History of Mosquitoborne Diseases in the United States and Implications for New Pathogens.

The introduction and spread of West Nile virus and the recent introduction of chikungunya and Zika viruses into the Americas have raised concern about the potential for various tropical pathogens to become established in North America. A historical analysis of yellow fever and malaria incidences in the United States suggests that it is not merely a temperate climate that keeps these pathogens from becoming established. Instead, socioeconomic changes are the most likely explanation for why these pathogens essentially disappeared from the United States yet remain a problem in tropical areas. In contrast to these anthroponotic pathogens that require humans in their transmission cycle, zoonotic pathogens are only slightly affected by socioeconomic factors, which is why West Nile virus became established in North America. In light of increasing globalization, we need to be concerned about the introduction of pathogens such as Rift Valley fever, Japanese encephalitis, and Venezuelan equine encephalitis viruses.

A semi-analytical model for estimating total suspended matter in highly turbid waters.

Total suspended matter (TSM) is related to water quality. High TSM concentrations limit underwater light availability, thus affecting the primary productivity of aquatic ecosystems. Accurate estimation of TSM concentrations in various waters with remote sensing technology is particularly challenging, as the concentrations and optical properties vary greatly among different waters. In this research, a semi-analytical model was established for Hangzhou Bay and Lake Taihu for estimating TSM concentration. The model construction proceeded in two steps. 1) Two indices of the model were calculated by deriving absorption and backscattering coefficients of suspended matter (ap(λ) and bbp(λ)) from the reflectance signal using a semi-analytical method. 2) The two indices were then weighted to derive TSM. The performance of the proposed model was tested using in situ reflectance and Geostationary Ocean Color Imager (GOCI) data. The derived TSM based on in situ reflectance and GOCI images both corresponded well with the in situ TSM with low mean relative error (32%, 41%), root mean square error (20.1 mg/L, 43.1 mg/L), and normalized root mean square error (33%, 55%). The model was further used for the slightly turbid Xin'anjiang Reservoir to demonstrate its applicability to derive ap(λ) and bbp(λ) in other water types. The results indicated that the form Rrs -1(λ1) - Rrs -1(λ2) could minimize the effect of CDOM absorption in deriving ap(λ) from the total absorption. The model exploited the different relationships between TSM concentration and multiband reflectance, thus improving the performance and application range in deriving TSM.

Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments.

The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m3 decrease in PM2.5, and a 1% increase in "heavy industry" results in a 0.07 µg/m3 increase in PM2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

Exploring for Municipality-Level Socioeconomic Variables Related to Zika Virus Incidence in Colombia.

Colombia experienced an outbreak of Zika virus infection during September 2015 until July 2016. This study aimed to identify the socioeconomic factors that at the municipality level correlate with this outbreak and therefore could have influenced its incidence. An analysis of publicly available, municipality-aggregated data related to eight potential explanatory socioeconomic variables was conducted. These variables are school dropout, low energy strata, social security system, savings capacity, tax, resources, investment, and debt. The response variable of interest in this study is the number of reported cases of Zika virus infection per people (projected) per square kilometer. Binomial regression models were performed. Results show that the best predictor variables of Zika virus occurrence, assuming an expected inverse relationship with socioeconomic status, are "school", "energy", and "savings". Contrary to expectations, proxies of socioeconomic status such as "investment", "tax", and "resources" were associated with an increase in the occurrence of Zika virus infection, while no association was detected for "social security" and "debt". Energy stratification, school dropout rate, and the percentage of the municipality's income that is saved conformed to the hypothesized inverse relationship between socioeconomic standing and Zika occurrence. As such, this study suggests these factors should be considered in Zika risk modeling.

Comparing different spatio-temporal modeling methods in dengue fever data analysis in Colombia during 2012-2015.

In this paper, we compare a variety of spatio-temporal conditional autoregressive models to a dengue fever dataset in Colombia, and incorporate an innovative data transformation method in the data analysis. In order to gain a better understanding on the effects of different niche variables in the epidemiological process, we explore Poisson-lognormal and binomial models with different Bayesian spatio-temporal modeling methods in this paper. Our results show that the selected model can well capture the variations of the data. The population density, elevation, daytime and night land surface temperatures are among the contributory variables to identify potential dengue outbreak regions; precipitation and vegetation variables are not significant in the selected spatio-temporal mixed effects model. The generated dengue fever probability maps from the model show a geographic distribution of risk that apparently coincides with the elevation gradient. The results in the paper provide the most benefits for future work in dengue studies.

Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management.

The valley reservoirs service as a critical resource for society by providing drinking water, power generation, recreation, and maintaining biodiversity. Management and assessment of the water environment in valley reservoirs are urgent due to the recent eutrophication and water quality deterioration. As an essential component of the water body, total suspended matter (TSM) hinder the light availability to underwater and then affect the photosynthesis of aquatic ecosystem. We used long-term HJ-1A/B dataset to track TSM variation and elucidating the driving mechanism of valley reservoirs. Taking a typical deep-valley reservoir (Xin'anjing Reservoir) as our case study, we constructed a TSM model with satisfactory performance (R2, NRMSE, and MRE values are 0.85, 18.57%, and 20%) and further derived the spatial-temporal variation from 2009 to 2017. On an intra-annual scale, the TSM concentration exhibited a significant increase from 2.13 ± 1.10 mg L-1 in 2009 to 3.94 ± 0.82 mg L-1 in 2017. On a seasonal scale, the TSM concentration in the entire reservoir was higher in the summer (3.36 ± 1.54 mg L-1) and autumn (2.74 ± 0.82 mg L-1) than in the spring (1.84 ± 1.27 mg L-1) and winter (1.44 ± 2.12 mg L-1). On a monthly scale, the highest and lowest mean TSM value occurred in June (4.66 ± 0.45 mg L-1) and January (0.67 ± 1.50 mg L-1), and the monthly mean TSM value increased from January to June, then dropped from June to December. Combing HJ-1A/B-derived TSM, climatological data, basin dynamic, and morphology of the reservoir, we elucidated the driving mechanism of TSM variation. The annual increase of TSM from long-term HJ-1A/B data indicated that the water quality of Xin'anjiang Reservoir was decreasing. The annual increase of phytoplankton jointed with an increase of built-up land and decrease of forest land in the basin may partially be responsible for the increasing trend in TSM. This study suggested that combining the long-term remote sensing data and in situ data could provide insight into the driving mechanism of water quality dynamic and improve current management efforts for local environmental management.

Factors of Concern Regarding Zika and Other Aedes aegypti-Transmitted Viruses in the United States.

The recent explosive outbreaks of Zika and chikungunya throughout the Americas has raised concerns about the threats that these and similar diseases may pose to the United States (U.S.). The commonly accepted association between tropical climates and the endemicity of these diseases has led to concerns about the possibility of their redistribution due to climate change and transmission arising from cases imported from endemic regions initiating outbreaks in the United States. While such possibilities are indeed well founded, the analysis of historical records not only confirms the potential critical role of traveling and globalization but also reveals that the climate in the United States currently is suitable for local transmission of these viruses. Thus, the main factors preventing these diseases from occurring in the United States today are more likely socioeconomic such as lifestyle, housing infrastructure, and good sanitation. As long as such conditions are maintained, it seems unlikely that local transmission will occur to any great degree, particularly in the northern states. Indeed, a contributing factor to explain the current endemicity of these diseases in less-developed American countries may be well explained by socioeconomic and some lifestyle characteristics in such countries.