Opportunistic Data Research Articles

Inferring UK COVID-19 fatal infection trajectories from daily mortality data: Were infections already in decline before the UK lockdowns?

The number of new infections per day is a key quantity for effective epidemic management. It can be estimated relatively directly by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen‐effective reproduction number, R, using data gathered from the clinical response to the disease. For coronavirus disease 2019 (Covid‐19/SARS‐Cov‐2), such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty, it is useful to retrospectively reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on first‐wave Covid‐19 deaths and the disease duration distribution suggests that fatal infections were in decline before full UK lockdown (24 March 2020), and that fatal infections in Sweden started to decline only a day or two later. An analysis of UK data using the model of Flaxman et al. gives the same result under relaxation of its prior assumptions on R, suggesting an enhanced role for non‐pharmaceutical interventions short of full lockdown in the UK context. Similar patterns appear to have occurred in the subsequent two lockdowns.

Citizen science data for urban planning: Comparing different sampling schemes for modelling urban bird distribution

• CS standardized data outperform opportunistic data for SDMs. • Opportunistic data fail to identify key areas for nightingale distribution. • Citizen Science projects aimed at SDM should be designed to avoid data bias. • Environmental gradients should be considered in CS project designs. • Recording species absences could improve opportunistic data quality.

Jamming in Eavesdropping on Throughput Maximization in Green Cognitive Radio Networks

This work considers a cognitive radio (CR) network consisting of a set of CR transmit-receive node pairs, one fusion center (FC), multiple primary user emulation attack (PUEA) nodes, an eavesdropper ( <inline-formula><tex-math notation="LaTeX">$E_{av}$</tex-math></inline-formula> ) node and a set of friendly jammers used for protection of CR data transmission from eavesdropping. At the initial time slot of the frame, simultaneous energy harvesting (EH) and spectrum sensing are done through power splitting (PS) mode. CR transmit nodes then amplify and forward the sensed samples of both the primary user (PU) and the PUEA to the FC for cooperative spectrum sensing (CSS). Based on the CSS decision, CR transmit nodes either perform EH over the entire duration or make an opportunistic data transmission in time division mode. The closed form expressions of the optimal sensing duration, power allocation factor and transmit power for each secondary user (SU) are found. The sum secondary throughput of the network is maximized under the constraints of meeting the sensing reliability of the PU, individual energy causality for each SU and the best selected jammer, interference at the PU receiver, individual secondary and secrecy outage probability. Simulation results show a performance gain on the maximum value of the sum secondary throughput by <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 17.56 and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 49.69 percent over the existing works.

How citizen science could improve species distribution models and their independent assessment.

Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer's participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

Fuzzy-Based Distributed Protocol for Vehicle-to-Vehicle Communication

This article models the multihop data-routing in vehicular ad-hoc networks as multiple criteria decision making (MCDM) in four steps. First, the criteria that have impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by fuzzy membership functions (FMFs), which are interpolated (curve fitting) based on the data collected from massive experimental simulations. Third, the analytical hierarchy process (AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and the Takagi–Sugeno–Kang (TSK) inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we design a distributed and opportunistic data routing protocol, namely, vehicular environment fuzzy router which targets vehicle-to-vehicle (V2V) communication and runs in two main processes—road segment selection (RSS) and relay vehicle selection (RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results show that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency.

An opportunistic data dissemination for autonomous vehicles communication

An opportunistic data dissemination for autonomous vehicles communication

The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

Opportunistically collected species occurrence data are often used for species distribution models (SDMs) when high-quality data collected through standardized recording protocols are unavailable. While opportunistic data are abundant, uncertainty is usually high, e.g. due to observer effects or a lack of metadata. To increase data quality and improve model performance, we filtered species records based on record attributes that provide information on the observation process or post-entry data validation. Data filtering does not only increase the quality of species records, it simultaneously reduces sample size, a trade-off that remains relatively unexplored. By controlling for sample size in a dataset of 255 species, we were able to explore the combined impact of data quality and sample size on model performance. We applied three data quality filters based on observers' activity, the validation status of a record in the database and the detail of a submitted record, and analyzed changes in AUC, Sensitivity and Specificity using Maxent with and without filtering. The impact of stringent filtering on model performance depended on (1) the quality of the filtered data: records validated as correct and more detailed records lead to higher model performance, (2) the proportional reduction in sample size caused by filtering and the remaining absolute sample size: filters causing small reductions that lead to sample sizes of more than 100 presences generally benefitted model performance and (3) the taxonomic group: plant and dragonfly models benefitted more from data quality filtering compared to bird and butterfly models. Our results also indicate that recommendations for quality filtering depend on the goal of the study, e.g. increasing Sensitivity and/or Specificity. Further research must identify what drives species’ sensitivity to data quality. Nonetheless, our study confirms that large quantities of volunteer generated and opportunistically collected data can make a valuable contribution to ecological research and species conservation.

Drivers of spatio-temporal variation in mosquito submissions to the citizen science project \u2018M\xfcckenatlas\u2019

Intensified travel activities of humans and the ever growing global trade create opportunities of arthropod-borne disease agents and their vectors, such as mosquitoes, to establish in new regions. To update the knowledge of mosquito occurrence and distribution, a national mosquito monitoring programme was initiated in Germany in 2011, which has been complemented by a citizen science project, the ‘Mückenatlas’ since 2012. We analysed the ‘Mückenatlas’ dataset to (1) investigate causes of variation in submission numbers from the start of the project until 2017 and to (2) reveal biases induced by opportunistic data collection. Our results show that the temporal variation of submissions over the years is driven by fluctuating topicality of mosquito-borne diseases in the media and large-scale climate conditions. Hurdle models suggest a positive association of submission numbers with human population, catch location in the former political East Germany and the presence of water bodies, whereas precipitation and wind speed are negative predictors. We conclude that most anthropogenic and environmental effects on submission patterns are associated with the participants’ (recording) behaviour. Understanding how the citizen scientists’ behaviour shape opportunistic datasets help to take full advantage of the available information.

CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19.

The factors affecting the penetration of certain diseases such as COVID-19 in society are still unknown. Internet of Things (IoT) technologies can play a crucial role during the time of crisis and they can provide a more holistic view of the reasons that govern the outbreak of a contagious disease. The understanding of COVID-19 will be enriched by the analysis of data related to the phenomena, and this data can be collected using IoT sensors. In this paper, we show an integrated solution based on IoT technologies that can serve as opportunistic health data acquisition agents for combating the pandemic of COVID-19, named CIoTVID. The platform is composed of four layers—data acquisition, data aggregation, machine intelligence and services, within the solution. To demonstrate its validity, the solution has been tested with a use case based on creating a classifier of medical conditions using real data of voice, performing successfully. The layer of data aggregation is particularly relevant in this kind of solution as the data coming from medical devices has a very different nature to that coming from electronic sensors. Due to the adaptability of the platform to heterogeneous data and volumes of data; individuals, policymakers, and clinics could benefit from it to fight the propagation of the pandemic.

TSVNet: Combining Time-Series and Opportunistic Sensing by Transfer Learning for Dynamic Thermal Sensation Estimation

The development of sensing technologies has enabled the estimation of human thermal sensation, which helps efficient heating, ventilation, and air conditioning control. Recently, machine learning techniques using physiological and environmental sensors have been proposed. However, in most existing literature, the personal thermal sensation is estimated using only opportunistic data as features, and evaluations are conducted in a static environment. In real situations, we are exposed to a dynamic environment, owing to human mobility and changes in airflow. Therefore, it is necessary to estimate personal thermal sensation in a dynamic surrounding environment. In this study, we propose TSVNet, a deep-learning-based method reflecting time-series changes to address the dynamic environment. We collected data for a total of 123 days, which included the dynamic environment data from 21 subjects, for the evaluation. The results indicate that our method improves F1-score by 5.8% compared with a baseline method. We also design a data balancing method for regression problems on imbalanced datasets, including time-series data. In addition, the result of the lookback time evaluation shows that the use of physiological information in the previous 10 minutes improves the performance of the method.

Insect responses to global change offer signposts for biodiversity and conservation

1. Insects have emerged as causes célèbres for widespread concern about human effects on global biodiversity. Here, we consider how insects provide opportunities both to understand the ecological effects of global change and to enhance environmental conservation.2. Despite a limited time frame and geographic extent of quantitative evidence, recent studies of changes to the abundance, distribution, and diversity of insects indicate temporally heterogeneous trends which vary among taxa, regions, and biotopes. These results suggest a) that insect numbers are responding to multiple stressors in the wider context of the changes to fitness, abundance, distributions, and biotic interactions that result from habitat and climate change; and b) that habitat specialists with narrow geographic ranges may be particularly at risk.3. Predictions of the effects of global change on insects based on macroecology and ecophysiology can be tested by combining approaches, including experiments and observations over gradients of latitude, elevation, and urbanization; as well as innovative quantitative analyses of data from standardised monitoring schemes and opportunistic data from historical collections and citizen science. Linking these complementary approaches helps to detect the mechanisms influencing insect responses to the interacting drivers of global change and to inform conservation.4. The impetus and debate provoked by recent high profile reports of insect declines provide opportunities to promote insect conservation, but also to obtain comprehensive evidence for the effects of global change on biodiversity and thus develop and communicate measures to mitigate the threats to ecosystems from global change.

Species distribution modelling is needed to support ecological impact assessments

Abstract Legislation commonly mandates the mitigation of impacts to biodiversity in planning and development processes, with potential impacts identified through some form of ecological impact assessment. Yet, protections for biodiversity are frequently undermined because the distributions of priority species are poorly known in most locations at the spatial scales required to inform planning decisions (i.e. c. 1–100 ha). Planning applications are often screened against opportunistic records to determine potential impacts to priority species. However, raw occurrence records provide information only on where a species has been detected and cannot be used to indicate if a species is likely to be absent from a site. Inferences drawn from these data on the likelihood of a species being present at a site can only be correctly interpreted through an appropriate species distribution modelling (SDM) framework that ensures assumptions about the data and models are formalised and documented. We argue that SDM frameworks must be integrated into ecological impact assessments to improve support for biodiversity protections within planning and development processes. Biases and uncertainties in opportunistic data create modelling challenges, but recent methodological advances can minimise their impacts on predictions. We advocate co‐production with practitioners of SDM tools, mapping products and best‐practice guidelines specific to planning processes. Policy implications. The integration of species distribution modelling frameworks into ecological impact assessments will strengthen biodiversity protections in planning and development processes by ensuring methodological transparency and rigour in the interpretation of species occurrence data.

Fishing Gear as a Data Collection Platform: Opportunities to Fill Spatial and Temporal Gaps in Operational Sub-Surface Observation Networks

While the open oceans are well covered by automated ocean measurement instruments, there is a lack of sub-surface oceanographic observation in coastal and shelf seas. Commercial fishing gear such as bottom trawls, pots, traps and long lines can act as a platform for sensors, which collect physical oceanographic data concurrently with normal fishing operations. The lack of observed in-situ ocean data in coastal and shelf seas limits operational oceanography, weather forecasting, maritime industries, and climate change monitoring. In addition, using fishing gear as an ocean observation platform has auxiliary benefits for fisheries management including stakeholder involvement. This study quantifies and compares the existing sub-surface in-situ data coverage with the spatial distribution of fishing activities. The results show that integration with fishing could contribute to filling some of the most pressing gaps in existing ocean observation systems in coastal and shelf seas. There are limitations inherent to opportunistic data collection, mainly related to spatial and temporal heterogeneity of fishing activities. However, we make the case that fishery-based observations have the potential to complement existing ocean observing systems in areas where oceanographic data are lacking and needed most in order to ensure long term sustainability of oceanographic monitoring.

Joint spectrum sensing, power, and bandwidth allocation for multiband cognitive radio systems

Multiband cognitive radio (CR) signalling allows CR networks to efficiently take advantage of the unused frequency bands. On the other hand, joint spectrum sensing and power allocation in CR systems has been shown to enable higher opportunities for CR transmission. In this work, the authors perform joint spectrum sensing and resource allocation (power and bandwidth) in a multiband CR system. This is achieved by defining an optimisation problem, which is formulated to maximise the average opportunistic CR data rate under constraints on interference, power budget, and total available bandwidth leading to a non-convex optimisation problem. As the original formulated optimisation problem is inherently non-convex, they convert it into a convex problem where the optimal solution is obtained using the Lagrange multipliers method and linear programming. For performance comparison, they consider the classical method based on equal bandwidth allocation for each subchannel. They provide several numerical results to contrast the performance of the proposed method compared to the classical method in terms of the average achievable CR data rate.

Medicinal cannabis in Thailand: 1-year experience after legalization.

Medicinal cannabis in Thailand: 1-year experience after legalization.

Pushing the Limits of Transmission Concurrency for Low Power Wireless Networks

Concurrent transmission (CT) has been widely adopted to optimize the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgment frame (ACK) collision at senders can also significantly diminish concurrency opportunities. In this article, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in a distributed manner. The key idea is to align the silent periods of concurrent transmitters. To achieve this goal, we align the end of data frames concurrently transmitted by several senders. Therefore, the potentially arriving ACKs can avoid a collision with ongoing data transmissions because the concurrent senders are in a listening state to wait for receivers’ ACKs for a short and fixed period. ALIGNER can be applied for both deterministic and opportunistic forwarding protocols. It optionally uses a random back-off and slotted ACK mechanism to avoid a potential collision among simultaneously arrived ACKs in opportunistic forwarding. In addition, ALIGNER adopts a tailor-made metrics to analyze the throughput benefit of concurrent transmission for both deterministic and opportunistic data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can significantly increase the concurrency opportunities in both deterministic (up to 105%) and opportunistic (up to 89.7%) forwarding compared with the state-of-the-art CT methods.

Capitalizing on opportunistic citizen science data to monitor urban biodiversity: A multi-taxa framework

Monitoring urban biodiversity is increasingly important, given the increasing anthropogenic pressures on biodiversity in urban areas. While the cost of broad-scale monitoring by professionals may be prohibitive, citizen science (also referred to as community science) will likely play an important role in understanding biodiversity responses to urbanization into the future. Here, we present a framework that relies on broad-scale citizen science data –– collected through iNaturalist –– to quantify (1) species-specific responses to urbanization on a continuous scale, capitalizing on globally-available VIIRS night-time lights data; and (2) community-level measures of the urbanness of a given biological community that can be aggregated to any spatial unit relevant for policy-decisions. We demonstrate the potential utility of this framework in the Boston metropolitan region, using >1000 species aggregated across 87 towns throughout the region. Of the most common species, our species-specific urbanness measures highlighted the expected difference between native and non-native species. Further, our biological community-level urbanness measures –– aggregated by towns –– negatively correlated with enhanced vegetation indices within a town and positively correlated with the area of impervious surface within a town. We conclude by demonstrating how towns can be ‘ranked’ promoting a framework where towns can be compared based on whether they over- or under-perform in the urbanness of their community relative to other towns. Ultimately, biodiversity conservation in urban environments will best succeed with robust, repeatable, and interpretable measures of biodiversity responses to urbanization, and involving the broader public in the derivation and tracking of these responses will likely result in increased bioliteracy and conservation awareness.

Opportunistic Data Collection in Cognitive Wireless Sensor Networks: Air–Ground Collaborative Online Planning

In this article, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the offline optimization of flight mode and transmission. To tackle these problems, we develop an air–ground collaborative online planning method. On the one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the Line-of-Sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air–ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

Cannot see the diversity for all the species: Evaluating inclusion criteria for local species lists when using abundant citizen science data.

Abundant citizen science data on species occurrences are becoming increasingly available and enable identifying composition of communities occurring at multiple sites with high temporal resolution. However, for species displaying temporary patterns of local occurrences that are transient to some sites, biodiversity measures are clearly dependent on the criteria used to include species into local species lists. Using abundant opportunistic citizen science data from frequently visited wetlands, we investigated the sensitivity of α‐ and β‐diversity estimates to the use raw versus detection‐corrected data and to the use of inclusion criteria for species presence reflecting alternative site use. We tested seven inclusion criteria (with varying number of days required to be present) on time series of daily occurrence status during a breeding season of 90 days for 77 wetland bird species. We show that even when opportunistic presence‐only observation data are abundant, raw data may not produce reliable local species richness estimates and rank sites very differently in terms of species richness. Furthermore, occupancy model based α‐ and β‐diversity estimates were sensitive to the inclusion criteria used. Total species lists (all species observed at least once during a season) may therefore mask diversity differences among sites in local communities of species, by including vagrant species on potentially breeding communities and change the relative rank order of sites in terms of species richness. Very high sampling effort does not necessarily free opportunistic data from its inherent bias and can produce a pattern in which many species are observed at least once almost everywhere, thus leading to a possible paradox: The large amount of biological information may hinder its usefulness. Therefore, when prioritizing among sites to manage or preserve species diversity estimates need to be carefully related to relevant inclusion criteria depending on the diversity estimate in focus.

MAXIMUM ENTROPY MODELING USING CITIZEN SCIENCE: USE CASE ON JACOBIN CUCKOO AS AN INDICATOR OF INDIAN MONSOON

Abstract. Due to a growing revolution of the citizen science era with the involvement of non-professionals in scientific tasks such as species observation, yields an opportunistic data for modeling and planning purposes. Such citizen science based scientific observations can be a sustainable option to answer many research questions. Here, citizen science data of the Clamator jacobinus bird is taken from Global Biodiversity Information facility to predict its habitat suitability through maximum entropy approach. The distribution data is divided into two monthly sets – June to October and November to May for critically analysing the probable climatic reasons for its migration and understanding the influence of climatic variables in its suitability during the Indian monsoon season and Southern Africa rainy season. Also, the influential role of different bioclimatic variables in determining the bird’s suitability is described in this paper and to predict how this bird will react to different climate change scenarios in 2050 year. The maximum entropy modeling is performed on both sets of data and results are surprisingly interesting, which verifies an Indian myth that this bird is harbinger of the monsoon in India. This study concluded that the precipitation during warmest and wettest quarter, and isothermality are the major factors in determining the migration of Clamator jacobinus, but, hot, dry and cold climate is not suitable for this bird suitability. Such study using the citizen science data can be used in biodiversity planning as well as in improving the agricultural economy because monsoon is considered as an auspicious season for functioning of biodiversity and agricultural tasks.