Persistent Hotspots Research Articles

Identification and characterization of spatio-temporal hotspots of forest fires in South Asia.

Forest fire is considered as one of the major threats to global biodiversity and a significant source of greenhouse gas emissions. Rising temperatures, weather conditions, and topography promote the incidences of fire due to human ignition in South Asia. Because of its synoptic, multi-spectral, and multi-temporal nature, remote sensing data can be a state of art technology for forest fire management. This study focuses on the spatio-temporal patterns of forest fires and identifying hotspots using the novel geospatial technique "emerging hotspot analysis tool" in South Asia. Daily MODIS active fire locations data of 15years (2003-2017) has been aggregated in order to characterize fire frequency, fire density, and hotspots. A total of 522,348 active fire points have been used to analyze risk of fires across the forest types. Maximum number of forest fires in South Asia was occurring during the January to May. Spatial analysis identified areas of frequent burning and high fire density in South Asian countries. In South Asia, 51% of forest grid cells were affected by fires in 15years. Highest number of fire incidences was recorded in tropical moist deciduous forest and tropical dry deciduous forest. The emerging hotspots analysis indicates prevalence of sporadic hotspots, followed by historical hotspots, consecutive hotspots, and persistent hotspots in South Asia. Of the seven South Asian countries, Bangladesh has highest emerging hotspot area (34.2%) in forests, followed by 32.2% in India and 29.5% in Nepal. Study results offer critical insights in delineation of fire vulnerable forest landscapes which will stand as a valuable input for strengthening management of fires in South Asia.

Characterizing Distribution of Forest Fires in Myanmar Using Earth Observations and Spatial Statistics Tool

This is the first of its kind work on the assessment of forest burnt area and fire hotspots of Myanmar using Landsat OLI data and spatial statistics tool. Burnt area analysis indicates 15.2% of vegetation area was affected by fires in 2017. Analysis of burnt area at state level indicates Kayah affected by more fires in 2017. Of the total vegetation fire occurrences from 2003 to 2017 about 44.7% were observed in the forested landscapes of Myanmar. The emerging hotspot analysis had shown the highest spatial extent of persistent hotspots followed by oscillating hotspots. Forest fire hotspots are mainly found in the states of Kayah, Shan, Bago, Nayi Pyi Taw, Magway, Mandalay, Chin, and Kayin. Overall earth observations based on 2003 to 2017 fire occurrences indicate a declining trend of fires in Myanmar. A comparison of the fire occurrences recorded by MODIS and VIIRS indicates that VIIRS is capable of detecting a greater number of fire incidences. The findings of the study would support in assessing the impact of fires on forest, its structure, composition, function, and provide valuable input for nationwide forest fire management.

Modeling Approaches to Predicting Persistent Hotspots in SCORE Studies for Gaining Control of Schistosomiasis Mansoni in Kenya and Tanzania.

BackgroundSome villages, labeled “persistent hotspots (PHS),” fail to respond adequately in regard to prevalence and intensity of infection to mass drug administration (MDA) for schistosomiasis. Early identification of PHS, for example, before initiating or after 1 or 2 years of MDA could help guide programmatic decision making.MethodsIn a study with multiple rounds of MDA, data collected before the third MDA were used to predict PHS. We assessed 6 predictive approaches using data from before MDA and after 2 rounds of annual MDA from Kenya and Tanzania.ResultsGeneralized linear models with variable selection possessed relatively stable performance compared with tree-based methods. Models applied to Kenya data alone or combined data from Kenya and Tanzania could reach over 80% predictive accuracy, whereas predicting PHS for Tanzania was challenging. Models developed from one country and validated in another failed to achieve satisfactory performance. Several Year-3 variables were identified as key predictors.ConclusionsStatistical models applied to Year-3 data could help predict PHS and guide program decisions, with infection intensity, prevalence of heavy infections (≥400 eggs/gram of feces), and total prevalence being particularly important factors. Additional studies including more variables and locations could help in developing generalizable models.

Differential impact of mass and targeted praziquantel delivery on schistosomiasis control in school-aged children: A systematic review and meta-analysis.

BackgroundSchistosomiasis is a widespread public health concern in the poorest regions of the world. The principal control strategy is regular praziquantel administration to school-aged children in endemic areas. With calls for the elimination of schistosomiasis as a public health problem, expanding praziquantel delivery to all community members has been advocated. This systematic review and meta-analysis compares the impact of community-wide and child-targeted praziquantel distribution on schistosomiasis prevalence and intensity in school-aged children.Methodology/Principal findingsWe searched MEDLINE, Embase and Web of Science to identify papers that reported schistosome prevalence before and after praziquantel administration, either to children only or to all community members. Extracted data included Schistosoma species, drug administration strategy, number of treatment rounds, follow-up interval and prevalence and intensity before and after treatment. We used inverse variance weighted generalised linear models to examine the impact of mass versus targeted drug administration on prevalence reduction, and weighted boxplots to examine the impact on infection intensity reduction. This study is registered with PROSPERO, number CRD42018095377.In total, 34 articles were eligible for systematic review and 28 for meta-analysis. Schistosoma mansoni was reported in 20 studies; Schistosoma haematobium in 19 studies, and Schistosoma japonicum in two studies. Results of generalised linear models showed no detectable difference between mass and targeted treatment strategies on prevalence reduction in school-aged children for S. mansoni (odds ratio 0.47, 95%CI 0.13–1.68, p = 0.227) and S. haematobium (0.41, 95%CI 0.06–3.03, p = 0.358). Box plots also showed no apparent differences in intensity reduction between the two treatment strategies.Conclusions/SignificanceThe results of this meta-analysis do not support the hypothesis that community-wide treatment is more effective than targeted treatment at reducing schistosomiasis infections in children. This may be due to the relatively small number of included studies, insufficient treatment coverage, persistent infection hotspots and unmeasured confounders. Further field-based studies comparing mass and targeted treatment are required.

Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China

Abstract Emissions from heavy-duty diesel trucks (HDDTs) pose a major threat to environment and human health. Understanding the behavior of pollutant emissions from HDDTs facilitate the formulation of traffic-related policy measures to mitigate the adverse effects. This study proposes a new method to estimate the emission inventory of HDDTs and analyze their spatiotemporal evolution characteristics. Multi-source data were fused to provide a complete picture of the transport environment. With the idea of modeling pollutant emissions by “single vehicle” and “road segment,” emission inventories were constructed with different spatiotemporal scales using localized emission factors. A spatiotemporal cube model was introduced to represent the high-resolution emission inventory. A hot-spot and local-outlier analysis were conducted to explore the spatiotemporal evolution mechanism of pollutant emissions. The megacity of Tianjin in China was taken as the area for the case study. The average daily emissions of CO, NOx, PM, and VOC are 12,978.18, 48,675.22, 712.6, and 1217.72 kg d−1, respectively. Temporally, the pollutant emission had a significant peak at 06:00, 11:00, and 18:00 and was affected by major festivals. Spatially, the distribution pattern of emission was policy-driven and closely related to its spatial location. It increases radially outward from the outer-ring road to the periphery. The hot-spot analysis identified 16 pollutant emission patterns. The road segment with persistent cold spots accounted for 48.27% of the roads, mainly distributed within the outer ring road. The road segments with intensifying and persistent hot spots accounted for 19.04% of roads, mainly distributed on the intercity highway. The locations with outlier values reached 12,027, accounting for 31.90%. The key time intervals of the occurrence of the outlier pattern are 11:00–12:00 and 01:00–02:00. Road segments showing only the low–low cluster pattern is mainly located within the outer ring roads. Those with only low–high outlier pattern exhibits a relatively scattered spatial distribution, which is affected by the heterogeneous distribution of HDDTs.

Geographic distribution of risk (\u201cHotspots\u201d) for HIV, HCV, and drug overdose among persons who use drugs in New York City: the importance of local history

AimsTo identify geographic “hotspots” for potential transmission of HIV and HCV and for drug overdose among persons who use heroin and cocaine in New York City and to examine historical continuities in problem drug use hotspots in the city.MethodsA total of 2714 study participants were recruited among persons entering Beth Israel substance use treatment programs. A structured questionnaire was administered and blood samples for HIV and HCV testing were collected. Hotspots for potential virus transmission were defined as ZIP codes with 10+ participants, 2+ persons infected with the virus and engaging in transmission behavior, and 2+ persons not infected and engaging in acquisition behavior. ZIP codes with 3+ persons with previous overdoses were considered potential hotspots for future overdoses.ResultsParticipants resided in 166/178 (93%) of the ZIP codes in New York City. Injecting drug use was reported in 150/178 (84%) of the ZIP codes. No zip codes were identified for injecting-related HIV transmission, 5 zip codes were identified for sexual HIV transmission, 3 for HCV transmission, and 8 for drug overdose. Many of the ZIP code potential hotspots were in neighborhoods long associated with drug use: Lower Eastside and Harlem in Manhattan, the South Bronx, and Central Brooklyn.DiscussionHeroin and cocaine use requiring treatment were reported from almost all ZIP codes in New York City, indicating needs for widely dispersed harm reduction services. Identified hotspots should be targeted for reducing sexual transmission of HIV, transmission of HCV, and drug overdoses. Some of the hotspots have persisted as problem drug use areas for 40 to over 100 years. Monitoring of drug use patterns in historical hotspot neighborhoods may permit early identification of and response to emerging drug use-related health problems. Persistent historical hotspots for problem drug use present a complex problem for implementing harm reduction services that deserve additional research.

Persistent Hotspots in Schistosomiasis Consortium for Operational Research and Evaluation Studies for Gaining and Sustaining Control of Schistosomiasis after Four Years of Mass Drug Administration of Praziquantel.

.Control of schistosomiasis presently relies largely on preventive chemotherapy with praziquantel through mass drug administration (MDA) programs. The Schistosomiasis Consortium for Operational Research and Evaluation has concluded five studies in four countries (Côte d’Ivoire, Kenya, Mozambique, and Tanzania) to evaluate alternative approaches to MDA. Studies involved four intervention years, with final evaluation in the fifth year. Mass drug administration given annually or twice over 4 years reduced average prevalence and intensity of schistosome infections, but not all villages that were treated in the same way responded similarly. There are multiple ways by which responsiveness to MDA, or the lack thereof, could be measured. In the analyses presented here, we defined persistent hotspots (PHS) as villages that achieved less than 35% reduction in prevalence and/or less than 50% reduction in infection intensity after 4 years of either school-based or community-wide MDA, either annually or twice in 4 years. By this definition, at least 30% of villages in each of the five studies were PHSs. We found no consistent relationship between PHSs and the type or frequency of intervention, adequacy of reported MDA coverage, and prevalence or intensity of infection at baseline. New research is warranted to identify PHSs after just one or a few rounds of MDA, and new adaptive strategies need to be advanced and validated for turning PHSs into responder villages.

A One-Health lens for anthrax

A One-Health lens for anthrax

Overlap of Pink-footed Shearwaters and central Chilean purse-seine fisheries: Implications for bycatch risk

Abstract Understanding susceptibility of seabirds to fisheries bycatch requires quantifying overlap of seabird at-sea habitat with fisheries’ distribution and effort. Pink-footed Shearwaters (Ardenna creatopus) are vulnerable seabirds that breed only in Chile. Recently, high rates of Pink-footed Shearwater bycatch (i.e. >1,500 observed mortalities 2015–2017) were documented by observers in central Chilean purse-seine fisheries. We present analysis of Pink-footed Shearwater at-sea movements and overlap with central Chilean purse-seine fleets targeting common sardine (Strangomera bentincki), Peruvian anchoveta (Engraulis ringens), and Chilean jack mackerel (Trachurus murphyi). To determine overlap during 2015–2017, we paired locations from 49 Pink-footed Shearwaters rearing nestlings at Isla Mocha, Chile, with locations and number of observed purse-seine sets in central Chile. Pink-footed Shearwaters typically visited waters ≤30 km offshore throughout central Chile. Foraging trip durations varied interannually, with longer trips in 2016, but all years revealed persistent foraging hotspots near Valdivia, the Gulf of Arauco, and Isla Mocha, Chile. Greatest overlap between Pink-footed Shearwaters and fisheries occurred with the sardine/anchoveta fleet near Valdivia (artisanal and industrial) and the Gulf of Arauco (artisanal); overlap with the jack mackerel fleet was minimal. Given Pink-footed Shearwater bycatch documented in these fisheries, this overlap may indicate risk of bycatch for these birds, although we did not directly quantify shearwater–fisheries interaction. Our results can inform further fishery monitoring efforts, as well as collaboration among scientists, managers, and fishers to identify, quantify, and reduce fisheries bycatch of Pink-footed Shearwaters within Chile and internationally.

Essential fish habitats and hotspots of nekto‐benthic diversity and density in the western Mediterranean

Abstract Since the 1990s, most currently assessed Mediterranean nekto‐benthic stocks have been exploited above maximum sustainable yield and have declined. This study explores the co‐occurrence of essential fish habitats for the most important nekto‐benthic resources exploited by bottom‐trawl fisheries in the western Mediterranean and areas of importance for nekto‐benthic communities. Fishery‐independent data obtained from Mediterranean scientific bottom‐trawl surveys were used to identify persistent hotspots of recruitment for the most important nekto‐benthic species around the Iberian Peninsula and the Balearic Islands between 2002 and 2016, as an indicator of their essential fish habitats. Likewise, hotspots of nekto‐benthic community diversity and density were also determined, as an estimate of key areas for nekto‐benthic communities. Areas of overlap of persistent hotspots of recruitment and of community species richness and density were mainly found on the deep shelf and the upper slope (100–200 m and 200–500 m deep respectively). These overlapping areas could be of particular interest in the development of fishery management plans aiming to implement an ecosystem approach to fisheries. Protection, through temporary or permanent closures, of the overlapping areas identified would contribute to improving both the sustainable exploitation of the main target species of bottom‐trawl fisheries and the conservation of nekto‐benthic communities.

Persistent Hot Spot Detection and Characterisation Using SLSTR

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 μm is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods.

Spatial Changes of Urban Heat Island Formation in the Colombo District, Sri Lanka: Implications for Sustainability Planning

The formation of surface urban heat islands (SUHIs) can cause significant adverse impacts on the quality of living in urban areas. Monitoring the spatial patterns and trajectories of UHI formations could be helpful to urban planners in crafting appropriate mitigation and adaptation measures. This study examined the spatial pattern of SUHI formation in the Colombo District (Sri Lanka), based on land surface temperature (LST), a normalized difference vegetation index (NDVI), a normalized difference built-up index (NDBI), and population density (PD) using a geospatial-based hot and cold spot analysis tool. Here, ‘hot spots’ refers to areas with significant spatial clustering of high variable values, while ‘cold spots’ refers to areas with significant spatial clustering of low variable values. The results indicated that between 1997 and 2017, 32.7% of the 557 divisions in the Colombo District persisted as hot spots. These hot spots were characterized by a significant clustering of high composite index values resulting from the four variables (LST, NDVI (inverted), NDBI, and PD). This study also identified newly emerging hot spots, which accounted for 49 divisions (8.8%). Large clusters of hot spots between both time points were found on the western side of the district, while cold spots were found on the eastern side of the district. The areas identified as hot spots are the more urbanized parts of the district. The emerging hot spots were in areas that had undergone landscape changes due to urbanization. Such areas are found between the persistent hot spots (western parts of the district) and persistent cold spots (eastern parts of the district). Generally, the spatial pattern of the emerging hot spots followed the pattern of urbanization in the district, which had been expanding from west to east. Overall, the findings of this study could be used as a reference in the context of sustainable landscape and urban planning for the Colombo District.

A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand.

Background: The Centers for Disease Control and Prevention reported that deaths from chronic respiratory diseases (CRDs) in Thailand increased by almost 13% in 2010, along with an increased burden related to the disease. Evaluating the geographical heterogeneity of CRDs is important for surveillance. Previous studies have indicated that socioeconomic status has an effect on disease, and that this can be measured with variables such as night-time lights (NTLs) and industrial density (ID). However, there is no understanding of how NTLs and ID correlate with CRDs. We compared spatial heterogeneity obtained by using local cluster detection methods for CRDs and by correlating NTLs and ID with CRDs. Methods: We applied the spatial scan statistic in SaTScan, as well as local indices of spatial association (LISA), Getis and Ord's local Gi*(d) statistic, and Pearson correlation. In our analysis, data were collected on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previously diagnosed CRDs by a physician from the National Socioeconomic Survey, which is a cross-sectional study conducted by the National Statistical Office of Thailand in 2010. Results: According to our findings, the spatial scan statistic, LISA, and the local Gi*(d) statistic revealed similar results for areas with the highest clustering of CRDs. However, the hotspots for the spatial scan statistic covered a wider area than LISA and the local Gi*(d) statistic. In addition, there were persistent hotspots in Bangkok and the perimeter provinces. NTLs and ID have a positive correlation with CRDs. Conclusions: This study demonstrates that all the statistical methods used could detect spatial heterogeneity of CRDs. NTLs and ID can serve as new parameters for determining disease hotspots by representing the population and industrial boom that typically contributes to epidemics.

A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand

Background: The Centers for Disease Control and Prevention reported that deaths from chronic respiratory diseases (CRDs) in Thailand increased by almost 13% in 2010, along with an increased burden related to the disease. Evaluating the geographical heterogeneity of CRDs is important for surveillance. Previous studies have indicated that socioeconomic status has an effect on disease, and that this can be measured with variables such as night-time lights (NTLs) and industrial density (ID). However, there is no understanding of how NTLs and ID correlate with CRDs. We compared spatial heterogeneity obtained by using local cluster detection methods for CRDs and by correlating NTLs and ID with CRDs. Methods: We applied the spatial scan statistic in SaTScan, as well as local indices of spatial association (LISA), Getis and Ord’s local Gi*(d) statistic, and Pearson correlation. In our analysis, data were collected on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previously diagnosed CRDs by a physician from the National Socioeconomic Survey, which is a cross-sectional study conducted by the National Statistical Office of Thailand in 2010. Results: According to our findings, the spatial scan statistic, LISA, and the local Gi*(d) statistic revealed similar results for areas with the highest clustering of CRDs. However, the hotspots for the spatial scan statistic covered a wider area than LISA and the local Gi*(d) statistic. In addition, there were persistent hotspots in Bangkok and the perimeter provinces. NTLs and ID have a positive correlation with CRDs. Conclusions: This study demonstrates that all the statistical methods used could detect spatial heterogeneity of CRDs. NTLs and ID can serve as new parameters for determining disease hotspots by representing the population and industrial boom that typically contributes to epidemics.

A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand

The Centers for Disease Control and Prevention reported that deaths from chronic respiratory diseases (CRDs) in Thailand increased by almost 13% in 2010, along with an increased burden related to the disease. Evaluating the geographical heterogeneity of CRDs is important for surveillance. Previous studies have indicated that socioeconomic status has an effect on disease, and that this can be measured with variables such as night-time lights (NTLs) and industrial density (ID). However, there is no understanding of how NTLs and ID correlate with CRDs. We compared spatial heterogeneity obtained by using local cluster detection methods for CRDs and by correlating NTLs and ID with CRDs. We applied the spatial scan statistic in SaTScan, as well as local indices of spatial association (LISA), Getis and Ord's local Gi*(d) statistic, and Pearson correlation. In our analysis, data were collected on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previously diagnosed CRDs by a physician from the National Socioeconomic Survey, which is a cross-sectional study conducted by the National Statistical Office of Thailand in 2010. According to our findings, the spatial scan statistic, LISA, and the local Gi*(d) statistic revealed similar results for areas with the highest clustering of CRDs. However, the hotspots for the spatial scan statistic covered a wider area than LISA and the local Gi*(d) statistic. In addition, there were persistent hotspots in Bangkok and the perimeter provinces. NTLs and ID have a positive correlation with CRDs. This study demonstrates that all the statistical methods used could detect spatial heterogeneity of CRDs. NTLs and ID can serve as new parameters for determining disease hotspots by representing the population and industrial boom that typically contributes to epidemics.

Substantial N2O emissions from peat decomposition and N fertilization in an oil palm plantation exacerbated by hotspots

It is unclear to what extent emissions of nitrous oxide (N2O) from drained histosols in the tropics may contribute to the atmospheric burden of greenhouse gases. In particular, there is a critical need to elucidate their magnitude in oil palm plantations on these soils. We examined spatio-temporal variations of N2O emissions from peat decomposition and nitrogen (N) fertilization in a plantation trial in Sumatra, which included three application rates: 0 (N0), 153 (N1) and 306 (N2) kg N ha−1 y−1. The spatially stratified sampling design distinguished the area around the palms which received fertilizer (9% of the surface) from the rest of the plot which was unfertilized. Annual emissions were substantial with rates of 22.1 ± 5.7, 12.8 ± 2.7 and 26.6 ± 5.7 kg N2O-N ha−1 in the N0, N1 and N2 treatments, respectively. These equal 9.3 ± 2.4, 5.4 ± 1.1 and 11.2 ± 2.4 Mg CO2eq ha−1 y−1, or 5–10 times emission rates in natural peatland forest. The site exhibited two persistent hotspots located in the unfertilized zone, contributing 33 and 46% of annual emissions in N0 and N2 while representing only 10% of the area sampled. The response of emissions to fertilization was exponential but restricted to the small N application area. At the plot scale and over the year, the impact of fertilized-induced emissions was minimal due to the prevalence of emissions from peat decomposition. Annual rates among treatments were similar when discarding the contribution of hotspots to evaluate N addition effect. High N2O emissions from peat decomposition in the tropics tend to be common within the restricted existing literature; which is in contrast with most recent IPCC emission factors. Our results emphasize the importance to integrate N2O emissions in greenhouse gas budgets of plantations on peat, despite the predominance of CO2 in total emissions.

Defining Persistent Hotspots: Areas That Fail to Decrease Meaningfully in Prevalence after Multiple Years of Mass Drug Administration with Praziquantel for Control of Schistosomiasis.

.Preventive chemotherapy with praziquantel for schistosomiasis morbidity control is commonly done by mass drug administration (MDA). MDA regimen is usually based on prevalence in a given area, and effectiveness is evaluated by decreases in prevalence and/or intensity of infection after several years of implementation. Multiple studies and programs now find that even within well-implemented, multiyear, annual MDA programs there often remain locations that do not decline in prevalence and/or intensity to expected levels. We term such locations “persistent hotspots.” To study and address persistent hotspots, investigators and neglected tropical disease (NTD) program managers need to define them based on changes in prevalence and/or intensity. But how should the data be analyzed to define a persistent hotspot? We have analyzed a dataset from an operational research study in western Tanzania after three annual MDAs using four different approaches to define persistent hotspots. The four approaches are 1) absolute percent change in prevalence; 2) percent change in prevalence; 3) change in World Health Organization guideline categories; 4) change (absolute or percent) in both prevalence and intensity. We compare and contrast the outcomes of these analyses. Our intent is to show how the same dataset yields different numbers of persistent hotspots depending on the approach used to define them. We suggest that investigators and NTD program managers use the approach most suited for their study or program, but whichever approach is used, it should be clearly stated so that comparisons can be made within and between studies and programs.

A Persistent Hotspot of Schistosoma mansoni Infection in a Five-Year Randomized Trial of Praziquantel Preventative Chemotherapy Strategies.

Persistent hotspots have been described after mass drug administration (MDA) for the control of schistosomiasis, but they have not been studied during the course of a multiyear MDA program. In data from a 5-year study of school-based and village-wide preventive chemotherapy strategies for Schistosoma mansoni, spatial scan statistics were used to find infection hotspots in 3 populations: 5- to 8-year-olds, 9- to 12-year-olds, and adults. Negative binomial regression was used to analyze changes from baseline, and receiver operating characteristic analyses were used to predict which villages would reach prevalence and intensity endpoints. We identified a persistent hotspot, not associated with study arm, where S. mansoni infection prevalence and intensity did not decrease as much as in villages outside the hotspot. Significant differences from baseline were realized after 1 year of MDA: we did not identify factors that moderated this relationship. Villages meeting specified endpoints at year 5 were predicted from prior year data with moderately high sensitivity and specificity. The MDA strategies were less effective at reducing prevalence and intensity in the hotspot compared with other villages. Villages that reached year 5 endpoints could be detected earlier, which may provide the opportunity to amend intervention strategies.

An Assessment of Non-geophysical Effects in Spaceborne GNSS Reflectometry Data From the UK TechDemoSat-1 Mission

An assessment of non-geophysical effects in spaceborne global navigation satellite system reflectometry (GNSS-R) data from the UK TechDemoSat-1 (TDS-1) mission is presented. TDS-1 was launched in July 2014 and provides the first new spaceborne GNSS-R data since the pioneering UK-disaster monitoring constellation experiment in 2003. Non-geophysical factors evaluated include ambient L-band noise, instrument operating mode, and platform-related parameters. The findings are particularly relevant to users of uncalibrated GNSS-R signals for the retrieval of geophysical properties of the Earth surface. Substantial attitude adjustments of the TDS-1 platform are occasionally found to occur that introduce large uncertainties in parts of the TDS-1 GNSS-R dataset, particularly for specular points located outside the main beam of the nadir antenna where even small attitude errors can lead to large inaccuracies in the geophysical inversion. Out of eclipse however, attitude adjustments typically remain smaller than 1.5°, with larger deviations of up to 10° affecting less than 5% of the overall sun-lit data. Global maps of L1 ambient noise are presented for both automatic and programmed gain modes of the receiver, revealing persistent L-band noise hotspots along the Equator that can reach up to 2.5 dB, most likely associated with surface reflection of signals from other GNSS transmitters and constellations. Sporadic high-power noise events observed in certain regions point to sources of human origin. Relevant conclusions of this study are that platform attitude knowledge is essential and that radiometric calibration of GNSS-R signals should be used whenever possible. Care should be taken when considering using noise measurements over the equatorial oceans for calibration purposes, as ambient noise and correlated noise in delay–Doppler maps both show more variation than might be expected over these regions.

Spatial distribution of Io’s volcanic activity from near-IR adaptive optics observations on 100 nights in 2013–2015

The extreme and time-variable volcanic activity on Jupiter’s moon Io is the result of periodic tidal forcing. The spatial distribution of Io’s surface heat flux provides an important constraint on models for tidal heat dissipation, yielding information on interior properties and on the depth at which the tidal heat is primarily dissipated. We analyze the spatial distribution of 48 hot spots based on more than 400 total hot spot detections in adaptive optics images taken on 100 nights in 2013–2015 (data presented in de Kleer and de Pater [2016] Time variability of Io’s volcanic activity from near-IR adaptive optics 13 observations on 100 nights in 2013–2015). We present full surface maps of Io at multiple near-infrared wavelengths for three epochs during this time period, and show that the longitudinal distribution of hot spots has not changed significantly since the Galileo mission.We find that hot spots that are persistently active at moderate intensities tend to occur at different latitudes/longitudes than those that exhibit sudden brightening events characterized by high peak intensities and subsequent decay phases. While persistent hot spots are located primarily between ± 30°N, hot spots exhibiting bright eruption events occur primarily between 40° and 65° in both the northern and southern hemispheres. In addition, while persistent hot spots occur preferentially on the leading hemisphere, all bright eruptions were detected on the trailing hemisphere, despite the comparable longitudinal coverage of our observations to both hemispheres. A subset of the bright hot spots which are not intense enough to qualify as outburst eruptions resemble outbursts in terms of temporal evolution and spatial distribution, and may be outbursts whose peak emission went unobserved, or else scaled-down versions of the same phenomenon. A statistical analysis finds that large eruptions are more spatially clustered and occur at higher latitudes than 95% of simulated datasets that assume that eruptions occur at random and independent locations.The preferential occurrence of bright, violent eruptions at higher latitudes supports the idea that a deeper magma source supplies these events, as has been previously hypothesized. The monotonic eastward progression of bright eruptions at southern latitudes from 300° to 200°W also suggests a possible eruption triggering mechanism operating across distances of ∼500 km. A comparison to tidal heating models finds a good correspondence between recent models incorporating a partially-fluid interior (Tyler et al. [2015] Astrophys. J., 218–222). and hot spots in the leading hemisphere as well as persistent hot spots. However, hot spots on the trailing hemisphere and bright eruptions do not match these models well, corresponding better to standard deep-mantle heating models (Segatz et al. [1988] Icarus, 75, 187–206) although this match is still imperfect.