Control Of Disease Outbreaks Research Articles

Antibiotic Use and Resistance Patterns in Escherichia coli Isolates from Rabbit Farms in Korhogo, Côte d'Ivoire

Backround: The use of antimicrobials in rabbit farming is essential for treating sick animals, controlling disease outbreaks, preventing illness, and promoting growth. However, their overuse and improper application contribute to the development and spread of resistant bacteria, posing a significant public health threat. This study aimed to assess antibiotic use and resistance patterns in Escherichia coli isolates from rabbit farms in Korhogo, Côte d'Ivoire. Methodology: Questionnaire was sent to 48 farmers, followed by the collection of fresh rabbit droppings from 25 farms. Escherichia coli was detected by inoculating droppings on chromogenic medium, followed by identification on the basis of biochemical characteristics. Resistance was studied using the Müeller-Hinton agar disc-diffusion method using 12 antibiotics. Results: Less than one-third of farmers (29.2%) used antibiotics, with oxytetracycline being the most commonly used. Antibiotics were primarily administered for digestive, skin, and respiratory diseases. The 56 E. coli isolates were highly resistant to colistin (82.1%), tetracycline (53.6%), amikacin (37.5%) and trimethoprim-sulfamethoxazole (32.1%). Resistance rate to cefotaxime, ceftriaxone, aztreonam, nalidixic acid and ciprofloxacin were very low (< 4%). However, all isolates were sensitive to amoxicillin + clavulanic acid and imipenem. The prevalence of ESBL E. coli was 3.6%. A significant number of E. coli isolate had a MAR index > 0.2. Conclusion: This study highlights the poor use of antibiotics on rabbit farms in Korhogo, with high levels of resistance to certain antibiotics of critical importance in human medicine. Hence the need to intensify awareness-raising and training of farmers in the rational use of antibiotics, in order to reduce the risk of dissemination of resistant bacteria within the population.

Application of fluid dynamics in modeling the spatial spread of infectious diseases with low mortality rate: A study using MUSCL scheme

Abstract This study presents a comprehensive mathematical framework that applies fluid dynamics to model the spatial spread of infectious diseases with low mortality rates. By treating susceptible, infected, and treated population densities as fluids governed by a system of partial differential equations, the study simulates the epidemic’s spatial dynamics. The Monotone Upwind Scheme for Conservation Laws is employed to enhance the accuracy of numerical solutions, providing a high-resolution approach for capturing disease transmission patterns. The model’s analogy between fluid flow and epidemic propagation reveals critical insights into how diseases disperse geographically, influenced by factors like human mobility and environmental conditions. Numerical simulations show that the model can predict the evolution of infection and treatment population densities over time, offering practical applications for public health strategies. Sensitivity analysis of the reproduction number highlights the influence of key epidemiological parameters, guiding the development of more efficient disease control measures. This work contributes a novel perspective to spatial epidemiology by integrating principles of fluid dynamics, aiding in the design of targeted interventions for controlling disease outbreaks.

Promising Probiotic Candidates for Sustainable Aquaculture: An Updated Review.

With the intensification of aquaculture to meet the rising demands of fish and shellfish, disease outbreaks during the larval and adult stages are a major challenge faced by aqua culturists. As the prophylactic use of vaccines and antibiotics has several limitations, research is now focused on sustainable alternatives to vaccines and antibiotics, e.g., medicinal plants, probiotics, postbiotics, prebiotics, and synbiotics, as promising candidates to strengthen the immune response of fish and shellfish and to control disease outbreaks. With respect to probiotics, numerous studies are available revealing their health-promoting and beneficial impacts in aquaculture. However, most studies focus on Bacillus and Lactobacillus species. Keeping in view the positive effects of probiotic lactic acid bacteria in aquaculture, researchers are now looking for other probiotic bacteria that can be used in aquaculture. Recently, many non-lactic acid bacteria (non-LAB), which are mainly host-associated, have been reported to reveal beneficial effects in fish and shellfish aquaculture. The main non-LAB probiotic genera are Bifidobacterium, Clostridium, Microbacterium, Micrococcus, Paenibacillus, Acinetobacter, Alcaligenes, Enterobacter, Phaeobacter Pseudoalteromonas, Pseudomonas, Pseudomonas, and Vibrio. Despite the promising effects of non-LAB probiotics, comparably, there is limited available information in this context. This review focuses only on probiotic strains that are non-LAB, mostly isolated from the host digestive tract or rearing water, and discusses their beneficial effects in fish and shellfish aquaculture. This review will provide detailed information on the use of various non-LAB bacteria and provide a roadmap to future studies on new probiotics for sustainable aquaculture.

Analysis Time-Delayed SEIR Model with Survival Rate for COVID-19 Stability and Disease Control

This paper presents a mathematical model to examine the transmission and stability dynamics of the SEIR model for COVID-19. To assess disease progression, the model incorporates a time delay for the time delay and survival rates. Then, we use the Routh–Hurwitz criterion, the LaSalle stability principle, and Hopf bifurcation analysis to look at disease-free and endemic equilibrium points. We investigate global stability using the Lyapunov function and simulate the model behavior with real COVID-19 data from Indonesia. The results confirm the impact of time delay on disease transmission, mitigation strategies, and population recovery rates, demonstrating that rapid interventions can significantly impact the course of the epidemic. The results indicate that a balance between transmission reduction and vaccination efforts is crucial for achieving long-term stability and controlling disease outbreaks. Finally, we estimate the degree of disease control and look at the rate of disease spread by simulating the genuine data.

Operational and Workforce Capacity Improvements for Supporting Public Health Emergency Management: Lessons Learned for Preparing for and Responding to 2014-2022 Ebola Outbreaks in Africa.

The first Ebola Virus Disease (EVD) cases in the 2021 Ebola outbreak were reported by the Democratic Republic of the Congo (DRC) Ministry of Health in February. However, 1 week later, the Guinea Ministry of Health reported its first EVD outbreak since April 2016. U.S. Centers for Disease Control (CDC) in-country operational and workforce capacity were built during the 2014-2016 Ebola outbreak response in West Africa and leveraged during the 2021 EVD outbreaks. During the 2014-2016 West Africa response and the 2021 EVD outbreaks, capacity and capability improvements in laboratory systems, risk communication, surveillance, epidemiology, infection prevention, and control were needed for a successful response. The overarching goal of CDC's operational and workforce capacity improvements was to strengthen countries' abilities to prevent, detect, and respond to outbreaks quickly. The Ebola outbreaks are examples of enhanced public health interventions where CDC has contributed as a partner with in-country ministries of health to save lives and control disease outbreaks. Lessons learned from the recent Ebola outbreaks indicate that a capacity-building approach has the potential application to other public health emergencies and contributes to strengthening global health security.

Vector-borne disease outbreak control via instant releases

This paper is devoted to the study of optimal release strategies to control vector-borne diseases, such as dengue, Zika, chikungunya and malaria. Two techniques are considered: the sterile insect one (SIT), which consists in releasing sterilized males among wild vectors in order to perturb their reproduction, and the Wolbachia one (presently used mainly for mosquitoes), which consists in releasing vectors, that are infected with a bacterium limiting their vectorial capacity, in order to replace the wild population by one with reduced vectorial capacity. In each case, the time dynamics of the vector population is modeled by a system of ordinary differential equations in which the releases are represented by linear combinations of Dirac measures with positive coefficients determining their intensity. We introduce optimal control problems that we solve numerically using ad-hoc algorithms, based on writing first-order optimality conditions characterizing the best combination of Dirac measures. We then discuss the results obtained, focusing in particular on the complexity and efficiency of optimal controls and comparing the strategies obtained. Mathematical modeling can help testing a great number of scenarios that are potentially interesting in future interventions (even those that are orthogonal to the present strategies) but that would be hard, costly or even impossible to test in the field in present conditions.

Integrating short- and full-length 16S rRNA gene sequencing to elucidate microbiome profiles in Pacific white shrimp (Litopenaeus vannamei) ponds.

Despite their immense economic value as a key aquaculture species, the production of Pacific white shrimp (Litopenaeus vannamei) faces significant challenges from intensive farming practices and disease outbreaks. Routine microbial profiling for disease surveillance could be a promising approach to anticipate and control disease outbreaks. To achieve this, accuracy in microbial profiling in shrimp ponds is crucial for enabling targeted action and prevention. Extensive documentation emphasizes that, beyond biological factors (related to the host, diet, or health status during the rearing period), technical elements, including sequencing techniques significantly influence bacterial community profiling. This study investigated the influence of short- and long-read sequencing of 16S rRNA genes on the microbial profiles in shrimp intestines, water, and sediments. The origin of the samples (intestine or environmental) in shrimp culture ponds primarily drove the observed differences in core microbial species. The ecological niches accounted for 56% of bacterial community variations in culture ponds. Both sequencing approaches showed consistent results in identifying higher-rank taxa and assessing alpha and beta diversity. However, at the species level, full-length 16S rRNA gene sequences provided better resolution than V3-V4 sequences. For routine microbial profiling in shrimp culture ponds, our study suggests that short-read sequences were sufficient for determining overall bacterial community.IMPORTANCEThis interdisciplinary study investigated the influence of sequencing techniques on bacterial communities profiling within Pacific white shrimp (Litopenaeus vannamei) ponds. By integrating aquaculture, microbiology, and environmental science, we revealed the role of ecological niches and factors like salinity and pH on microbiota diversity and composition in shrimp intestines, pond water, and sediment. Additionally, we compared the taxonomic resolution using partial versus full-length 16S rRNA gene sequences, highlighting the value of longer amplicons for precise identification of key taxa. These findings provide novel insights into microbial dynamics underlying environmental effects in shrimp aquaculture. Comprehensive characterization of the pond microbiome could lead to management strategies that promote shrimp health and productivity. Furthermore, the potential of a multi-omics approach for integrating complementary data streams to elucidate environment-microbiome-host interactions was highlighted.

Clinical Performance of the LiquidArray® Gastrointestinal VER 1.0 Assay in Patients with Suspected Gastroenteritis.

Rapid and accurate pathogen identification is essential for the proper management of patients with infectious gastroenteritis, as well as for a better control of disease outbreaks. This observational, non-interventional, single-site study evaluated the diagnostic accuracy of LiquidArray® Gastrointestinal VER 1.0, a multiplex PCR syndromic panel capable of detecting up to 26 clinically relevant enteropathogens. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and likelihood ratio (LR) were evaluated using stool samples from 1512 patients with suspected gastroenteritis and were compared to seven competitor assays. LiquidArray® Gastrointestinal VER 1.0 showed a very low invalid rate (0.5% at initial testing, down to 0% after repeat) and high sensitivity (>90% for most detected targets) and specificity (>99% for all detected targets). Accordingly, the PPV and NPV were high (>90% for most targets and >99% for all targets, respectively). The analytical performance of LiquidArray® Gastrointestinal VER 1.0 was also excellent as to co-amplification capability, cross-reactivity and assay precision. This study demonstrates the excellent clinical performance of LiquidArray® Gastrointestinal VER 1.0 and its suitability for implementation in clinical routine for the rapid and accurate diagnosis of infectious gastroenteritis.

The development of RT-RPA and CRISPR-Cas12a based assay for sensitive detection of Hirame novirhabdovirus

Hirame novirhabdovirus (HIRRV) is a highly pathogenic fish virus that poses a significant threat to the farming of a variety of economic fish. Due to no commercial vaccines and effective drugs available, sensitive and rapid detection of HIRRV at latent and early stages is important and critical for the control of disease outbreaks. However, most of the current methods for HIRRV detection have a large dependence on instruments and operations. For better detection of HIRRV, we have established a detection technology based on the reverse transcription and recombinase polymerase amplification (RT-RPA) and CRISPR/Cas12a to detect the N gene of HIRRV in two steps. Following the screening of primer pairs, the reaction temperature and time for RPA were optimized to be 40 °C and 32min, respectively, and the CRISPR/Cas12a reaction was performed at 37 °C for 15min. The whole detection procedure including can be accomplished within 1 h, with a detection sensitivity of about 8.7 copies/μl. The detection method exhibited high specificity with no cross-reaction to the other Novirhabdoviruses IHNV and VHSV, allowing naked-eye color-based interpretation of the detection results through lateral flow (LF) strip or fluorescence under violet light. Furthermore, the proliferation dynamic of HIRRV in the spleen of flounder were comparatively detected by LF- and fluorescence-based RPA-CRISPR/Cas12a assay in comparison to qRT-PCR at the early infection stage, and the results showed that the viral positive signal could be firstly detected by the two RPA-CRISPR/Cas12a based methods at 6 hpi, and then by qRT-PCR at 12 hpi. Overall, our results demonstrated that the developed RPA-CRISPR/Cas12a method is a stable, specific, sensitive and more suitable in the field, which has a significant effect on the prevention of HIRRV. RT-RPA-Cas12a-mediated assay is a rapid, specific and sensitive detection method for visual and on-site detection of HIRRV, which shows a great application promise for the prevention of HIRRV infections.

Supplementing Nile tilapia (Oreochromis niloticus) (Linnaeus, 1758) larvae with dietary beta-glucan could improve their growth, survival, immune function, intestinal and liver histomorphology

Abstract Β-glucan exerts a positive impact and consider as a functional feed additives to enhance fish growth, immunity, control disease outbreaks and pathogen infections by increasing functional, immune and decreasing harmful responses. The present study aimed to determine the most effective dietary level of beta-glucan (βG) to improve Nile tilapia Oreochromis niloticus (L.) fry growth performance, feed utilization, and hematological indicators. Seven isonitrogenous (32% crude protein) and isocaloric diets (18 MJ/kg) were formulated. All diets were identical except for the variation in βG levels.The basal experimental diet (control diet) had no βG added. Diets 2–7 each contained βG at levels of 250, 500, 1000, 2000, 4000, and 8000 mg/kg diet, respectively. A total of 840 fish with an average body weight of 0.2±0.01 g were allocated into the seven experimental treatments (in triplicate). The trial lasted for 120 days, Nile tilapia fry growth performance and feed utilization were significantly (P≤0.05) higher in all treatments receiving βG diets than in the control diet. The fish fed with ßG0.1 diet showed the highest significant growth induces and the best FCR values. The survival (S%) of the fish also showed a significant increase in the βG diets up to ßG0.8 levels, when compared to the other experimental diet groups. All hematological parameters increased (P≤0.05) in fish fed with a diet supplemented up to ßG0.8 compared to the control group. Histological examination of the intestine and liver in the control group revealed histopathological alterations in the villi. On the other hand, the groups treated with βG had longer and structurally normal villi. The most well-preserved intestinal tissue and the tallest villi were observed in ßG0.1, followed by ßG0.050 and ßG0.8 groups. In the liver, the control group exhibited fatty degeneration, necrosis and pyknosis whereas the ßG0.1 group displayed the most preserved hepatic tissue, followed by ßG0.8 and ßG0.2 groups. The results indicate that according to FBW-based broken-line model analysis, the optimal dietary level of βG for Nile tilapia fry to exhibit superior growth and diet utilization efficiency associated with the best FCR for Nile tilapia should be 0.12% βG /kg diet. To increase the immunity of fish and improve the properties of hematological and histopathological indices, the dose can be increased to 0.8% βG without any adverse effects.

Molecular characterization, spatio-temporal expression patterns of crtc2 gene and its immune roles in yellow catfish (Pelteobagrus fulvidraco)

cAMP response element binding (CREB) protein 2 (CRTC2) is a transcriptional coactivator of CREB and plays an important role in the immune system. Thus far, the physiological roles of Crtc2 in teleost are still poorly understood. In this study, the crtc2 gene was identified and characterized from yellow catfish (Pelteobagrus fulvidraco; therefore, the gene is termed as pfcrtc2), and its evolutionary and molecular characteristics as well as potential immunity-related roles were investigated. Our results showed that the open reading frame of pfcrtc2 was 2346 bp in length, encoding a protein with 781 amino acids. Gene structure analysis revealed its existence of 14 exons and 13 introns. A phylogenetic analysis proved that the tree of crtc2 was clustered into five groups, exhibiting a similar evolutionary topology with species evolution. Multiple protein sequences alignment demonstrated high conservation of the crtc2 in various vertebrates with similar structure. Syntenic and gene structural comparisons further established that crtc2 was highly conserved, implying its similar roles in diverse vertebrates. Tissue distribution pattern detected by quantitative real-time PCR showed that the pfcrtc2 gene was almost expressed in all detected tissues except for eyes, with the highest expression levels in the gonad, indicating that Crtc2 may play important roles in various tissues. In addition, pfcrtc2 was transcribed at all developmental stages in yellow catfish, showing the highest expression levels at 12 h after fertilization. Finally, the transcriptional profiles of crtc2 were significantly increased in yellow catfishes injected with Aeromonas hydrophila or Poly I:C, which shared a consistent change pattern with four immune-related genes including IL-17A, IL-10, MAPKp38, and NF-κBp65, suggesting pfCrtc2 may play critical roles in preventing both exogenous bacteria and virus invasion. In summary, our findings lay a solid foundation for further studies on the functions of pfcrtc2, and provide novel genetic loci for developing new strategies to control disease outbreak in teleost.

Complex vaccination strategies prevent the emergence of vaccine resistance.

Vaccination is the most effective tool to control infectious diseases. However, the evolution of vaccine resistance, exemplified by vaccine resistance in SARS-CoV-2, remains a concern. Here, we model complex vaccination strategies against a pathogen with multiple epitopes-molecules targeted by the vaccine. We found that a vaccine targeting one epitope was ineffective in preventing vaccine escape. Vaccine resistance in highly infectious pathogens was prevented by the full-epitope vaccine, that is, one targeting all available epitopes, but only when the rate of pathogen evolution was low. Strikingly, a bet-hedging strategy of random administration of vaccines targeting different epitopes was the most effective in preventing vaccine resistance in pathogens with the low rate of infection and high rate of evolution. Thus, complex vaccination strategies, when biologically feasible, may be preferable to the currently used single-vaccine approaches for long-term control of disease outbreaks, especially when applied to livestock with near 100% vaccination rates.

PECULIARITIES OF TRAINING PERSONNEL RESOURCES OF THE PUBLIC HEALTH SYSTEM IN THE SPHERE OF EPIDEMIOLOGY

Aim. An analysis of educational approaches to the training of specialists in the public health system in the field of epidemiology in Ukraine and the world was carried out with the definition of the main competencies, in accordance with the best educational international practices. Conducting an analysis of educational approaches to the training of specialists in the public health system in the field of epidemiology in Ukraine and the world with the definition of core competencies, in accordance with the best educational international practices. Materials and methods. Critical narrative literature review, based on scientific works indexed in the scientometric databases Medline, Google Scholar, PubMed, with directory of open access journals (Directory of Open Access Journals (DOAJ)) and data from the global network educational (TEPHINET) was conducted. Results. The world experience of training specialists in the field of epidemiology, with the aim of strengthening the health care system, shows that efforts are being directed to the training of practically oriented specialists. The main subject areas are necessary for the modern training of epidemiologists in the field of public health: epidemiological method and biostatistics, epidemiological surveillance and biosafety, epidemiology of infectious and non-infectious diseases, communication, management and leadership, computer skills. An epidemiologist must possess basic competencies: apply epidemiological research methods, synthesis of knowledge and interpretation of data to monitor diseases, respond to outbreaks, manage public health surveillance, manage staff and resources, develop written and oral communications. There is a valid opinion that competences in the field of epidemiology should be standardized, which would make the epidemiologist universal in different countries aimed at formation qualified and experienced epidemiologists who can detect, investigate and control disease outbreaks; conduct observations; analyze epidemiological data; measure the impact of interventions; and conduct applied epidemiological studies. Conclusions. The practical orientation of educational programs should provide a strong component for creating a sustainable capacity to respond to emergency situations in the field of health care at the national, regional and global levels. The Ukrainian approach to the training of epidemiologists requires changes using programs that combine theoretical learning with the predominant experience of practical work, taking into account the basic competencies with elements of mentoring and peer training, should meet modern standards of andragogy and approaches to motivating future specialists to learn throughout their lives.

Su ürünleri yetiştiriciliğinde protozoan parazitlerine karşı etkili bitkisel ilaçlar

In industrial aquaculture, producing at high stock densities is inevitable to maximize efficiency and harvest per unit volume. Antibiotics, disinfectants, and other chemicals have become necessary to prevent and control disease outbreaks in intensive fish farming practices. However, the negative impacts of the use of synthetic chemical drugs on environmental health have sparked discussions, making research into alternative treatments inevitable. Medicinal plants offer promising solutions for disease control due to their natural, biodegradable, and antimicrobial properties. The therapeutic properties of plants have been known and safely used in traditional medicine for a long time. The idea that medicinal plants can be utilized in aquaculture as a safer, sustainable, and environmentally friendly practice has begun to be embraced. Although numerous treatment protocols have been developed against metazoan parasites in aquaculture, managing protozoan parasites still poses a significant challenge. A detailed literature review has identified that more than 30 plant species have the potential to control many protozoan pathogens in fish, including Ichthyophthirius multifiliis and Cryptocaryon irritans. Studies highlight the effectiveness of plant-derived compounds in combating parasites and promoting growth, supporting immunity, serving as antibacterial agents, and even as anaesthetics. Since research on medicinal plants predominantly targets commercially valuable fish farming, there is a recognized need for expanded studies on their application in shellfish farming. Collaboration among researchers, institutions, and farmers is crucial in increasing awareness of local medicinal plants and promoting their use. The use of plants with medicinal properties in aquaculture represents a promising path for disease control and sustainable production. Adopting these natural alternatives could align with responsible agricultural practices and meet the urgent need to mitigate the environmental impacts of traditional treatments in the aquaculture industry.

Evaluating infectious disease outbreak potential and mitigation effectiveness on cruise ships

The cruise ship sector is a major part of the tourism industry, and an estimated over 30 million passengers are transformed worldwide each year. Cruise ships bring diverse populations into proximity for many days, facilitating the transmission of respiratory illnesses. The objective of this study is to develop a modeling framework to inform the development of viable disease risk management policies and measures to control disease outbreaks on cruises. Our model, parameterized and calibrated using the data of the COVID-19 outbreak on the Diamond Princess cruise ship in 2020, is used to assess the impact of the mitigation measures such as mask wearing, vaccination, on-board and pre-traveling testing measures. Our results indicate mask wearing in public places as the cheapest and most affordable measure can drop the number of cumulative confirmed cases by almost 50%. This measure along with the vaccination by declining the number of the cumulative confirmed cases by more than 94% is the most effective measure to control outbreaks on cruises. According to our findings, outbreaks are more predominant in the passenger population than the crew members, however, the protection measures are more beneficial if they are applied by both crew members and passengers. Regarding the testing measure, pre-traveling testing is more functional than the on-board testing to control outbreaks on cruises.

Using a general practice research database to assess the spatio-temporal COVID-19 risk

BackgroundIn Flanders, general practitioners (GPs) were among the first ones to collect data regarding COVID-19 cases. Intego is a GPs’ morbidity registry in primary care with data collected from the electronic medical records from a sample of general practices. The Intego database contain elaborate information regarding patient characteristics, such as comorbidities. At the national level, the Belgian Public Health Institute (Sciensano) recorded all test-confirmed COVID-19 cases, but without other patient characteristics.MethodsSpatio and spatio-temporal analyses were used to analyse the spread of COVID-19 incidence at two levels of spatial aggregation: the municipality and the health sector levels. Our study goal was to compare spatio-temporal modelling results based on the Intego and Sciensano data, in order to see whether the Intego database is capable of detecting epidemiological trends similar to those in the Sciensano data. Comparable results would allow researchers to use these Intego data, and their wealth of patient information, to model COVID-19-related processes.ResultsThe two data sources provided comparable results. Being a male decreased the odds of having COVID-19 disease. The odds for the age categories (17,35], (35,65] and (65,110] of being a confirmed COVID-19 case were significantly higher than the odds for the age category [0,17]. In the Intego data, having one of the following comorbidities, i.e., chronic kidney disease, heart and vascular disease, and diabetes, was significantly associated with being a COVID-19 case, increasing the odds of being diagnosed with COVID-19.ConclusionWe were able to show how an alternative data source, the Intego data, can be used in a pandemic situation. We consider our findings useful for public health officials who plan intervention strategies aimed at monitor and control disease outbreaks such as that of COVID-19.

First Report of Agroathelia rolfsii Causing Southern Blight on Cowpea in China

Cowpea (Vigna unguiculata L.) is a legume consumed as a high-quality plant protein source in many parts of the world. In August 2023, it was observed that a plant disease affected cowpea growth in Yiyang (28.34°N, 112.55°E), China. The average disease incidence was 10%, resulting in 8.5% economic losses in approximately 3,000 m2. The symptoms initially appeared as brown lesions near the stem-soil interface and the lesions were colonized by white mycelia. As the disease progressed, the disease symptoms included constriction and brown staining at the base of the stem, covered with a small amount of white mycelia. Eventually, the entire plants withered and collapsed and many sclerotia were scattered on the ground around the diseased stem. Twenty samples (10 sclerotia and 10 diseased tissue fragments) were collected from symptomatic plants for causal agent isolation. Samples were disinfected with 70% ethanol for 30 s, 5% NaClO for 1 min, rinsed three times with sterile water, dried and placed on potato dextrose agar (PDA) plates at 28℃ in the dark. In total, 20 isolates were obtained by the hyphal tip method (Terrones et al. 2022) and showed a consistent phenotype of white cottony mycelia on PDA with an growth rate of 12.9 to 21.3 mm/day (n = 20). Sclerotia formed at five to eight days post inoculation, were initially whitish, turning beige and eventually dark brown. The diameter of mature sclerotia ranged from 0.89 to 2.13 mm (mean = 1.64±0.29 mm; n =50). For pathogen identification, ITS1/ITS4 (White et al. 1990) and EF1-983F/EF1-2218R (Rehner and Buckley 2005) primers were used to amplify the internal transcribed spacer regions (ITS) and translation elongation factor-1 alpha gene (TEF-1α), respectively. The sequences of all 20 isolates showed 99% to 100% similarity with Agroathelia rolfsii sequences from GenBank by BLAST analysis. The sequences of two representative strains, ID1 and ID4, were deposited in GenBank. The ITS sequences of ID1 (OR689482) and ID4 (OR689481) were ＞99% similar to A. rolfsii strain QJ7 (593/596 bp; MZ750983) and A. rolfsii strain Kale078 (565/568 bp; MN872304), respectively. Also, TEF-1α sequences of ID1 (OR713735) and ID4 (OR713736) were ＞99% similar to the sequences of A. rolfsii strain HS-Sr (1073/1073 bp; OL416131) and A. rolfsii strain MSB1-2 (1070/1075 bp; MN702790), respectively. Phylogenetic analysis based on ITS and TEF1-α sequences indicated that ID1 and ID4 clustered into the A. rolfsii clade. Based on morphology and sequence analyses, the isolates ID1 and ID4 were identified as A. rolfsii (anamorph Sclerotium rolfsii). Pathogenicity tests were conducted three times on healthy 30-day-old cowpea seedlings. Five plants were inoculated with 6-day-old mycelial discs (6 mm) of ID1 or ID4 at the base of the seedlings (n = 30) while four plants were inoculated with a sterile PDA disc as a control (n = 12). All seedlings were cultivated in a greenhouse with a temperature of 26°C to 28°C and relative humidity 60% to 80% with a 14/10 h light/dark photoperiod. Eight days later, all the fungal inoculated seedlings showed symptoms including brown necrosis and collapse of the stems, and eventual withering of the seedlings. Control plants remained asymptomatic. The causal pathogens were reisolated in PDA plates and identified by ITS sequence analysis, completing Koch's postulates. To our knowledge, this is the first report of A. rolfsii causing southern blight on cowpea in China. Early accurate diagnosis will help farmers to adopt suitable practices to control disease outbreaks and reduce losses.

QR codes and automated decision-making in the COVID-19 pandemic

In this article, we explore Quick Response (QR) codes (machine-readable optical labels that link to information) and how, after a period of having fallen out of favor, they have been reactivated and have come to underpin COVID-19 automation and contact-tracing efforts. During the pandemic, they were used especially for “safe entry” and other kinds of check-in to locations to facilitate contact tracing. In this context, QR codes facilitate automated decision-making in relation to infectious disease surveillance and disease outbreak control. However, the use of QR codes for contact-tracing purposes has enjoyed mixed success and its implementation has encountered several challenges, as we illustrate through a case study that explores QR codes and COVID contact tracing in Singapore and Australia.

Impact of climate change on potential distribution of Dickeya zeae causal agent of stalk rot of maize in Sialkot district Pakistan

Maize (Zea mays) is an influential crop in its production across the world. However, the invasion of many phytopathogens greatly affects the maize crop yield at various hotspot areas. Of many diseases, bacterial stalk rot of maize caused by Dickeya zeae results in severe yield reduction, thus the need for efficient management is important. Further, to produce epidemiological information for control of disease outbreaks in the hot spot regions of Sialkot District, Punjab Pakistan, extensive field surveys during 2021 showed that out of 266 visited areas, the highest disease incidence ranging from 66.5 to 78.5% while the lowest incidence was ranging from 9 to 20%. The Maxent modeling revealed that among 19 environmental variables, four variables including temperature seasonality (bio-4), mean temperature of the wettest quarter (bio-8), annual precipitation (bio-12), and precipitation of driest month (bio-14) were significantly contributing to disease distribution in current and coming years. The study outcomes revealed that disease spread will likely increase across four tehsils of Sialkot over the years 2050 and 2070. Our findings will be helpful to policymakers and researchers in devising effective disease management strategies against bacterial stalk rot of maize outbreaks in Sialkot, Pakistan.

High performance Legionella pneumophila source attribution using genomics-based machine learning classification.

Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.