Stand-alone Program Research Articles

Graduate programs in physical, engineering, and underwater acoustics at the Naval Postgraduate School

The Departments of Physics and of Electrical and Computer Engineering at the Naval Postgraduate School offer graduate programs in acoustics leading to MS and PhD degrees in applied physics and engineering acoustics. Engineering acoustics degrees can be completed in either traditional or distance learning modes. The departments also offer stand-alone academic certificate programs in fundamentals of engineering acoustics, underwater acoustics, and sonar system applications, with a set of three certificates leading to a MS degree in engineering acoustics. MS and PhD programs are interdisciplinary, with courses and laboratory work drawn principally from the fields of physics and electrical engineering. Subjects covered include waves and oscillations; fundamentals of physical and structural acoustics; the generation, propagation, and reception of sound in the ocean; civilian and military applications of sonar systems; and acoustic signal processing. Topics of recent theses and dissertations include development and field testing of novel sensors for atmospheric and ocean acoustics, modeling and measurements of ambient noise and sound propagation in the ocean, sound scattering in underwater waveguides, acoustic vector sensors and vector field properties, acoustic communications, noise interferometry, time reversal in acoustics, geo-acoustic inversion, acoustic remote sensing of the ocean, and acoustics of autonomous underwater and aerial vehicles.

Integrated Management of Type 2 Diabetes and Gestational Diabetes in the Context of Multi-Morbidity in Africa: A Systematic Review.

Introduction:Many adults diagnosed with gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM) also have other known or unknown comorbid conditions. The rising prevalence of GDM and T2DM within a broader context of multimorbidity can best be addressed through an integrated management response, instead of stand-alone programs targeting specific infectious and/or chronic diseases.Aim:To describe GDM and T2DM screening, care and cost-effectiveness outcomes in the context of multimorbidity through integrated interventions in Africa.Methods:A systematic review of all published studies was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Risk Of Bias in Non-randomised Studies of Interventions (ROBINS-I) was used to assess risk of bias. Data synthesis was conducted using narrative synthesis of included studies.Results:A total of 9 out of 13 included studies reported integrated diabetes mellitus (DM) screening, 7 included integrated care and 9 studies addressed cases of newly detected DM who were asymptomatic in pre-diabetes stage. Only 1 study clearly analysed cost-effectiveness in home-based care; another 5 did not evaluate cost-effectiveness but discussed potential cost benefits of an integrated approach to DM screening and care. Compared to partial integration, only 2 fully integrated interventions yielded tangible results regarding DM screening, care and early detection of cases despite many that reported barriers to its sustainability.Conclusion:Though few, integrated interventions for screening and/or care of DM in the context of multimorbidity within available resources in health systems throughout Africa exist and suggest that this approach is possible and could improve health outcomes.

DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery

We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambiguity of boundaries and incomplete sequences. It scored consistently high for performance metrics while breaching accuracy of >98% when tested across a large number of validated instances. During multiple benchmarkings DeepPlnc consistently outperformed all the compared tools and maintained a highly significant lead in the range of 2.5%- 4.6% from the second best performing tool (p-value << 0.01). DeepPlnc was used to annotate a de novo assembled transcriptome of a himalayan species where again it suggested its much better suitability for genome and transcriptome annotation purposes than the existing tools. DeepPlnc has been made freely available as a web-server and stand-alone program at https://scbb.ihbt.res.in/DeepPlnc/.

CpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein-Protein Interactions.

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods—even if high throughput—are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful—experimentally verified—associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

A pilot study on the perspectives of regulators and institutions of management education in India

The Indian government set out to make higher education in general and management education, in particular, more accessible to the general public. It indicates that the government wants every firm to be professionalized and every sector to have effective administrators. To achieve this goal, the government liberally approved the establishment of many management institutions around the country. The AICTE, UGC, and other institutions have been established as regulators to supervise management education and its progress and direction. As a result, the country now has over 3700 institutions. All of these institutions have unique names and structures, and some are standalone programs (PGDM), others are university departments, and several being connected institutes. There has been a considerable qualitative and quantitative transformation in management education over the last 40 years. This provided adequate clarity and direction.Various business school administrations have shown much enthusiasm for building management institutions, but less attention is made to maintaining quality management education.However, the stakeholders have a wide range of opinions about the institution's outcome. To put it another way, numerous management institutions have failed to meet the expectations of regulators, industry, and society. Although, as requested by the government, management education is available to ordinary students. However, the quality of management education falls short of expectations.This paper attempts to comprehend the regulatory framework, implementation, and quality outcome difficulties.A structured questionnaire is provided to the respondents for the aim of the study, and the data is evaluated with appropriate statistical methods, and accounting inferences are derived.

Knowledge, Attitude and Practices of Pharmacovigilance and Adverse Drug Reaction Reporting Among Pharmacists Working at Alkarak Governorate, Jordan

Background: Pharmacovigilance (PV) is the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug related problem. Since adverse drug reactions (ADRs) are considered worldwide as one of the most common public health problems that affect all groups of patients; the assessment of healthcare providers' knowledge, attitude, and practice of PV and ADRs reporting will provide an in-depth look at the reasons behind the lack of reports. Objectives: This study aims to assess the overall knowledge, attitude, and practice of PV and ADRs reporting among pharmacists working at Alkarak Governorate, Jordan. Methods: A cross-sectional study was conducted during the period from 20th February till 20th April 2021. All employed pharmacists working at Alkarak Governorate were eligible to participate using a structured-interview based-questionnaire. The Bivariate correlation test with the Pearson's (r) was used to assess the correlations between metric variables, independent samples t-test and the one-way ANOVA tests were also applied. The SPSS IBM program version 21 and the Stand-Alone FACTOR program were used for the statistical data analysis. The level of significance was set to be ≤ 0.050. Results: The majority of the pharmacists were females (74.4%), most of respondents (84.9%) had a Bachelor's degree in pharmacy. The pharmacists showed a moderately overall knowledge of PV (58.7%), positive attitudes toward their perceived ADRs reporting importance (78%), and toward selective ADRs reporting (62.8%), a moderate effort exerted by those pharmacists regarding the practice of ADRs reporting process (55.8%). The main barrier was that pharmacists did not know how to report (72.1%). Conclusion: Pharmacists had a moderate PV and ADRs reporting knowledge on average. Positive attitudes toward perceived ADRs reporting importance and moderate level of agreement on focusing on the well-known and serious ADRs. The result denotes a moderate effort exerted by those pharmacists on ADRs reporting practices. Pharmacists faced a moderately high perceived difficulties and extra burden (barriers).

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.

MotivationRecently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins.ResultsHere, we showed that by integrated deep learning with physics-inferred secondary structures, co-evolutionary information and multiple sequence-alignment sampling, we can achieve RNA contact-map prediction at a level of accuracy similar to that in protein contact-map prediction. More importantly, highly accurate prediction for top L long-range contacts can be assured for those RNAs with a high effective number of homologous sequences (Neff > 50). The initial use of the predicted contact map as distance-based restraints confirmed its usefulness in 3D structure prediction.Availability and implementationSPOT-RNA-2D is available as a web server at https://sparks-lab.org/server/spot-rna-2d/ and as a standalone program at https://github.com/jaswindersingh2/SPOT-RNA-2D.Supplementary information Supplementary data are available at Bioinformatics online.

Shry: Application of Canonical Augmentation to the Atomic Substitution Problem.

A common approach for studying a solid solution or disordered system within a periodic ab initio framework is to create a supercell in which certain amounts of target elements are substituted with other elements. The key to generating supercells is determining how to eliminate symmetry-equivalent structures from many substitution patterns. Although the total number of substitutions is on the order of trillions, only symmetry-inequivalent atomic substitution patterns need to be identified, and their number is far smaller than the total. Our developed Python software package, which is called Shry (Suite for High-throughput generation of models with atomic substitutions implemented by Python), allows the selection of only symmetry-inequivalent structures from the vast number of candidates based on the canonical augmentation algorithm. Shry is implemented in Python 3 and uses the CIF format as the standard for both reading and writing the reference and generated sets of substituted structures. Shry can be integrated into another Python program as a module or can be used as a stand-alone program. The implementation was verified through a comparison with other codes with the same functionality, based on the total numbers of symmetry-inequivalent structures, and also on the equivalencies of the output structures themselves. The provided crystal structure data used for the verification are expected to be useful for benchmarking other codes and also developing new algorithms in the future.

An open-source toolbox for Multi-patient Intracranial EEG Analysis (MIA)

Intracranial EEG (iEEG) performed during the pre-surgical evaluation of refractory epilepsy provides a great opportunity to investigate the neurophysiology of human cognitive functions with exceptional spatial and temporal precisions. A difficulty of the iEEG approach for cognitive neuroscience, however, is the potential variability across patients in the anatomical location of implantations and in the functional responses therein recorded. In this context, we designed, implemented, and tested a user-friendly and efficient open-source toolbox for Multi-Patient Intracranial data Analysis (MIA), which can be used as standalone program or as a Brainstorm plugin. MIA helps analyzing event related iEEG signals while following good scientific practice recommendations, such as building reproducible analysis pipelines and applying robust statistics. The signals can be analyzed in the temporal and time-frequency domains, and the similarity of time courses across patients or contacts can be assessed within anatomical regions. MIA allows visualizing all these results in a variety of formats at every step of the analysis. Here, we present the toolbox architecture and illustrate the different steps and features of the analysis pipeline using a group dataset collected during a language task.

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles.

We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images.

DDGun: an untrained predictor of protein stability changes upon amino acid variants.

Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔG prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔG values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔG of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔG, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔG prediction.

Discrete Biochemical Systems Theory.

Almost every biomedical systems analysis requires early decisions regarding the choice of the most suitable representations to be used. De facto the most prevalent choice is a system of ordinary differential equations (ODEs). This framework is very popular because it is flexible and fairly easy to use. It is also supported by an enormous array of stand-alone programs for analysis, including many distinct numerical solvers that are implemented in the main programming languages. Having selected ODEs, the modeler must then choose a mathematical format for the equations. This selection is not trivial as nearly unlimited options exist and there is seldom objective guidance. The typical choices include ad hoc representations, default models like mass-action or Lotka-Volterra equations, and generic approximations. Within the realm of approximations, linear models are typically successful for analyses of engineered systems, but they are not as appropriate for biomedical phenomena, which often display nonlinear features such as saturation, threshold effects or limit cycle oscillations, and possibly even chaos. Power-law approximations are simple but overcome these limitations. They are the key ingredient of Biochemical Systems Theory (BST), which uses ODEs exclusively containing power-law representations for all processes within a model. BST models cover a vast repertoire of nonlinear responses and, at the same time, have structural properties that are advantageous for a wide range of analyses. Nonetheless, as all ODE models, the BST approach has limitations. In particular, it is not always straightforward to account for genuine discreteness, time delays, and stochastic processes. As a new option, we therefore propose here an alternative to BST in the form of discrete Biochemical Systems Theory (dBST). dBST models have the same generality and practicality as their BST-ODE counterparts, but they are readily implemented even in situations where ODEs struggle. As a case study, we illustrate dBST applied to the dynamics of the aryl hydrocarbon receptor (AhR), a signal transduction system that simultaneously involves time delays and stochasticity.

The effect of armed conflict on intimate partner violence: Evidence from the Boko Haram insurgency in Nigeria

Intimate partner violence (IPV) is the most common form of violence against women in both conflict and non-conflict settings but in conflict settings it often receives less attention than other forms of gender-based violence (GBV), such as conflict-related sexual violence. To examine whether increased rates of IPV are linked to conflict we use data from Domestic Violence module of the Nigerian Demographic and Health Survey (NDHS) collected in 2008 and 2013 and spatially link them to the Boko Haram (BH) actor file of the Armed Conflict Location and Events Database (ACLED). To estimate whether the BH insurgency is associated with increases in IPV we use a quasi-experimental approach, employing a kernel-based difference-in-difference model. We also examine the effect of the BH insurgency on women’s likelihood of experiencing controlling behavior from a husband or partner, women’s autonomy in household decision-making and their control over their own earnings. We find that the presence of BH increases the probability that women experience physical or sexual IPV by about 4 percentage points after controlling for known correlates of IPV; partner’s alcohol use, previous exposure to IPV and condoning IPV as a social norm. Further, we find controlling behaviors from husbands/partners – another form of IPV - are heightened in locations that are impacted by the BH insurgency. In these places women’s risk of experiencing controlling behavior increases by 14 percentage points, indicating that the BH insurgency exacerbates another form of IPV; behaviors that are often pre-cursors to physical and sexual IPV. Our results underscore the need for policy makers to prioritize programs that respond to and prevent IPV in conflict affected settings. Effective program responses can be both integrated into sectoral programs and delivered as standalone programs alongside other interventions that provide services to communities living in conflict-affected settings.

A Tailored Web-Based Video Intervention (ParentCoach) to Support Parents With Children With Sleeping Problems: User-Centered Design Approach.

BackgroundMany parents frequently struggle with undesirable or problematic behavior (ie, temper tantrums and whining) displayed by their child. To support parents in promoting positive parenting skills (ie, recognizing challenging situations and reacting appropriately), the interactive video e-learning tool ParentCoach was developed. The tool aims to teach parents generic behavioral responses by means of situational learning, tailoring, and problem solving. The first demonstration focused on sleeping problems.ObjectiveThe aim of this paper is to illustrate the user-centered development of ParentCoach.MethodsWe conducted usability, understandability, and acceptance tests among the target group (29 parents, 7 youth health care professionals, and 4 individuals with former lower health literacy) in different phases of the development process via focus groups, interviews, and surveys. This allowed for relevant insights on specifications and user requirements to guide the development and revision of the tool in each iteration.ResultsIterative testing and development allowed for the final demonstration of ParentCoach to be experienced as a relevant and accessible parenting intervention that can be used as a stand-alone program or in combination with another program.ConclusionsThis paper elaborates on the iterative development process and its benefits for the final demonstration of ParentCoach.

Disparities in Telehealth Utilization in a Population of Publicly Insured Children During the COVID-19 Pandemic.

Telehealth became a crucial vehicle for health care delivery in the United States during the COVID-19 pandemic. However, little research exists on inequities in telehealth utilization among the pediatric population. This study examines disparities in telehealth utilization in a population of publicly insured children. This observational, retrospective study used administrative data from Alabama's stand-alone Children's Health Insurance Program, ALL Kids. Rates of any telehealth use for March to December 2020 were examined. In addition-to capture lack of health care utilization-rates of having no medical claims were examined and compared with March to December 2019 and 2018. Multinomial logit models were estimated to investigate how telehealth use and having no medical claims (reference category: having medical claims but no telehealth) were associated with race/ethnicity, rural-urban residence, and family income. Of the 106,478 enrollees over March to December 2020, 13.4% had any telehealth use and 24.7% had no medical claims. The latter was greater than no medical claims in 2019 (19.5%) and 2018 (20.7%). Black and Hispanic children had lower odds of any telehealth use (odds ratio [OR]: 0.81, P < 0.01; OR: 0.68, P < 0.01) and higher odds of no medical claims (OR: 1.11, P < 0.05; OR: 1.73, P < 0.05) than non-Hispanic White children. Rural residents had lower odds of telehealth use than urban residents. Those in the highest family income-based fee group had higher odds of telehealth use than the lowest family income-based fee group. As telehealth will likely continue to play an important role in health care delivery, additional efforts/investments are required to ensure telehealth does not further exacerbate inequities in pediatric health care access.

Kajian termodinamik kinerja AC split: studi kasus pada bangunan pendidikan dengan menerapkan program komputer

This paper presents a thermodynamic study on energy performance of a split AC system with R-32 for the application of educational buildings at the Bali State Polytechnic. The study was conducted using a standalone computer program U-CoolS V.1.12 which was developed on the EES (Engineering Equation Solver) V11,334 program. A thermodynamic model was developed and validated using data from the technical specifications of various types of split AC. Thermodynamic studies were carried out based on variations in ambient temperature in the Bali Region. The effect of two operating parameters of a split AC system, namely the condensing temperature in the condenser and the evaporation temperature of the refrigerant in the evaporator, on the compressor performance and energy performance of the AC system is simulated. Thermodynamic simulation results show the strong influence of condensing temperature and evaporation temperature on compressor performance and overall energy performance of the split AC system. It was found that for 1 °C increase in condensing temperature, the compressor power increased quite significantly around 2.1%, the cooling capacity of the split AC system decreased by 1.8%, COP and EER also decreased by 3.9%. Meanwhile, for 1 °C increase in evaporation temperature, it was found an increase in compressor power consumption of around 0.31%, an increase in cooling capacity of 4.4% and an increase in COP and EER of 4.1%. The results of this thermodynamic study also show that the split AC system performance using R-32 refrigerant has a very good COP range from 3.27 to 5.95 with the implication that R-32 refrigerant has the potential to be a refrigerant for a split AC system.

Peer support facilitates post-traumatic growth in first responders: A literature review

Introduction First responders are routinely exposed to traumatic workplace incidents that can result in post-traumatic stress disorder (PTSD). First responder organizations utilize different types of peer led programs to support first responders following a critical incident including the Critical Incident Stress Management Program (CISM) and stand-alone peer support programs. Little research has been done to determine the benefits or limitations of stand-alone peer support programs on the mental health of first responders. The aim of this project was to understand what characteristics of peer support facilitate post-traumatic growth (PTG) in first responders. Methods A literature search was completed using the City University of Seattle library database, ResearchGate and Google Scholar. The studies used in this review were found on EBCSOhost, ProQuest, PubMed and Wiley Online. This review includes literature published since 2006 that references post-traumatic growth in first responders, and/or their preferences for support, their coping styles and organizational impacts on growth. Thematic analysis was conducted to identify the themes and patterns in the final 10 research papers. Results This literature review identified five themes of formal and informal peer interactions that support PTG in first responders. (1) Peer support assists first responders with the processing of traumatic events and (2) managing organizational stressors. (3) Peer support alone may not lead to PTG and requires the use of adaptive coping behaviours. (4) First responders who actively engage with peer support experience increased well-being and (5) relational safety with peers encourages disclosure that leads to post-traumatic growth. Conclusion Formal and informal peer support contribute to post-traumatic growth (PTG) in first responders. Further research needs to be done to differentiate between the effects of trained peer support and support from colleagues.

Specificity of Parenting Program Component Effects: Relational, Behavioral, and Cognitive Approaches to Children's Conduct Problems.

Childhood conduct problems (eg, defiance, anger, and aggression) compromise child and family well-being and development.1 Evidence-based programs to support parents in managing children's conduct problems exist, but most families do not have access to these programs.2 Implementation costs, certification and supervision demands, and limited possibilities to personalize evidence-based programs hinder their scalability. To improve the care that families receive, we need to identify and understand discrete therapeutic processes, rather than comprehensive treatment protocols, that can be flexibly implemented to effectively reduce key risk factors for children's conduct problems, and make them widely available as low-cost stand-alone therapy components.3 The present study therefore tests the effects of 3 stand-alone parenting program components.

Partitioning, a Novel Approach to Mitigate the Risk and Impact of African Swine Fever in Affected Areas.

As African swine fever (ASF) continues to expand geographically, supplementary control strategies are needed to reduce disease risk and impact in affected areas. Full depopulation is central to current ASF control efforts, and its efficacy depends on surveillance and timely disease reporting, while resulting in large losses regardless of the producers' efforts to promptly detect, report, and contain the disease. This disconnect between prompt detection and reporting, and subsequent farm losses, can deter producers to invest in ASF detection and control. Alternative approaches are needed to incentivize individual producers to invest in early detection and reporting. We postulate that commercial swine farms may be effectively partitioned in separate units, or subpopulations, to which biosecurity, surveillance and control can be applied. The suggested Partitioning framework relies on three main components: 1. external and internal biosecurity to reduce the risk of ASF introduction and maintain separate subpopulations; 2. cost-effective on-farm ASF surveillance to enhance early detection; 3. response plans at the unit level, including culling of affected subpopulations, and demonstration of freedom from disease on the remaining ones. With such Partitioning approach, individual producers may reduce ASF risk on a farm and in the region, while also reducing ASF outbreak losses via targeted depopulation of affected units. It requires relevant legislation to incorporate the notion of within-farm subpopulations and provide a regulatory framework for targeted depopulation and substantiation of disease freedom. Its design should be tailored to fit individual farms. Partitioning can be an effective public-private partnership approach for ASF risk reduction. It should be driven by industry, as its benefits are accrued mainly by individual producers, but regulatory oversight is key to ensure proper implementation and avoid further disease spread. Partitioning's value is greatest for producers in ASF-affected regions, but ASF-free areas could also benefit from it for preparedness and early detection. It could also be adapted to other transboundary animal diseases and can be implemented as a stand-alone program or in conjunction with other efforts such as zoning and compartmentalization. Partitioning would contribute to the improved resilience and sustainability of the global pork industry and will benefit consumers and society through improved food security and animal welfare.