Control Policies Research Articles

Dynamic event-triggered optimal control for stochastic interconnected nonlinear systems with matched disturbances via adaptive dynamic programming

This paper presents a dynamic event-triggered adaptive dynamic programming (DETADP) algorithm to study the optimal control issue of stochastic interconnected nonlinear systems with matched disturbances. By constructing an improved cost function related to the actuator faults for each auxiliary subsystem, the original control issue is converted into finding an array of decentralized optimal control policies. In order to eliminate the effect of matching perturbations, an integral sliding mode control strategy is developed using the DETADP algorithm to ensure optimal performance for general nonlinear dynamics. When the system trajectories converge to a sliding-mode surface, equivalent sliding mode dynamics are transformed into reformulated auxiliary systems with a modified cost function. Then, a DETADP algorithm is introduced under an identifier-critic network framework, where the identifier aims to determine the stochastic dynamic, the critic aims to acquire the solutions of Hamilton–Jacobi–Bellman(HJB) equations. Furthermore, a novel dynamic event-triggering condition is designed to determine the occurrence of an event by introducing a dynamic variable. By using the Lyapunov stability theory, all signals in the closed-loop system are proved to be uniformly ultimately bounded. Finally, practical simulation examples are conducted to validate the effectiveness of the designed method.

Refined prediction of SO2 concentration around Chinese coking enterprises and exposure risk assessment of different populations based on buffer Latin hypercube

Coking enterprises in China are recognized as significant sources of SO2 emissions, making them a key industry with high levels of SO2 intensity. Assessing the health risks for different populations around these coking enterprises nationwide is challenging due to the lack of clarity regarding SO2 concentrations at varying distances from these facilities. To address this issue, we developed a buffer Latin hypercube XGBoost particle swarm optimization (BLH-XGB-PSO) algorithm that combines efficient global search capabilities and accurate prediction performance. This algorithm enables effective traversal of monitoring points located at varying distances and directions around the enterprise while automatically seeking optimal model parameters. Using this model, we accurately predicted the concentration of SO2 every 0.5 km within a 10 km radius around China's coking enterprises in 2017, achieving a high prediction accuracy with an R2 value of 0.97. The prediction results indicate that the highest concentration of SO2 in the vicinity of Chinese coking enterprises is observed in the central region of Shanxi province (65.88 μg/m3). The average annual concentration of SO2 around all production enterprises amounts to 27.9 μg/m3. Furthermore, we conducted an assessment on the impact of coking enterprises on different age groups, genders, and regions regarding the number of affected individuals, health exposure risks, and control effectiveness. Our findings reveal that in 2017, around 5.5 thousand newborns (14.5% male, 85.5% female) had a hazard quotient (HQ) exceeding threshold value which poses potential human health risks. The implementation of the control policy successfully prevented 64,375 thousand people from being affected by higher concentrations of SO2. Greater attention should be devoted to the health risks faced by newborns residing in proximity to coking enterprises, with a particular emphasis on female infants.

Breast cancer: do the current policies mean anything?

Aim Breast cancer remains one of the most prevalent malignancies globally with a high mortality rate. South Africa has a number of policies in place designed to ensure that minimum delays are experienced by patients from first symptom to initial treatment. This study was initiated to determine where the longest delays occurred during the patient’s journey, from the first symptom to the first treatment. This information, combined with the existing policy, identifies shortfalls in the breast cancer diagnostic and treatment pathways that must be addressed. Methods A cross-sectional study was undertaken in the Department of Medical Oncology, University of Pretoria/Steve Biko Academic Hospital between April 2020 and August 2021, analysing six timelines from first symptom to first treatment in patients with newly diagnosed breast cancer. Data was obtained by the researchers from the patients on their first visit relating to the delays experienced from first symptom to final biopsy. Clinical information was obtained from the patients’ source records once a treatment decision had been reached and treatment started where applicable. Results The longest delays experienced were between the patients’ first visit to any medical facility to date of diagnostic biopsy. Delays due to COVID-19 were in the minority with 13/79 (16.4%) patients reporting having experienced COVID-19 related delays. Key Area 2 of the Breast Cancer Prevention and Control Policy makes provision for patients to be referred directly to a regional/tertiary/quaternary medical facility for further screening, diagnosis and treatment within 21-60 days from the first visit. In the study’s cohort, a mean of four months from first visit to diagnostic biopsy was recorded. Overall delays of longer than 12 months due to non-representative biopsies (range 1-3) were recorded in three patients. Twenty-nine patients (36.7%) presented to three medical facilities prior to diagnostic biopsy with 10/79 (12.6%) and 1/79 (1.2%) presenting to four and five institutions, respectively, before the diagnostic biopsy. Conclusions The delays patients experienced highlights the lack of knowledge about the urgency and correct referral of suspected malignancy cases. Despite the number of promulgated policies relating to fast track systems via specialist breast units, fiscal and human capital deficiencies negatively impact the attempts to diagnose and treat patients timeously and increase survival.

Phenotypic evidence of deltamethrin resistance and identification of selective sweeps in Anopheles mosquitoes on the Bijagós Archipelago, Guinea-Bissau

Vector control in the Bijagós Archipelago of Guinea-Bissau currently relies on pyrethroid insecticide-treated nets. However, data on insecticide resistance in Guinea-Bissau is limited. This study identified deltamethrin resistance in the Anopheles gambiae sensu lato complex on Bubaque island using WHO tube tests in November 2022. Whole genome sequencing of An. gambiae sensu stricto mosquitoes identified six single nucleotide polymorphisms (SNPs) previously associated with, or putatively associated with, insecticide resistance: T791M, L995F, N1570Y, A1746S and P1874L in the vgsc gene, and L119V in the gste2 gene. Twenty additional non-synonymous SNPs were identified in insecticide-resistance associated genes. Four of these SNPs were present at frequencies over 5% in the population: T154S, I126F and G26S in the vgsc gene and A65S in ace1. Genome wide selection scans using Garud’s H12 statistic identified two selective sweeps: one in chromosome X and one in chromosome 2R. Both selective sweeps overlap with metabolic genes previously associated with insecticide resistance, including cyp9k1 and the cyp6aa/cyp6p gene cluster. This study presents the first phenotypic testing for deltamethrin resistance and the first whole genome sequence data for Anophelesgambiae mosquitoes from the Bijagós, contributing data of significance for vector control policy in this region.

Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles

To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.

Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning

Identifying the time-varying control schemes that maximize storage performance is critical to the commercial deployment of geological carbon storage (GCS) projects. However, the optimization process typically demands extensive resource-intensive simulation evaluations, which poses significant computational challenges and practical limitations. In this study, we presented the multimodal latent dynamic (MLD) model, a novel deep learning framework for fast flow prediction and well control optimization in GCS operations. The MLD model implicitly characterizes the forward compositional simulation process through three components: a representation module that learns compressed latent representations of the system, a transition module that approximates the evolution of the system states in the low-dimensional latent space, and a prediction module that forecasts the flow responses for given well controls. A novel model training strategy combining a regression loss and a joint-embedding consistency loss was introduced to jointly optimize the three modules, which enhances the temporal consistency of the learned representations and ensures multi-step prediction accuracy. Unlike most existing deep learning models designed for systems with specific parameters, the MLD model supports arbitrary input modalities, thereby enabling comprehensive consideration of interactions between diverse types of data, including dynamic state variables, static spatial system parameters, rock and fluid properties, as well as external well settings. Since the MLD model mirrors the structure of a Markov decision process (MDP) that computes state transitions and rewards (i.e., economic calculation for flow responses) for given states and actions, it can serve as an interactive environment to train deep reinforcement learning agents. Specifically, the soft actor-critic (SAC) algorithm was employed to learn an optimal control policy that maximizes the net present value (NPV) from the experiences gained by continuous interactions with the MLD model. The efficacy of the proposed approach was first compared against commonly used simulation-based evolutionary algorithm and surrogate-assisted evolutionary algorithm on a deterministic GCS optimization case, showing that the proposed approach achieves the highest NPV, while reducing the required computational resources by more than 60%. The framework was further applied to the generalizable GCS optimization case. The results indicate that the trained agent is capable of harnessing the knowledge learned from previous scenarios to provide improved decisions for newly encountered scenarios, demonstrating promising generalization performance.

Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Smoking is the leading behavioural risk factor for mortality globally, accounting for more than 175 million deaths and nearly 4·30 billion years of life lost (YLLs) from 1990 to 2021. The pace of decline in smoking prevalence has slowed in recent years for many countries, and although strategies have recently been proposed to achieve tobacco-free generations, none have been implemented to date. Assessing what could happen if current trends in smoking prevalence persist, and what could happen if additional smoking prevalence reductions occur, is important for communicating the effect of potential smoking policies. In this analysis, we use the Institute for Health Metrics and Evaluation's Future Health Scenarios platform to forecast the effects of three smoking prevalence scenarios on all-cause and cause-specific YLLs and life expectancy at birth until 2050. YLLs were computed for each scenario using the Global Burden of Disease Study 2021 reference life table and forecasts of cause-specific mortality under each scenario. The reference scenario forecasts what could occur if past smoking prevalence and other risk factor trends continue, the Tobacco Smoking Elimination as of 2023 (Elimination-2023) scenario quantifies the maximum potential future health benefits from assuming zero percent smoking prevalence from 2023 onwards, whereas the Tobacco Smoking Elimination by 2050 (Elimination-2050) scenario provides estimates for countries considering policies to steadily reduce smoking prevalence to 5%. Together, these scenarios underscore the magnitude of health benefits that could be reached by 2050 if countries take decisive action to eliminate smoking. The 95% uncertainty interval (UI) of estimates is based on the 2·5th and 97·5th percentile of draws that were carried through the multistage computational framework. Global age-standardised smoking prevalence was estimated to be 28·5% (95% UI 27·9-29·1) among males and 5·96% (5·76-6·21) among females in 2022. In the reference scenario, smoking prevalence declined by 25·9% (25·2-26·6) among males, and 30·0% (26·1-32·1) among females from 2022 to 2050. Under this scenario, we forecast a cumulative 29·3 billion (95% UI 26·8-32·4) overall YLLs among males and 22·2 billion (20·1-24·6) YLLs among females over this period. Life expectancy at birth under this scenario would increase from 73·6 years (95% UI 72·8-74·4) in 2022 to 78·3 years (75·9-80·3) in 2050. Under our Elimination-2023 scenario, we forecast 2·04 billion (95% UI 1·90-2·21) fewer cumulative YLLs by 2050 compared with the reference scenario, and life expectancy at birth would increase to 77·6 years (95% UI 75·1-79·6) among males and 81·0 years (78·5-83·1) among females. Under our Elimination-2050 scenario, we forecast 735 million (675-808) and 141 million (131-154) cumulative YLLs would be avoided among males and females, respectively. Life expectancy in 2050 would increase to 77·1 years (95% UI 74·6-79·0) among males and 80·8 years (78·3-82·9) among females. Existing tobacco policies must be maintained if smoking prevalence is to continue to decline as forecast by the reference scenario. In addition, substantial smoking-attributable burden can be avoided by accelerating the pace of smoking elimination. Implementation of new tobacco control policies are crucial in avoiding additional smoking-attributable burden in the coming decades and to ensure that the gains won over the past three decades are not lost. Bloomberg Philanthropies and the Bill & Melinda Gates Foundation.

Exposure to tropospheric ozone and NO2 in the ambient air of Tehran metropolis: Spatiotemporal distribution and inhalation health risk assessment

BackgroundAir pollution has emerged as a critical challenge in the 21st century, necessitating alert monitoring of key pollutants such as ozone and nitrogen dioxide (NO2)F. These pollutants are exacerbated by urbanization and industrialization, posing significant health risks, particularly in densely populated metropolitan areas. ObjectiveThe aim of this study was inhalation health risk assessment of NO2 and ozone in various scenarios and spatiotemporal Distribution Interpolation. MethodsData were collected from the Tehran Air Quality Control Center, encompassing 18 monitoring stations from March 2019 to February 2022. Health risk assessments (HRA) were performed for adult age groups across three different exposure scenarios (3, 8, and 12 h) And a sensitivity analysis was also conducted to determine the relative importance of each parameter in assessing the risks of each scenario. ResultsThe hazard index (HI) values for NO2 and ozone in all three scenarios were found to range from 0.24 to 1.56 and 0.04 to 0.49, respectively and body weight is a risk-lowering factor in sensitivity analyses. The interpolation results of ozone, NOx, and NO2 using the IDW approach from March 2019 to February 2022 showed that overall, the north and northeast of Tehran had the highest concentrations of NO2 and NOx, and the north and west had the highest concentrations of ozone. SignificanceThis study underscores the HRA with exposure to NO2 and ozone, particularly for individuals spending more than 8 h outdoors. Given Tehran's high population density and persistent air pollution, it is imperative to implement effective control policies to safeguard public health. The findings advocate for limiting outdoor activity to less than 8 h per day, especially for vulnerable occupational groups. Additionally, the study highlights the importance of considering body weight in health risk assessments.

Effectiveness of UEFA's regulation for European football financial management: A comprehensive systematic review and meta-analysis

BackgroundThe Union of European Football Association's (UEFA) Financial Fair Play (FFP) regulations are a topic of ongoing debate. This study aims to evaluate and quantify the FFP's effect on clubs' financial performance and critically review the regulatory changes introduced by the UEFA in 2022 through the new Financial Sustainability Regulations (FSR). MethodsA systematic review and meta-analysis were conducted using the Web of Science and Scopus databases up to December 31, 2023, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. ResultsThis systematic review included 22 articles; a meta-analysis was conducted on 52 financial measures derived from 20 studies of 11 articles. The meta-analysis’ main result—obtained by comparing two different financial measures (profitability versus solvency)—revealed that FFP's effect on profitability measures was significant, with a value of 0.151 (p = 0.050); however, the effect size for solvency measures was not significant, with a value of 0.049 (p = 0.639). ContributionsThis systematic review revealed variability in the results of the studies analysed, reflecting contextual factors' influence, which underlines the need for a more adaptive and specific approach to clubs’ financial control policies. The meta-analysis found that the type of financial measure employed (profitability versus solvency) was a notable source of variability among the studies, as its moderating effect was significant. Consequently, the FFP exerted contrasting effects on profitability and solvency. LimitationsA significant level of heterogeneity was observed in the financial measures analysed, predominantly because of the different samples and periods across the included studies. ConclusionsThis study corroborates FFP's mixed and limited impact on financial performance, highlighting the need for stricter control in European football, which aligns with the new FSR. Our study underscores aspects that future research should address to deepen knowledge of UEFA regulations' efficiency in enhancing football's financial sustainability.

Event Based Control for Network Security Systems Subject to Actuator Saturation

Abstract As a typical nonlinear link, actuator saturation exists in most network security protection systems. It easily disrupts the closed-loop performance of the system, leading to instability. The event triggering control policy generally adopts an aperiodic sampling mechanism and transmits information only when the triggering condition is met, which can save system communication and computing resources. An event-triggered controller is designed for a network security system with actuator saturation, and the system stabilization is realized. The attractor domain of the system is estimated by solving optimization problems in the form of linear matrix inequalities. Simulation results show the validity of the conclusions obtained in this paper.

Synchronization of Delayed Memristor-Based Neural Networks via Pinning Control With Local Information.

In this article, a novel pinning control method, only requiring information from partial nodes, is developed to synchronize drive-response memristor-based neural networks (MNNs) with time delay. An improved mathematical model of MNNs is established to describe the dynamic behaviors of MNNs accurately. In the existing literature, pinning controllers for synchronization of drive-response systems were designed based on information of all nodes, but in some specific situations, the control gains may be very large and challenging to realize in practice. To overcome this problem, a novel pinning control policy is developed to achieve synchronization of delayed MNNs, which depends only on local information of MNNs, for reducing communication and calculation burdens. Furthermore, sufficient conditions for synchronization of delayed MNNs are provided. Finally, numerical simulation and comparative experiments are conducted to verify the effectiveness and superiority of the proposed pinning control method.

Australia as the Destination for Study Abroad: International Students’ Mobility

International students have become one of the main populations in Australian universities. However, due to the COVID-19 pandemic, many international students decided to go to other countries and regions for their higher education due to the border control policies in Australia. Based on the social cognitive career and motivation theory, this study explored the motivations and reasons why Chinese postgraduate international students decided to return to Australia after the Australian government officially re-opened the border for on-campus and face-to-face education and why Chinese postgraduate international students decided to study in Australia instead of other countries, particularly during the COVID-19 pandemic. Based on the general inductive approach with semi-structured interviews, focus group activities, and member-checking interviews, 20 Chinese postgraduate international students’ stories were captured. The findings indicated that financial, academic, and personal considerations are significant in the participants’ decision-making processes. The results will fill the research gaps and become a reference in international student mobilities, decisions, and decision-making processes of international students, particularly during the COVID-19 pandemic in the Australian university environment.

Prevalence of Breast Cancer Screening in Asia: Systematic Review and Meta-Analysis.

Breast cancer is one of the leading causes of cancer-related mortality among women above 50 years of age. This systematic review and meta-analysis aimed at identifying the prevalence and trend of breast cancer screening among selected Asian countries. We searched three databases including, PubMed, Web of Science, and Scopus using our search terms. Two independent reviewers screened titles and abstracts applying the inclusion and exclusion criteria specified in the PROSPERO (registration ID: CRD42023401516) and any conflicts were resolved through discussion. Two independent reviewers and conflicts also completed the full-text screening, which was solved. Relevant information was extracted in an Excel sheet and random effects meta-analysis was performed to identify the pooled estimate of breast cancer screening in Asia using Stata 17. We identified 41 studies from 14 selected Asian countries reporting breast cancer screening rates from 2012-2023. The pooled estimate of four countries (China, Iran, Japan, and Korea) with more than three data points on clinical breast examination and mammographic screening showed a screening rate of 24% (95% CI: 0.20 - 0.27) and 18% (95% CI: (0.14 - 0.21) in the last ten years respectively. Most Asian countries do not have sufficient data on breast cancer screening due to a lack of nationally representative surveys or national-level cancer registries. Therefore, it is crucial to strengthen cancer control policies including breast cancer screening programs.

Public Support for Tobacco Control Policies Among Mississippi Adults

Public Support for Tobacco Control Policies Among Mississippi Adults

Adaptive Inverse Nonlinear Optimal Control Based on Finite-Time Concurrent Learning and Semidefinite Programming.

This article investigates the problem of inverse optimal control (IOC) for a class of nonlinear affine systems. An adaptive IOC approach is proposed to recover the cost functional using only the system state data, which integrates the finite-time concurrent learning (FTCL) technique and the semidefinite programming (SDP) technique. First, an identifier neural network (NN) is employed to approximate the unknown nonlinear control policy, and an FTCL-based update law is proposed to estimate the weights of the identifier NN online, which removes the traditional persistent excitation (PE) condition. Moreover, the finite-time convergence as well as the uniformly ultimately boundness (UUB) of estimation error of the identifier NN weights are analysed according to whether or not there exists the identifier NN approximation error. Then, with the help of a value NN for approximating the value function, an SDP problem with a quadratic objective function can be set up for determining the weighting matrices of the cost functional. Finally, simulation results are presented to validate the proposed method.

Efficient Queue Control Policies for Latency-Critical Traffic in Mobile Networks

Efficient Queue Control Policies for Latency-Critical Traffic in Mobile Networks

Model Predictive Control of Spatially Distributed Systems with Spatio-Temporal Logic Specifications

In this paper, for spatially distributed systems, we propose a new method of model predictive control with spatio-temporal logic specifications. We formulate the finite-time control problem with specifications described by SSTLf (signal spatio-temporal logic over finite traces) formulas. In the problem formulation, the feasibility is guaranteed by representing control specifications as a penalty in the cost function. Time-varying weights in the cost function are introduced to satisfy control specifications as well as possible. The finite-time control problem can be written as a mixed integer programming (MIP) problem. According to the policy of model predictive control (MPC), the control input can be generated by solving the finite-time control problem at each discrete time. The effectiveness of the proposed method is presented through a numerical example.

Design of a conceptual model of open innovation for the decentralization of the science, technology, and innovation system in Colombia from an organizational ecology perspective

Science, technology, and innovation (STI) systems are fundamental to the economic development of any nation. However, their high hierarchy and centralization create inequities for the more dispersed regions to access their benefits. Traditional approaches to STI system decentralization have been through public control and investment policies, posing a challenge for emerging economies. Given these conditions, it is necessary to explore alternative approaches such as open innovation (OI), which can facilitate bringing the STI system to the regions by breaking its hierarchical structure; and organizational ecology (OE), which can contribute to the construction of ecosystemic appropriation of STI in the regions. The objective of this research is to propose a conceptual model that addresses the need to decentralize Colombia's STI system through an alternative approach to public policy governance, utilizing OI and OE. The methodology used for this research is Design Science Research (DSR), which allows for the creation of an artifact-type model, validated through the representational validation technique, supported by a cross-impact analysis matrix completed by 67% members of the subregional STI committees in the department of Caldas. The result is a conceptual model that integrates the components of Colombia's STI system, decentralizes them through OI factors, and ensures the ecosystemic appropriation of STI in the regions through OE factors. Model criteria, such as organizational readiness, collaborative capacity, absorptive capacities, intellectual capital, technological capital, and local niche, are presented as key elements in the decentralization of the STI system and the ecosystemic appropriation in the co-creation of a mutualistic STI system in Colombia's regions. The findings of the model represent an integrated model that unfolds sequentially; the first phase develops the decentralization through OI factors, and the second phase develops the ecosystemic appropriation from OE factors. This research contributes an integrated OI and OE model as an alternative to the traditional STI system decentralization approach from public policy governance and nation-region control, overcoming the hierarchical barrier of the system and granting ecosystemic appropriation of science, technology, and innovation in the regions.

Chest CT features and risk factors for patients with Omicron variant pneumonia: a multicenter retrospective clinical study.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Omicron variant infection has become widespread in China as a result of the alterations in epidemic control and prevention policies. We identified the clinical characteristics and lung computed tomography (CT) imaging characteristics of patients infected during the early stage of the Omicron BA.5 wave in Shanghai to provide a guide to the diagnosis, treatment, and prognosis of infection. Clinical information and lung CT imaging characteristics of patients with Omicron variant infection admitted to three designated hospitals in Shanghai from March to June 2022 were analyzed retrospectively. A total of 958 patients were included in the analysis. Among the patients, 169 (17.64%) had pneumonia confirmed by CT, of whom 70.41% (119/169) had lesions in < 10% of the lung area. Older age, unvaccinated status, and comorbid chronic lung disease, cerebrovascular disease, kidney disease, or Alzheimer`s disease were associated with poor prognosis. In patients with coronavirus disease 2019 (COVID-19) pneumonia, a large lesion size was associated with a poor prognosis. Age ≥ 65 years, unvaccinated status, fever > 5 days, and lymphocyte count < 0.5×109/L were risk factors for pneumonia. Age ≥ 65 years, unvaccinated status, fever > 5 days, and lymphocyte count < 0.5×109/L can be used to identify high-risk individuals who warrant a CT scan to screen for COVID-19 pneumonia, especially during the period of Omicron variant predominance. Concurrently, the importance of immunization should be emphasized to help people withstand the effects of Omicron variant infection.

Progress made in digitalizing antimicrobial resistance surveillance in a One Health approach in Kenya.

Antimicrobial resistance (AMR) is a major threat to global public health, affecting human and animal health, agriculture, food safety, and the environment. The control of AMR is often challenging, particularly when data are scanty or siloed in individual sectors. To develop evidence-based control policies for AMR, an electronic information system that integrates AMR data from various sectors, in a One Health approach, is critical. Acknowledging the interconnectedness of AMR in humans, animals, and the environment and the need to assess the AMR burden using a One Health approach, Kenya's National Antimicrobial Stewardship Interagency Committee (NASIC), with support from FIND, integrated human and animal health AMR data at the national AMR data repository and developed the One Health AMR Surveillance System (OHAMRS). The OHAMRS comprises two core digital components: interoperability middleware for integrating data from various sources and a DHIS2 web portal for the analysis and visualization of AMR surveillance data from the human and animal health sectors. These components are scalable for future inclusion of data from other One Health sectors, e.g., the environment, food/feed, and aquaculture sectors. The OHAMRS has 42 dashboards that facilitate the presentation, interpretation, and dissemination of actionable information relating to AMR, including 17 dashboards for human and animal health priority pathogens and 8 for drug-resistance indicators. The priority pathogen dashboards provide visualization of antimicrobial susceptibility patterns, resistance and susceptibility trends, resistance tables, and geospatial susceptibility maps. Other dashboards include surveillance sites and specimen reports, data completeness, data reconciliation, sample testing workload, a One Health intersectoral dashboard, and other reporting tools for diverse stakeholders. Digitalizing AMR surveillance through a One Health lens is pivotal to understand AMR prevalence and patterns across various sectors. The OHAMRS provides comprehensive data analysis and presentation, informing policymaking on AMR control. Digital tools such as the OHAMRS are vital in facilitating the availability of data and actionable information on AMR required to address the AMR crisis in Kenya.