Surveillance Methods Research Articles

Comparison of Different Reverse Transcriptase-Polymerase Chain Reaction-Based Methods for Wastewater Surveillance of SARS-CoV-2: Exploratory Study.

Many countries have applied the wastewater surveillance of the COVID-19 pandemic to their national public health monitoring measures. The most used methods for detecting SARS-CoV-2 in wastewater are quantitative reverse transcriptase-polymerase chain reaction (RT-qPCR) and reverse transcriptase-droplet digital polymerase chain reaction (RT-ddPCR). Previous comparison studies have produced conflicting results, thus more research on the subject is required. This study aims to compare RT-qPCR and RT-ddPCR for detecting SARS-CoV-2 in wastewater. It also aimed to investigate the effect of changes in the analytical pipeline, including the RNA extraction kit, RT-PCR kit, and target gene assay, on the results. Another aim was to find a detection method for low-resource settings. We compared 2 RT-qPCR kits, TaqMan RT-qPCR and QuantiTect RT-qPCR, and RT-ddPCR based on sensitivity, positivity rates, variability, and correlation of SARS-CoV-2 gene copy numbers in wastewater to the incidence of COVID-19. Furthermore, we compared 2 RNA extraction methods, column- and magnetic-bead-based. In addition, we assessed 2 target gene assays for RT-qPCR, N1 and N2, and 2 target gene assays for ddPCR N1 and E. Reverse transcription strand invasion-based amplification (RT-SIBA) was used to detect SARS-CoV-2 from wastewater qualitatively. Our results indicated that the most sensitive method to detect SARS-CoV-2 in wastewater was RT-ddPCR. It had the highest positivity rate (26/30), and its limit of detection was the lowest (0.06 gene copies/µL). However, we obtained the best correlation between COVID-19 incidence and SARS-CoV-2 gene copy number in wastewater using TaqMan RT-qPCR (correlation coefficient [CC]=0.697, P<.001). We found a significant difference in sensitivity between the TaqMan RT-qPCR kit and the QuantiTect RT-qPCR kit, the first having a significantly lower limit of detection and a higher positivity rate than the latter. Furthermore, the N1 target gene assay was the most sensitive for both RT-qPCR kits, while no significant difference was found between the gene targets using RT-ddPCR. In addition, the use of different RNA extraction kits affected the result when the TaqMan RT-qPCR kit was used. RT-SIBA was able to detect SARS-CoV-2 RNA in wastewater. As our study, as well as most of the previous studies, has shown RT-ddPCR to be more sensitive than RT-qPCR, its use in the wastewater surveillance of SARS-CoV-2 should be considered, especially if the amount of SARS-CoV-2 circulating in the population was low. All the analysis steps must be optimized for wastewater surveillance as our study showed that all the analysis steps including the compatibility of the RNA extraction, the RT-PCR kit, and the target gene assay influence the results. In addition, our study showed that RT-SIBA could be used to detect SARS-CoV-2 in wastewater if a qualitative result is sufficient.

A data fusion method for maritime traffic surveillance: The fusion of AIS data and VHF speech information

The more comprehensive information supply for Vessel Traffic Service (VTS), the greater capacity for maritime traffic surveillance. To supply ship intention information contained in very high frequency (VHF) speech for VTS, we propose an automatic identification system (AIS) data and VHF speech information fusion method. In the first part of this method, the feature information of VHF speech is extracted by Bert-BiLSTM-CRF-based entity extraction method, likes location, ship name, and ship intention information, etc. In the second part, we design a spatio-temporal range to filter AIS data, the temporal range is determined by receive time of VHF speech, and the spatial range is determined by the location information extracted from VHF speech. In the third part, by measuring the ship name similarity between AIS data and VHF speech, the barriers to fusion are successfully overcome. The similarity is calculated by combining with Chinese pinyin character similarity and Arabic number similarity and Chinese pinyin characters are encoded by pronunciation characters, calculated by the dynamic time warping (DTW) distance. Through experiments, the method can effectively provide comprehensive information for maritime traffic surveillance, and even when crews misreport ship name within a small deviation, our method still possesses a great fusion capability.

Head and neck cancer surveillance: The value of computed tomography and clinical exam

PurposeThe recurrence of head and neck cancer (HNC) is most prevalent during the initial two years following curative treatment, underscoring the criticality of regular surveillance for HNC survivors. This study aims to evaluate the effectiveness of computed tomography (CT) imaging and clinical physical examination (CE) in HNC surveillance, assessing whether these imaging protocols meet the current treatment limitations confronting HNC specialists. MethodsRetrospective chart review of a 9-year experience with head and neck cancer patients at a single, academic tertiary care center. Demographic data was collected along with data regarding whether the recurrences were detected primarily through CE, flexible endoscopic exam (scope exam), or CT or CT/PET scan. Subsets of the data were analyzed and compared by sensitivity, specificity, and negative predictive values. Results264 HNC patients were identified. 72 total recurrences (27 %) were noted. The method of initial detection spurring further investigation was imaging in 42 (58.3 %) patients, CE (33.3 %) in 24 patients, scope exam in 6 (8.4 %) patients. Overall, 65 (90.3 %) patients had imaging that showed recurrence regardless of method of initial detection. Sensitivity, (87.1 % vs 70.5 %), and specificity (93.95 % vs 96.9 %) were noted for CT and CE respectively. Combined sensitivity and specificity for CT and CE was 96.2 % and 91.05 % respectively. ConclusionThe data suggests that imaging could provide sufficient methods of HNC surveillance despite limitations the COVID-19 pandemic presents.

How Does Listening to “Fetal Heartbeats” Affect the Feelings of Pregnant Women?

OBJECTIVES: This study was conducted to evaluate the impact of listening to fetal heartbeats (FHBs) on the feelings of normal and high-risk pregnancies (HRPs), and to learn the colors and geometrical figures representing their feelings. STUDY DESIGN: This retrospective study consisted of 291 pregnant women who applied for a non-stress test (NST). Participants were divided into two groups; 1: Normal pregnancies (Group1) and 2: HRPs (Group2). Demographic variables, feelings in response to listening to FHBs (negative, neutral, positive), grade mark (0-10) of feelings, color (warm, neutral, cold), and figure (simple, complex) preferences were retrospectively evaluated. RESULTS: Of the 291 women, 172 (59.1%) Group 1 participants were compared to 119 (40.9%) Group 2 participants. Group 1 and Group 2 had similar feelings (p&gt;0.05), however, the rate of negative feelings was higher in Group 2 (11.6% vs. 20.2%). Higher marks were observed in Group 2 with happiness and peace/relaxation after listening to FHBs. Fear was the lowest mark in women with positive/neutral feelings in both groups. Women in Group 1 preferred warm colors regardless of their feelings. Women in Group 2 who have positive feelings chose warm colors, whereas women who felt neutral or negative more commonly chose cold (55.5%) and neutral (41.7%) colors (p=0.017). Independent of feelings, simple figures were predominately preferred (p=0.04). CONCLUSION: NST is more than a perinatal surveillance method and listening to FHBs has a positive impact on the feelings of pregnancies. Warm colors were commonly related to positive feelings. Simple figures were preferred regardless of feelings.

Genomics for Arbovirus Surveillance: Considerations for Routine Use in Public Health Laboratories.

The emergence and re-emergence of arthropod-borne viruses is a public health threat. For routine surveillance in public health laboratories, cost-effective and reproducible methods are essential. In this review, we address the technical considerations of high-throughput sequencing methods (HTS) for arbovirus surveillance in national health laboratories, focusing on pre-sequencing, sequencing, and post-sequencing approaches, underlining the importance of robust wet and dry laboratory workflows for reproducible analysis. We aim to provide insights for researchers and clinicians interested in arbovirus, diagnosis, and surveillance by discussing current advances in sequencing methods and bioinformatics pipelines applied to arboviruses.

Shoulder Pain in Competitive Swimmers: A Multi-Site Survey Study.

There are 2.8 million youth competitive swimmers in the United States (US), and shoulder pain is the most common complaint among swimmers. To determine prevalence of shoulder pain, disability, and dissatisfaction in youth competitive swimmers. A secondary purpose was to determine influence of age, competitive swimming experience, and previous history of shoulder pain or injury on shoulder pain, disability, and dissatisfaction, and finally to determine if sex, geographic area, and participation in a second sport relate to shoulder pain, disability, and dissatisfaction. Multi-site cross-sectional design. Six-hundred and seventy-one swimmers aged 9-17 years from six states in the US completed surveys which included demographics, the Penn Shoulder Score (PSS), and the Disability of Arm Shoulder Hand (DASH) Sports. Independent t-tests were used to compare pain, disability, dissatisfaction, the influence of age, sex, participation in second sport, geographic region, and history of shoulder pain. Linear regression analyses were performed to determine the interaction of these variables with reported pain and disability. Forty-nine percent of swimmers had shoulder symptoms. Greater shoulder pain and disability were reported in 15 to 17-year-olds compared to 9 to 10-year-olds (pain and disability: p<0.001), in swimmers with greater versus less years of experience (pain and disability: p<0.001), and in swimmers from eastern versus western states (pain: p=0.001, disability: p=0.0014). Swimmers not participating in a second sport had higher shoulder dissatisfaction (p=0.002). History of prior shoulder pain/traumatic injury was the best indicator of increased pain, disability, and dissatisfaction (p<0.001). Almost half of swimmers surveyed had shoulder pain, with a higher prevalence found in older age groups, those with greater experience, and in those with prior shoulder pain or traumatic injury. Further research should investigate shoulder pain prevention programs, and surveillance methods are recommended to identify symptomatic swimmers who may benefit from referral to prevent further pain and disability. III.

Surveillance of influenza A and B viruses from community and hospital wastewater treatment plants.

Influenza virus is a well-known pathogen that can cause epidemics and pandemics. Several surveillance methods are being followed to monitor the transmission patterns and spread of influenza in the community. Wastewater-based Epidemiology (WBE) can serve as an additional tool to detect the presence of influenza viruses. The current study primarily focuses on surveillance of Influenza A and Influenza B in wastewater treatment plant (WWTP) samples. A total of 100 wastewater samples were collected in July (n = 50) and August (n = 50) 2023 from four different WWTPs in Manipal and Udupi, district of Karnataka, India. The WWTP samples were processed and tested by Real-Time reverse transcriptase PCR (RT-PCR). The data generated was analysed in comparison with the clinical Influenza cases. Of the 100 samples, 18 (18%) tested positive for Influenza A virus and 2 (2%) tested positive for Influenza B virus, with a viral load ranging 1.4 x 102-2.2 x 103 gc/L for influenza A virus and 5.2 x 103-7.7 x 103gc/L for influenza B virus. On correlating the WWTP positivity with clinical case, it was found that influenza clinical cases and virus positivity in wastewater increased simultaneously, emphasizing WBE as a concurrent method for monitoring influenza virus activity.

Malay Translation and Validation of the Oslo Sports Trauma Research Center Questionnaire on Health Problems.

The Oslo Sports Trauma Research Center Questionnaire on Health Problems (OSTRC-H) is a proven surveillance method to register acute injury, overuse injury, or illness in athletes. However, it has not yet been translated into Malay. To translate, culturally adapt, and validate the OSTRC-H into Malay using internationally established guidelines. Cohort study (diagnosis); Level of evidence, 3. The Malay translation of the OSTRC-H (OSTRC-H.M) was distributed to 54 elite (ie, professional) and recreational athletes. Face validity, content validity, and construct validity were assessed for the validity measurement properties, while test-retest and internal consistency were used for reliability measurement properties. The content validity index was satisfactory at 0.93. The OSTRC-H.M showed good internal consistency, with a Cronbach α value of .84. Confirmatory factor analysis showed a good model for construct validity, with values of 0.99 and 0.04 for comparative fit index and root mean square error of approximation, respectively. Per-item intraclass correlation coefficients (ICCs) for test-retest reliability ranged from 0.55 to 0.80. The overall test-retest reliability of the questionnaire was good (ICC, 0.8; 95% CI, 0.63-0.88; P < .001). The OSTRC-H.M was found to be valid and reliable and therefore acceptable for application among Malay-speaking athletes.

A scoping review of stroke registers in Sub-Saharan Africa.

Stroke registers are recommended as a key priority by the Lancet Neurology World Stroke Organization Commission for Stroke, 2023, and the African Stroke Leaders' Summit, 2022. This scoping review aims to map where stroke registers have been implemented in Sub-Saharan Africa (SSA). The article then compares and critiques the methods and definitions used and summarizes key results from the registers. The scoping review searched EMBASE, MEDLINE, and CABI Global Health databases and included all studies with a prospective longitudinal design in SSA, where adult acute stroke was the primary condition studied. Articles were screened against inclusion and exclusion criteria independently by two authors. We identified 42 unique stroke registers from 48 individual studies. The registers were located in 19 countries, with 19 from East Africa, 15 West Africa, 6 Central Africa, and 2 from Southern Africa. Cumulatively, the registers recruited 12,345 participants with stroke, the median number of participants was 183 (interquartile range (IQR): 121-312), and the range was 50-1018. Only one study was a population-based register, and 41 were hospital-based registers. Of the hospital-based registers, 29 were single site, 10 were conducted at two sites, and 2 at three sites. Twenty-three (54.7%) of the registers were located in the capital city of their respective country, and only one of the hospital-based registers was in a self-described rural area. Length of recruitment ranged from 4 months to 6 years; the median length of recruitment was 12 months. Methodology and definitions were heterogenous between the registers. Only seven (19.4%) registers referenced the WHO STEPwise approach to implementing stroke registers. Twenty-seven (64.3%) registers used the WHO definition of stroke. The mean neuroimaging rate was 84%, and ranged from 0% to 100%. Stroke severity was measured using the National Institute of Health Stroke Scale (NIHSS) in 22 (52.4%) registers, four registers used the Glasgow Coma Scale (GCS), two registers used the miniNIHSS, one used the Scandinavian Stroke Scale, one modified Rankin Scale (mRS), and 11 registers did not report a stroke severity measure. Seventeen (40.5%) registers used the mRS to measure function, six registers used Barthel Index alone, and three registers used both mRS and Barthel Index. Only two registers included a quality-of-life measure, the EQ-5D. Eight registers included a quality-of-care measure, and 26 (61.9%) registers recorded socioeconomic status or a socioeconomic status proxy, most frequently educational attainment. This scoping review found high heterogeneity of methods and definitions used by stroke registers, with low uptake of the WHO stepwise method of stroke surveillance. A drive to standardize methodology would improve the comparability of stroke data in SSA. The shared use of educational attainment by registers in our review may enable future meta-analyses of inequities in stroke in SSA. Incorporating health-related quality-of-life measures, such as EQ-5D, into stroke registers should be encouraged, bringing a patient perspective, and allow the estimation of quality-adjusted life years lost to stroke. Agreement on a standardized register methodology or further promotion and uptake of the WHO stepwise method is essential to produce comparable data to improve stroke prevention and care.

Genetic Diversity in Salmonella enterica in Outbreaks of Foodborne and Zoonotic Origin in the USA in 2006-2017.

Whole genome sequencing is replacing traditional laboratory surveillance methods as the primary tool to track and characterize clusters and outbreaks of the foodborne and zoonotic pathogen Salmonella enterica (S. enterica). In this study, 438 S. enterica isolates representing 35 serovars and 13 broad vehicle categories from one hundred epidemiologically confirmed outbreaks were evaluated for genetic variation to develop epidemiologically relevant interpretation guidelines for Salmonella disease cluster detection. The Illumina sequences were analyzed by core genome multi-locus sequence typing (cgMLST) and screened for antimicrobial resistance (AR) determinants and plasmids. Ninety-three of the one hundred outbreaks exhibited a close allele range (less than 10 allele differences with a subset closer than 5). The remaining seven outbreaks showed increased variation, of which three were considered polyclonal. A total of 16 and 28 outbreaks, respectively, showed variations in the AR and plasmid profiles. The serovars Newport and I 4,[5],12:i:-, as well as the zoonotic and poultry product vehicles, were overrepresented among the outbreaks, showing increased variation. A close allele range in cgMLST profiles can be considered a reliable proxy for epidemiological relatedness for the vast majority of S. enterica outbreak investigations. Variations associated with mobile elements happen relatively frequently during outbreaks and could be reflective of changing selective pressures.

Effects of States' Methods for Estimating Nonfatal Overdose, United States, 2021.

Previous nonfatal overdose is a key risk factor for drug overdose death; however, current nonfatal overdose surveillance is limited to people who receive medical attention. We identified states that tracked nonfatal overdoses treated in prehospital and hospital care settings, assessed the effects of different surveillance methods on the magnitude of nonfatal overdose estimates, and estimated states' nonfatal-to-fatal overdose ratio. Two analysts independently reviewed state websites to characterize states' methods of capturing nonfatal overdose events from December 2022 through February 2023. We collected information on surveillance methods in 5 states that met the inclusion criteria, including data source, measure specification, drug(s) involved, and whether states performed deduplication or published mutually exclusive measure specifications to capture unique events across care settings. We calculated nonfatal-to-fatal overdose ratios to assess the effects of different data sources on estimates of nonfatal overdoses. Illinois, Maine, North Carolina, and West Virginia used syndromic surveillance data and New Jersey used hospital discharge data to track nonfatal overdose-related emergency department visits. Illinois and West Virginia tracked nonfatal overdose-related encounters with emergency medical services. Other states tracked opioid overdoses reversed following naloxone administration by emergency medical services, law enforcement, and community members. Maine, New Jersey, and West Virginia published nonfatal overdose information by using mutually exclusive measure specifications; the number of nonfatal overdoses per fatal overdose in these states ranged from approximately 5:1 to 14:1. Establishing a standard framework to combine data from existing national surveillance systems in prehospital and hospital care settings can improve nonfatal overdose estimates and enable comparisons between jurisdictions to help decision makers identify areas most in need of essential services.

Public health preparedness, syndromic surveillance, and response during the largest religious gathering at the Catholic pilgrimage center of Velankanni in South India: 2016.

The dynamicity and mobility of the population in a mass gathering setting pose a challenge to traditional disease surveillance methods and strain the local health services. Velankanni is one of the most sacred Christian pilgrimage places located in Nagapattinam, Tamil Nadu, India. We participated in the Velankanni festival to describe the public health preparedness, surveillance, and response activities carried out during the festival. This was a cross-sectional study. We reviewed the national and international guidelines and published literature and discussed with the key stakeholders. We developed a checklist to observe public health preparedness activities. We facilitated the staff and monitored the activities by the implementers. We established the syndromic surveillance in the designated locations of the event and used tracker software to capture the data. Emergency medical teams were formed with trained health personnel to respond to medical emergencies. The team monitored all the public health activities. There are 59 primary care public health facilities and nine ambulatory Mobile Medical Units, with 160 medical officers available at the site. Of the 16,169 persons who attended the medical camps, 9863 (61%) were males and 8408 (52%) were aged 15-44. Acute diarrheal disease was the most frequent of the reported syndromes, followed by injuries, acute febrile illness, and animal bites. There was no outbreak of any disease either identified or reported. Our findings suggest that risk assessments should be used, and establishing an Incident Command Center is vital for executing command and control mechanisms during mass gatherings.

Detection of influenza virus in urban wastewater during the season 2022/2023 in Sicily, Italy.

Seasonal influenza generally represents an underestimated public health problem with significant socioeconomic implications. Monitoring and detecting influenza epidemics are important tasks that require integrated strategies. Wastewater-based epidemiology (WBE) is an emerging field that uses wastewater data to monitor the spread of disease and assess the health of a community. It can represent an integrative surveillance tool for better understanding the epidemiology of influenza and prevention strategies in public health. We conducted a study that detected the presence of Influenza virus RNA using a wastewater-based approach. Samples were collected from five wastewater treatment plants in five different municipalities, serving a cumulative population of 555,673 Sicilian inhabitants in Italy. We used the RT-qPCR test to compare the combined weekly average of Influenza A and B viral RNA in wastewater samples with the average weekly incidence of Influenza-like illness (ILI) obtained from the Italian national Influenza surveillance system. We also compared the number of positive Influenza swabs with the viral RNA loads detected from wastewater. Our study investigated 189 wastewater samples. Cumulative ILI cases substantially overlapped with the Influenza RNA load from wastewater samples. Influenza viral RNA trends in wastewater samples were similar to the rise of ILI cases in the population. Therefore, wastewater surveillance confirmed the co-circulation of Influenza A and B viruses during the season 2022/2023, with a similar trend to that reported for the weekly clinically confirmed cases. Wastewater-based epidemiology does not replace traditional epidemiological surveillance methods, such as laboratory testing of samples from infected individuals. However, it can be a valuable complement to obtaining additional information on the incidence of influenza in the population and preventing its spread.

Summary of the Centers for Disease Control and Prevention's Self-reported Traumatic Brain Injury Survey Efforts.

Surveillance of traumatic brain injury (TBI), including concussion, in the United States has historically relied on healthcare administrative datasets, but these methods likely underestimate the true burden of TBI. The Centers for Disease Control and Prevention (CDC) has recently added TBI prevalence questions to several national surveys. The objective of this article is to summarize their recent efforts and report TBI prevalence estimates. Surveys. Adult and youth respondents to a series of national surveys. Recent nationally representative surveys with either 12-month or lifetime TBI prevalence questions were identified. For each data source, survey methodology, TBI definition, question wording, and prevalence estimates were examined. TBI prevalence varied depending on the question wording and data source. Overall 12-month prevalence of concussion/TBI among adults ranged from 2% to 12% while overall lifetime prevalence of concussion or TBI ranged from 19% to 29%. Overall 12-month prevalence of concussion/TBI among children and adolescents was 10% while 12-month prevalence of sports- and recreation-related concussion for youth ranged from 7% to 15%. Overall lifetime prevalence of TBI among youth ranged from 6% to 14%. Survey data based on self-reported concussions and TBIs resulted in larger prevalence estimates than would be expected based on traditional surveillance methods. Analyses of the various surveys shows that how the questions are asked and what terminology is used can notably affect the estimates observed. Efforts can be made to optimize and standardize data collection approaches to ensure consistent measurement across settings and populations.

Opportunities to strengthen respiratory virus surveillance systems in Australia: lessons learned from the COVID-19 response.

Disease surveillance data was critical in supporting public health decisions throughout the coronavirus disease 2019 (COVID-19) pandemic. At the same time, the unprecedented circumstances of the pandemic revealed many shortcomings of surveillance systems for viral respiratory pathogens. Strengthening of surveillance systems was identified as a priority for the recently established Australian Centre for Disease Control, which represents a critical opportunity to review pre-pandemic and pandemic surveillance practices, and to decide on future priorities, during both pandemic and inter-pandemic periods. On 20 October 2022, we ran a workshop with experts from the academic and government sectors who had contributed to the COVID-19 response in Australia on 'The role of surveillance in epidemic response', at the University of New South Wales, Sydney, Australia. Following the workshop, we developed five recommendations to strengthen respiratory virus surveillance systems in Australia, which we present here. Our recommendations are not intended to be exhaustive. We instead chose to focus on data types that are highly valuable yet typically overlooked by surveillance planners. Three of the recommendations focus on data collection activities that support the monitoring and prediction of disease impact and the effectiveness of interventions (what to measure) and two focus on surveillance methods and capabilities (how to measure). Implementation of our recommendations would enable more robust, timely, and impactful epidemic analysis.

Participatory Disease Surveillance for the Early Detection of Cholera-Like Diarrheal Disease Outbreaks in Rural Villages in Malawi: Prospective Cohort Study.

Cholera-like diarrheal disease (CLDD) outbreaks are complex and influenced by environmental factors, socioeconomic conditions, and population dynamics, leading to limitations in traditional surveillance methods. In Malawi, cholera is considered an endemic disease. Its epidemiological profile is characterized by seasonal patterns, often coinciding with the rainy season when contamination of water sources is more likely. However, the outbreak that began in March 2022 has extended to the dry season, with deaths reported in all 29 districts. It is considered the worst outbreak in the past 10 years. This study aims to evaluate the feasibility and outcomes of participatory surveillance (PS) using interactive voice response (IVR) technology for the early detection of CLDD outbreaks in Malawi. This longitudinal cohort study followed 740 households in rural settings in Malawi for 24 weeks. The survey tool was designed to have 10 symptom questions collected every week. The proxies' rationale was related to exanthematic, ictero-hemorragica for endemic diseases or events, diarrhea and respiratory/targeting acute diseases or events, and diarrhea and respiratory/targeting seasonal diseases or events. This work will focus only on the CLDD as a proxy for gastroenteritis and cholera. In this study, CLDD was defined as cases where reports indicated diarrhea combined with either fever or vomiting/nausea. During the study period, our data comprised 16,280 observations, with an average weekly participation rate of 35%. Maganga TA had the highest average of completed calls, at 144.83 (SD 10.587), while Ndindi TA had an average of 123.66 (SD 13.176) completed calls. Our findings demonstrate that this method might be effective in identifying CLDD with a notable and consistent signal captured over time (R2=0.681404). Participation rates were slightly higher at the beginning of the study and decreased over time, thanks to the sensitization activities rolled out at the CBCCs level. In terms of the attack rates for CLDD, we observed similar rates between Maganga TA and Ndindi TA, at 16% and 15%, respectively. PS has proven to be valuable for the early detection of epidemics. IVR technology is a promising approach for disease surveillance in rural villages in Africa, where access to health care and traditional disease surveillance methods may be limited. This study highlights the feasibility and potential of IVR technology for the timely and comprehensive reporting of disease incidence, symptoms, and behaviors in resource-limited settings.

Historical review of surveillance methods, insecticide spray, and epidemiology of malaria in Bannu zone, Khyber Pakhtunkhwa, Pakistan

Historical review of surveillance methods, insecticide spray, and epidemiology of malaria in Bannu zone, Khyber Pakhtunkhwa, Pakistan

From pixels to plant health: accurate detection of banana Xanthomonas wilt in complex African landscapes using high-resolution UAV images and deep learning

Bananas and plantains are vital for food security and smallholder livelihoods in Africa, but diseases pose a significant threat. Traditional disease surveillance methods, like field visits, lack accuracy, especially for specific diseases like Xanthomonas wilt of banana (BXW). To address this, the present study develops a Deep-Learning system to detect BXW-affected stems in mixed-complex landscapes within the Eastern Democratic Republic of Congo. RGB (Red, Green, Blue) and multispectral (MS) images from unmanned aerial vehicles UAVs were utilized using pansharpening algorithms for improved data fusion. Using transfer learning, two deep-learning model architectures were used and compared in our study to determine which offers better detection capabilities. A single-stage model, Yolo-V8, and the second, a two-stage model, Faster R-CNN, were both employed. The developed system achieves remarkable precision, recall, and F1 scores ranging between 75 and 99% for detecting healthy and BXW-infected stems. Notably, the RGB and PAN UAV images perform exceptionally well, while MS images suffer due to the lower spatial resolution. Nevertheless, specific vegetation indexes showed promising performance detecting healthy banana stems across larger areas. This research underscores the potential of UAV images and Deep Learning models for crop health assessment, specifically for BXW in complex African systems. This cutting-edge deep-learning approach can revolutionize agricultural practices, bolster African food security, and help farmers with early disease management. The study’s novelty lies in its Deep-Learning algorithm development, approach with recent architectures (Yolo-V8, 2023), and assessment using real-world data, further advancing crop-health assessment through UAV imagery and deep-learning techniques.

Comparison of Barrier Surveillance Algorithms for Directional Sensors and UAVs.

Border surveillance and the monitoring of critical infrastructure are essential components of regional and industrial security. In this paper, our purpose is to study the intricate nature of surveillance methods used by hybrid monitoring systems utilizing Pan-Tilt-Zoom (PTZ) cameras, modeled as directional sensors, and UAVs. We aim to accomplish three occasionally conflicting goals. Firstly, at any given moment we want to detect as many intruders as possible with special attention to newly arriving trespassers. Secondly, we consider it equally important to observe the temporal movement and behavior of each intruder group as accurately as possible. Furthermore, in addition to these objectives, we also seek to minimize the cost of sensor usage associated with surveillance. During the research, we developed and analyzed several interrelated, increasingly complex algorithms. By leveraging RL methods we also gave the system the chance to find the optimal solution on its own. As a result we have gained valuable insights into how various components of these algorithms are interconnected and coordinate. Building upon these observations, we managed to develop an efficient algorithm that takes into account all three criteria mentioned above.

Campylobacter assessment along the Spanish food chain: Identification of key points.

Campylobacteriosis, caused by Campylobacter spp., is one of the most important foodborne zoonotic diseases in the world and a common cause of gastroenteritis. In the European Union, campylobacteriosis is considered the most common zoonotic disease, with over 10,000 cases in 2020 alone. This high occurrence highlights the need of more efficient surveillance methods and identification of key points. Herein, we evaluated and identified key points of Campylobacter spp. occurrence along the Spanish food chain during 2015-2020, based on the following variables: product, stage and region. We analysed a dataset provided by the Spanish Agency for Food Safety and Nutrition using a machine learning algorithm (random forests). Campylobacter presence was influenced by the three selected explanatory variables, especially by product, followed by region and stage. Among the studied products, meat, especially poultry and sheep, presented the highest probability of occurrence of Campylobacter, where the bacterium was present in the initial, intermediate and final stages (e.g., wholesale, retail) of the food chain. The presence in final stages may represent direct consumer exposure to the bacteria. By using the random forest method, this study contributes to the identification of Campylobacter key points and the evaluation of control efforts in the Spanish food chain.