Worst-case Assumptions Research Articles

Calculation method for the fail-safe design of steel-steel contacts subject to fretting fatigue based on a worst-case assumption

The lower fretting-fatigue strengths of component connections compared to plain fatigue strengths is a notable characteristic of strength assessments. It can be attributed to the additional tribological stress, which is dependent on the contact pressure, the slip amplitude and the selected material pairing. By using a double-actuated flat-pad test bench with slip control, a separate analysis of these influencing variables can be carried out. The materials investigated are limited to the class of steel materials, specifically to C22 + N, C45 + N, C45 + QT, 34CrNiMo6 + QT, and 42CrMo4 + QT, because of their frequent use in shaft–hub connections. Using fretting factors derived from the flat-pad tests, a local approach within the framework of the FKM guideline (Analytical strength assessment of components in mechanical engineering) was proposed for fail-safe design where there is fretting fatigue. The tribological stress parameters of slip amplitude and contact pressure are taken into account by means of a worst-case-concept. The critical stress parameters were found at a contact pressure of 40MPa and a slip amplitude of 10μm. The calculation method was validated on a connecting-rod connection with a maximum error of 14% in the determination of the degree of utilization.

The practice of blood cross-match request and transfusion in surgical patients at Debre Markos comprehensive Specialized Hospital, Debre Markos, Ethiopia, 2021/2022. A prospective study

BackgroundBlood transfusions play a great role to improve oxygen delivery to tissues for indicated surgical patients. Preoperative ordering of blood, especially in elective surgery, is often based on the worst-case assumptions, demanding large quantities of blood or overestimating the anticipated blood loss, of which little is ultimately used. This study aimed to assess the practices of blood requisite and transfusion in surgical patients. MethodAn institutional-based prospective study was conducted from September to February 2021/2022, in Debre Markos comprehensive Specialized Hospital. Socio-demographic data like age, sex, ASA status, type of anesthesia, and type of surgeries were taken preoperatively. The number of cross-matched and transfused data were collected from patient charts throughout the perioperative periods. Efficacy of blood utilization was evaluated using the following indices like cross-match to transfusion ratio (C/T ratio), transfusion probability (%T), and transfusion index (TI); a ratio of 2.5 and below, A value of 30% and above, and values of 0.5 or more respectively were considered indicative of significant blood usage and this study is registered with a research unique identifying number of researchregistry7989 and reported in line with STROCSS 2021 guideline. ResultIn all procedures, among cross-matched blood units 64.1% were unutilized. Depending on the urgency of the procedures about 77.7% of cross-matched blood units were not utilized in elective patients. In emergency procedures, the majority (64.3%) of cross-matched units were transfused. The overall blood transfusion indices result was C/T ratio, %T, and TI was 2.9, 33.5%, and 0.7, respectively. The overall blood transfusion indices for elective and emergency surgery are set respectively as follows C/T ratio (4.6 vs 1.5), %T (22 vs 78.8), and TI (0.4 vs 1.9). Among elective procedures, blood utilization was significant in orthopedic surgery with the value of C/T ratio 1.3, %T 66, and TI 1.4. In the rest of the elective procedures, blood transfusion indices were not significant. ConclusionThe overall blood utilization indices in emergency surgical patients were better than in elective and preoperative grouping, screening, and hold (GSH) is sufficient for elective surgical procedures to decrease wastage of valuable supplies.

A Predictive Command Governor-Based Adaptive Cruise Controller With Collision Avoidance for Non-Connected Vehicle Following

This paper presents a command governor (CG) based adaptive cruise controller (ACC) that is applied in simulation to normal driving scenarios and emergency stopping scenarios. The vehicle-following case study used in this paper involves a heavy-duty ego vehicle and a light-duty <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-connected</i> lead vehicle (i.e., the ego vehicle does not communicate with the lead vehicle and can only infer the lead vehicles’ position and velocity states through its own sensors). Typically, to ensure constraints in the presence of disturbances, receding horizon based ACCs will assume some known worst-case behavior of the lead vehicle. In the presence of a stochastic, non-connected lead vehicle, however, achieving such a guarantee requires a worst-case assumption on the behavior of the lead vehicle for all future time. In this work, the CG assumes a lead vehicle velocity profile that will be achieved with a prescribed level of certainty, based on a stochastic characterization of lead vehicle behavior that has been informed by actual on-road data. The CG ensures safe following distance under this probabilistic lead vehicle assumption. Here, “safe following distance” is based on the ego vehicle’s ability to come to a stop without collision if the lead vehicle were to suddenly brake at maximum deceleration after proceeding at a velocity profile that is prescribed based on a statistical lower bound on lead vehicle velocity. Ultimately, the CG ensures that the worst-case safe following distance is satisfied with a prescribed probability, thereby paralleling chance-constrained CG formulations. Simulation results for a heavy-duty truck indicate that the CG-based ACC outperforms a PID-ACC in terms of fuel economy and drivability. Additionally, the CG-ACC approach was able to ensure rear-end collision avoidance in emergency stopping simulations.

Explicit Explore, Exploit, or Escape (E^4): near-optimal safety-constrained reinforcement learning in polynomial time

In reinforcement learning (RL), an agent must explore an initially unknown environment in order to learn a desired behaviour. When RL agents are deployed in real world environments, safety is of primary concern. Constrained Markov decision processes (CMDPs) can provide long-term safety constraints; however, the agent may violate the constraints in an effort to explore its environment. This paper proposes a model-based RL algorithm called Explicit Explore, Exploit, or Escape (E^{4}), which extends the Explicit Explore or Exploit (E^{3}) algorithm to a robust CMDP setting. E^4 explicitly separates exploitation, exploration, and escape CMDPs, allowing targeted policies for policy improvement across known states, discovery of unknown states, as well as safe return to known states. E^4 robustly optimises these policies on the worst-case CMDP from a set of CMDP models consistent with the empirical observations of the deployment environment. Theoretical results show that E^4 finds a near-optimal constraint-satisfying policy in polynomial time whilst satisfying safety constraints throughout the learning process. We then discuss E^4 as a practical algorithmic framework, including robust-constrained offline optimisation algorithms, the design of uncertainty sets for the transition dynamics of unknown states, and how to further leverage empirical observations and prior knowledge to relax some of the worst-case assumptions underlying the theory.

“Cellular Network Densification Increases Radio-Frequency Pollution”: True or False?

A very popular theory circulating among non-scientific communities claims that the massive deployment of Base Stations (BSs) over the territory, a.k.a. cellular network densification, always triggers an uncontrolled and exponential increase of human exposure to Radio Frequency “Pollution” (RFP). To face such concern in a way that can be understood by the layman, in this work we develop a very simple model to compute the RFP, based on a set of worst-case and conservative assumptions. We then provide closed-form expressions to evaluate the RFP variation in a pair of candidate 5G deployments, subject to different densification levels. Results, obtained over a wide set of representative 5G scenarios, dispel the myth: cellular network densification triggers an RFP decrease (up to three orders of magnitude) when the radiated power from the BS is adjusted to ensure a minimum sensitivity at the cell edge. Eventually, we analyze the conditions under which the RFP may increase when the network is densified (e.g., when the radiated power does not scale with the cell size), proving that the amount of RFP is always controlled. Finally, the results obtained by simulation confirm the outcomes of the RFP model.

Uncertainties and Design of Active Aerodynamic Attitude Control in Very Low Earth Orbit

This paper discusses the design and the performance achievable with active aerodynamic attitude control in very low Earth orbit, i.e., below 450 km in altitude. A novel real-time algorithm is proposed for selecting the angles of deflection of aerodynamic actuators providing the closest match to the control signal computed by a selected control law. The algorithm is based on a panel method for the computation of the aerodynamic coefficients and relies on approximate environmental parameters estimation and worst-case scenario assumptions for the re-emission properties of space materials. Discussion of results is performed by assuming two representative pointing maneuvers, for which momentum wheels and aerodynamic actuators are used synergistically. A quaternion feedback proportional-integral-derivative (PID) controller implemented in discrete time is assumed to determine the control signal at a sampling frequency of 1 Hz. The outcome of a Monte Carlo analysis, performed for a wide range of orbital conditions, shows that the target attitude is successfully achieved for the vast majority of the cases, thus proving the robustness of the approach in the presence of environmental uncertainties and realistic attitude hardware limitations.

A Novel Decentralized Consensus-Based Tracking Control for Exploration of Saturn's Rings

A better comprehension of the formation of Saturn's rings is crucial to understand our solar system. Many questions remain unanswered despite the successes of the Cassini and Voyager missions to Saturn. Traditional large spacecraft prove to be problematic within planetary rings because collisions and consequent mission failure increase dramatically in the rings’ dynamically dense environment. Using a swarm of small unmanned probes with decentralized control and communication structures that are equipped with research instruments distributed amongst the swarm is a possible solution. This paper presents a novel, decentralized consensus-based tracking control algorithm for guiding such a swarm. This control algorithm is characterized by a trajectory generator based on worst-case assumptions about both communication structures and the ring particle environment. Optimal model parameters, numerically calculated through the Monte Carlo method with the goal of minimizing fuel consumption and avoiding collisions, are used to generate an optimal reference trajectory for the swarm of spacecraft. A feedback tracking control with the optimal reference trajectory is designed under the assumption of unreliable and non-instantaneous communication among individual swarm agents. The effectiveness of the proposed control algorithm is validated numerically in MATLAB/Simulink.

Cross-feature trained machine learning models for QoT-estimation in optical networks

The ever-increasing demand for global internet traffic, together with evolving concepts of software-defined networks and elastic-optical-networks, demand not only the total capacity utilization of underlying infrastructure but also a dynamic, flexible, and transparent optical network. In general, worst-case assumptions are utilized to calculate the quality of transmission (QoT) with provisioning of high-margin requirements. Thus, precise estimation of the QoT for the lightpath (LP) establishment is crucial for reducing the provisioning margins. We propose and compare several data-driven machine learning (ML) models to make an accurate calculation of the QoT before the actual establishment of the LP in an unseen network. The proposed models are trained on the data acquired from an already established LP of a completely different network. The metric considered to evaluate the QoT of the LP is the generalized signal-to-noise ratio (GSNR), which accumulates the impact of both nonlinear interference and amplified spontaneous emission noise. The dataset is generated synthetically using a well-tested GNPy simulation tool. Promising results are achieved, showing that the proposed neural network considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin. Furthermore, we also analyze the impact of cross-features and relevant features training on the proposed ML models’ performance.

How Relevant Are Direct Emissions of Microplastics into Freshwater from an LCA Perspective?

Microplastics are ubiquitous in ecosystems and a lot of research is being performed to understand their environmental fate and effects on organisms. However, the release and impact of MP has so far not been considered in LCA studies. This is due to missing information on the inventory side about microplastic releases and missing Characterization Factors to quantify the effects of MP. The goal of this study was to elucidate the relevance of MP release into freshwaters from an LCA perspective, by using worst-case assumptions. In accordance with the USEtox framework, an interim and simplified Characterization Factor for the impact category of freshwater ecotoxicity was calculated to be 3231 PAF·m3·d·kg−1. Applying this Characterization Factor, two LCA case studies were conducted, one on a polyester T-Shirt and one with a shower gel containing microplastics. The results show a small contribution of microplastics to the freshwater ecotoxicity for a scenario with state-of-the-art wastewater treatment. Different scenarios varying in microplastic release and removal during wastewater treatment and a sensitivity analysis of the Characterization Factor allowed identifying the potential range of the microplastic contribution to the overall ecotoxicity. In conclusion, the inclusion of microplastic release into LCA only marginally influences the overall environmental effects of the two products in the LCA case studies.

Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing program.

Mass antigen testing programs have been challenged because of an alleged insufficient specificity, leading to a large number of false positives. The objective of this study is to derive a lower bound of the specificity of the SD Biosensor Standard Q Ag-Test in large scale practical use. Based on county data from the nationwide tests for SARS-CoV-2 in Slovakia between 31.10.-1.11. 2020 we calculate a lower confidence bound for the specificity. As positive test results were not systematically verified by PCR tests, we base the lower bound on a worst case assumption, assuming all positives to be false positives. 3,625,332 persons from 79 counties were tested. The lowest positivity rate was observed in the county of Rožňava where 100 out of 34307 (0.29%) tests were positive. This implies a test specificity of at least 99.6% (97.5% one-sided lower confidence bound, adjusted for multiplicity). The obtained lower bound suggests a higher specificity compared to earlier studies in spite of the underlying worst case assumption and the application in a mass testing setting. The actual specificity is expected to exceed 99.6% if the prevalence in the respective regions was non-negligible at the time of testing. To our knowledge, this estimate constitutes the first bound obtained from large scale practical use of an antigen test.

Worst-case analysis for a leader–follower partially observable stochastic game

Although Partially Observable Stochastic Games (POSGs) provide a powerful mathematical paradigm for modeling multi-agent dynamic decision making under uncertainty and partial information, they are notoriously hard to solve (e.g., the common-payoff POSGs are NEXP-complete) and have an extensive data requirement on each agent. The latter may represent a serious challenge to a defending agent if he/she has limited knowledge of its adversary. A worst-case analysis can significantly reduce both model computational complexity and data requirements regarding the adversary; further, a (near) optimal worst-case policy may represent a useful guide for action selection for risk-averse defenders (e.g., benchmarks). This article introduces a worst-case analysis to a leader–follower POSG where: (i) the defending leader has little knowledge of the adversarial follower’s reward structure, level of rationality, and process for gathering and transmitting data relevant for decision making; (ii) the objective is to determine a best worst-case value function and a control strategy for the leader. We show that the worst-case assumption transforms this POSG into a more computationally tractable single-agent problem with a simple sufficient statistic. However, the value function can be non-convex, in contrast with the value function of a partially observable Markov decision process. We design an iterative solution procedure for computing a lower bound of the leader’s value function and its control policy for the finite horizon case. This approach was numerically illustrated to support decision making in a security example.

COVID-19 modeling and non-pharmaceutical interventions in an outpatient dialysis unit.

This paper describes a data-driven simulation study that explores the relative impact of several low-cost and practical non-pharmaceutical interventions on the spread of COVID-19 in an outpatient hospital dialysis unit. The interventions considered include: (i) voluntary self-isolation of healthcare personnel (HCPs) with symptoms; (ii) a program of active syndromic surveillance and compulsory isolation of HCPs; (iii) the use of masks or respirators by patients and HCPs; (iv) improved social distancing among HCPs; (v) increased physical separation of dialysis stations; and (vi) patient isolation combined with preemptive isolation of exposed HCPs. Our simulations show that under conditions that existed prior to the COVID-19 outbreak, extremely high rates of COVID-19 infection can result in a dialysis unit. In simulations under worst-case modeling assumptions, a combination of relatively inexpensive interventions such as requiring surgical masks for everyone, encouraging social distancing between healthcare professionals (HCPs), slightly increasing the physical distance between dialysis stations, and—once the first symptomatic patient is detected—isolating that patient, replacing the HCP having had the most exposure to that patient, and relatively short-term use of N95 respirators by other HCPs can lead to a substantial reduction in both the attack rate and the likelihood of any spread beyond patient zero. For example, in a scenario with R0 = 3.0, 60% presymptomatic viral shedding, and a dialysis patient being the infection source, the attack rate falls from 87.8% at baseline to 34.6% with this intervention bundle. Furthermore, the likelihood of having no additional infections increases from 6.2% at baseline to 32.4% with this intervention bundle.

Laser safety assessments supported by analyses of reflections from metallic targets irradiated by high-power laser light

When using kilowatt-class lasers in outdoor environments, ensuring laser safety turns out to be a complex issue due to the large safety areas that must be respected. For the special cases of collimated or focused laser radiation reflected from ideally flat but naturally rough metallic surfaces, the classical laser hazard analysis is deemed insufficient. In order to investigate the corresponding hazard areas for the aforementioned cases, we performed experiments on laser-matter interactions. Using high-power laser radiation, we studied the spatial and temporal reflection characteristics from four different metallic samples. For the evaluation of total reflection characteristics, we performed curve-fitting methods comprising Gaussian-like specular components, diffuse scattering components according to the ABg-scatter model and Lambertian components. For the investigation of occurring caustics, we developed a dedicated model in order to assess the divergence of the contained structures as a function of distance. Our evaluations have shown that the majority of the reflected power is scattered and based on these findings, that resulting nominal optical hazard distance values, even under worst-case assumptions, are significantly smaller than those of the non-reflected laser beam.

Fine-Grained Cryptography Revisited

Fine-grained cryptographic primitives are secure against adversaries with bounded resources and can be computed by honest users with less resources than the adversaries. In this paper, we revisit the results by Degwekar, Vaikuntanathan, and Vasudevan in Crypto 2016 on fine-grained cryptography and show the constructions of three key fundamental fine-grained cryptographic primitives: one-way permutations, hash proof systems (which in turn implies a public-key encryption scheme against chosen chiphertext attacks), and trapdoor one-way functions. All of our constructions are computable in \(\mathsf {NC^1}\) and secure against (non-uniform) \(\mathsf {NC^1}\) circuits under the widely believed worst-case assumption \(\mathsf {NC^1}\subsetneq \mathsf{\oplus L/poly}\).

Thrust measurements and evaluation of asymmetric infrared laser resonators for space propulsion

Since modern propulsion systems are insufficient for large-scale space exploration, a breakthrough in propulsion physics is required. Amongst different concepts, the EMDrive is a proposed device claiming to be more efficient in converting energy into propulsive forces than classical photon momentum exchange. It is based on a microwave resonator inside a tapered cavity. Recently, Taylor suggested using a laser instead of microwaves to boost thrust by many orders of magnitude due to the higher quality factor of optical resonators. His analysis was based on the theory of quantised inertia by McCulloch, who predicted that an asymmetry in mass surrounding the device and/or geometry is responsible for EMDrive-like forces. We put this concept to the test in a number of different configurations using various asymmetrical laser resonators, reflective cavities of different materials and size as well as fiber-optic loops, which were symmetrically and asymmetrically shaped. A dedicated high precision thrust balance was developed to test all these concepts with a sensitivity better than pure photon thrust, which is the force equivalent to the radiation pressure of a laser for the same power that is used to operate each individual devices. In summary, all devices showed no net thrust within our resolution at the Nanonewton range, meaning that any anomalous thrust must be below state-of-the-art propellantless propulsion. This puts strong limits on all proposed theories like quantised inertia by at least 4 orders of magnitude for the laboratory-scale geometries and power levels used with worst case assumptions for the theoretical predictions.

Limited Visibility Aware Motion Planning for Autonomous Valet Parking Using Reachable Set Estimation.

Autonomous driving helps drivers avoid paying attention to keeping to a lane or keeping a distance from the vehicle ahead. However, the autonomous driving is limited by the need to park upon the completion of driving. In this sense, automated valet parking (AVP) system is one of the promising technologies for enabling drivers to free themselves from the burden of parking. Nevertheless, the driver must continuously monitor the automated system in the current automation level. The main reason for monitoring the automation system is due to the limited sensor range and occlusions. For safety reasons, the current field of view must be taken into account, as well as to ensure comfort and to avoid unexpected and harsh reactions. Unfortunately, due to parked vehicles and structures, the field of view in a parking lot is not sufficient for considering new obstacles coming out of occluded areas. To solve this problem, we propose a method that estimates the risks for unobservable obstacles by considering worst-case assumptions. With this method, we can ensure to not act overcautiously while moving safe. As a result, the proposed method can be a proactive approach to consider the limited visibility encountered in a parking lot. In the proposed method, occlusion can be efficiently reflected in the planning process. The potential of the proposed method is evaluated in a variety of simulations.

Post-Quantum Cryptosystems for Internet-of-Things: A Survey on Lattice-Based Algorithms

The latest quantum computers have the ability to solve incredibly complex classical cryptography equations particularly to decode the secret encrypted keys and making the network vulnerable to hacking. They can solve complex mathematical problems almost instantaneously compared to the billions of years of computation needed by traditional computing machines. Researchers advocate the development of novel strategies to include data encryption in the post-quantum era. Lattices have been widely used in cryptography, somewhat peculiarly, and these algorithms have been used in both; (a) cryptoanalysis by using lattice approximation to break cryptosystems; and (b) cryptography by using computationally hard lattice problems (non-deterministic polynomial time hardness) to construct stable cryptographic functions. Most of the dominant features of lattice-based cryptography (LBC), which holds it ahead in the post-quantum league, include resistance to quantum attack vectors, high concurrent performance, parallelism, security under worst-case intractability assumptions, and solutions to long-standing open problems in cryptography. While these methods offer possible security for classical cryptosytems in theory and experimentation, their implementation in energy-restricted Internet-of-Things (IoT) devices requires careful study of regular lattice-based implantation and its simplification in lightweight lattice-based cryptography (LW-LBC). This streamlined post-quantum algorithm is ideal for levelled IoT device security. The key aim of this survey was to provide the scientific community with comprehensive information on elementary mathematical facts, as well as to address real-time implementation, hardware architecture, open problems, attack vectors, and the significance for the IoT networks.

The contribution of commercial fireworks to both local and personal air quality in Auckland, New Zealand.

Firework displays produce large amounts of particulate matter (PM), contributing to poor air quality in local areas. Since short-term exposure to particulate matter correlates with increased mortality risks, these celebrations may impact both human health and the environment. Little is known about the particulate matter produced from recreational fireworks, as most studies have focused on professional large-scale events. In New Zealand, it is common for consumer fireworks to be ignited within residential areas during the Guy Fawkes celebration around 5 November. To better understand the contribution of individual fireworks on local air quality, ambient PM10 sampling was conducted in the 10 days surrounding Guy Fawkes Day in Auckland, New Zealand. These data were supplemented with measurements of firework emissions from 11 different individual products, including smoke bombs, sparklers, and Roman candles. Filter sampling results indicated that personal fireworks can contribute to ground level ambient air quality during celebrations, increasing ambient PM10 concentrations by 21.6 μg m-3 over a 12-h sampling period. The use of personal fireworks can expose consumers to PM10 concentrations much higher, up to 9.51 mg m-3 from individual sparkler use under worst-case scenario assumptions. The inhalation of sparkler emissions for just 8 min can lead to an exposure to PM10 mass greater than that from daily recommended limits (50 μg m-3 exposure over 24 h). X-ray fluorescence (XRF) analysis indicated that potassium (K) and strontium (Sr) can be used as tracers for local firework use and that arsenic (As) may be an important contaminant during Guy Fawkes celebrations. The PM from personal fireworks contained large amounts of chlorine (Cl), which may be indicative of perchlorate oxidizers. In addition, lead (Pb) was observed in the PM generated from two of the colored sparklers, which were marketed as "safer" alternatives to more explosive firework products.

Environmental risk assessment of the DvSSJ1 dsRNA and the IPD072Aa protein to non-target organisms

ABSTRACT Event DP-Ø23211-2 (hereafter referred to as DP23211) maize expresses the DvSSJ1 double-stranded RNA (DvSSJ1 dsRNA) and the IPD072Aa protein, encoded by the ipd072Aa gene. DvSSJ1 dsRNA and the IPD072Aa protein each provide control of corn rootworms (Diabrotica spp.) when expressed in plants. As part of the environmental risk assessment (ERA), the potential hazard to non-target organisms (NTOs) exposed to the DvSSJ1 dsRNA and the IPD072Aa protein expressed in DP23211 maize was assessed. Worst-case estimated environmental concentrations (EECs) for different NTO functional groups (pollinators and pollen feeders, soil dwelling detritivores, predators and parasitoids, aquatic detritivores, insectivorous birds, and wild mammals) were calculated using worst-case assumptions. Several factors that reduce exposure to NTOs under more realistic environmental conditions were applied, when needed to provide more environmentally relevant EECs. Laboratory bioassays were conducted to assess the activity of DvSSJ1 dsRNA or the IPD072Aa protein against selected surrogate species, and margins of exposure (MOEs) were calculated by comparing the Tier I hazard study results to worst-case or refined EECs. Based on specificity and MOE values, DvSSJ1 dsRNA and the IPD072Aa protein expressed in DP23211 maize are not expected to be harmful to NTO populations at environmentally relevant concentrations.

Effects of COVID-19 on HIV services: the case of Malawi

The coronavirus (COVID-19) pandemic is a global health emergency. It presents a significant threat for people living with HIV and AIDS and for the global response to HIV. Changes that countries have set in motion within the health systems and economies to adapt to the COVID-19 pandemic threaten to deprioritize the urgent, ongoing needs of people living with HIV and derail decades of hard-won progress in the response to HIV, TB and other diseases. Disruptions to supply chain systems for key HIV commodities, logistics and reporting systems have limited the ability for countries like Malawi to maintain or extend HIV-related services, as well as to set up adequate COVID-19 control measures. In such countries where there is high burden of HIV, it is a priority to ensure continuity of ART during the pandemic. HIV related deaths could increase substantially during the COVID-19 pandemic under reasonable worst-case assumptions about interruptions to HIV services. It is highly recommended for the COVID-19 response to be informed and grounded in rights-based and community-centred approaches. True progress will require addressing deep-seated structural inequalities to protect the most marginalized. There is a need to actively involve People Living with HIV (PLHIV) when making crucial decisions about their health during pandemics and that HIV-related policies need not be overlooked during the pandemic if the successes of the decades-long fight against HIV/AIDS have to be maintained.