Search results for: HIV sentinel surveillance

Authors: M. Hemnath, S. Kannan, R. Kiran, K. Thanigaivelu

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This paper is based on exploring the possible opportunities and applications using Power Line Communication (PLC) for security and surveillance operations. Various research works are done for introducing PLC into onboard vehicle communication and networking (CAN, LIN etc.) and various international standards have been developed. Wireless power transfer (WPT) is also an emerging technology which is studied and tested for recharging purposes. In this work we present a system which embeds the detection and the response into one which eliminates the need for dedicated network for data transmission. Also we check the feasibility for integrating wireless power transfer system into this proposed security system for transmission of power to detection unit wirelessly from the response unit.

Keywords: power line communication, wireless power transfer, surveillance

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Authors: Dina A. Amu, Fifi N. Afifah, Catur Rosidati, Tirton Nefianto

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Indonesia was shaken up by the avian influenza cases that had caused the country suffered losses of millions of dollars. The avian influenza case had even been suspected as a bioterrorism attack since it was an uncommon case in epidemiology. Furthermore, this avian influenza virus is a high pathogenic one and Indonesia has the highest case of fatality rate in the world. Bioterrorism threats or epidemic potential disease outbreaks currently does not exist in Tanjung Priok port yet. However, the surveillance system enhancement on epidemic potential diseases should be taken as a prevention, especially because Indonesia is currently facing the ASEAN Economic Society (AES). Therefore, this research evaluates the health surveillance system which is organized by Control, Quarantine and Surveillance Department, Health Office of Tanjung Priok Port. This study uses qualitative-evaluative method which utilizes Urgency Seriousness Growth (USG) method to determine priority issues and Root Cause analysis to determine the cause of prior problem. The result of this research shows that the implementation of epidemic potential disease surveillance in Tanjung Priok port has not done in the best possible way. It is because the lack of time allocation and the succinctness of the check list of ship's environmental health inspection. Therefore, Health Ministry of Indonesia should recruit more employees at the health office of Tanjung Priok port, hold a simulation of ship's inspection and simplify the list for ship's environmental health inspection.

Keywords: surveillance, epidemic potential disease, port health, bioterrorism

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Authors: Humphrey Agevi, Harrison Tsingalia, Richard Onwonga, Shem Kuyah

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Most of the world ecosystems have been affected by the effects of climate change. Efforts have been made to mitigate against climate change effects. While most studies have been done in forest ecosystems and pure plant plantations, trees on farms including agroforestry have only received attention recently. Agroforestry systems and tree cover on agricultural lands make an important contribution to climate change mitigation but are not systematically accounted for in the global carbon budgets. This study sought to: (i) determine tree diversity in different agroforestry practices; (ii) determine tree biomass in different agroforestry practices. Study area was determined according to the Land degradation surveillance framework (LSDF). Two study sites were established. At each of the site, a 5km x 10km block was established on a map using Google maps and satellite images. Way points were then uploaded in a GPS helped locate the blocks on the ground. In each of the blocks, Nine (8) sentinel clusters measuring 1km x 1km were randomized. Randomization was done in a common spreadsheet program and later be downloaded to a Global Positioning System (GPS) so that during surveys the researchers were able to navigate to the sampling points. In each of the sentinel cluster, two farm boundaries were randomly identified for convenience and to avoid bias. This led to 16 farms in Kakamega South and 16 farms in Kakamega North totalling to 32 farms in Kakamega Site. Species diversity was determined using Shannon wiener index. Tree biomass was determined using allometric equation. Two agroforestry practices were found; homegarden and hedgerow. Species diversity ranged from 0.25-2.7 with a mean of 1.8 ± 0.10. Species diversity in homegarden ranged from 1-2.7 with a mean of 1.98± 0.14. Hedgerow species diversity ranged from 0.25-2.52 with a mean of 1.74± 0.11. Total Aboveground Biomass (AGB) determined was 13.96±0.37 Mgha-1. Homegarden with the highest abundance of trees had higher above ground biomass (AGB) compared to hedgerow agroforestry. This study is timely as carbon budgets in the agroforestry can be incorporated in the global carbon budgets and improve the accuracy of national reporting of greenhouse gases.

Keywords: agroforestry, allometric equations, biomass, climate change

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Authors: Endalkachew Abebe Kebede, Bojin Bojinov, Andon Vasilev Andonov, Orhan Dengiz

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Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study.

Keywords: landsat 9, leaf spectrometer, sentinel 2, UAV

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

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Authors: Bing-Bing E. Goh

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Plant counting is a labour intensive and time-consuming task for the farmers. However, it is an important indicator for farmers to make decisions on subsequent field management. This study is to evaluate the potential of Sentinel-2 images using statistical analysis to retrieve information on plant density for monitoring, especially during critical period at the beginning of March. The model was calibrated with in-situ data from 19 winter wheat fields in Republic of Ireland during the crop growing season in 2019-2020. The model for plant density resulted in R2 = 0.77, RMSECV = 103 and NRMSE = 14%. This study has shown the potential of using Sentinel-2 to estimate plant density and quantify plant establishment to effectively monitor crop progress and to ensure proper field management.

Keywords: winter wheat, remote sensing, crop monitoring, multivariate analysis

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Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

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Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

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Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

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Authors: Özlem Canbolat, Sevgisun Kapucu

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The aim of this study was to determine the effect of a computer-assisted glycemic surveillance protocol on nursing workload in intensive care unit. The study is completed in an Education and Research Hospital in Ankara with the attendance of volunteered 19 nurse who had been worked in reanimation unit. Nurses used the written protocol and computer-assisted glycemic surveillance protocol for glycemic follow-up approach of the intensive care patients. Nurses used the written protocol first in the glycemic follow-up of the patient, then used the computer-assisted protocol. (Nurses used the written protocol first, then the computer-assisted protocol in the glycemic follow-up of the patient). Less time was spent in glycemic control with computerized protocol than written protocol and this difference is statistically significant (p < 0.001). It was determined that the computerized protocol application was completed in about 10 seconds (25% shorter) than the written protocol implementation. The computer-assisted glycemic surveillance protocol was found to be more easy and appropriate by nurses and the satisfaction level of the users was higher than with written protocol. While 79% of the nurses find it confusing to implement the written protocol, 79% were satisfied with the use of computerized protocol.

Keywords: computer-assisted protocol, glycemic control, insulin infusion protocol, intensive care, nursing workload

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Authors: Mohamed El Kory Yacoub, Ahmed Bezeid El Mamy Beyatt, Djibril Barry, Yanogo Pauline, Nicolas Meda

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Introduction: Rift Valley Fever is a zoonotic arbovirosis that severely affects ruminants, as well as humans. It causes abortions in pregnant females and deaths in young animals. The disease occurs during heavy rains followed by large numbers of mosquito vectors. The objective of this work is to evaluate the surveillance system for Rift Valley Fever. Methods: We conducted an evaluation of the Rift Valley Fiver surveillance system. Data were collected from the analysis of the national database of the Mauritanian Network of Animal Disease Epidemiological Surveillance at the Ministry of Rural Development, of RVF cases notified from the whole national territory, of questionnaires and interviews with all persons involved in RVF surveillance at the central level. The quality of the system was assessed by analyzing the quantitative attributes defined by the Centers for Disease Control and Prevention. Results: In 2019, 443 cases of RVF were notified by the surveillance system, of which 36 were positive. Among the notified cases of Rift Valley Fever, the 0- to the 3-year-old age group of small ruminants was the most represented with 49.21% of cases, followed by 33.33%, which was recorded in large ruminants in the 0 to 7-year-old age group, 11.11% of cases were older than seven years. The completeness of the data varied between 14.2% (age) and 100% (species). Most positive cases were recorded between October and November 2019 in seven different regions. Attribute analysis showed that 87% of the respondents were able to use the case definition well, and 78.8% said they were familiar with the reporting and feedback loop of the Rift Valley Fever data. 90.3% of the respondents found it easy, while 95% of them responded that it was easy for them to transmit their data to the next level. Conclusions: The epidemiological surveillance system for Rift Valley Fever in Mauritania is simple and representative. However, data quality, stability, and responsiveness are average, as the diagnosis of the disease requires laboratory confirmation and the average delay for this confirmation is long (13 days). Consequently, the lack of completeness of the recorded data and of description of cases in terms of time-place-animal, associated with the delay between the stages of the surveillance system can make prevention, early detection of epidemics, and the initiation of measures for an adequate response difficult.

Keywords: evaluation, epidemiological surveillance system, rift valley fever, mauritania, ruminants

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Authors: Mike Dee

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Urban public spaces are sutured with a range of surveillance and sensor technologies that claim to enable new forms of ‘data based citizen participation’, but also increase the tendency for ‘function-creep’, whereby vast amounts of data are gathered, stored and analysed in a broad application of urban surveillance. This kind of monitoring and capacity for surveillance connects with attempts by civic authorities to regulate, restrict, rebrand and reframe urban public spaces. A direct consequence of the increasingly security driven, policed, privatised and surveilled nature of public space is the exclusion or ‘unfavourable inclusion’ of those considered flawed and unwelcome in the ‘spectacular’ consumption spaces of many major urban centres. In the name of urban regeneration, programs of securitisation, ‘gentrification’ and ‘creative’ and ‘smart’ city initiatives refashion public space as sites of selective inclusion and exclusion. In this context of monitoring and control procedures, in particular, children and young people’s use of space in parks, neighbourhoods, shopping malls and streets is often viewed as a threat to the social order, requiring various forms of remedial action. This paper suggests that cities, places and spaces and those who seek to use them, can be resilient in working to maintain and extend democratic freedoms and processes enshrined in Marshall’s concept of citizenship, calling sensor and surveillance systems to account. Such accountability could better inform the implementation of public policy around the design, build and governance of public space and also understandings of urban citizenship in the sensor saturated urban environment.

Keywords: citizenship, public space, surveillance, young people

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Taekyun Doo, Cheongmin Ji, Manpyo Hong

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Like a closed-circuit television (CCTV), video surveillance system is widely placed for gathering video from unspecified people to prevent crime, surveillance, or many other purposes. However, abuse of CCTV brings about concerns of personal privacy invasions. In this paper, we propose an encryption method to protect personal privacy system in H.264 compressed video bitstream with encrypting only regions of interest (ROI). There is no need to change the existing video surveillance system. In addition, encrypting ROI in compressed video bitstream is a challenging work due to spatial and temporal drift errors. For this reason, we propose a novel drift mitigation method when ROI is encrypted. The proposed method was implemented by using JM reference software based on the H.264 compressed videos, and experimental results show the verification of our proposed methods and its effectiveness.

Keywords: H.264/AVC, video encryption, privacy protection, post compression, region of interest

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Authors: Jean-Philippe Amat, Timothée Vergne, Aymeric Hans, Bénédicte Ferry, Pascal Hendrikx, Jackie Tapprest, Barbara Dufour, Agnès Leblond

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The surveillance of infectious diseases is necessary to describe their occurrence and help the planning, implementation and evaluation of risk mitigation activities. However, the exact number of detected cases may remain unknown whether surveillance is based on serological tests because identifying seroconversion may be difficult. Moreover, incomplete detection of cases or outbreaks is a recurrent issue in the field of disease surveillance. This study addresses these two issues. Using a viral animal disease as an example (equine viral arteritis), the goals were to establish suitable rules for identifying seroconversion in order to estimate the number of cases and outbreaks detected by a surveillance system in France between 2006 and 2013, and to assess the sensitivity of this system by estimating the total number of outbreaks that occurred during this period (including unreported outbreaks) using a capture-recapture model. Data from horses which exhibited at least one positive result in serology using viral neutralization test between 2006 and 2013 were used for analysis (n=1,645). Data consisted of the annual antibody titers and the location of the subjects (towns). A consensus among multidisciplinary experts (specialists in the disease and its laboratory diagnosis, epidemiologists) was reached to consider seroconversion as a change in antibody titer from negative to at least 32 or as a three-fold or greater increase. The number of seroconversions was counted for each town and modeled using a unilist zero-truncated binomial (ZTB) capture-recapture model with R software. The binomial denominator was the number of horses tested in each infected town. Using the defined rules, 239 cases located in 177 towns (outbreaks) were identified from 2006 to 2013. Subsequently, the sensitivity of the surveillance system was estimated as the ratio of the number of detected outbreaks to the total number of outbreaks that occurred (including unreported outbreaks) estimated using the ZTB model. The total number of outbreaks was estimated at 215 (95% credible interval CrI95%: 195-249) and the surveillance sensitivity at 82% (CrI95%: 71-91). The rules proposed for identifying seroconversion may serve future research. Such rules, adjusted to the local environment, could conceivably be applied in other countries with surveillance programs dedicated to this disease. More generally, defining ad hoc algorithms for interpreting the antibody titer could be useful regarding other human and animal diseases and zoonosis when there is a lack of accurate information in the literature about the serological response in naturally infected subjects. This study shows how capture-recapture methods may help to estimate the sensitivity of an imperfect surveillance system and to valorize surveillance data. The sensitivity of the surveillance system of equine viral arteritis is relatively high and supports its relevance to prevent the disease spreading.

Keywords: Bayesian inference, capture-recapture, epidemiology, equine viral arteritis, infectious disease, seroconversion, surveillance

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Authors: Sophonie Sarielle Jean Jacques Bertrand

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Background: Acute flaccid paralysis (PFA) is the sudden weakness or paralysis of muscles seen in children under 15 years of age. According to the WHO, PFA remains a real public health problem. For Haiti, the PFA represents a national priority. This study aims to describe the epidemiological profile of cases of Acute Flaccid Paralysis (PFA) in Haiti from 2018-2020. Methods: A descriptive cross-sectional study covering the period of 2018-2021 was carried out. epidemiological surveillance data PFA exported to Integrated Monitoring Evaluation Surveillance (MESI) were used. Sociodemographic variables were studied. Prevalence and clinical mortality rate were calculated. Epi Info 7.2 and Excel 2016 were used for data analysis. Results: 76 AFP cases were recorded for the period, or 13 (17%) in 2018, 23 (30%) in 2019, 8 (11%) in 2020 32 (42%) in 2021. Children aged 5-14 years accounted for 36% of cases (n= 26). The M/F sex ratio was 0.52, with a predominance of the female sex. The clinical mortality rate was 2.6%. The prevalence was 1.77/100,000 people. Conclusion: From 2018-2021, 76 cases of PFA cases were recorded in the 10 departments of the country, of which the West department was the most affected. Maintaining high vaccination coverage and a standard acute flaccid paralysis surveillance system are essential for the eradication of this condition. Strengthen epidemiological surveillance of PFA.

Keywords: epidemiology, PFA, Haïti, MESI

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Authors: E. Bennion, A. Acharya, S. Barnes, D. Ferrell, S. Luckett-Cole, G. Mower, J. Nelson, Y. Nguyen

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Objective: This study aimed to evaluate the validity of a firearm injury query in the Early Notification of Community-based Epidemics syndromic surveillance system. Syndromic surveillance data are used at the Utah Department of Health for early detection of and rapid response to unusually high rates of violence and injury, among other health outcomes. The query of interest was defined by the Centers for Disease Control and Prevention and used chief complaint and discharge diagnosis codes to capture initial emergency department encounters for firearm injury of all intents. Design: Two epidemiologists manually reviewed electronic health records of emergency department visits captured by the query from April-May 2020, compared results, and sent conflicting determinations to two arbiters. Results: Of the 85 unique records captured, 67 were deemed probable, 19 were ruled out, and two were undetermined, resulting in a positive predictive value of 75.3%. Common reasons for false positives included non-initial encounters and misleading keywords. Conclusion: Improving the validity of syndromic surveillance data would better inform outbreak response decisions made by state and local health departments. The firearm injury definition could be refined to exclude non-initial encounters by negating words such as “last month,” “last week,” and “aftercare”; and to exclude non-firearm injury by negating words such as “pellet gun,” “air gun,” “nail gun,” “bullet bike,” and “exit wound” when a firearm is not mentioned.

Keywords: evaluation, health information system, firearm injury, syndromic surveillance

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Authors: Berceste Gülçin Özdemir

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The representation of women characters in cinema has been discussed for centuries. In cinema where dominant narrative codes prevail and scopophilic views exist over women characters, passive stereotypes of women are observed in the representation of women characters. In films shot from a woman’s point of view in Turkish Cinema and even in the films outside the main stream in which the stories of women characters are told, the fact that women characters are discussed on the basis of feminist film theories triggers the question: ‘Are feminist films produced in Turkish Cinema?’ The spaces that are used in the representation of women characters are observed to be used as spaces that convert characters into passive subjects on the basis of the space factor in the narrative. The representation of women characters in the possible surveillance spaces integrates the characters and compresses them in these spaces. In this study, narrative analysis was used to investigate women characters representation in the surveillance spaces. For the study framework, firstly a case study films are selected, and in the second level, women characters representations in surveillance spaces are argued by narrative analysis using feminist film theories. Two questions are argued with feminist film theories: ‘Why do especially women directors represent their female characters to viewers by representing them in surveillance spaces?’ and ‘Can this type of presentation contribute to the feminist film practice and become important with regard to feminist film theories?’ The representation of women characters in a passive and observed way in surveillance spaces of the narrative reveals the questioning of also the discourses of films outside of the main stream. As films that produce alternative discourses and reveal different cinematic languages, those outside the main stream are expected to bring other points of view also to the representation of women characters in spaces. These questionings are selected as the baseline and Turkish films such as Watch Tower and Mustang, directed by women, were examined. This examination paves the way for discussions regarding the women characters in surveillance spaces. Outcomes can be argued from the viewpoint of representation in the genre by feminist film theories. In the context of feminist film theories and feminist film practice, alternatives should be found that can corporally reveal the existence of women in both the representation of women characters in spaces and in the usage of the space factor.

Keywords: feminist film theory, representation, space, women directors

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Authors: Ella Sèdé Maforikan

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Accurate land cover mapping is essential for effective environmental monitoring and natural resources management. This study focuses on assessing the classification performance of two satellite datasets and evaluating the impact of different input feature combinations on classification accuracy in the Beterou catchment, situated in the northern part of Benin. Landsat-8 and Sentinel-2 images from June 1, 2020, to March 31, 2021, were utilized. Employing the Random Forest (RF) algorithm on Google Earth Engine (GEE), a supervised classification categorized the land into five classes: forest, savannas, cropland, settlement, and water bodies. GEE was chosen due to its high-performance computing capabilities, mitigating computational burdens associated with traditional land cover classification methods. By eliminating the need for individual satellite image downloads and providing access to an extensive archive of remote sensing data, GEE facilitated efficient model training on remote sensing data. The study achieved commendable overall accuracy (OA), ranging from 84% to 85%, even without incorporating spectral indices and terrain metrics into the model. Notably, the inclusion of additional input sources, specifically terrain features like slope and elevation, enhanced classification accuracy. The highest accuracy was achieved with Sentinel-2 (OA = 91%, Kappa = 0.88), slightly surpassing Landsat-8 (OA = 90%, Kappa = 0.87). This underscores the significance of combining diverse input sources for optimal accuracy in land cover mapping. The methodology presented herein not only enables the creation of precise, expeditious land cover maps but also demonstrates the prowess of cloud computing through GEE for large-scale land cover mapping with remarkable accuracy. The study emphasizes the synergy of different input sources to achieve superior accuracy. As a future recommendation, the application of Light Detection and Ranging (LiDAR) technology is proposed to enhance vegetation type differentiation in the Beterou catchment. Additionally, a cross-comparison between Sentinel-2 and Landsat-8 for assessing long-term land cover changes is suggested.

Keywords: land cover mapping, Google Earth Engine, random forest, Beterou catchment

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Authors: Ernest Tambo, Ghislaine Madjou, Jeanne Y. Ngogang, Shenglan Tang, Zhou XiaoNong

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Implementing national to community-based 'One Health' surveillance approach for human, animal and environmental consequences mitigation offers great opportunities and value-added in sustainable development and wellbeing. 'One Health' surveillance approach global partnerships, policy commitment and financial investment are much needed in addressing the evolving threats and epidemics crises mitigation in African countries. The paper provides insights onto how China-Africa health development cooperation in promoting “One Health” surveillance approach in response advocacy and mitigation. China-Africa health development initiatives provide new prospects in guiding and moving forward appropriate and evidence-based advocacy and mitigation management approaches and strategies in attaining Universal Health Coverage (UHC) and Sustainable Development Goals (SDGs). Early and continuous quality and timely surveillance data collection and coordinated information sharing practices in malaria and other diseases are demonstrated in Comoros, Zanzibar, Ghana and Cameroon. Improvements of variety of access to contextual sources and network of data sharing platforms are needed in guiding evidence-based and tailored detection and response to unusual hazardous events. Moreover, understanding threats and diseases trends, frontline or point of care response delivery is crucial to promote integrated and sustainable targeted local, national “One Health” surveillance and response approach needs implementation. Importantly, operational guidelines are vital in increasing coherent financing and national workforce capacity development mechanisms. Strengthening participatory partnerships, collaboration and monitoring strategies in achieving global health agenda effectiveness in Africa. At the same enhancing surveillance data information streams reporting and dissemination usefulness in informing policies decisions, health systems programming and financial mobilization and prioritized allocation pre, during and post threats and epidemics crises programs strengths and weaknesses. Thus, capitalizing on “One Health” surveillance and response approach advocacy and mitigation implementation is timely in consolidating Africa Union 2063 agenda and Africa renaissance capabilities and expectations.

Keywords: Africa, one health approach, surveillance, response

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

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Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

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Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand the smallholder farming communities within Scotland by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted in conjunction with text mining of the data in search of common themes, words, and topics found within the text. Results from bi-grams and topic modelling uncover four main topics of interest within the data pertaining to aspects of livestock husbandry: feeding, breeding, slaughter, and disposal. These topics were found amongst both the poultry and pig sub-forums. Topic modeling appears to be a useful method of unsupervised classification regarding this form of data, as it has produced clusters that relate to biosecurity and animal welfare. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter and Facebook/Meta, in addition to time series analysis to highlight temporal patterns.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, smallholding, social media, web scraping, sentiment analysis, geolocation, text mining, NLP

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Authors: Sisay Tiroro Salato

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Background: Ethiopia has been implementing the maternal death surveillance and response system to provide real-time actionable information, including causes of death and contributing factors. Analysis of maternal mortality surveillance data was conducted to identify the causes and underlying factors in Addis Ababa, Ethiopia. Methods: We carried out a retrospective surveillance data analysis of 324 maternal deaths reported in Addis Ababa, Ethiopia, from 2017 to 2021. The data were extracted from the national maternal death surveillance and response database, including information from case investigation, verbal autopsy, and facility extraction forms. The data were analyzed by computing frequency and presented in numbers, proportions, and ratios. Results: Of 324 maternal deaths, 92% died in the health facilities, 6.2% in transit, and 1.5% at home. The mean age at death was 28 years, ranging from 17 to 45. The maternal mortality ratio per 100,000 live births was 77for the five years, ranging from 126 in 2017 to 21 in 2021. The direct and indirect causes of death were responsible for 87% and 13%, respectively. The direct causes included obstetric haemorrhage, hypertensive disorders in pregnancy, puerperal sepsis, embolism, obstructed labour, and abortion. The third delay (delay in receiving care after reaching health facilities) accounted for 57% of deaths, while the first delay (delay in deciding to seek health care) and the second delay (delay in reaching health facilities) and accounted for 34% and 24%, respectively. Late arrival to the referral facility, delayed management after admission, andnon-recognition of danger signs were underlying factors. Conclusion: Over 86% of maternal deaths were attributed by avoidable direct causes. The majority of women do try to reach health services when an emergency occurs, but the third delays present a major problem. Improving the quality of care at the healthcare facility level will help to reduce maternal death.

Keywords: maternal death, surveillance, delays, factors

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Authors: Jinsiang Shaw, Pik-Hoe Chen

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This paper aims to integrate human recognition, motion recognition, and object tracking technologies without requiring a pre-training database model for motion recognition or the unknown object itself. Furthermore, it can simultaneously track multiple users and multiple objects. Unlike other existing human motion recognition methods, our approach employs a rule-based condition method to determine if a user hand is approaching or departing an object. It uses a background subtraction method to separate the human and object from the background, and employs behavior features to effectively interpret human object-grabbing actions. With an object’s histogram characteristics, we are able to isolate and track it using back projection. Hence, a moving object trajectory can be recorded and the object itself can be located. This particular technique can be used in a camera surveillance system in a shopping area to perform real-time intelligent surveillance, thus preventing theft. Experimental results verify the validity of the developed surveillance algorithm with an accuracy of 83% for shoplifting detection.

Keywords: Automatic Tracking, Back Projection, Motion Recognition, Shoplifting

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Authors: D. Madhushanka, Y. Liu, H. C. Fernando

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Fires are one of the foremost factors of land surface disturbance in diverse ecosystems, causing soil erosion and land-cover changes and atmospheric effects affecting people's lives and properties. Generally, the severity of the fire is calculated as the Normalized Burn Ratio (NBR) index. This is performed manually by comparing two images obtained afterward. Then by using the bitemporal difference of the preprocessed satellite images, the dNBR is calculated. The burnt area is then classified as either unburnt (dNBR<0.1) or burnt (dNBR>= 0.1). Furthermore, Wildfire Severity Assessment (WSA) classifies burnt areas and unburnt areas using classification levels proposed by USGS and comprises seven classes. This procedure generates a burn severity report for the area chosen by the user manually. This study is carried out with the objective of producing an automated tool for the above-mentioned process, namely the World Wildfire Severity Assessment Tool (WWSAT). It is implemented in Google Earth Engine (GEE), which is a free cloud-computing platform for satellite data processing, with several data catalogs at different resolutions (notably Landsat, Sentinel-2, and MODIS) and planetary-scale analysis capabilities. Sentinel-2 MSI is chosen to obtain regular processes related to burnt area severity mapping using a medium spatial resolution sensor (15m). This tool uses machine learning classification techniques to identify burnt areas using NBR and to classify their severity over the user-selected extent and period automatically. Cloud coverage is one of the biggest concerns when fire severity mapping is performed. In WWSAT based on GEE, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for both pre-fire and post-fire image compositing. The parallel processing capabilities and preloaded geospatial datasets of GEE facilitated the production of this tool. This tool consists of a Graphical User Interface (GUI) to make it user-friendly. The advantage of this tool is the ability to obtain burn area severity over a large extent and more extended temporal periods. Two case studies were carried out to demonstrate the performance of this tool. The Blue Mountain national park forest affected by the Australian fire season between 2019 and 2020 is used to describe the workflow of the WWSAT. This site detected more than 7809 km2, using Sentinel-2 data, giving an error below 6.5% when compared with the area detected on the field. Furthermore, 86.77% of the detected area was recognized as fully burnt out, of which high severity (17.29%), moderate-high severity (19.63%), moderate-low severity (22.35%), and low severity (27.51%). The Arapaho and Roosevelt National Forest Park, California, the USA, which is affected by the Cameron peak fire in 2020, is chosen for the second case study. It was found that around 983 km2 had burned out, of which high severity (2.73%), moderate-high severity (1.57%), moderate-low severity (1.18%), and low severity (5.45%). These spots also can be detected through the visual inspection made possible by cloud-free images generated by WWSAT. This tool is cost-effective in calculating the burnt area since satellite images are free and the cost of field surveys is avoided.

Keywords: burnt area, burnt severity, fires, google earth engine (GEE), sentinel-2

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Authors: Nobuyuki Takemoto, Ai Koyanagi, Masanori Yasuda, Hiroshi Yamamoto

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Background: Fluorescence imaging (FI) is one of the methods to identify sentinel lymph nodes (SLNs). However, the procedure is technically complicated and requires procedural skills, as SLN biopsy must be conducted in dim light conditions. As an improved version of this method, we introduced a combined method (Combined mixed dye and fluorescence; CMF) consisting of indigo carmine blue dye and FI. The direct visualization of SLNs under shadowless surgical light conditions is facilitated by the addition of the blue dye. We compared the SLN detection rates of CMF with that of the indocyanine green (ICG) dye method (ICG-D). Methods: A total of 202 patients with stage ≤ IIA breast cancer who underwent breast conservative therapy with separate incision from January 2004 to February 2017 were reviewed. Details of the two methods are as follows: (1) ICG-D: 2ml of ICG (10mg) was used and the green-stained SLNs were resected via a 3-4cm axillary incision; (2) CMF: A combination of 1ml of ICG (5mg) and 1-3ml of indigo carmine (4-12mg) was used. Using Photodynamic Eye (PDE), a 1.5-2 cm incision was made near the point of disappearance of the fluorescence and SLNs with intermediate color of blue and green were resected. Results: There were 92 ICG-D and 110 CMF cases. CMF resulted in a significantly higher detection rate than ICG-D (96.4% vs. 83.7%; p=0.003). This difference was particularly notable in those aged ≥ 60 years (98.3% vs. 74.3%) and individuals with BMI ≥ 25kg/m2 (90.3% vs. 58.3%). Conclusion: CMF is an effective method to identify SLNs which is safe, efficient, and cost-effective. Furthermore, radiation exposure can be avoided, and it can be performed in institutes without nuclear medicine facilities. CMF achieves a high SLN identification rate, and most of this procedure is feasible under shadowless surgical light conditions. CMF can reliably perform SLN biopsy even in those aged ≥ 60 years and individuals with BMI ≥ 25 kg/m2.

Keywords: sentinel lymph node biopsy, identification rate, indocyanine green (ICG), indigocarmine, fluorescence

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Authors: Raheleh Farzanmanesh, Christopher J. Weston

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Mangroves are forest ecosystems located in the inter-tidal regions of tropical and subtropical coastlines that provide many valuable economic and ecological benefits for millions of people, such as preventing coastal erosion, providing breeding, and feeding grounds, improving water quality, and supporting the well-being of local communities. In addition, mangroves capture and store high amounts of carbon in biomass and soils that play an important role in combating climate change. The decline in mangrove area has prompted government and private sector interest in mangrove conservation and restoration projects to achieve multiple Sustainable Development Goals, from reducing poverty to improving life on land. Mangrove aboveground biomass plays an essential role in the global carbon cycle, climate change mitigation and adaptation by reducing CO2 emissions. However, little information is available about the effectiveness of mangrove sustainable management on mangrove change area and aboveground biomass (AGB). Here, we proposed a method for mapping, modeling, and assessing mangrove area and AGB in two Global Environment Facility (GEF) blue forests projects based on Sentinel-2 Level 1C imagery during their conservation lifetime. The SVR regression model was used to estimate AGB in Tahiry Honko project in Madagascar and the Abu Dhabi Blue Carbon Demonstration Project (Abu Dhabi Emirates. The results showed that mangrove forests and AGB declined in the Tahiry Honko project, while in the Abu Dhabi project increased after the conservation initiative was established. The results provide important information on the impact of mangrove conservation activities and contribute to the development of remote sensing applications for mapping and assessing mangrove forests in blue carbon initiatives.

Keywords: blue carbon, mangrove forest, REDD+, aboveground biomass, Sentinel-2

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Authors: Walid Abdallah, Noureddine Boudriga

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Wireless sensor networks have shown their effectiveness in the deployment of many critical applications especially in the military domain. Border surveillance is one of these applications where a set of wireless sensors are deployed along a country border line to detect illegal intrusion attempts to the national territory and report this to a control center to undergo the necessary measures. Regarding its nature, this wireless sensor network can be the target of many security attacks trying to compromise its normal operation. Particularly, in this application the deployment and location of sensor nodes are of great importance for detecting and tracking intruders. This paper proposes a location-based authentication and key distribution mechanism to secure wireless sensor networks intended for border surveillance where the key establishment is performed using elliptic curve cryptography and identity-based public key scheme. In this scheme, the public key of each sensor node will be authenticated by keys that depend on its position in the monitored area. Before establishing a pairwise key between two nodes, each one of them must verify the neighborhood location of the other node using a message authentication code (MAC) calculated on the corresponding public key and keys derived from encrypted beacon messages broadcast by anchor nodes. We show that our proposed public key authentication and key distribution scheme is more resilient to node capture and node replication attacks than currently available schemes. Also, the achievement of the key distribution between nodes in our scheme generates less communication overhead and hence increases network performances.

Keywords: wireless sensor networks, border surveillance, security, key distribution, location-based

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Authors: Benjamin Osei-Tutu, Henrietta Awewole Kolson

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Background: Acute gastroenteritis is one of the frequently reported Out-Patient Department (OPD) cases. However, the causative pathogens of these cases are rarely identified at the OPD due to delay in laboratory results or failure to obtain specimens before antibiotics is administered. Method: A retrospective review of surveillance data from the Adentan Municipality, Accra, Ghana that were recorded in the National foodborne disease surveillance system of Ghana, was conducted with the main aim of describing the epidemiology and food practice of cases reported from the Adentan Municipality. The study involved a retrospective review of surveillance data kept on patients who visited health facilities that are involved in foodborne disease surveillance in Ghana, from January 2015 to December 2016. Results: A total of 375 cases were reviewed and these were classified as viral hepatitis (hepatitis A and E), cholera (Vibrio cholerae), dysentery (Shigella sp.), typhoid fever (Salmonella sp.) or gastroenteritis. Cases recorded were all suspected case and the average cases recorded per week was 3. Typhoid fever and dysentery were the two main clinically diagnosed foodborne illnesses. The highest number of cases were observed during the late dry season (Feb to April), which marks the end of the dry season and the beginning of the rainy season. Relatively high number of cases was also observed during the late wet seasons (Jul to Oct) when the rainfall is the heaviest. Home-made food and street vended food were the major sources of suspected etiological food, recording 49.01% and 34.87% of the cases respectively. Conclusion: Majority of cases recorded were classified as gastroenteritis due to the absence of laboratory confirmation. Few cases were classified as typhoid fever and dysentery based on clinical symptoms presented. Patients reporting with foodborne diseases were found to consume home meal and street vended foods as their predominant source of food.

Keywords: accra, etiologic food, food poisoning, gastroenteritis, illness, surveillance

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Authors: M. Sulieman, H. Arabiyat, H. Ali, K. Potiszil, I. Abbas, R. English, P. King, I. Brown, P. Drew

Abstract:

Introduction: The incidence of breast ductal Carcinoma in Situ (DCIS) has been increasing; it currently represents up 20-25% of all breast carcinomas. Some aspects of DCIS management are still controversial, mainly due to the heterogeneity of its clinical presentation and of its biological and pathological characteristics. In DCIS, histological diagnosis obtained preoperatively, carries the risk of sampling error if the presence of invasive cancer is subsequently diagnosed. The mammographic extent over than 4–5 cm and the presence of architectural distortion, focal asymmetric density or mass on mammography are proven important risk factors of preoperative histological under staging. Intracystic papillary cancer (IPC) is a rare form of breast carcinoma. Despite being previously compared to DCIS it has been shown to present histologically with invasion of the basement membrane and even metastasis. SLNB – Carries the risk of associated comorbidity that should be considered when planning surgery for DCIS and IPC. Objectives: The aim of this Audit was to better define a ‘high risk’ group of patients with pre-op diagnosis of non-invasive cancer undergoing breast conserving surgery, who would benefit from sentinel node biopsy. Method: Retrospective data collection of all patients with ductal carcinoma in situ over 5 years. 636 patients identified, and after exclusion criteria applied: 394 patients were included. High risk defined as: Extensive micro-calcification >40mm OR any mass forming DCIS. IPC: Winpath search from for the term ‘papillary carcinoma’ in any breast specimen for 5 years duration;.29 patients were included in this group. Results: DCIS: 188 deemed high risk due to >40mm calcification or a mass forming (radiological or palpable) 61% of those had a mastectomy and 32% BCS. Overall, in that high-risk group - the number with invasive disease was 38%. Of those high-risk DCIS pts 85% had a SLN - 80% at the time of surgery and 5% at a second operation. For the BCS patients - 42% had SLN at time of surgery and 13% (8 patients) at a second operation. 15 (7.9%) pts in the high-risk group had a positive SLNB, 11 having a mastectomy and 4 having BCS. IPC: The provisional diagnosis of encysted papillary carcinoma is upgraded to an invasive carcinoma on final histology in around a third of cases. This has may have implications when deciding whether to offer sentinel node removal at the time of therapeutic surgery. Conclusions: We have defined a ‘high risk’ group of pts with pre-op diagnosis of non-invasive cancer undergoing BCS, who would benefit from SLNB at the time of the surgery. In patients with high-risk features; the risk of invasive disease is up to 40% but the risk of nodal involvement is approximately 8%. The risk of morbidity from SLN is up to about 5% especially the risk of lymphedema.

Keywords: breast ductal carcinoma in Situ (DCIS), intracystic papillary carcinoma (IPC), sentinel node biopsy (SLNB), high-risk, non-invasive, cancer disease

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Authors: Shamas Ul Hassan, Nasir Mukhtar, Sajjad Ur Rehman, Asghar Ali Mian, Iftikhar Hussain, Muhammad Safdar Anjum

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The Mycoplasma gallisepticum (MG) is the major cause of economic looses in birds which is transmitted by free flying birds in the environment. These demands for improving the biosecurity measures at farm level including proper disposal of farm mortality and other wastes along with the inclusion of zoos and wild life parks in the MG surveillance programme. For the purpose of doing surveillance of MG in different pet, game and free flying birds a total of 12 samples each of peacocks, pheasants, ducks, pigeons, parrots, and house crows were included in the first ever study of its nature in Pakistan. During the study, the relevant samples along with recording clinical and postmortem findings were subjected to sero-prevalence, culture isolation and PCR system. Further PCR being more sensitive proves to be a better epidemiological tool. Seropositive findings revealed in peacocks, pheasants, ducks, pigeons, parrots, and crows were 66.7%, 58.3%, 41.7%, 41.7%, 16.7% and 16.7% respectively with some free flying birds giving ambiguous reactions. Whereas in the same order the culture/isolation positive results were recorded as 25%, 16.7%, 8.3%, 16.7%, 16.7%, and 25%. The samples were further confirmed on the basis of 732 bp product in PCR system. High rate of prevalence of MG in the pet, game and free flying birds regardless to their clinical findings demands to improve the biosecurity measures at the farm level with the minimum interaction of these birds with commercial poultry. Further the proper and timely disposal of all sorts of carcasses contaminated litter and wasted feed in such ways that the free flying birds are denied of picking up at those wastages. Moreover, MG surveillance system including the advances diagnostic techniques in wildlife parks and zoos be devised with proper timely preventive and therapeutic measures. The study proves that a variety of birds other then chicken either with or without clinical exhibitions carry MG organism which could be the potential source of infection for commercial poultry. The routine surveillance will be done to reduce the economic losses in poultry production.

Keywords: epidemiology, Mycoplasma gallisepticum (MG), free flying birds, surveillance, PCR

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