Effective surveillance systems for vectorborne diseases in urban settings and translation of the data into action:a scoping review

Background:Vector-borne diseases(VBDs)continue to represent a global threat,with“old”diseases like malaria,and“emergent”or“re-emergent”ones like Zika,because of an increase in international trade,demographic growth,and rapid urbanization.In this era of globalization,surveillance is a key element in controlling VBDs in urban settings,but surveillance alone cannot solve the problem.A review of experiences is of interest to examine other solution elements.The objectives were to assess the different means of VBD surveillance in urban environments,to evaluate their potential for supporting public health actions,and to describe the tools used for public health actions,the constraints they face,and the research and health action gaps to be filled.Main body:For this scoping review we searched peer-reviewed articles and grey literature published between 2000 and 2016.Various tools were used for data coding and extraction.A quality assessment was done for each study reviewed,and descriptive characteristics and data on implementation process and transferability were analyzed in all studies.After screening 414 full-text articles,we retained a total of 79 articles for review.The main targets of the articles were arboviral diseases(65.8%)and malaria(16.5%).The positive aspects of many studies fit within the framework of integrated vector management.Public awareness is considered a key to successful vector control programs.Advocacy and legislation can reinforce both empowerment and capacity building.These can be achieved by collaboration within the health sector and with other sectors.Research is needed to develop well designed studies and new tools for surveillance and control.Conclusions:The need for surveillance systems in urban settings in both developing and developed countries was highlighted.Countries face the same challenges relating to human,financial,and structural resources.These findings also constitute a wake-up call for governments,academia,funders,and World Health Organization to strengthen control programs and enhance VBD research in urban environments.

Application of robust face recognition in video surveillance systems

In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis(FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.

Strengthening Africa's response to Mpox(monkeypox):insights from historical outbreaks and the present global spread

Mpox,formerly known as monkeypox,is a viral zoonotic disease endemic to Central and West Africa that has posed significant public health challenges since its identification in 1970.Despite decades of experience in managing outbreaks,the 2022-2024 Mpox outbreaks exposed substantial gaps in global preparedness and response,leading the World Health Organization(WHO)to declare a Public Health Emergency of International Concern(PHEIC)in 2022.The resurgence of cases in Europe in 2022 and the more recent emergence of the virulent clade Ib in the Democratic Republic of the Congo(DRC)in 2024 have highlighted a critical need for improved proactive and response strategies to curb the epidemic.This narrative review examines the historical and recent epidemiology of Mpox in Africa and explores the factors that have limited effective management.These include objective influences such as viral mutations,zoonotic transmission patterns,and environmental changes like deforestation,as well as subjective factors,including delayed responses,limited vaccine availability,cessation of smallpox vaccinations,and inequitable access to healthcare.In particular,the review emphasizes the ongoing disparities in global health equity,as wealthier nations have been able to secure vaccines and therapeutics quickly,while endemic regions in Africa continue to struggle with limited resources.The review also discusses how socio-economic and cultural factors,combined with weak public health infrastructure and inadequate surveillance systems,perpetuate cycles of outbreak in vulnerable populations.Furthermore,the emergence of clade Ib in 2024,with its higher virulence and mortality rates among children,particularly in rural areas,underscores the urgency of addressing the evolving epidemiological landscape of Mpox.In response to these challenges,this review recommends strengthening healthcare infrastructure,enhancing surveillance systems,ensuring equitable access to vaccines and treatments,and integrating environmental management into public health strategies.Global collaboration remains essential to provide African countries with the resources and support needed to manage and prevent future outbreaks effectively.Without these measures,the world risks a prolonged public health crisis with far-reaching consequences for both Africa and the global community.

A Triplet-Branch Convolutional Neural Network for Part-Based Gait Recognition

Intelligent vision-based surveillance systems are designed to deal with the gigantic volume of videos captured in a particular environment to perform the interpretation of scenes in form of detection,tracking,monitoring,behavioral analysis,and retrievals.In addition to that,another evolving way of surveillance systems in a particular environment is human gait-based surveillance.In the existing research,several methodological frameworks are designed to use deep learning and traditional methods,nevertheless,the accuracies of these methods drop substantially when they are subjected to covariate conditions.These covariate variables disrupt the gait features and hence the recognition of subjects becomes difficult.To handle these issues,a region-based triplet-branch Convolutional Neural Network(CNN)is proposed in this research that is focused on different parts of the human Gait Energy Image(GEI)including the head,legs,and body separately to classify the subjects,and later on,the final identification of subjects is decided by probability-based majority voting criteria.Moreover,to enhance the feature extraction and draw the discriminative features,we have added soft attention layers on each branch to generate the soft attention maps.The proposed model is validated on the CASIA-B database and findings indicate that part-based learning through triplet-branch CNN shows good performance of 72.98%under covariate conditions as well as also outperforms single-branch CNN models.

Surveillance and response systems for elimination of tropical diseases: summary of a thematic series in Infectious Diseases of Poverty

The peer-reviewed journal Infectious Diseases of Poverty provides a new platform to engage with,and disseminate in an open-access format,science outside traditional disciplinary boundaries.The current piece reviews a thematic series on surveillance-response systems for elimination of tropical diseases.Overall,22 contributions covering a broad array of diseases are featured–i.e.clonorchiasis,dengue,hepatitis,human immunodeficiency virus/acquired immune deficiency syndrome(HIV/AIDS),H7N9 avian influenza,lymphatic filariasis,malaria,Middle East respiratory syndrome(MERS),rabies,schistosomiasis and tuberculosis(TB).There are five scoping reviews,a commentary,a letter to the editor,an opinion piece and an editorial pertaining to the theme“Elimination of tropical disease through surveillance and response”.The remaining 13 articles are original contributions mainly covering(i)drug resistance;(ii)innovation and validation in the field of mathematical modelling;(iii)elimination of infectious diseases;and(iv)social media reports on disease outbreak notifications released by national health authorities.Analysis of the authors’affiliations reveals that scientists from the People’s Republic of China(P.R.China)are prominently represented.Possible explanations include the fact that the 2012 and 2014 international conferences pertaining to surveillance-response mechanisms were both hosted by the National Institute of Parasitic Diseases(NIPD)in Shanghai,coupled with P.R.China’s growing importance with regard to the control of infectious diseases.Within 4 to 22 months of publication,three of the 22 contributions were viewed more than 10000 times each.With sustained efforts focusing on relevant and strategic information towards control and elimination of infectious diseases,Infectious Diseases of Poverty has become a leading journal in the field of surveillance and response systems in infectious diseases and beyond.

Are surveillance response systems enough to effectively combat and contain the Ebola outbreak?

The epidemic of the Ebola virus infection in West Africa in 2014 has become a worldwide concern.Due to the nature of the disease,which has an extremely high mortality potential,this outbreak has received much attention from researchers and public health workers.An article entitled“Need of surveillance response systems to combat Ebola outbreaks and other emerging infectious diseases in African countries,”published in the journal Infectious Diseases of Poverty in August 2014,concluded that a good surveillance system to monitor disease transmission dynamics is essential and needs to be implemented to combat the outbreak.Issues regarding the limitation of the passive surveillance system have been raised by Professor Viroj Wiwanitkit,who emphasizes the need for an active disease detection system such as mass screening in this letter to editor.The different function between passive and active surveillance system in combating the disease outbreak has been agreed upon by Ernest Tambo et al.There have also been discussions between Wiwanitkit and Tambo et al.on the following issues:(i)the extreme resource limitations in outbreak areas,(ii)new technology to improve the available systems.Further recommendations echoed in this letter to editor by Wiwanitkit,who outlined the research priorities on the development of appropriate combined disease monitoring systems and good policy to allocate available tools and technology in resource-limited settings for epidemic scenarios.The journal’s editor,Professor Xiao-Nong Zhou,has therefore collated all parts of these discussions between authors in this letter to editor paper,in order to further promote research on a combined active and passive system to combat the present extending Ebola outbreak.

Smart Objects Identification System for Robotic Surveillance

Video surveillance is an active research topic in computer vision.In this paper,humans and cars identifcation technique suitable for real time video surveillance systems is presented.The technique we proposed includes background subtraction,foreground segmentation,shadow removal,feature extraction and classifcation.The feature extraction of the extracted foreground objects is done via a new set of afne moment invariants based on statistics method and these were used to identify human or car.When the partial occlusion occurs,although features of full body cannot be extracted,our proposed technique extracts the features of head shoulder.Our proposed technique can identify human by extracting the human head-shoulder up to 60%–70%occlusion.Thus,it has a better classifcation to solve the issue of the loss of property arising from human occluded easily in practical applications.The whole system works at approximately 16 29 fps and thus it is suitable for real-time applications.The accuracy for our proposed technique in identifying human is very good,which is 98.33%,while for cars identifcation,the accuracy is also good,which is 94.41%.The overall accuracy for our proposed technique in identifying human and car is at 98.04%.The experiment results show that this method is efective and has strong robustness.

An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities

Innovations on the Internet of Everything(IoE)enabled systems are driving a change in the settings where we interact in smart units,recognized globally as smart city environments.However,intelligent video-surveillance systems are critical to increasing the security of these smart cities.More precisely,in today’s world of smart video surveillance,person re-identification(Re-ID)has gained increased consideration by researchers.Various researchers have designed deep learningbased algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision problems.In this line of research,we designed an adaptive feature refinementbased deep learning architecture to conduct person Re-ID.In the proposed architecture,the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel attention.In addition,the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature maps.Furthermore,the model’s effectiveness is validated on the CUHK01 and CUHK02 datasets.When compared with existing approaches,the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%,respectively.

SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

With the evolution of video surveillance systems,the requirement of video storage grows rapidly;in addition,safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events.As most of the scene in the surveillance video are redundant and contains no information needs attention,we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly.Our goal is to improve the condensation rate to reduce more storage size,and increase the accuracy in abnormal detection.As the trajectory feature is the key to both goals,in this paper,a new method for feature extraction of moving object trajectory is proposed,and we use the SOINN(Self-Organizing Incremental Neural Network)method to accomplish a high accuracy abnormal detection.In the results,our method is able to shirk the video size to 10%storage size of the original video,and achieves 95%accuracy of abnormal event detection,which shows our method is useful and applicable to the surveillance industry.

Combating infectious diseases of poverty: a year on

The Infectious Diseases of Poverty journal,launched a year ago,is a platform to engage outside the traditional disciplinary boundaries,and disseminate high quality science towards the improvement of health.This paper reviews the milestone achievements during its first year of operation.The journal has filled an important niche,addressing some of the main priorities in the Global Report for Research on Infectious Diseases of Poverty.Highlights include the publication of three thematic issues on health systems,surveillance and response systems,as well as co-infection and syndemics.The thematic issues have foregrounded the importance and innovation that can be achieved through transdisciplinary research.The journal has been indexed by PubMed since April 2013,with the publication of a total of 38 articles.Finally,the journal is delivering to wider range readers both in developing and developed countries with sustained efforts with a focus on relevant and strategic information towards elimination of infectious diseases of poverty.