Tool For Disease Surveillance Research Articles

Tiled Amplicon Sequencing Enables Culture-free Whole-Genome Sequencing of Pathogenic Bacteria From Clinical Specimens.

Pathogen sequencing is an important tool for disease surveillance and demonstrated its high value during the COVID-19 pandemic. Viral sequencing during the pandemic allowed us to track disease spread, quickly identify new variants, and guide the development of vaccines. Tiled amplicon sequencing, in which a panel of primers is used for multiplex amplification of fragments across an entire genome, was the cornerstone of SARS-CoV-2 sequencing. The speed, reliability, and cost-effectiveness of this method led to its implementation in academic and public health laboratories across the world and adaptation to a broad range of viral pathogens. However, similar methods are not available for larger bacterial genomes, for which whole-genome sequencing typically requires in vitro culture. This increases costs, error rates and turnaround times. The need to culture poses particular problems for medically important bacteria such as Mycobacterium tuberculosis, which are slow to grow and challenging to culture. As a proof of concept, we developed two novel whole-genome amplicon panels for M. tuberculosis and Streptococcus pneumoniae. Applying our amplicon panels to clinical samples, we show the ability to classify pathogen subgroups and to reliably identify markers of drug resistance without culturing. Development of this work in clinical settings has the potential to dramatically reduce the time of diagnosis of drug resistance for multiple drugs in parallel, enabling earlier intervention for high priority pathogens.

Evaluation of wastewater percent positive for assessing epidemic trends - A case study of COVID-19 in Shangrao, China

ObjectiveThis study aims to assess the feasibility of evaluating the COVID-19 epidemic trend through monitoring the positive percentage of SARS-CoV-19 RNA in wastewater. MethodThe study collected data from January to August 2023, including the number of reported cases, the positive ratio of nucleic acid samples in sentinel hospitals, the incidence rate of influenza-like symptoms in students, and the positive ratio of wastewater samples in different counties and districts in Shangrao City. Wastewater samples were obtained through grabbing and laboratory testing was completed within 24 h. The data were then normalized using Z-score normalization and analyzed for lag time and correlation using the xcorr function and Spearman correlation coefficient. ResultsA total of 2797 wastewater samples were collected. The wastewater monitoring study, based on sampling point distribution, was divided into two phases. Wuyuan County consistently showed high levels of positive ratio in wastewater samples in both phases, reaching peak values of 91.67% and 100% respectively. The lag time analysis results indicated that the peak positive ratio in all wastewater samples in Shangrao City appeared around 2 weeks later compared to the other three indicators. The correlation analysis revealed a strong linear correlation across all four types of data, with Spearman correlation coefficients ranging from 0.783 to 0.977, all of which were statistically significant. ConclusionThe positive ratio of all wastewater samples in Shangrao City accurately reflected the COVID-19 epidemic trend from January to August 2023. This study confirmed the lag effect of wastewater percent positive and its strong correlation with the reported incidence rate and the positive ratio of nucleic acid samples in sentinel hospitals, supporting the use of wastewater percent positive monitoring as a supplementary tool for infectious disease surveillance in the regions with limited resources.

Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities

Wastewater has emerged as a crucial tool for infectious disease surveillance, offering a valuable means to bridge the equity gap between underserved communities and larger urban municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. In this study, we tested if detecting SARS-CoV-2 in wastewater can forecast outbreaks in rural communities.Under the CDC National Wastewater Surveillance program, we monitored the SARS-CoV-2 in the wastewater of five rural communities and a small city in Idaho (USA). We then used a particle filter method coupled with a stochastic susceptible-exposed-infectious-recovered (SEIR) model to infer active case numbers using quantities of SARS-CoV-2 in wastewater.Our findings revealed that while high daily variations in wastewater viral load made real-time interpretation difficult, the SEIR model successfully factored out this noise, enabling accurate forecasts of the Omicron outbreak in five of the six towns shortly after initial increases in SARS-CoV-2 concentrations were detected in wastewater. The model predicted outbreaks with a lead time of 0 to 11 days (average of 6 days +/- 4) before the surge in reported clinical cases.This study not only underscores the viability of wastewater-based epidemiology (WBE) in rural communities—a demographic often overlooked in WBE research—but also demonstrates the potential of advanced epidemiological modeling to enhance the predictive power of wastewater data. Our work paves the way for more reliable and timely public health guidance, addressing a critical gap in the surveillance of infectious diseases in rural populations.

Risk factors of foodborne parasitic diseases still remain in China: a WeChat-based knowledge, attitudes, and practices study

ObjectiveWith the progress of economic globalization and food diversification, foodborne parasitic diseases pose a significant public health challenge in China. This study aimed to explore the knowledge, attitudes, and practices regarding foodborne parasitic diseases among the Chinese population via WeChat, which is a promising tool for disease surveillance and health education.MethodsUsing a questionnaire, this cross-sectional study was conducted on September 25, 2023. Participants completed a structured questionnaire by scanning a QR code provided in a tweet from the WeChat official account of the Jiangsu Institute of Parasite Disease. Multivariable logistic regression analysis was employed to explore potential independent determinants of adequate knowledge of foodborne parasitic diseases, and the positive attitude and good practice rates of the participants were calculated.ResultsIn total, 5,675 valid questionnaires were collected via the WeChat official account. Most participants (79.91%) fell within the age range of 20–40 years, with a higher representation of males (53.18%), and 89.80% were of Han Chinese ethnicity. It was found that 76.65% of the participants had adequate level of knowledge. Multivariable logistic regression analysis revealed that individuals of Hui nationality (OR = 0.57, 95% CI: 0.40–0.81, p = 0.002), clinicians (OR = 0.13, 95% CI: 0.09–0.19, p < 0.001), teachers (OR = 0.48, 95% CI: 0.34–0.69, p < 0.001), and government staff (OR = 0.40, 95% CI: 0.30–0.53, p < 0.001) had significantly higher levels of knowledge. Among the participants, 33.9% reported consuming raw fish or drunken shrimp, 10.6% would still try to consume raw fish despite the risk of parasitic infection, and 84.1% did not use separate cutting boards for raw and cooked foods in their kitchens.ConclusionsAlthough majority of the Chinese public (76.65%) demonstrates adequate level of knowledge on foodborne parasitic diseases, there is a need to enhance personal hygiene practices and dietary habits, particularly the utilization of distinct cutting boards and the consumption of raw fish. A WeChat official account is an accessible tool for spreading foodborne parasitic diseases related health information to the public. WeChat-based health education should be implemented to enhance public awareness regarding the prevention and control of foodborne parasitic diseases.

New Approaches to Plant Pathogen Detection and Disease Diagnosis.

Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.

Key Characteristics and Perception of Different Outbreak Surveillance Systems in Côte d'Ivoire: Cross-Sectional Survey Among Users.

Accurate and timely infectious disease surveillance is pivotal for effective public health responses. An important component of this is the disease surveillance tools used. Understanding views and experiences of users is crucial for informing policy decisions and ensuring the seamless functioning of surveillance systems. In this study, we aimed to assess the user perceptions of 3 disease surveillance tools used in Côte d'Ivoire, namely, MAGPI, District Health Information Software 2 (DHIS2), and Surveillance Outbreak Response Management and Analysis System (SORMAS), the latter was implemented in 2021 within a pilot scheme. We conducted interviews and a web-based survey distributed to users of the 3 surveillance tools. The survey assessed users' views of the surveillance tools' usefulness, ease of use, feelings toward the tool, conditions that may influence the use, and other characteristics. The descriptive analysis compared responses from SORMAS, MAGPI, and DHIS2 users, providing a comprehensive evaluation of their experiences. Among the 159 respondents who actively use one of the systems, MAGPI was the most widely used surveillance tool among respondents (n=127, 79.9%), followed by DHIS2 (n=108, 67.9%), and SORMAS (n=25, 15.7%). In terms of users' perceptions, SORMAS, despite its limited implementation, emerged as a tool that allows for data analysis and had the most comprehensive set of functionalities. DHIS2 was appreciated for its frequency of report provision, although users reported occasional IT system failures. MAGPI was recognized for its ease of use but was reported to lack certain functionalities offered by the other surveillance systems. This study offers valuable insights into the perceptions of disease surveillance tools users in Côte d'Ivoire. While all systems were positively regarded, each exhibited strengths and weaknesses addressing different needs and functionalities. Policy makers and health officials can use these findings to enhance existing tools or consider a unified approach for infectious disease surveillance systems. Understanding users' perspectives allows them to optimize the choice of surveillance tools, ultimately strengthening public health responses in Côte d'Ivoire and potentially serving as a model for other countries facing similar decisions in their health care systems.

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand

BackgroundTimely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by clinical testing, healthcare-seeking behaviour or access to care.MethodsWe construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods.ResultsWe analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand’s second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022.ConclusionsEstimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time.

Evaluation of chimeric recombinant antigens for the serodiagnosis of Trypanosoma cruzi in dogs: a promising tool for Chagas disease surveillance

BackgroundChagas disease (CD), a neglected parasitic disease caused by Trypanosoma cruzi, poses a significant health threat in Latin America and has emerged globally because of human migration. Trypanosoma cruzi infects humans and over 100 other mammalian species, including dogs, which are important sentinels for assessing the risk of human infection. Nonetheless, the serodiagnosis of T. cruzi in dogs is still impaired by the absence of commercial tests. In this study, we investigated the diagnostic accuracy of four chimeric recombinant T. cruzi IBMP antigens (IBMP-8.1, IBMP-8.2, IBMP-8.3, and IBMP-8.4) for detecting anti-T. cruzi antibodies in dogs, using latent class analysis (LCA).Methods We examined 663 canine serum samples, employing indirect ELISA with the chimeric antigens. LCA was utilized to establish a latent variable as a gold standard for T. cruzi infection, revealing distinct response patterns for each antigen.Results The IBMP (Portuguese acronym for the Molecular Biology Institute of Paraná) antigens achieved area under the ROC curve (AUC) values ranging from 90.9% to 97.3%. The highest sensitivity was attributed to IBMP-8.2 (89.8%), while IBMP-8.1, IBMP-8.3, and IBMP-8.4 achieved 73.5%, 79.6%, and 85.7%, respectively. The highest specificity was observed for IBMP-8.4 (98.6%), followed by IBMP-8.2, IBMP-8.3, and IBMP-8.1 with specificities of 98.3%, 94.4%, and 92.7%, respectively. Predictive values varied according to prevalence, indicating higher effectiveness in endemic settings.Conclusions Our findings underscore the remarkable diagnostic performance of IBMP-8.2 and IBMP-8.4 for the serodiagnosis of Trypanosoma cruzi in dogs, representing a promising tool for the diagnosis of CD in dogs. These chimeric recombinant antigens may not only enhance CD surveillance strategies but also hold broader implications for public health, contributing to the global fight against this neglected tropical disease.Graphical

Exosomal membrane proteins analysis using a silicon nanowire field effect transistor biosensor

Exosomes are of great significance in clinical diagnosis, due to their high homology with parental generation, which can reflect the pathophysiological status. However, the quantitative and classification detection of exosomes is still faced with the challenges of low sensitivity and complex operation. In this study, we develop an electrical and label-free method to directly detect exosomes with high sensitivity based on a Silicon nanowire field effect transistor biosensor (Si-NW Bio-FET). First, the impact of Debye length on Si-NW Bio-FET detection was investigated through simulation. The simulation results demonstrated that as the Debye length increased, the electrical response to Si-NW produced by charged particle at a certain distance from the surface of Si-NW was greater. A Si-NW Bio-FET modified with specific antibody CD81 on the nanowire was fabricated then used for detection of cell line-derived exosomes, which achieved a low limit of detection (LOD) of 1078 particles/mL in 0.01 × PBS. Furthermore, the Si-NW Bio-FETs modified with specific antibody CD9, CD81 and CD63 respectively, were employed to distinguish exosomes derived from human promyelocytic leukemia (HL-60) cell line in three different states (control group, lipopolysaccharide (LPS) inflammation group, and LPS + Romidepsin (FK228) drug treatment group), which was consistent with nano-flow cytometry. This study provides a highly sensitive method of directly quantifying exosomes without labeling, indicating its potential as a tool for disease surveillance and medication instruction.

Evaluating storage conditions and enhancement strategies on viral biomarker recovery for WBE applications.

Wastewater-based epidemiology (WBE) is a valuable disease surveillance tool. However, little is known on how factors such as transportation, storage, and wastewater characteristics influence the accuracy of the quantification methods. Hence, this study investigated the impact of storage temperatures and physicochemical characteristics of wastewater on SARS-CoV-2 and influenza A stability using droplet digital PCR. Additionally, strategies to enhance viral recovery were explored. Municipal influent wastewater stored between ±25 and -80 °C was assessed for a period of 84 days to determine viral degradation. Degradation up to 94.1% of influenza A and SARS-CoV-2 was observed in all samples with the highest at ±25 °C. Viral degradation was correlated to the changes in wastewater physicochemical characteristics. The low degradation observed of SARS-CoV-2 in the spiked pellets were indicative of viral adhesion to wastewater solids, which correlated with changes in pH. Ultrasonication frequencies ranging from 4 to 16 kHz, increased SARS-CoV-2 concentrations in the supernatant between 3.30 and 35.65%, indicating viral RNA attachment to wastewater solids. These results highlight the importance of additional pretreatment methods for maximizing RNA recovery from wastewater samples. Based on these findings, it was deduced that wastewater preservation studies are essential, and pretreatment should be included in the WBE methodology.

Optimisation of analytical methods for tuberculosis drug detection in wastewater: A multinational study

Wastewater-based epidemiology (WBE) is a robust tool for disease surveillance and monitoring of pharmaceutical consumption. However, monitoring tuberculosis (TB) drug consumption faces challenges due to limited data availability. This study aimed to optimise methods for detecting TB drugs in treated and untreated wastewater from four African countries: South Africa, Nigeria, Kenya, and Cameroon. The limit of detection (LOD) for these drugs ranged from a minimum of 2.20 (±1.02) for rifampicin to a maximum of 2.95 (±0.79) for pyrazinamide. A parallel trend was observed concerning the limit of quantification (LOQ), with rifampicin reporting the lowest average LOQ of 7.33 (±3.44) and pyrazinamide showing the highest average LOQ of 9.81 (±2.64). The variance in LOD and LOQ values could be attributed to factors such as drug polarity. Erythromycin and rifampicin exhibited moderately polar LogP values (2.6 and 2.95), indicating higher lipid affinity and lower water affinity.Conversely, ethambutol, pyrazinamide, and isoniazid displayed polar LogP values (−0.059, −0.6, and −0.7), suggesting lower lipid affinity and greater water affinity. The study revealed that storing wastewater samples for up to 5 days did not result in significant drug concentration loss, with concentration reduction remaining below 1 log throughout the storage period. Application of the optimised method for drug detection and quantification in both treated and untreated wastewater unveiled varied results. Detection frequencies varied among drugs, with ethambutol consistently most detected, while pyrazinamide and isoniazid were least detected in wastewater from only two countries. Most untreated wastewater samples had undetectable drug concentrations, ranging from 1.21 ng/mL for erythromycin to 54.61 ng/mL for isoniazid. This variability may suggest differences in drug consumption within connected communities. In treated wastewater samples, detectable drug concentrations ranged from 1.27 ng/mL for isoniazid to 10.20 ng/mL for ethambutol. Wastewater treatment plants exhibited variable removal efficiencies for different drugs, emphasising the need for further optimisation. Detecting these drugs in treated wastewater suggests potential surface water contamination and subsequent risks of human exposure, underscoring continued research's importance.

Endoscopic and histologic utility of transnasal endoscopy in pediatric eosinophilic esophagitis.

Unsedated transnasal endoscopy (TNE) is an alternative method of examining the esophageal mucosa in pediatric patients with eosinophilic esophagitis (EoE), reducing cost, time, and risk associated with frequent surveillance esophagogastroduodenoscopies (EGD). Adequacy of transnasal esophageal biopsies for the evaluation of eosinophilic esophagitis histologic scoring system (EoEHSS) has not yet been evaluated. We compared procedure times, endoscopic findings, and EoEHSS scoring for EoE patients undergoing TNE versus standard EGD. Sixty-six TNE patients and 132 EGD controls matched for age (mean age 14.0 years) and disease status (29.3% active) were included. Compared to patients undergoing standard EGD, patients undergoing TNE spent 1.94 h less in the GI suite (p < 0.0001), with comparable occurrence rates of all visual endoscopic findings and most EoEHSS components. TNE serves as a useful tool for long-term disease surveillance, and consideration should be given to its use in clinical trials for EoE.

Knowledge, perceptions, and practices around zoonotic diseases among actors in the livestock trade in the Lake Victoria crescent ecosystem in East Africa.

Zoonotic diseases such as anthrax, rabies, brucellosis, and Rift Valley fever pose a direct threat to health and undercut livelihoods in the communities in which they occur. A combination of anthropogenic and animal activities like migration and interaction with wildlife and their respective parasites and vectors drives the emergence and re-emergence of zoonotic diseases. Consequently, One Health interdisciplinary approaches that incorporate social scientists can provide key insights into complex local perceptions. The approach calls for collaboration between the human and animal health sectors, including the sharing of disease surveillance data necessary to alleviate disease impacts. Livestock traders interact closely with livestock, which puts them at elevated risk of infection and creates conditions by which they may spread zoonotic disease. It is thus essential to examine practices among actors involved in the livestock trade to understand the most appropriate ways to mitigate these risks. A qualitative study was conducted among the actors in the livestock trade in Busia County on their knowledge and perceptions of zoonotic diseases and practices that may contribute to the spread, control, and prevention of zoonotic disease transmission. A thematic analysis framework was used to categorize and synthesize data from in-depth interviews (IDIs), key informant interviews (KIIs), and structured observations. Whereas participants could list livestock diseases, they could not identify which ones were zoonoses, demonstrating insufficient knowledge of zoonosis. They identify sick animals by checking for dropped ears, excess mucus production, diarrhea, bloody urinal discharge, and general animal activity levels. To prevent the spread of these diseases, they wash their animals, isolate sick animals from the rest of the stock, and vaccinate their animals. They seek help from animal health professionals for sick animals as part of curative practices. This shows that they perceive the diseases as serious and that they need to be attended to by professionals. The results also show that they perceive animals from outside the region to be more vulnerable to diseases compared to those from within. The actors in the livestock trade engage in practices like skinning dead animals before burying them; to them, this is a normal practice. Some also consume dead carcasses. These increase the risk of zoonotic disease transmission. The actors involved in the livestock trade are critical in the prevention and elimination of zoonotic diseases; hence, they need to be involved when developing intervention programs and policies for animal health extension services. Training them as a continuum of animal health workers blends lay and professional knowledge, which, alongside their intense contact with large numbers of animals, becomes a critical disease surveillance tool. Increasing awareness of zoonoses by using multi-disciplinary teams with social scientists is urgently needed so that practices like skinning dead animals before disposing of them and consumption of dead carcasses can be minimized.

COVID-19 led to an increased online search interest for "urticaria"

Background/Aim: Google Trends is a powerful tool for health information access and disease surveillance, including COVID-19 data forecasting. Cutaneous manifestations of COVID-19 include urticarial rash, which is multifactorial and may indicate a better prognosis. Aim of this study was to analyse the search queries for the term "urticaria" during the six years period (2017-2022) and assess the relationship between relative search volume (RSV) and a global rise in COVID-19 cases. Methods: This study examined search queries for "urticaria" from 2017 to 2022, exploring the relationship between RSV and global COVID-19 cases. Data on "urticaria" search volume in various countries were collected using Google Trends. Microsoft Excel and correlation analysis were used to analyse the data and investigate the association between search volume and monthly COVID-19 case rise. Results: During 2017-2019, the mean RSV for "urticaria" was 69.9 ± 10.1, which increased to 84.1 ± 8.6 during 2020-2022. The difference in means was statistically significant (p &lt; 0.001). The mean RSV in 2022 (94.3) was the highest among all years studied. The increase in RSV from 2020 to 2022 moderately correlated (r = 0.56) with monthly COVID-19 case rise (p &lt; 0.001). Nicaragua showed the highest search interest, while Africa, North America, Asia and Pacific, the Middle East and Europe had low search interest. Conclusions: This study identified a significant increase in search volume for "urticaria" during the COVID-19 pandemic. Nicaragua had the highest search interest, while English-speaking countries displayed low interest. Language variation, cultural differences and COVID-19-related health concerns may influence public interest in urticaria. These findings can aid public health officials in understanding public health concerns and tailoring effective health communication strategies.

Clusters of hepatitis D in Iraq, 2018 to 2022: A geographic analysis

The regional distribution of hepatitis D is evaluated by the utilization of the disease surveillance tool SaTScan and its spatial scan statistic. The statistic for the spatial scan was used in detecting the area that has a significantly higher prevalence of hepatitis. The spatial scan statistic determined Najaf, Babil, Wasit, Maysan, Dha Qar, Basrah, and Muthanna to be most likely to constitute areas of cluster presence for hepatitis D. The relative risk (RR) for men was 1.49 (P &lt; 0.001) and that for females was 1.46 (P &lt; 0.001). After considering all the parameters under this model, we found that the most probable location for clustering hepatitis in the country is the central areaNajaf, Babil, Wasit, Maysan Dha Qar Muthanna Diyala Baghdad Karbala Salah Ad Din and Tam'mim with a relative risk of 6.65(P &lt; 0.001).

Mhealth tools for community-based infectious disease surveillance in Africa: a scoping review protocol

IntroductionCountries in the WHO’s African region have found community-based surveillance useful in ensuring the effectiveness of the Integrated Disease Surveillance and Response strategy. This approach encourages community participation in the...

Evaluation of Metabarcoding Methods for Plant Disease Surveillance

Metabarcoding holds great potential for general plant pathogen surveillance by providing an untargeted profile of the host microbiome. However, the standard marker utilized for microbiome analysis of prokaryotes, the 16 rRNA gene, offers limited diagnostic utility as it provides phylogenetic resolution primarily at the genus level, and universal primers often co-amplify plant DNA. Here, we evaluated two recently published universal primer sets targeting the DNA gyrase and RNA polymerase ß subunit genes ( gyrB and rpoB, respectively), relative to a plant discriminating 16S primer set (799F/1115R), as disease surveillance tools. Comparative analysis of a mock bacterial community, as well as naturally infected citrus variegated chlorosis samples, indicated that the gyrB method displayed optimal performance in the amplification of DNA across a broad taxonomic spectrum of plant-pathogenic bacteria, providing resolution at the species and, in some cases, subspecies levels. It also generated high-quality datasets with minimal to no co-amplification of plant DNA, outperforming the rpoB and 16S assays in these regards. Further evaluation revealed that the gyrB method displayed an overall linear trend in the detection of several diverse bacterial pathogens; however, it was at least an order of magnitude less sensitive than a standard real-time PCR assay. Finally, we demonstrate the potential of this method to disentangle a mixed population of Pantoea spp. associated with a rice-bacterial blight outbreak and identify putative novel pathogens that would otherwise be overlooked with conventional PCR-based tests. This work represents steps toward establishing a robust, untargeted metabarcoding method for general plant disease surveillance. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

49 Leveraging Big Data Analytics to Improve Disease Surveillance and Welfare in Pig Production

Abstract Large amounts of data are routinely collected in pig production, including operational farm data and, more recently, data from digital tools such as remote and on-site sensing technologies. Furthermore, additional sources of information, such as demographic, economic, and weather variables, can be combined with farm data. Integrating and analyzing such data can generate valuable insights and data-driven decision-making tools for disease surveillance, animal welfare, and production system optimization. This presentation discusses two examples of big data integration and analysis in the context of optimizing management practices in pig production. The first example pertains to a data analytics project related to total transportation losses (TTL) of market-weight pigs, an important animal welfare concern that economically impacts producers and abattoirs. After merging and checking data for errors, the final dataset included 4.5M+ pigs from 420 farms loaded in 26,819 shipments delivered to two abattoirs. Results from a generalized additive mixed model (GAMM) technique showed that the risk of loss per shipment was constant for temperature-humidity index (THI) values between −10 and 10. Additionally, a strong interaction between wind speed and precipitation was associated with TTL. TTL were strongly associated with the type of driver and the distance traveled, in addition to weather variables. The second example pertains to swine disease surveillance, in which data from a multi-site system, where pigs are transported between farms after each particular production phase, were investigated. Reports of 76,566 shipments across sites were obtained and used to characterize pig movement flow, conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and assess the potential impact of each farm type on disease dissemination across the system. Topological properties of networks were investigated, such as the number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Overall, the results indicated that gilt development units, nursery, and sow farms have a more central role in the pig production hierarchical structure. As such, they are potential major factors in the introduction and spread of diseases in the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to infection. Additionally, the characteristics of a disease should have strong implications for biosecurity practices across production sites.