Showing papers by "James Cook University published in 2021"

Half a century of global decline in oceanic sharks and rays

Abstract: Overfishing is the primary cause of marine defaunation, yet declines in and increasing extinction risks of individual species are difficult to measure, particularly for the largest predators found in the high seas1-3. Here we calculate two well-established indicators to track progress towards Aichi Biodiversity Targets and Sustainable Development Goals4,5: the Living Planet Index (a measure of changes in abundance aggregated from 57 abundance time-series datasets for 18 oceanic shark and ray species) and the Red List Index (a measure of change in extinction risk calculated for all 31 oceanic species of sharks and rays). We find that, since 1970, the global abundance of oceanic sharks and rays has declined by 71% owing to an 18-fold increase in relative fishing pressure. This depletion has increased the global extinction risk to the point at which three-quarters of the species comprising this functionally important assemblage are threatened with extinction. Strict prohibitions and precautionary science-based catch limits are urgently needed to avert population collapse6,7, avoid the disruption of ecological functions and promote species recovery8,9.

CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images.

Abstract: Background and Objective The Coronavirus 2019, or shortly COVID-19, is a viral disease that causes serious pneumonia and impacts our different body parts from mild to severe depending on patient’s immune system. This infection was first reported in Wuhan city of China in December 2019, and afterward, it became a global pandemic spreading rapidly around the world. As the virus spreads through human to human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Preventing viral spreading requires early detection of positive cases and to treat infected patients as quickly as possible. The need for COVID-19 testing kits has increased, and many of the developing countries in the world are facing a shortage of testing kits as new cases are increasing day by day. In this situation, the recent research using radiology imaging (such as X-ray and CT scan) techniques can be proven helpful to detect COVID-19 as X-ray and CT scan images provide important information about the disease caused by COVID-19 virus. The latest data mining and machine learning techniques such as Convolutional Neural Network (CNN) can be applied along with X-ray and CT scan images of the lungs for the accurate and rapid detection of the disease, assisting in mitigating the problem of scarcity of testing kits. Methods Hence a novel CNN model called CoroDet for automatic detection of COVID-19 by using raw chest X-ray and CT scan images have been proposed in this study. CoroDet is developed to serve as an accurate diagnostics for 2 class classification (COVID and Normal), 3 class classification (COVID, Normal, and non-COVID pneumonia), and 4 class classification (COVID, Normal, non-COVID viral pneumonia, and non-COVID bacterial pneumonia). Results The performance of our proposed model was compared with ten existing techniques for COVID detection in terms of accuracy. A classification accuracy of 99.1% for 2 class classification, 94.2% for 3 class classification, and 91.2% for 4 class classification was produced by our proposed model, which is obviously better than the state-of-the-art-methods used for COVID-19 detection to the best of our knowledge. Moreover, the dataset with x-ray images that we prepared for the evaluation of our method is the largest datasets for COVID detection as far as our knowledge goes. Conclusion The experimental results of our proposed method CoroDet indicate the superiority of CoroDet over the existing state-of-the-art-methods. CoroDet may assist clinicians in making appropriate decisions for COVID-19 detection and may also mitigate the problem of scarcity of testing kits.

Red seaweed (Asparagopsis taxiformis) supplementation reduces enteric methane by over 80 percent in beef steers

Abstract: The red macroalgae (seaweed) Asparagopsis spp. has shown to reduce ruminant enteric methane (CH4) production up to 99% in vitro. The objective of this study was to determine the effect of Asparagopsis taxiformis on CH4 production (g/day per animal), yield (g CH4/kg dry matter intake (DMI)), and intensity (g CH4/kg ADG); average daily gain (ADG; kg gain/day), feed conversion efficiency (FCE; kg ADG/kg DMI), and carcass and meat quality in growing beef steers. Twenty-one Angus-Hereford beef steers were randomly allocated to one of three treatment groups: 0% (Control), 0.25% (Low), and 0.5% (High) A. taxiformis inclusion based on organic matter intake. Steers were fed 3 diets: high, medium, and low forage total mixed ration (TMR) representing life-stage diets of growing beef steers. The Low and High treatments over 147 days reduced enteric CH4 yield 45 and 68%, respectively. However, there was an interaction between TMR type and the magnitude of CH4 yield reduction. Supplementing low forage TMR reduced CH4 yield 69.8% (P <0.01) for Low and 80% (P <0.01) for High treatments. Hydrogen (H2) yield (g H2/DMI) increased (P <0.01) 336 and 590% compared to Control for the Low and High treatments, respectively. Carbon dioxide (CO2) yield (g CO2/DMI) increased 13.7% between Control and High treatments (P = 0.03). No differences were found in ADG, carcass quality, strip loin proximate analysis and shear force, or consumer taste preferences. DMI tended to decrease 8% (P = 0.08) in the Low treatment and DMI decreased 14% (P <0.01) in the High treatment. Conversely, FCE tended to increase 7% in Low (P = 0.06) and increased 14% in High (P <0.01) treatment compared to Control. The persistent reduction of CH4 by A. taxiformis supplementation suggests that this is a viable feed additive to significantly decrease the carbon footprint of ruminant livestock and potentially increase production efficiency.

Six modes of co-production for sustainability

Abstract: The promise of co-production to address complex sustainability challenges is compelling. Yet, co-production, the collaborative weaving of research and practice, encompasses diverse aims, terminologies and practices, with poor clarity over their implications. To explore this diversity, we systematically mapped differences in how 32 initiatives from 6 continents co-produce diverse outcomes for the sustainable development of ecosystems at local to global scales. We found variation in their purpose for utilizing co-production, understanding of power, approach to politics and pathways to impact. A cluster analysis identified six modes of co-production: (1) researching solutions; (2) empowering voices; (3) brokering power; (4) reframing power; (5) navigating differences and (6) reframing agency. No mode is ideal; each holds unique potential to achieve particular outcomes, but also poses unique challenges and risks. Our analysis provides a heuristic tool for researchers and societal actors to critically explore this diversity and effectively navigate trade-offs when co-producing sustainability. Co-production includes diverse aims, terminologies and practices. This study explores such diversity by mapping differences in how 32 initiatives from 6 continents co-produce diverse outcomes for the sustainable development of ecosystems at local to global scales.

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Abstract: The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this article is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

Motivating or manipulating: the influence of health-protective behaviour and media engagement on post-COVID-19 travel

Abstract: COVID-19 (Coronavirus Disease) is a contagious global pandemic that has impacted tourism in 2020. Tourist behaviour and destination image are significantly influenced by the tourist’s perception of...

The MPA Guide: A framework to achieve global goals for the ocean

Abstract: Marine Protected Areas (MPAs) are conservation tools intended to protect biodiversity, promote healthy and resilient marine ecosystems, and provide societal benefits. Despite codification of MPAs i...

Global declines in coral reef calcium carbonate production under ocean acidification and warming

Abstract: Ocean warming and acidification threaten the future growth of coral reefs. This is because the calcifying coral reef taxa that construct the calcium carbonate frameworks and cement the reef together are highly sensitive to ocean warming and acidification. However, the global-scale effects of ocean warming and acidification on rates of coral reef net carbonate production remain poorly constrained despite a wealth of studies assessing their effects on the calcification of individual organisms. Here, we present global estimates of projected future changes in coral reef net carbonate production under ocean warming and acidification. We apply a meta-analysis of responses of coral reef taxa calcification and bioerosion rates to predicted changes in coral cover driven by climate change to estimate the net carbonate production rates of 183 reefs worldwide by 2050 and 2100. We forecast mean global reef net carbonate production under representative concentration pathways (RCP) 2.6, 4.5, and 8.5 will decline by 76, 149, and 156%, respectively, by 2100. While 63% of reefs are projected to continue to accrete by 2100 under RCP2.6, 94% will be eroding by 2050 under RCP8.5, and no reefs will continue to accrete at rates matching projected sea level rise under RCP4.5 or 8.5 by 2100. Projected reduced coral cover due to bleaching events predominately drives these declines rather than the direct physiological impacts of ocean warming and acidification on calcification or bioerosion. Presently degraded reefs were also more sensitive in our analysis. These findings highlight the low likelihood that the world's coral reefs will maintain their functional roles without near-term stabilization of atmospheric CO2 emissions.

A simple and effective F0 knockout method for rapid screening of behaviour and other complex phenotypes.

Abstract: Hundreds of human genes are associated with neurological diseases, but translation into tractable biological mechanisms is lagging. Larval zebrafish are an attractive model to investigate genetic contributions to neurological diseases. However, current CRISPR-Cas9 methods are difficult to apply to large genetic screens studying behavioural phenotypes. To facilitate rapid genetic screening, we developed a simple sequencing-free tool to validate gRNAs and a highly effective CRISPR-Cas9 method capable of converting >90% of injected embryos directly into F0 biallelic knockouts. We demonstrate that F0 knockouts reliably recapitulate complex mutant phenotypes, such as altered molecular rhythms of the circadian clock, escape responses to irritants, and multi-parameter day-night locomotor behaviours. The technique is sufficiently robust to knockout multiple genes in the same animal, for example to create the transparent triple knockout crystal fish for imaging. Our F0 knockout method cuts the experimental time from gene to behavioural phenotype in zebrafish from months to one week.

Ipilimumab alone or ipilimumab plus anti-PD-1 therapy in patients with metastatic melanoma resistant to anti-PD-(L)1 monotherapy: a multicentre, retrospective, cohort study.

Abstract: Summary Background Anti-PD-1 therapy (hereafter referred to as anti-PD-1) induces long-term disease control in approximately 30% of patients with metastatic melanoma; however, two-thirds of patients are resistant and will require further treatment. We aimed to determine the efficacy and safety of ipilimumab plus anti-PD-1 (pembrolizumab or nivolumab) compared with ipilimumab monotherapy in patients who are resistant to anti-PD-(L)1 therapy (hereafter referred to as anti-PD-[L]1). Methods This multicentre, retrospective, cohort study, was done at 15 melanoma centres in Australia, Europe, and the USA. We included adult patients (aged ≥18 years) with metastatic melanoma (unresectable stage III and IV), who were resistant to anti-PD-(L)1 (innate or acquired resistance) and who then received either ipilimumab monotherapy or ipilimumab plus anti-PD-1 (pembrolizumab or nivolumab), based on availability of therapies or clinical factors determined by the physician, or both. Tumour response was assessed as per standard of care (CT or PET–CT scans every 3 months). The study endpoints were objective response rate, progression-free survival, overall survival, and safety of ipilimumab compared with ipilimumab plus anti-PD-1. Findings We included 355 patients with metastatic melanoma, resistant to anti-PD-(L)1 (nivolumab, pembrolizumab, or atezolizumab), who had been treated with ipilimumab monotherapy (n=162 [46%]) or ipilimumab plus anti-PD-1 (n=193 [54%]) between Feb 1, 2011, and Feb 6, 2020. At a median follow-up of 22·1 months (IQR 9·5–30·9), the objective response rate was higher with ipilimumab plus anti-PD-1 (60 [31%] of 193 patients) than with ipilimumab monotherapy (21 [13%] of 162 patients; p Interpretation In patients who are resistant to anti-PD-(L)1, ipilimumab plus anti-PD-1 seemed to yield higher efficacy than ipilimumab with a higher objective response rate, longer progression-free, and longer overall survival, with a similar rate of grade 3–5 toxicity. Ipilimumab plus anti-PD-1 should be favoured over ipilimumab alone as a second-line immunotherapy for these patients with advanced melanoma. Funding None.

Combating ecosystem collapse from the tropics to the Antarctic

Abstract: Globally, collapse of ecosystems—potentially irreversible change to ecosystem structure, composition and function—imperils biodiversity, human health and well‐being. We examine the current state and recent trajectories of 19 ecosystems, spanning 58° of latitude across 7.7 M km2, from Australia's coral reefs to terrestrial Antarctica. Pressures from global climate change and regional human impacts, occurring as chronic ‘presses’ and/or acute ‘pulses’, drive ecosystem collapse. Ecosystem responses to 5–17 pressures were categorised as four collapse profiles—abrupt, smooth, stepped and fluctuating. The manifestation of widespread ecosystem collapse is a stark warning of the necessity to take action. We present a three‐step assessment and management framework (3As Pathway Awareness, Anticipation and Action) to aid strategic and effective mitigation to alleviate further degradation to help secure our future.

The role of Indigenous peoples and local communities in effective and equitable conservation

Abstract: Debate about what proportion of the Earth to protect often overshadows the question of how nature should be conserved and by whom. We present a systematic review and narrative synthesis of 169 publications investigating how different forms of governance influence conservation outcomes, paying particular attention to the role played by Indigenous peoples and local communities. We find a stark contrast between the outcomes produced by externally controlled conservation, and those produced by locally controlled efforts. Crucially, most studies presenting positive outcomes for both well-being and conservation come from cases where Indigenous peoples and local communities play a central role, such as when they have substantial influence over decision making or when local institutions regulating tenure form a recognized part of governance. In contrast, when interventions are controlled by external organizations and involve strategies to change local practices and supersede customary institutions, they tend to result in relatively ineffective conservation at the same time as producing negative social outcomes. Our findings suggest that equitable conservation, which empowers and supports the environmental stewardship of Indigenous peoples and local communities represents the primary pathway to effective long-term conservation of biodiversity, particularly when upheld in wider law and policy. Whether for protected areas in biodiversity hotspots or restoration of highly modified ecosystems, whether involving highly traditional or diverse and dynamic local communities, conservation can become more effective through an increased focus on governance type and quality, and fostering solutions that reinforce the role, capacity, and rights of Indigenous peoples and local communities. We detail how to enact progressive governance transitions through recommendations for conservation policy, with immediate relevance for how to achieve the next decade’s conservation targets under the UN Convention on Biological Diversity.

Coral Probiotics: Premise, Promise, Prospects.

Abstract: The use of Beneficial Microorganisms for Corals (BMCs) has been proposed recently as a tool for the improvement of coral health, with knowledge in this research topic advancing rapidly. BMCs are defined as consortia of microorganisms that contribute to coral health through mechanisms that include (a) promoting coral nutrition and growth, (b) mitigating stress and impacts of toxic compounds, (c) deterring pathogens, and (d) benefiting early life-stage development. Here, we review the current proposed BMC approach and outline the studies that have proven its potential to increase coral resilience to stress. We revisit and expand the list of putative beneficial microorganisms associated with corals and their proposed mechanismsthat facilitate improved host performance. Further, we discuss the caveats and bottlenecks affecting the efficacy of BMCs and close by focusing on the next steps to facilitate application at larger scales that can improve outcomes for corals and reefs globally.

Neuromorphic context-dependent learning framework with fault-tolerant spike routing

Abstract: Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%-16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. In addition, the proposed system can be applied in real-time learning and decision-making applications, brain-machine integration, and the investigation of brain cognition during learning.

Deep Learning Based Multistep Solar Forecasting for PV Ramp-Rate Control Using Sky Images

Abstract: Solar forecasting is one of the most promising approaches to address the intermittent photovoltaic (PV) power generation by providing predictions before upcoming ramp events. In this article, a novel multistep forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time series models (e.g., recurrent neural network (RNN) or long short-term memory) and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal–spatial information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting-based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach.

CerebelluMorphic: large-scale neuromorphic model and architecture for supervised motor learning

Abstract: The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article presents a large-scale cerebellar network model for supervised learning, as well as a cerebellum-inspired neuromorphic architecture to map the cerebellar anatomical structure into the large-scale model. Our multinucleus model and its underpinning architecture contain approximately 3.5 million neurons, upscaling state-of-the-art neuromorphic designs by over 34 times. Besides, the proposed model and architecture incorporate 3411k granule cells, introducing a 284 times increase compared to a previous study including only 12k cells. This large scaling induces more biologically plausible cerebellar divergence/convergence ratios, which results in better mimicking biology. In order to verify the functionality of our proposed model and demonstrate its strong biomimicry, a reconfigurable neuromorphic system is used, on which our developed architecture is realized to replicate cerebellar dynamics during the optokinetic response. In addition, our neuromorphic architecture is used to analyze the dynamical synchronization within the Purkinje cells, revealing the effects of firing rates of mossy fibers on the resonance dynamics of Purkinje cells. Our experiments show that real-time operation can be realized, with a system throughput of up to 4.70 times larger than previous works with high synaptic event rate. These results suggest that the proposed work provides both a theoretical basis and a neuromorphic engineering perspective for brain-inspired computing and the further exploration of cerebellar learning.

Extending the natural adaptive capacity of coral holobionts

Abstract: C.R.V. acknowledges funding from the German Research Foundation (DFG) (grants 433042944 and 458901010). R.S.P. acknowledges funding from King Abdullah University of Science and Technology (grant FCC/1/1973-51-01). J.E.P. acknowledges funding from the University of South Florida Research & Innovation Internal Awards Program (grant 0142687). K.M.Q. acknowledges funding from the Australian Institute of Marine Science (AIMS). E.M.M. was supported by the Mote Eminent Scholarship and the National Science Foundation (NSF) (OCE-1452538). M.A. acknowledges funding from King Abdullah University of Science and Technology (grant FCC/1/1973-36-01).

A Comparative Study on Photovoltaic MPPT Algorithms Under EN50530 Dynamic Test Procedure

Abstract: Dynamic performance of maximum power point tracking (MPPT) algorithms is important to ensure high-power output under practical operating conditions. In this article, after reviewing three dynamic test procedures, including stepped operation procedure, day-by-day operation procedure, and EN50530 dynamic test procedure, three typical MPPT algorithms such as the fixed-step-size perturb and observe (P&O), variable-step-size incremental conductance, and hybrid-step-size beta method are evaluated experimentally under the EN50530 dynamic test procedure. Two dynamic EN50530 test sequences are adopted for the performance evaluation to cover different irradiance changing conditions. The PV model for EN50530 dynamic test sequences is built, and the effects of wrong-step changes by using three MPPT algorithms are analyzed systematically. The experimental comparison of three MPPT algorithms in terms of the tracking routines, accumulated energy, and tracking efficiency is presented. The research shows that the 0.5% fixed-step-size P&O may fail to track the MPP due to the tracking drift, whereas the beta algorithm exhibits the highest tracking efficiency under both dynamic sequences. The average tracking efficiency improvement of the beta algorithm compared with other two algorithms are experimentally measured as $\text{24.2}\%$ and $\text{18.8}\%$ , respectively.

Does High Public Trust Amplify Compliance with Stringent COVID-19 Government Health Guidelines? A Multi-country Analysis Using Data from 102,627 Individuals

Abstract: Purpose To examine how public trust mediates the people’s adherence to levels of stringent government health policies and to establish if these effects vary across the political regimes. Methods This study utilizes data from two large-scale surveys: the global behaviors and perceptions at the onset of COVID-19 pandemic and the Oxford COVID-19 Government Response Tracker (OxCGRT). Linear regression models were used to estimate the effects of public trust and strictness of restriction measures on people’s compliance level. The model accounted for individual and daily variations in country-level stringency of preventative measures. Differences in the dynamics between public trust, the stringent level of government health guidelines and policy compliance were also examined among countries based on political regimes. Results We find strong evidence of the increase in compliance due to the imposition of stricter government restrictions. The examination of heterogeneous effects suggests that high public trust in government and the perception of its truthfulness double the impact of policy restrictions on public compliance. Among political regimes, higher levels of public trust significantly increase the predicted compliance as stringency level rises in authoritarian and democratic countries. Conclusion This study highlights the importance of public trust in government and its institutions during public health emergencies such as the COVID-19 pandemic. Our results are relevant and help understand why governments need to address the risks of non-compliance among low trusting individuals to achieve the success of the containment policies.

Mapping subnational HIV mortality in six Latin American countries with incomplete vital registration systems

Abstract: Background Human immunodeficiency virus (HIV) remains a public health priority in Latin America. While the burden of HIV is historically concentrated in urban areas and high-risk groups, subnational estimates that cover multiple countries and years are missing. This paucity is partially due to incomplete vital registration (VR) systems and statistical challenges related to estimating mortality rates in areas with low numbers of HIV deaths. In this analysis, we address this gap and provide novel estimates of the HIV mortality rate and the number of HIV deaths by age group, sex, and municipality in Brazil, Colombia, Costa Rica, Ecuador, Guatemala, and Mexico. Methods We performed an ecological study using VR data ranging from 2000 to 2017, dependent on individual country data availability. We modeled HIV mortality using a Bayesian spatially explicit mixed-effects regression model that incorporates prior information on VR completeness. We calibrated our results to the Global Burden of Disease Study 2017. Results All countries displayed over a 40-fold difference in HIV mortality between municipalities with the highest and lowest age-standardized HIV mortality rate in the last year of study for men, and over a 20-fold difference for women. Despite decreases in national HIV mortality in all countries—apart from Ecuador—across the period of study, we found broad variation in relative changes in HIV mortality at the municipality level and increasing relative inequality over time in all countries. In all six countries included in this analysis, 50% or more HIV deaths were concentrated in fewer than 10% of municipalities in the latest year of study. In addition, national age patterns reflected shifts in mortality to older age groups—the median age group among decedents ranged from 30 to 45 years of age at the municipality level in Brazil, Colombia, and Mexico in 2017. Conclusions Our subnational estimates of HIV mortality revealed significant spatial variation and diverging local trends in HIV mortality over time and by age. This analysis provides a framework for incorporating data and uncertainty from incomplete VR systems and can help guide more geographically precise public health intervention to support HIV-related care and reduce HIV-related deaths.