Traditional Surveillance Research Articles

Introduction of wastewater surveillance of SARS-CoV-2 in Ukraine

Abstract Since the Russian invasion, Ukraine has faced humanitarian crises and challenges in diseases surveillance. The primary healthcare (PHC) system struggled with growing numbers of internally displaced and wounded people. In October 2022, the PATH-led, USAID-funded Support TB Control Efforts in Ukraine (STBCEU) project, supported implementation of wastewater surveillance (WWS) for SARS-CoV-2 in four oblasts on request of the Ministry of Health (MoH), expanding to 18 oblasts by October 2023. Samples were collected from 64 sites twice per week and tested for SARS-CoV-2 and influenza A/B using BioExtract® Premium Mag test kits in regional laboratories. From October 2022 to April 2024, 4,784 samples were collected at municipal aqueducts, with SARS-CoV-2 delta and omicron variants detected across all regions. WWS sample results were compared with routine surveillance to assess trends in concentrations and reported cases among population. Gradual decrease in SARS-CoV-2 concentration to zero by May 29, 2023, coincided with a decrease in morbidity. In the last week of August 2023, the concentration of the SARS-CoV-2 in wastewater began its gradual increase with the beginning of new cases registration among the population within 2 weeks, by mid-September. From October 2023, SARS-CoV-2 dominated in 96.6% of positive WWS testing results; in other specimens, influenza and respiratory viruses have been identified. The implementation of WWS in Ukraine highlighted its effectiveness as a public health tool in settings where traditional surveillance methods are disrupted, providing timely data on the viral pathogens for outbreaks prediction and responses, underscoring its role in preventive health measures. The MOH of Ukraine recommended extending the method nationwide for routine surveillance of SARS-CoV-2 and other pathogens, enhancing Ukraine’s capacity to manage public health threats and strengthen the resilience of its health system against future crises. Key messages • WWS in Ukraine underscores its effectiveness as a public health tool, providing timely data on viral pathogens for outbreak prediction and response in disrupted settings. • Notable decline in SARS-CoV-2 concentration in wastewater correlates with reduced morbidity, highlighting WWS’s predictive value.

Utility of syndromic surveillance for the surveillance of healthcare-associated infections in resource-limited settings: a narrative review

Globally, Healthcare-associated infections (HCAIs) pose a significant threat to patient safety and healthcare systems. In low- and middle-income countries (LMICs), the lack of adequate resources to manage HCAIs, as well as the weak healthcare system, further exacerbate the burden of these infections. Traditional surveillance methods that rely on laboratory tests are cost-intensive and impractical in these settings, leading to ineffective monitoring and delayed management of HCAIs. The rates of HCAIs in resource-limited settings have not been well established for most LMICs, despite their negative consequences. This is partly due to costs associated with surveillance systems. Syndromic surveillance, a part of active surveillance, focuses on clinical observations and symptoms rather than laboratory confirmation for HCAI detection. Its cost-effectiveness and efficiency make it a beneficial approach for monitoring HCAIs in LMICs. It provides for early warning capabilities, enabling timely identification and response to potential HCAI outbreaks. Syndromic surveillance is highly sensitive and this helps balance the challenge of low sensitivity of laboratory-based surveillance systems. If syndromic surveillance is used hand-in-hand with laboratory-based surveillance systems, it will greatly contribute to establishing the true burden of HAIs in resource-limited settings. Additionally, its flexibility allows for adaptation to different healthcare settings and integration into existing health information systems, facilitating data-driven decision-making and resource allocation. Such a system would augment the event-based surveillance system that is based on alerts and rumours for early detection of events of outbreak potential. If well streamlined and targeted, to monitor priority HCAIs such as surgical site infections, hospital-acquired pneumonia, diarrheal illnesses, the cost and burden of the effects from these infections could be reduced. This approach would offer early detection capabilities and could be expanded into nationwide HCAI surveillance networks with standardised data collection, healthcare worker training, real-time reporting mechanisms, stakeholder collaboration, and continuous monitoring and evaluation. Syndromic surveillance offers a promising strategy for combating HCAIs in LMICs. It provides early warning capabilities, conserves resources, and enhances patient safety. Effective implementation depends on strategic interventions, stakeholder collaboration, and ongoing monitoring and evaluation to ensure sustained effectiveness in HCAI detection and response.

Mobile Phone Syndromic Surveillance for Respiratory Conditions in an Emergency (COVID-19) Context in Colombia: Representative Survey Design.

Syndromic surveillance for respiratory infections such as COVID-19 is a crucial part of the public health surveillance toolkit as it allows decision makers to detect and prepare for new waves of the disease in advance. However, it is labor-intensive, costly, and increases exposure to survey personnel. This study assesses the feasibility of conducting a mobile phone-based respiratory syndromic surveillance program in a middle-income country during a public health emergency, providing data to support the inclusion of this method in the standard infection control protocols at the population level. This study aims to assess the feasibility of a national active syndromic surveillance system for COVID-19 disease in Colombia. In total, 2 pilots of syndromic mobile phone surveys (MPSs) were deployed using interactive voice response technology in Colombia (367 complete surveys in March 2022 and 451 complete surveys in April and May 2022). Respondents aged 18 years and older were sampled using random digit dialing, and after obtaining consent, they were sent a 10-minute survey with modules on sociodemographic status, respiratory symptoms, past exposure to COVID-19 infection and vaccination status, preferences about COVID-19 vaccination, and information source for COVID-19. Pilot 1 used a nationally representative sample while pilot 2 used quota sampling to yield representative results at the regional level. In this work, we assessed the performance characteristics of the survey pilots and compared the demographic information collected with a nationally representative household survey. For both pilots, contact rates were between 1% and 2%, while participation rates were above 80%. The results revealed that younger, female, and higher educated participants were more likely to participate in the syndromic survey. Survey rates as well as demographics, COVID-19 vaccination status, and prevalence of respiratory symptoms are reported for both pilots. We found that respondents of the MPSs are more likely to be younger and female. In a COVID-19 pandemic setting, using an interactive voice response MPS to conduct syndromic surveillance may be a transformational, low-risk, and feasible method to detect outbreaks. This evaluation expects to provide a path forward to the inclusion of MPSs as a traditional surveillance method.

Automated Recognition of Snow-Covered and Icy Road Surfaces Based on T-Net of Mount Tianshan

The Tianshan Expressway plays a crucial role in China’s “Belt and Road” strategy, yet the extreme climate of the Tianshan Mountains poses significant traffic safety risks, hindering local economic development. Efficient detection of hazardous road surface conditions (RSCs) is vital to address these challenges. The complexity and variability of RSCs in the region, exacerbated by harsh weather, make traditional surveillance methods inadequate for real-time monitoring. To overcome these limitations, a vision-based artificial intelligence approach is urgently needed to ensure effective, real-time detection of dangerous RSCs in the Tianshan road network. This paper analyzes the primary structures and architectures of mainstream neural networks and explores their performance for RSC recognition through a comprehensive set of experiments, filling a research gap. Additionally, T-Net, specifically designed for the Tianshan Expressway engineering project, is built upon the optimal architecture identified in this study. Leveraging the split-transform-merge structure paradigm and asymmetric convolution, the model excels in capturing detailed information by learning features across multiple dimensions and perspectives. Furthermore, the integration of channel, spatial, and multi-head attention modules enhances the weighting of key features, making the T-Net particularly effective in recognizing the characteristics of snow-covered and icy road surfaces. All models presented in this paper were trained on a custom RSC dataset, compiled from various sources. Experimental results indicate that the T-Net outperforms fourteen once state-of-the-art (SOTA) models and three models specifically designed for RSC recognition, with 97.44% accuracy and 9.79% loss on the validation set.

Mapping data-driven strategies in improving health care and patient satisfaction

Health services involve data analytics, central to healthcare service delivery, research, and planning. Based on the literature, this specific article seeks to enlighten the reader about data analytics in health services by discussing its advantages, shortcomings, and issues that come with it. Section one mapping outlines how data analytics optimizes the results of health service delivery through risk factor definition, disease detection, and case-by-case management. It helps inform disease trajectory, determine drug interaction, monitor disease, support decision-making, and enhance service delivery quality. The research also examines data analysis in health services, medicine, and pharmaceuticals. Several big data analytics applications have been identified as relevant to drug development, personalized medication, and specialized trials. Evaluations that rely on biomarkers differentiate between patients who are convenient for specific therapies, improving the patient's status and developing health care services. The third section also stresses the role of data analytics in enhancing efficiency and profitability. They involve detecting fraud, abuse, and unnecessary medical activities, creating savings and better financial returns. Data analytics also leads to identifying high-cost patients and ways of managing healthcare costs, increasing revenues and profitability. The fourth section discusses how big data complements traditional public health surveillance and outbreak platforms. It facilitates collecting data, analyzing different sources, and identifying special patterns or outliers to detect the outbreak early enough, contain it, or allocate resources to control it. It anticipates and addresses the dangers to the public's well-being and effectively controls episodes of infectious diseases. However, some key issues are associated with data analytics implementation, such as data quality, governance, privacy, bias, integration, expertise, and ethics, among others. Therefore, solving these challenges to achieve the optimum shifting of health service paradigms through data analytics in inpatient treatment, organizational management, and affordability is imperative.

Enhancing Examination Integrity: Comparing Intelligent Surveillance Systems verses Traditional Methods in Ogun State Universities

The burgeoning issue of examination malpractice in Nigerian universities has prompted the investigation into more effective surveillance solutions. This study addressed this persistent challenge by comparing the efficacy of Intelligent Surveillance Systems (ISS) with traditional surveillance method of curbing examination malpractices. Utilizing mixed research design (descriptive and experimental). Using a sample of 394 lecturers obtained from seven selected public and private universities in Ogun State, Nigeria the researcher carried out a survey to determine respondents’ perception on the use of ISS relative to traditional approach for controlling examination malpractices. Quantitative analysis (t-test and standard deviation) of data collected through a structured questionnaire, indicated that ISS significantly outperforms traditional methods in both detecting and controlling examination malpractices, as highlighted by statistical measures such as t-values and standard deviations values obtained. These findings suggest that ISS can play a crucial role in enhancing the integrity of examinations. The study recommends the adoption of ISS in universities in Ogun and other Nigerian states with similar challenges.

Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists’ mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists’ sampling effort is focused more in Barcelona’s lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city’s upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists’ sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Anomaly detection in crowded scenes: technologies, challenges and opportunities

The paper discusses advancements in intelligent video surveillance systems, particularly focused on anomaly detection in crowded environments. These systems aim to enhance public safety by automatically detecting unusual behavior and potential threats in real-time. Traditional video surveillance, relying heavily on human monitoring, faces limitations like reduced concentration and slow response times. In contrast, intelligent surveillance uses machine learning and AI algorithms to process vast amounts of video data, identifying patterns that deviate from normal behavior. Crowd anomaly detection is essential in densely populated areas like transportation hubs, stadiums, and public squares. The diversity of anomalies, ranging from minor disruptions to serious threats such as theft or terrorist attacks, presents a challenge for these systems. Anomalies can be difficult to detect due to their unpredictable nature, and what constitutes an anomaly varies depending on the context. The paper highlights the need for robust systems that can adapt to various environmental conditions and distinguish between normal variations and genuine threats. While supervised machine learning models show promise, they often require large amounts of labeled data, which is difficult to obtain in real-world settings. Unsupervised models and deep learning techniques, such as Convolution Neural Networks, have been effective in analyzing crowd behavior and detecting anomalies. However, these methods still face challenges, including scalability, high false positive rates, and the need for real-time processing in large-scale environments. The paper concludes by addressing the limitations of current crowd anomaly detection methods, such as their computational costs, ethical concerns, and inability to detect novel anomalies. It suggests directions for future research, including the integration of advanced learning techniques to improve system performance and scalability

SMS-Based Active Surveillance of Adverse Events following Immunization in Children: The VigiVax Study.

Underreporting is the main limitation of spontaneous reporting systems. This cohort-event monitoring study aims to examine the potential of short message service (SMS)-based surveillance compared to traditional surveillance systems. Using VigiVax software, parents of vaccinated children aged two years or younger, in the period March 2021-May 2022, received a single SMS inquiry about adverse events following immunization (AEFI). Responses were collected, validated by health operators and integrated with the information on electronic immunization registries. AEFI reports were automatically submitted to the Italian Pharmacovigilance system. Among 254,160 SMS messages sent, corresponding to 451,656 administered doses (AD), 71,643 responses were collected (28.2% response rate), and 21,231 of them (8.3%) reported AEFI. After a seriousness assessment based on clinical criteria, 50 reports (0.24%) were classified as serious. Among these, a causality assessment identified 31 reports at least potentially related to the vaccination (RR: 6.86/100,000 AD). Febrile seizures following MMRV (measles, mumps, rubella, varicella) vaccination accounted for 11 of these 31 cases, with an incidence of 32 per 100,000 AD. No fatal outcomes were reported. Our findings support the highly favorable risk profile of pediatric vaccinations and the possibility to improve spontaneous reporting through the integration of digital technologies.

Outbreak-associated Salmonella Baildon found in wastewater demonstrates how sewage monitoring can supplement traditional disease surveillance.

Non-typhoidal Salmonella is a common cause of gastroenteritis worldwide, but current non-typhoidal Salmonella surveillance is suboptimal. Here, we evaluated the utility of wastewater monitoring to enhance traditional surveillance for this foodborne pathogen. In June 2022, we tested raw sewage collected twice a week from two treatment plants in central Pennsylvania for non-typhoidal Salmonella and characterized isolates using whole-genome sequencing. We recovered 43 Salmonella isolates from wastewater samples, differentiated by genomic analysis into seven serovars: 16 Panama (37.2%), 9 Senftenberg (20.9%), 8 Baildon (18.6%), and 3 or fewer of four other serovars. We assessed genetic relatedness and epidemiologic links between these wastewater isolates with those from patients with salmonellosis. All S. Baildon isolates from wastewater were genetically similar to those associated with a known contemporaneous salmonellosis outbreak. S. Baildon from wastewater and 42 outbreak-related isolates in the national outbreak detection database had the same core genome multilocus sequence typing, and outbreak code differed by zero or one single polynucleotide polymorphism. One of the 42 outbreak-related isolates was obtained from a patient residing in the wastewater sample collection catchment area, which serves approximately 17000 people. S. Baildon is a rare serovar (reported in <1% cases nationally, over five years). Our study underscores the value of monitoring sewage from a defined population to supplement traditional surveillance methods for the evidence of Salmonella infections and to determine the extent of outbreaks.IMPORTANCEDuring the COVID-19 pandemic, monitoring for SARS-CoV-2 in wastewater was highly effective in identifying the variants of concern earlier than clinical surveillance methods. Here, we show that monitoring domestic sewage can also augment traditional reporting of foodborne illnesses to public health authorities. Our study detected multiple Salmonella enterica serovars in samples from two wastewater treatment plants in central Pennsylvania. Using whole-genome sequencing, we demonstrated that the isolates of variant S. Baildon clustered with those from a foodborne salmonellosis outbreak that occurred in a similar time frame. Cases were primarily from Pennsylvania, and one individual lived within the wastewater treatment catchment area. This study highlights the effectiveness of domestic sewage testing as a proactive public health strategy to track and respond to infectious disease outbreaks.

Molecular xenomonitoring as an indicator of microfilaraemia prevalence for lymphatic filariasis in Samoa in 2019

BackgroundLymphatic filariasis (LF) is a globally significant, vector-borne, neglected tropical disease that can result in severe morbidity and disability. As the World Health Organization (WHO) Global Programme to Eliminate Lymphatic Filariasis makes progress towards LF elimination, there is greater need to develop sensitive strategies for post-intervention surveillance. Molecular xenomonitoring (MX), the detection of pathogen DNA in vectors, may provide a sensitive complement to traditional human-based surveillance techniques, including detection of circulating filarial antigen and microfilaraemia (Mf). This study aims to explore the relationship between human Mf prevalence and the prevalence of polymerase chain reaction (PCR)-positive mosquitoes using MX.MethodsThis study compared Mf and MX results from a 2019 community-based survey conducted in 35 primary sampling units (PSUs) in Samoa. This study also investigated concordance between presence and absence of PCR-positive mosquitoes and Mf-positive participants at the PSU level, and calculated sensitivity and negative predictive values for each indicator using presence of any Mf-positive infection in humans or PCR-positive mosquitoes as a reference. Correlation between prevalence of filarial DNA in mosquitoes and Mf in humans was estimated at the PSU and household/trap level using mixed-effect Bayesian multilevel regression analysis.ResultsMf-positive individuals were identified in less than half of PSUs in which PCR-positive mosquito pools were present (13 of 28 PSUs). Prevalence of PCR-positive mosquitoes (each species separately) was positively correlated with Mf prevalence in humans at the PSU level. Analysed at the species level, only Aedes polynesiensis demonstrated strong evidence of positive correlation (r) with human Mf prevalence at both PSU (r: 0.5, 95% CrI 0.1–0.8) and trap/household levels (r: 0.6, 95% CrI 0.2–0.9).ConclusionsFindings from this study demonstrate that MX can be a sensitive surveillance method for identifying residual infection in low Mf prevalence settings. MX identified more locations with signals of transmission than Mf-testing. Strong correlation between estimated PCR-positive mosquitoes in the primary vector species and Mf in humans at small spatial scales demonstrates the utility of MX as an indicator for LF prevalence in Samoa and similar settings. Further investigation is needed to develop MX guidelines to strengthen the ability of MX to inform operational decisions.Graphical abstract

Research progress of lipid-derived parameters in atherosclerotic cardiovascular disease

Dyslipidemia stands as an autonomous peril in the realm of atherosclerotic cardiovascular maladies. Prompt identification and timely intervention in the case of dyslipidemia hold promise for substantially curbing the onset and fatality rates associated with coronary heart disease. Traditional lipid surveillance metrics employed in clinical settings, such as low-density lipoprotein cholesterol, exhibit notable limitations. Conversely, lipid-derived parameters emerge as formidable contenders, demonstrating a capacity to amalgamate and quantify disparate risk factors and multifactorial etiologies inherent in a given disease. By encompassing a broader spectrum of information than singular indices, these parameters offer a more profound insight into disease progression by virtue of their grounding in the physiological intricacies of lipid metabolism. Drawing upon extant domestic and international guidelines and research, this discourse delineates and synthesizes four lipid-derived parameters with promising clinical applications: atherogenic index of plasma, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio, apolipoprotein B/A1 ratio, and lipoprotein combine index, and forwards a perspective grounded in current strides in clinical research.

Long COVID Illness: Disparities in Understanding and Receipt of Care in Emergency Department Populations

BackgroundMost Long COVID studies rely on traditional surveillance methods that miss underserved populations who use emergency departments (EDs) as their primary health care source. ObjectivesIn medically-underserved ED populations, we sought to determine 1) whether there are gaps in awareness and self-declared understanding about Long COVID illness, and 2) the prevalence, impact, and receipt of care for Long COVID symptoms. Design, Setting and ParticipantsCross-sectional, convenience sample survey study of adult patients at 11 geographically representative US EDs from 12/2022-10/2023. MeasurementsAwareness and self-declared understanding about Long COVID illness; prevalence, impact, and receipt of care for Long COVID symptoms. ResultsOf 1618 eligible patients,1455 (89.9%) agreed to participate: 33.4% African American, 30.9% Latino/a, and 17.1% lacked primary care; 33.2% had persistent COVID symptoms lasting > 1-month, 20.3% had symptoms > 3-months; 49.8% with Long COVID symptoms missed work/school because of symptoms; 30.3% of all participants and 33.5% of participants who had Long COVID symptoms had prior awareness and self-declared understanding of Long COVID. Characteristics associated with poor understanding of Long COVID were: African American race (adjusted odds ratio [aOR] 3.68, 95% confidence interval [CI] 2.66-5.09) and Latino/a ethnicity (aOR 3.16, 95% CI 2.15-4.64). Participants lacking primary care were less likely to have received Long COVID care (24.6% vs 51.2%; difference 26.6%; 95% CI 13.7-36.9%). Conclusions/RelevanceDespite high prevalence and impact of Long COVID symptoms, most of this ED population had limited awareness and self-declared understanding of Long COVID and many had not received care. EDs should consider development of protocols for diagnosis, education, and treatment of Long COVID illness.

Sequential use of capsular typing and whole-genome sequencing-based analysis for transmission of carbapenem-resistant Acinetobacter baumannii in a tertiary medical center

BackgroundDuring the COVID-19 pandemic, there has been an increasing trend in healthcare-associated infections (HAIs) caused by carbapenem-resistant Acinetobacter baumannii (CRAB), posting a global public health concern. The heightened sensitivity of whole-genome sequencing (WGS) renders it an optimal and potent tool for monitoring outbreaks and tracing the transmission routes of nosocomial pathogens. MethodWe collected CRAB isolates from March 1, 2023, to April 6, 2023 in Chang Gung Memorial Hospital Lin Kou branch, a tertiary medical center in northern Taiwan. Any two or more isolates with the same identifiable capsular K-locus (KL) types were selected, and analyzed via WGS to identify putative transmission clusters, combined with epidemiologic and retrospective analysis on medical records to confirm risk factors and hidden transmission chains. ResultA total of 48 non-redundant CRAB isolates were collected, belonging to ST2 of Pasteur MLST scheme and identifiable KL types of KL2, KL3, KL9, KL10, KL22, KL52. Excluding the KL types that was only found in 1 case, KL2 (n = 9, 22.5 %), KL3 (n = 24, 60 %), KL9 (n = 3, 7.5 %), and KL10 (n = 4, 10 %) were selected for further WGS analysis. Four distinct transmission clusters comprised of 2, 3, 10, and 23 cases were identified on a basis of phylogenetic status. 12 probable transmission chains were revealed, and 2 hidden transmission routes can be speculated. ConclusionThis study referred to some hidden transmission chains that may be missed from traditional surveillance measures. Despite its low prevalence and high cost currently, implementing WGS could be a efficient, prompt, and unequivocal option for future MDRO infection control.

Surveillance Metrics and History of the COVID-19 Pandemic in Central Asia: Updated Epidemiological Assessment.

This study updates the COVID-19 pandemic surveillance in Central Asia we conducted during the first year of the pandemic by providing 2 additional years of data for the region. The historical context provided through additional data can inform regional preparedness and early responses to infectious outbreaks of either the SARS-CoV-2 virus or future pathogens in Central Asia. First, we aim to measure whether there was an expansion or contraction in the pandemic in Central Asia when the World Health Organization (WHO) declared the end of the public health emergency for the COVID-19 pandemic on May 5, 2023. Second, we use dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history. Third, we aim to provide historical context for the course of the pandemic in Central Asia. Traditional surveillance metrics, including counts and rates of COVID-19 transmissions and deaths, and enhanced surveillance indicators, including speed, acceleration, jerk, and persistence, were used to measure shifts in the pandemic. To identify the appearance and duration of variants of concern, we used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID). We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether regional speed was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the sample period. Speed for the region had remained below the outbreak threshold for 7 months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant, the coefficients were relatively small in magnitude (0.125 and 0.347, respectively). Furthermore, the shift parameters for either of the 2 most recent weeks around May 5, 2023, were both significant and negative, meaning the clustering effect of new COVID-19 cases became even smaller in the 2 weeks around the WHO declaration. From December 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of speed equal to 10 became entirely insignificant for the first time in March 2023. Although COVID-19 continues to circulate in Central Asia, the rate of transmission remained well below the threshold of an outbreak for 7 months ahead of the WHO declaration. COVID-19 appeared to be endemic in the region and no longer reached the threshold of a pandemic. Both standard and enhanced surveillance metrics suggest the pandemic had ended by the time of the WHO declaration.

Developing a digital data platform for surveillance of food and water-borne pathogens in North East India: insight for public health advocacy.

Robust digital infrastructure is vital and the need of the hour, especially in the healthcare sector, for real-time data generation, analysis, and quick decision-making. Food- and water-borne illnesses represent a prominent cause of morbidity and mortality worldwide. India, a developing nation with diverse cultures and food practices, poses a high risk of food-borne diseases and outbreaks, yet is often underreported and ineffectively researched. Also, the unique socio-economic and environmental factors of the Northeast (NE) region contribute to the high burden of food-borne diseases. To address these trepidations, the Indian Council of Medical Research (ICMR) has undertaken a study for the surveillance of food-borne pathogens in NE India. The present study focuses on the development of a digital database system for the systematic surveillance of foodborne disease outbreaks, aiming to address the gaps in traditional surveillance methods and improve disease detection and response capabilities. The digital system integrates mobile applications, web-based platforms, and advanced analytics tools to enable real-time data collection, dissemination, and analysis of food-borne illness data. Additionally, the secure and scalable nature of the system enhances data accuracy and accessibility, making it a valuable tool for enhancing food-borne disease surveillance efforts in resource-constrained settings.

Spatiotemporal analysis using deep learning and fuzzy inference for evaluating broiler activities

Observing poultry activity is crucial for assessing their health status; however, the inspection process is often time-consuming and labor-intensive, particularly in cases involving large numbers of chickens. Inexperienced breeders may also misjudge their activity levels, potentially missing opportunities for prevention and treatment. This study integrates traditional video surveillance with an advanced monitoring system to identify various broiler behaviors in a breeding environment. A two-stage deep learning approach is employed: in the first stage, the broilers are detected, and in the second stage, five key body points (head, abdomen, two legs, and tail) are identified. A skeleton-based model is then developed centered around the abdomen, with six angles calculated using trigonometric methods. These angles are analyzed by a long short-term memory network to estimate behaviors such as “Standing”, “Walking”, “Resting”, “Eating”, “Preening”, and “Flapping”, selecting the behavior with the highest probability. Dual-layer fuzzy logic inference systems were used to evaluate the proportion of time broilers spent in static versus dynamic states, providing a robust determination of their activity levels. Validated in a mixed-sex breeding environment, the proposed system achieved accuracies of at least 85.2% for identifying broiler type, 79.2% for identifying body parts, and 50.8% for identifying behaviors. The activity level evaluation results were consistent with those conducted by experienced poultry experts.

Estimating time-varying cholera transmission and oral cholera vaccine effectiveness in Haiti and Cameroon, 2021-2023.

In 2023, cholera affected approximately 1 million people and caused more than 5000 deaths globally, predominantly in low-income and conflict settings. In recent years, the number of new cholera outbreaks has grown rapidly. Further, ongoing cholera outbreaks have been exacerbated by conflict, climate change, and poor infrastructure, resulting in prolonged crises. As a result, the demand for treatment and intervention is quickly outpacing existing resource availability. Prior to improved water and sanitation systems, cholera, a disease primarily transmitted via contaminated water sources, also routinely ravaged high-income countries. Crumbling infrastructure and climate change are now putting new locations at risk - even in high-income countries. Thus, understanding the transmission and prevention of cholera is critical. Combating cholera requires multiple interventions, the two most common being behavioral education and water treatment. Two-dose oral cholera vaccination (OCV) is often used as a complement to these interventions. Due to limited supply, countries have recently switched to single-dose vaccines (OCV1). One challenge lies in understanding where to allocate OCV1 in a timely manner, especially in settings lacking well-resourced public health surveillance systems. As cholera occurs and propagates in such locations, timely, accurate, and openly accessible outbreak data are typically inaccessible for disease modeling and subsequent decision-making. In this study, we demonstrated the value of open-access data to rapidly estimate cholera transmission and vaccine effectiveness. Specifically, we obtained non-machine readable (NMR) epidemic curves for recent cholera outbreaks in two countries, Haiti and Cameroon, from figures published in situation and disease outbreak news reports. We used computational digitization techniques to derive weekly counts of cholera cases, resulting in nominal differences when compared against the reported cumulative case counts (i.e., a relative error rate of 5.67% in Haiti and 0.54% in Cameroon). Given these digitized time series, we leveraged EpiEstim-an open-source modeling platform-to derive rapid estimates of time-varying disease transmission via the effective reproduction number ( ). To compare OCV1 effectiveness in the two considered countries, we additionally used VaxEstim, a recent extension of EpiEstim that facilitates the estimation of vaccine effectiveness via the relation among three inputs: the basic reproduction number ( ), , and vaccine coverage. Here, with Haiti and Cameroon as case studies, we demonstrated the first implementation of VaxEstim in low-resource settings. Importantly, we are the first to use VaxEstim with digitized data rather than traditional epidemic surveillance data. In the initial phase of the outbreak, weekly rolling average estimates of were elevated in both countries: 2.60 in Haiti [95% credible interval: 2.42-2.79] and 1.90 in Cameroon [1.14-2.95]. These values are largely consistent with previous estimates of in Haiti, where average values have ranged from 1.06 to 3.72, and in Cameroon, where average values have ranged from 1.10 to 3.50. In both Haiti and Cameroon, this initial period of high transmission preceded a longer period during which oscillated around the critical threshold of 1. Our results derived from VaxEstim suggest that Haiti had higher OCV1 effectiveness than Cameroon (75.32% effective [54.00-86.39%] vs. 54.88% [18.94-84.90%]). These estimates of OCV1 effectiveness are generally aligned with those derived from field studies conducted in other countries. Thus, our case study reinforces the validity of VaxEstim as an alternative to costly, time-consuming field studies of OCV1 effectiveness. Indeed, prior work in South Sudan, Bangladesh, and the Democratic Republic of the Congo reported OCV1 effectiveness ranging from approximately 40% to 80%. This work underscores the value of combining NMR sources of outbreak case data with computational techniques and the utility of VaxEstim for rapid, inexpensive estimation of vaccine effectiveness in data-poor outbreak settings.

Integrating the exposome and one health approach to national health surveillance: an opportunity for Latin American countries in health preventive management.

The exposome approach, emphasizing lifelong environmental exposures, is a holistic framework exploring the intricate interplay between genetics and the environment in shaping health outcomes. Complementing this, the one health approach recognizes the interconnectedness of human and ecological health within a shared ecosystem, extending to planetary health, which encompasses the entire planet. Integrating Disease Surveillance Systems with exposome, one health, and planetary health signifies a paradigm shift in health management, fostering a comprehensive public health framework. This publication advocates for combining traditional health surveillance with exposome and one health/planetary health approach, proposing a three-step approach: ecological analysis, territorial intervention in identified issues, and an analytical phase for assessing interventions. Particularly relevant for Latin American countries facing a double burden of diseases, integrating the exposome into traditional health surveillance proves cost-effective by leveraging existing data and environmental measurements. In conclusion, the integration of exposome and one health approaches into traditional health surveillance presents a robust framework for monitoring population health, especially in regions like Latin America with complex health challenges. This innovative approach enables tailored interventions, disease outbreak predictions, and a holistic understanding of the intricate links between human health and the environment, offering substantial benefits for public health and disease prevention despite existing challenges.

Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning

In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments.