Risk assessment of influenza transmission between workers and pigs on US indoor hog growing units.

On pig farms ample opportunity exists for pig-to-human and human-to-pig (cross-species) influenza transmission. The purpose of this study was to assess the risks of cross-species influenza transmission within an indoor pig grower unit in the United States and to prioritize data gaps. Using the World Organization for Animal Health risk assessment framework we evaluated influenza transmission across two risk pathways: 1. What is the likelihood that based on current conditions on a single typical hog grower-finisher facility in the Midwest (US), during a single production cycle, at least one hog becomes infected with an influenza virus associated with swine (either H1N1, H3N2, or H1N2) [step 1a] and that at least one worker becomes infected as a result [step 1b] and that the worker develops symptoms [step 1c]? And 2. What is the likelihood that, based on current conditions on a single typical hog grower-finisher facility in the Midwest (US), during a single production cycle, at least one worker becomes infected with an influenza virus associated with people (either H1N1, H3N2, or H1N2) [step 2a] and that at least one pig becomes infected as a result [step 2b] and that the pig(s) develop(s) symptoms [step 2c]? Semi-quantitative probability and uncertainty assessments were based on literature review including passive and active influenza surveillance data. We assumed a typical pig-grower farm has capacity for 4,000 pigs, two workers, and minimal influenza control measures. Probability and uncertainty categories were assessed for each risk step and the combined risk pathway. The combined risk assessment for risk pathway one was estimated to be Very Low for H1N1 and H1N2 with an overall High level of uncertainty. The combined risk assessment for risk pathway two was estimated to be Extremely Low for H1N1 and H3N2 with a High degree of uncertainty. Scenario analyses in which influenza control measures were assumed to be implemented separately (implementing vaccinating sows, mass vaccinating incoming pigs or improved personal protective equipment adherence) showed no reduction in the combined risk category. When implementing three influenza control methods altogether, the combined risk could be reduced to Extremely Low for risk pathway one and remained Extremely Low for risk pathway two. This work highlights that multiple influenza control methods are needed to reduce the risks of inter-species influenza transmission on swine farms.

A stochastic compartmental model to simulate intra- and inter-species influenza transmission in an indoor swine farm.

Common in swine production worldwide, influenza causes significant clinical disease and potential transmission to the workforce. Swine vaccines are not universally used in swine production, due to their limited efficacy because of continuously evolving influenza viruses. We evaluated the effects of vaccination, isolation of infected pigs, and changes to workforce routine (ensuring workers moved from younger pig batches to older pig batches). A Susceptible-Exposed-Infected-Recovered model was used to simulate stochastic influenza transmission during a single production cycle on an indoor hog growing unit containing 4000 pigs and two workers. The absence of control practices resulted in 3,957 pigs [0-3971] being infected and a 0.61 probability of workforce infection. Assuming incoming pigs had maternal-derived antibodies (MDAs), but no control measures were applied, the total number of infected pigs reduced to 1 [0-3958] and the probability of workforce infection was 0.25. Mass vaccination (40% efficacious) of incoming pigs also reduced the total number of infected pigs to 2362 [0-2374] or 0 [0-2364] in pigs assumed to not have MDAs and have MDAs, respectively. Changing the worker routine by starting with younger to older pig batches, reduced the number of infected pigs to 996 [0-1977] and the probability of workforce infection (0.22) in pigs without MDAs. In pigs with MDAs the total number of infected pigs was reduced to 0 [0-994] and the probability of workforce infection was 0.06. All other control practices alone, showed little improvement in reducing total infected pigs and the probability of workforce infection. Combining all control strategies reduced the total number of infected pigs to 0 or 1 with a minimal probability of workforce infection (<0.0002-0.01). These findings suggest that non-pharmaceutical interventions can reduce the impact of influenza on swine production and workers when efficacious vaccines are unavailable.

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.

Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach.

Water clarity (generally quantified as the Secchi disk depth: SDD) is a key variable for assessing environmental changes in lakes. Using remote sensing we calculated and elucidated the SDD dynamics in lakes in the Inner Mongolia-Xinjiang Lake Zone (IMXL) from 1986 to 2018 in response to variations in temperature, rainfall, lake area, normalized difference vegetation index (NDVI) and Palmer's drought severity index (PDSI). The results showed that the lakes with high SDD values are primarily located in the Xinjiang region at longitudes of 75°-93° E. In contrast, the lakes in Inner Mongolia at longitudes of 93°-118° E generally have low SDD values. In total, 205 lakes show significant increasing SDD trends (P < 0.05), with a mean rate of 0.15 m per decade. In contrast, 75 lakes, most of which are located in Inner Mongolia, exhibited significant decreasing trends with a mean rate of 0.08 m per decade (P < 0.05). Pooled together, an overall increase is found with a mean rate of 0.14 m per decade. Multiple linear regression reveals that among the five variables selected to explain the variations in SDD, lake area accounts for the highest proportion of variance (25%), while temperature and rainfall account for 12% and 10%, respectively. In addition, rainfall accounts for 52% of the variation in humidity, 8% of the variation in lake area and 7% of the variation in NDVI. Temperature accounts for 27% of the variation in NDVI, 39% of the variation in lake area and 22% of the variation in PDSI. Warming and wetting conditions in IMXL thus promote the growth of vegetation and cause melting of glaciers and expansion of lake area, which eventually leads to improved water quality in the lakes in terms of higher SDD. In contrast, lakes facing more severe drought conditions, became more turbid.

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.

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.

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.