis. The strongest overall association of ozone and pediatric respiratory disease was in Atlanta (OR = 1.08, 95% PI: 1.06, 1.11), followed by Dallas (OR = 1.04, 95% PI: 1.01, 1.07) and St. Louis (OR = 1.03, 95% PI: 0.99, 1.07). Patterns of association across levels of neighborhood SES in each city suggested stronger ORs in low compared to high SES areas, with some evidence of non-linear effect modification. CONCLUSIONS: Results suggest that ozone is associated with pediatric respiratory morbidity in multiple US cities; neighborhood SES may modify this association in a non-linear manner. In each city, children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying rates of respiratory morbidity.

Assessment of neighbourhood-level socioeconomic status as a modifier of air pollution-asthma associations among children in Atlanta.

BACKGROUND: A broad literature base provides evidence of association between air pollution and paediatric asthma. Socioeconomic status (SES) may modify these associations; however, previous studies have found inconsistent evidence regarding the role of SES. METHODS: Effect modification of air pollution-paediatric asthma morbidity by multiple indicators of neighbourhood SES was examined in Atlanta, Georgia. Emergency department (ED) visit data were obtained for 5-18 years old with a diagnosis of asthma in 20-county Atlanta during 2002-2008. Daily ZIP Code Tabulation Area (ZCTA)-level concentrations of ozone, nitrogen dioxide, fine particulate matter and elemental carbon were estimated using ambient monitoring data and emissions-based chemical transport model simulations. Pollutant-asthma associations were estimated using a case-crossover approach, controlling for temporal trends and meteorology. Effect modification by ZCTA-level (neighbourhood) SES was examined via stratification. RESULTS: We observed stronger air pollution-paediatric asthma associations in 'deprivation areas' (eg, ≥20% of the ZCTA population living in poverty) compared with 'non-deprivation areas'. When stratifying analyses by quartiles of neighbourhood SES, ORs indicated stronger associations in the highest and lowest SES quartiles and weaker associations among the middle quartiles. CONCLUSIONS: Our results suggest that neighbourhood-level SES is a factor contributing vulnerability to air pollution-related paediatric asthma morbidity in Atlanta. Children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying asthma ED rates. Inconsistent findings of effect modification among previous studies may be partially explained by choice of SES stratification criteria, and the use of multiplicative models combined with differing baseline risk across SES populations.

Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution.

Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) over the state of Georgia for a seven-year period (2002-2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO2 predicted most poorly due to limitations in coal combustion plume monitoring and modeling. For the other pollutants studied, 54-88% of the spatiotemporal variance (Pearson R(2) from cross-validation) was captured, with ozone and PM2.5 predicted best. The optimized fusion approach developed provides daily spatial field estimates of air pollutant concentrations and uncertainties that are consistent with observations, emissions, and meteorology.