Effective Surveillance System Research Articles

Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance During Arbaeenia Mass Gatherings in Iraq.

Large gatherings often involve extended and intimate contact among individuals, creating environments conducive to the spread of infectious diseases. Despite this, there is limited research utilizing outbreak detection algorithms to analyze real syndrome data from such events. This study sought to address this gap by examining the implementation and efficacy of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq. For the study, 10 data collectors conducted field data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10 healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in Iraq. Various outbreak detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported syndromes. During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10 health clinics along a route in Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited syndromes, with influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent syndromes included food poisoning (21.2%, n=1048), heatstroke (17.7%, n=875), febrile rash (9.0%, n=446), and gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM) algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA) algorithms for detecting small shifts. Effective public health surveillance systems are crucial during mass gatherings to swiftly identify and address emerging health risks. Utilizing advanced algorithms and real-time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the protection of public health during these gatherings.

An analysis of the social, cultural, and ecological factors that affect the implementation of biosecurity measures on smallholder commercial swine farms in Italy in the context of an emerging African Swine Fever outbreak

African Swine Fever (ASF) is a contagious viral disease that infects wild and domesticated swine. In early 2022, the virus was found in wild boar in the Apennine mountains of mainland Italy33https://storymaps.arcgis.com/stories/9fe6aa3980ca438cb9c7e8d656358f35. Since then, it has spread from wild boar to domesticated swine. To control the spread of ASF, an effective surveillance system and the implementation of strict biosecurity measures on farms are required yet are unevenly implemented across husbandry systems. Smallholder farms in particular are known to have low levels of biosecurity. In the Apennine mountains of Italy, small commercial farms have been found to have low levels of biosecurity despite being located in areas with high densities of wild boar, and, hence, being high-risk sites for potential ASF incursion and subsequent diffusion. To address the question as to why the level of biosecurity is low, interviews and participant observation were conducted with smallholder commercial farmers. The interviews identified the social, cultural, and ecological factors that affect the implementation of biosecurity measures in small commercial swine farms in the Apennines. Farmers expressed knowledge of priority biosecurity measures and an overall willingness to follow rules and regulations; however, the application of the measures in practice was uneven across farms. Economic, political, and ecological constraints as well as farmer beliefs about biosecurity emerged as important factors affecting the implementation of biosecurity measures. These include economic constraints, challenges posed by the mountain environment, a shifting regulatory environment, and ideas about animal welfare. Other important factors include cultural factors such as the use of traditional agricultural methods and customer expectations about viewing animals, time constraints and the perceived hassle of implementing the measures, farmer age, farmer relationships with government officials and veterinarians, and the role of pigs in reducing farm waste. The study confirmed that wild boar are present in high numbers and in close proximity to smallholder commercial farms in the Apennines.

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis.

Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.

The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

Road potholes have a well-known impact on driving quality and safety. Therefore, timely mitigation of potholes is critical for the safety of road users. However, efficient and timely maintenance relies on the presence of an effective process for pothole detection. Currently, transportation agencies primarily rely on manual inspection and road user reporting. These methods are subjective, prone to inaccuracy, and some are also laborious and time-consuming. An ideal pothole detection system would be accurate, objective, automated, and relatively inexpensive. In this context, accuracy encompasses three distinct performance areas: detection, localization, and size estimation. This study explores the potential of utilizing a mobile light detection and ranging (LiDAR) for accurate detection and size estimation, along with a global navigation satellite system (GNSS) receiver for localization, to develop an effective pothole surveillance system. To achieve this objective, the study proposes a four-step framework. Firstly, the LiDAR data are processed to generate ring-wise cross-sectional images. Secondly, a deep learning object detection network is trained to predict the presence and size of potholes. Thirdly, the ring-wise inferences are aggregated to produce a final decision. Lastly, the aggregated inferences are synchronized with GNSS locations to generate inspection maps. The system’s performance was validated using multiple road strips, never seen by the model, containing potholes of different sizes and shapes. The results demonstrated the effectiveness and accuracy of the proposed system. Overall, this research contributes to the research on LiDAR-based pothole inspection by proposing a novel four-step framework and incorporating it into an end-to-end pothole detection system, which can greatly improve the efficiency of pothole maintenance and enhance the safety of road users.

Genotypic Characterization of Mycobacterium Tuberculosis Strains Resistant to Rifampicin, Isoniazid and Second-Line Antibiotics in Chad

Monitoring drug resistance and identifying the genetic basis of resistance to first- and second-line anti-tuberculosis (anti-TB) drugs provides important information to optimize patient care. The present study was an observational, cross-sectional and analytical study aimed at determining the prevalence of resistance genes to rifampicin, isoniazid and resistant and multi-resistant genes with mutations associated with second-line anti-tuberculosis drugs (Levofloxacin Amikacin, Kanamycin and Capreomycin) among strains of Mycobacterium tuberculosis in nine hospitals in four provinces of Chad with three new molecular techniques. The TB-LAMP molecular technique made it possible to detect 264 (25.14%) strains of Mycobacterium tuberculosis complex among 1050 people referred for tuberculosis research, including 37 cases in relapse and 237 new cases under treatment or not without clinical improvement. The average age of the patients was 34.15 years with the extremes ranging from 12 to 77 years. The sex ratio was 3.33 in favor of men. The GeneXpert made it possible to confirm the 264 positive cases of Mycobacterium tuberculosis (MTB+) of which 76% of men and 24% of women had the MTB+ profile sensitive to rifampicin, and 80% of men and 20% of women harbored the Mycobacterium complex. rifampicin-resistant tuberculosis. The determination of resistance genes including 13% (rpoB) to rifampicin, 4% (inhA) to isoniazid and 8% (rpoB/KatG and/or KatG/inhA) multi-resistant (MDR) genes was carried out using the Line Probe Assay technique. This technique also made it possible to determine resistance genes with associated mutations including 13.6% (gyrA and gyrB), 7.6% (eis), 6.1% (rrs and eis) and 10.6% being mutations affecting the different alleles on the chromosomes of the Mycobacterium tuberculosis complex. This study highlighted the emergence of resistance to rifampicin and isoniazid as well as resistance with mutations associated with second-line anti-tuberculosis drugs. It raises the need to implement an effective surveillance system to detect the resistance of Mycobacterium tuberculosis to anti-tuberculosis drugs in Chad and even in Central Africa.

Diversity of Trichinella species in carnivores from Bosnia and Herzegovina

BackgroundIn Bosnia and Herzegovina, domestic and wild carnivores represent a significant driver for the transmission and ecology of zoonotic pathogens, especially those of parasitic aetiology. Nevertheless, there is no systematic research of Trichinella species in animals that have been conducted in Bosnia and Herzegovina, even though trichinellosis is considered the most important parasitic zoonosis. The available results of the few studies carried out in Bosnia and Herzegovina are mainly related to the confirmation of parasitic larvae in the musculature of domestic pigs and wild boars or data related to trichinellosis in humans. The objective of our study was to present the findings of a comprehensive investigation into the species composition of Trichinella among 11 carnivorous species within the territory of Bosnia and Herzegovina, as follows: red fox (Vulpes vulpes), grey wolf (Canis lupus), brown bear (Ursus arctos), wildcat (Felis silvestris), pine marten (Martes martes), European badger (Meles meles), weasel (Mustela nivalis), European polecat (Mustela putorius), Eurasian lynx (Lynx lynx), but also dog (Canis lupus familiaris) and cat (Felis catus).ResultsIn the period 2013–2023, carnivore musculature samples (n = 629), each consisting of 10 g of muscle tissue, were taken post-mortem and individually examined using the artificial digestion method. In the positive samples (n = 128), molecular genotyping and identification of parasitic larvae of Trichinella spp. were performed using a PCR-based technique up to the species/genotype level. Positive samples were used for basic PCR detection of the genus Trichinella (rrnS rt-PCR technique) and genotyping (rrnl-EVS rt-PCR technique). The Trichinella infection was documented for the first time in Bosnia and Herzegovina among red foxes, grey wolves, brown bears, dogs, badgers and Eurasian lynx, with a frequency rate of 20.3%. Additionally, the presence of T. britovi infection was newly confirmed in Bosnia and Herzegovina, marking the initial documented cases. Furthermore, both T. britovi and T. pseudospiralis infections were observed in the wildcat population, whereas T. britovi and T. spiralis infections were detected in pine martens. Consistent with previous research, our findings align particularly regarding carnivores, with data from other countries such as Germany, Finland, Romania, Poland and Spain, where T. britovi exhibits a wider distribution (62.5–100%) compared to T. spiralis (0.0–37.5%). T. britovi is more common among sylvatic carnivores (89.0%), while T. spiralis prevails in wild boars (62.0%), domestic swine (82.0%) and rodents (75.0%).ConclusionThe results of our study represent the first molecular identification of species of the genus Trichinella in Bosnia and Herzegovina. Additionally, our findings underscore the necessity for targeted epidemiological studies to thoroughly assess trichinellosis prevalence across diverse animal populations. Considering the relatively high frequency of trichinellosis infection in investigated animal species and its public health implications, there is an evident need for establishing an effective trichinellosis surveillance system in Bosnia and Herzegovina.

Antibiograms, multidrug resistance, and milk-related parameters of bacteria isolated from milk of dairy cattle in Phatthalung, Thailand.

Milk, a nutritious food, is widely consumed in human diets; however, contamination by micro-organisms can negatively impact its quality and consumer health. Contamination by micro-organisms affects the quality of milk, which can affect the quality of the milk production chain. This study aimed to determine the changes in milk composition and antibiotic susceptibility related to bacteria isolated from dairy cow milk. Raw milk samples were collected from 72 dairy cows. All milk samples were subjected to the California Mastitis Test (CMT) for CMT score determination. We also investigated milk composition, bacterial culture (BC), and antibiotic susceptibility. About 47.22% and 30.56% of dairy cattle were positive for CMT + BC and automatic somatic cell count (ASCC) + BC, respectively. Fecal appearance and animal age were found to be risk factors for ASCC + BC positivity in dairy cattle. Bacteria were found in approximately 76% of milk samples, with the most common isolated species being α-hemolytic Streptococcus spp., coagulase-negative Staphylococcus spp., and Escherichia coli. Of these, 70% are resistant to at least one antibiotic. Variation in the multidrug resistance pattern was high in Klebsiella spp. Fecal appearance and animal age are risk factors for ASCC + BC positivity in dairy cattle. This study identified antibiotic and multidrug resistance patterns, which require comprehensive studies and effective surveillance systems. Remarkably, the use of antibiotic therapy in dairy cattle should be monitored.

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

BackgroundIn Zimbabwe, anthrax is endemic with outbreaks being reported almost annually in livestock, wildlife, and humans over the past 40 years. Accurate modelling of its spatial distribution is key in formulating effective control strategies. In this study, an Ensemble Species Distribution Model was used to model the current and future distribution of anthrax occurrence in Zimbabwe.MethodsBioclimatic variables derived from the Beijing Climate Centre Climate System Model were used to model the disease. Collinearity testing was conducted on the 19 bioclimatic variables and elevation to remove redundancy. Variables that had no collinearity were used for anthrax habitat suitability modelling. Two future climate change scenarios for different Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 were used. Model evaluation was done using true skill, Kappa statistics and receiver operating characteristics.ResultsThe results showed that under current bioclimatic conditions, eastern and western districts of Zimbabwe were modelled as highly suitable, central districts moderately suitable and southern parts marginally suitable for anthrax occurrence. Future predictions demonstrated that the suitable (8%) and highly suitable (7%) areas for anthrax occurrence would increase under RCP4.5 scenario. In contrast, a respective decrease (11%) and marginal increase (0.6%) of suitable and highly suitable areas for anthrax occurrence were predicted under the RCP8.5 scenario. The percentage contribution of the predictors varied for the different scenarios; Bio6 and Bio18 for the current scenario, Bio2, Bio4 and Bio9 for the RCP4.5 and Bio3 and Bio15 for the RCP8.5 scenarios.ConclusionsThe study revealed that areas currently suitable for anthrax should be targeted for surveillance and prevention. The predicted future anthrax distribution can be used to guide and prioritise surveillance and control activities and optimise allocation of limited resources. In the marginally to moderately suitable areas, effective disease surveillance systems and awareness need to be put in place for early detection of outbreaks. Targeted vaccinations and other control measures including collaborative ‘One Health’ strategies need to be implemented in the predicted highly suitable areas. In the southern part where a high decrease in suitability was predicted, continued monitoring would be necessary to detect incursions early.

Current Status of Surveillance Systems for Human Respiratory Syncytial Virus

To conduct timely surveillance of the seasonal characteristics and disease burden of Human Respiratory Syncytial Virus (HRSV) in various geographical regions of China, and further develop more precise and effective prevention and intervention strategies, there is an urgent need for China to establish a nationwide, effective, and stable HRSV surveillance system. Through combining the current status of domestic and international HRSV surveillance systems and the existing surveillance framework in China, this study proposed an HRSV surveillance type applicable to China based on different surveillance purposes, and considering the feasibility of implementation. This article aimed to provide solid scientific and technical support to monitor the dynamic changes of HRSV epidemic timely, carry out a risk assessment and early warning, and further understand the disease burden of HRSV in China. It also helps to improve the diagnosis, prevention, and control of the HRSV diseases research and development, use, and evaluation of HRSV vaccines and drugs in China.

A smartphone-based crowd-sourced real-time surveillance platform (apple snail inspector) for the invasive snails: a design and development study

BackgroundThe large amphibious freshwater apple snail is an important invasive species in China, but there is currently no method available for their surveillance. The development and popularization of smartphones provide a new platform for research on surveillance technologies for the early detection and effective control of invasive species.MethodsThe ASI surveillance system was developed based on the infrastructure of the WeChat platform and Amap. The user can directly enter the game interface through the WeChat port on their mobile phone, and the system automatically obtains their location. The user can then report the location of apple snails. The administrator can audit the reported information, and all information can be exported to Microsoft Excel version 2016 for analysis. The map was generated by ArcGIS 10.2 and was used to characterize the spatial and temporal distribution of apple snails in Jiangsu Province.ResultsThe architecture of ASI consists of three parts: a mobile terminal, a server terminal and a desktop terminal. We published more than 10 tweets on the official WeChat account of the system to announce it to the public, and a total of 207 users in 2020 and 2021 correctly reported sightings of apple snails. We identified 550 apple snails breeding sites in 2020 and 2021, featuring ponds (81%), parks (17%) and farmland (2%). In addition, most of the locations contained snail eggs, and the reporting times mainly occurred between May and September.ConclusionsThe ASI is an effective surveillance system that can be used to identify the breeding locations of apple snails and provides the basis of prevention and control for its dispersal. Its successful development and operation provide new potential avenues for surveillance of other public health issues.Graphical

Wastewater Surveillance of SARS-CoV-2 RNA in a Prison Facility

Prison populations are unlikely to have access to prompt, effective medical care as the general population. Therefore, vaccination and effective surveillance systems have been recommended to mitigate coronavirus disease 2019 (COVID-19) transmission in prison settings. This pilot study aimed to assess the application of wastewater-based epidemiology (WBE) in a prison to act as an early warning tool for COVID-19 transmission. In this study, weekly wastewater samples (n = 21) were collected for 21 weeks from a prison facility in New Orleans, LA, USA, and analyzed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the results were compared with the number of confirmed cases during the same period. SARS-CoV-2 was concentrated using two methods and quantified via RT-qPCR using CDC N1 and N2 assays. Overall, SARS-CoV-2 was detected in eight samples (38%). An equal number of samples tested positive for SARS-CoV-2 using the two concentrating methods, indicating the effectiveness of both methods for building-scale WBE. Despite limited clinical testing in the studied prison facility, instances of SARS-CoV-2 detection in wastewater prior to the diagnosis of COVID-19 depict the potential use of wastewater surveillance in detecting the presence of early and averting outbreaks in asymptomatic COVID-19 patients.

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand

BackgroundDengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs.MethodsTo evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document}, Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility.ResultsIn the simulation study, Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document} and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document} and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies.ConclusionsBayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.

Design and Practical Application of a Cost Effective Intelligent Female Surveillance System Using GPS, GSM, and Arduino Technology

This paper suggests a cost effective, portable and intelligent surveillance system for women that they can utilize in risky or emergency situations. In this design, an Arduino Nano microcontroller, a GPS module, and a GSM module are the main parts used. Whenever an emergency arises, the user can instantly send an emergency message to a preset phone number along with their current location. The emergency activation button is in a handy place on the gadget, which is tiny enough to carry in a pocket or purse. This device will improve women's protection and security, particularly in places with high crime rates or poor public safety.

Establishing a national indicator-based surveillance system for hospital bed utilization by COVID-19 patients in the Philippines.

In March 2020, the Philippine Department of Health (DOH) designed and rapidly implemented a national surveillance system for the utilization of hospital beds by patients with coronavirus disease (COVID-19) to produce complete and timely data for use by various levels of governance in response to the COVID-19 pandemic. The DOH launched the DOH DataCollect (DDC) Bed Tracker system, a web-based application that collects information from all 1906 public and private hospitals and infirmaries across the country using a modular data collection tool. Data on the maximum number of occupied COVID-19-designated beds (n=28 261), hospital bed utilization rate (71.7%), and mechanical ventilator number (n=1846) and utilization rate (58.5%) were recorded in September 2021 during the Delta surge of cases in the Philippines. Data on human resources, personal protective equipment and supplies, and other operational indicators were added to the system during various modifications. Information from the DDC was used to inform the COVID-19 response and operations at national and local levels and facilitated research at academic and nongovernmental agencies. The development of the DDC system demonstrates that an effective surveillance system for use by all health-care facilities is achievable through strong national leadership, the use of available technology and adaptive information systems, and the establishment of networks across different health facilities and stakeholders.

A brief history of rabies in Haiti until the adoption of the One Health approach for its epidemiological surveillance and control

Rabies was first identified in dogs and humans by a French veterinarian, Jean Lompagieu Lapole, in 1788 in the colony of Saint-Domingue, which later became Haiti. Rabies has continued to persist in the country despite some intermittent successes in combating this disease over the past 20 years. We are trying to understand why rabies, which has officially existed in the country for around 250 years, is still classified as a neglected disease and continues to result in cases of human mortality. For a significant period, until the implementation of the Haiti Animal Rabies Surveillance Program (HARSP) in 2011, there was no robust initiative to fight against rabies based on regular vaccination campaigns and an effective epidemiological surveillance system despite certain efforts made by the Ministry of Agriculture and Pan American Health Organization Project of Strengthening Public Agricultural Services (RESEPAG): Projet de Renforcement des Services publics Agricoles (PAHO) to increase dog vaccinations. The HARSP program was established with technical assistance from the Center for Disease Prevention and Control of Infectious Diseases (CDC) to control and eliminate rabies in the short term, according to the One Health approach. It was a passive surveillance system based on strengthening the rabies diagnostic capacity of the Veterinary Laboratory, training veterinary and public health technicians, and supporting rabies vaccination campaigns. This approach has resulted in effective cooperation between the Ministry of Agriculture and the Ministry of Public Health based on the “One Health” enabling the Ministry of Public Health to report daily on bite to the Ministry of Agriculture for further investigations. HARSP marked a pivotal moment in the Rabies Control and Elimination Program. Between January 2013 and December 2017, there were 9,342 reported human exposures to animals suspected of carrying rabies in Haiti. The number of Human exposures to rabies suspected animals in Haiti from January 2013 to December 2017 was 9,342 heads. Among these cases, 2.4% were confirmed, 4.9% probable, 17.8% suspected, and 74.6% discarded. The One Health rabies program has demonstrated the potential for achieving control of both animal and human rabies in Haiti if the ministries of Agriculture and Health commit unequivocally to continue their collaborative development efforts and adhere to HARSP methodology, including vaccinating over 70% of the dog population.

The barriers and facilitators of implementing a national laboratory-based AMR surveillance system in Cambodia: key informants' perspectives and assessments of microbiology laboratories.

Collecting data on antimicrobial resistance (AMR) is an essential approach for defining the scope of the AMR problem, developing evidence-based interventions and detecting new and emerging resistances. Our study aimed to identify key factors influencing the implementation of a laboratory-based AMR surveillance system in Cambodia. This will add additional insights to the development of a sustainable and effective national AMR surveillance system in Cambodia and other low- and middle-income countries. Key informants with a role in governing or contributing data to the laboratory-based surveillance system were interviewed. Emerging themes were identified using the framework analysis method. Laboratories contributing to the AMR surveillance system were assessed on their capacity to conduct quality testing and report data. The laboratory assessment tool (LAT), developed by the World Health Organisation (WHO), was adapted for assessment of a diagnostic microbiology laboratory covering quality management, financial and human resources, data management, microbiology testing performance and surveillance capacity. Key informants identified inadequate access to laboratory supplies, an unsustainable financing system, limited capacity to collect representative data and a weak workforce to be the main barriers to implementing an effective surveillance system. Consistent engagement between microbiology staff and clinicians were reported to be a key factor in generating more representative data for the surveillance system. The laboratory assessments identified issues with quality assurance and data analysis which may reduce the quality of data being sent to the surveillance system and limit the facility-level utilisation of aggregated data. A weak surveillance network and poor guidance for outbreak response were also identified, which can reduce the laboratories' opportunities in detecting critical or emerging resistance occurring in the community or outside of the hospital's geographical coverage. This study identified two primary concerns: ensuring a sustainable and quality functioning of microbiology services at public healthcare facilities and overcoming sampling bias at sentinel sites. These issues hinder Cambodia's national AMR surveillance system from generating reliable evidence to incorporate into public health measures or clinical interventions. These findings suggest that more investments need to be made into microbiology diagnostics and to reform current surveillance strategies for enhanced sampling of AMR cases at hospitals.

Poliovirus serological assay after the cVDPV1 outbreak in Papua New Guinea: a cross-sectional study from 2020 to 2021

In June 2018, a type 1 circulating vaccine-derived poliovirus (cVDPV1) outbreak was declared in Papua New Guinea (PNG), resulting in a total of 26 paralytic confirmed cases. Eight vaccination campaign rounds with bivalent oral poliovirus vaccine (bOPV) were carried out in response. Prevalence of neutralizing polio antibodies in children was assessed two years after the outbreak response was completed. We conducted a cross-sectional serological survey among children aged 6 months-10 years selected from six provinces in PNG to evaluate seroprevalence of neutralizing polio antibodies to the three poliovirus serotypes and analyse sociodemographic risk factors. We included 984 of 1006 enrolled children in the final analysis. The seroprevalence of neutralizing polio antibodies for serotype 1, 2 and 3 was 98.3% (95% CI: 97.4-98.9), 63.1% (95% CI: 60.1-66.1) and 95.0% (95% CI: 93.6-96.3), respectively. Children <1 year had significantly lower type 1 seroprevalence compared to older children (p<0.001); there were no significant differences in seroprevalence among provinces. PNG successfully interrupted transmission of cVDPV1 with several high coverage bOPV campaigns and seroprevalence remained high after two years. The emergence of cVDPV strains underscores the importance of maintaining high levels of routine immunization coverage and effective surveillance systems for early detection. World Health Organization through a Rotary International IPPC grant.

COVID-19 Testing Among People with HIV: A Population Level Analysis Based on Statewide Data in South Carolina.

People with HIV (PWH) are at an elevated risk of developing severe COVID-19 outcomes because of compromised immunity and more comorbidities. However, existing literature suggests a lower rate of COVID-testing among PWH. This study aimed to explore the temporal trend of county-level COVID-19 testing rate and multi-level predictors of COVID-19 ever-testing among PWH in South Carolina (SC). Leveraging linked statewide HIV and COVID-19 datasets, we defined the study population as all adult (18 + years) PWH who were alive on March 2020 and living in SC. PWH with a COVID-19 testing record between March 2020 and October 2021 were defined as COVID-19 ever-testers. Logistic regression and generalized mixed models were used to investigate the association of PWH's demographic profile, HIV clinical characteristics (e.g., CD4 count, viral load), comorbidities, and social factors with COVID-19 testing among PWH. Among 15,660 adult PWH, 8,005 (51.12%) had ever tested for COVID-19 during the study period (March 2020-October 2021). PWH with older age, being male, and Hispanics were less likely to take COVID-19 testing, while men who have sex with men or injection drug users were more likely to take COVID-19 testing. PWH with higher recent viral load (10,000-100,000 copies/ml vs. <200 copies/ml: adjusted odds ratio [AOR]: 0.64, 95%CI: 0.55-0.75) and lower CD4 counts (> 350 cells/mm3 vs. <200 cells/mm3: AOR: 1.25, 95%CI: 1.09-1.45) had lower odds for COVID-19 testing. Additionally, PWH with lower comorbidity burden and those living in rural areas were less likely to be tested for COVID-19. Differences in COVID-19 test-seeking behaviors were observed among PWH in the current study, which could help provide empirical evidence to inform the prioritization of further disease monitoring and targeted intervention. More efforts on building effective surveillance and screening systems are needed to allow early case detection and curbing disease transmission among older, male, Hispanic, and immune-suppressed PWH, especially in rural areas.

Private Continuous Survival Analysis with Distributed Multi-Site Data.

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site data (e.g., from a number of local/regional health systems) need to be considered. Leveraging distributed data across multiple sites for epidemiological analysis poses significant challenges. Due to the sensitive nature of epidemiological data, it is imperative to design distributed solutions that provide strong privacy protections. Current privacy solutions often assume a central site, which is responsible for aggregating the distributed data and applying privacy protection before sharing the results (e.g., aggregation via secure primitives and differential privacy for sharing aggregate results). However, identifying such a central site may be difficult in practice and relying on a central site may introduce potential vulnerabilities (e.g., single point of failure). Furthermore, to support clinical interventions and inform policy decisions in a timely manner, epidemiological analysis need to reflect dynamic changes in the data. Yet, existing distributed privacy-protecting approaches were largely designed for static data (e.g., one-time data sharing) and cannot fulfill dynamic data requirements. In this work, we propose a privacy-protecting approach that supports the sharing of dynamic epidemiological analysis and provides strong privacy protection in a decentralized manner. We apply our solution in continuous survival analysis using the Kaplan-Meier estimation model while providing differential privacy protection. Our evaluations on a real dataset containing COVID-19 cases show that our method provides highly usable results.

Spatiotemporal analysis of Rabies outbreaks in herbivores in Tocantins as a subsidy for the detection of epizootics

An effective epidemiological surveillance system is essential to help control rabies, as it allows the early detection of unexpected increases in the disease as well as the identification of geographic areas with populations that need greater attention from the Official Veterinary Services (OVS). The present study aimed to comprehend the behavior of rabies outbreaks in herbivores, through a temporal analysis using a control diagram based on moving averages, and a spatial diffusion mapping. A spatial clustering analysis in rabies outbreaks was performed using both nearest neighbor analysis and ST-DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Additionally, associated risk factors and the effectiveness of rabies vaccination was assessed using a chi-square (χ²) test and odds ratios as the association measure. The study detected rabies outbreaks in state of Tocantins in certain years, especially in 2015. Spatial analysis revealed clustering (nearest neighbor index = 0.555; p &lt; 0.05) in intermediate regions of Araguaína e Palmas. Risk factors identified included shelter linked to relief, vegetation, and anthropic factors. The findings of this study have significant implications for the control of rabies in herbivores, providing valuable information to guide vaccination efforts and the control of hematophagous bats, thus aiding in disease prevention and control actions not only Tocantins state but also in other geographic regions grappling with paralytic rabies.