Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health

Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zika virus, and now COVID-19 have been impacting global health defences, and aggressively flourishing with the rise of global travel, urbanization, climate change, and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework

The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks. The gaze of these networks has in recent years turned increasingly towards applications in healthcare. It was perhaps inevitable that COVID-19, a global disease propagating health and economic devastation, should capture the attention and resources of the world's computer scientists in academia and industry. The potential for AI to support the response to the pandemic has been proposed across a wide range of clinical and societal challenges, including disease forecasting, surveillance and antiviral drug discovery. This is likely to continue as the impact of the pandemic unfolds on the world's people, industries and economy but a surprising observation on the current pandemic has been the limited impact AI has had to date in the management of COVID-19. This correspondence focuses on exploring potential reasons behind the lack of successful adoption of AI models developed for COVID-19 diagnosis and prognosis, in front-line healthcare services. We highlight the moving clinical needs that models have had to address at different stages of the epidemic, and explain the importance of translating models to reflect local healthcare environments. We argue that both basic and applied research are essential to accelerate the potential of AI models, and this is particularly so during a rapidly evolving pandemic. This perspective on the response to COVID-19, may provide a glimpse into how the global scientific community should react to combat future disease outbreaks more effectively.Comment: Accepted in Nature Machine Intelligenc

RELATING RSV GENETIC DIVERSITY TO GLOBAL TRANSMISSION DYNAMICS

Many studies of Respiratory Syncytial Virus (RSV) have relied on analyses of the Major Surface Glycoprotein G gene (G gene). Global transmission patterns have not been well studied due to lack of systematic global surveillance efforts. This study used phylogenetic analysis of full genome data, categorized by geo-region, to determine the sources of RSV A and B infection in Chile and Houston, Texas. Additionally, disease severity studies have generally focused on outcomes associated with a single genotype. In this study we developed a statistical phylogenetic approach to explore the relationship between tree topology and disease severity. Disease severity data included if the patient was given oxygen, if they were hospitalized, and if they were admitted to an intensive care unit. Global data was downloaded from GenBank, separated into RSV A and RSV B, aligned, and manually optimized. The United States and Canada region was overrepresented in the publicly available data, so subsampling was conducted to reduce selection bias. Starting trees were generated from the subsampled datasets using RAxML. Geographic traits and trait state transition rates were jointly estimated in a Bayesian statistical framework using BEAST. The global transmission network was estimated using the Bayesian stochastic search variable selection and a constant population with a HKY genetic substitution model. Trait associations were calculated using BaTS. For RSV A, the time to most recent common ancestor (tMRCA) was 1963.40 (95% BCI: 1946.15, 1969.60). For RSV B, the tMRCA was 1963.80 (95% BCI: 1959.50, 1967.33). Europe and Central Asia was a key source of RSV A and B transmissions for both Chile and Houston. In addition, the Middle East and North Africa and Latin America and the Caribbean were sources of RSV transmission into Houston. For the RSV A clinical data, there were significant associations between disease severity and tree topology when analyzing all three traits together (AI 3.13 p\u3c0.01, PS 22.39 p\u3c0.01) and for oxygen (AI 0.98 p\u3c0.01, PS 9.32 p\u3c0.01) and hospitalization independently (AI 1.92 p\u3c0.01, PS 11.78 p\u3c0.01). No significant association was found between tree topology and ICU admission. No significant associations were found in the RSV B clinical data, which may be due to the small sample size and homogeneous outcomes in this group. Improved surveillance systems are needed to gain a better understanding of global transmission patterns to complement studies done of local transmission patterns, as global introductions play an important role in local outbreaks. Identifying genetic mutations that lead to more severe outcomes may help researchers target vaccine development

Application of Species Distribution Modeling for Avian Influenza surveillance in the United States considering the North America Migratory Flyways.

Highly Pathogenic Avian Influenza (HPAI) has recently (2014-2015) re-emerged in the United States (US) causing the largest outbreak in US history with 232 outbreaks and an estimated economic impact of $950 million. This study proposes to use suitability maps for Low Pathogenic Avian Influenza (LPAI) to identify areas at high risk for HPAI outbreaks. LPAI suitability maps were based on wild bird demographics, LPAI surveillance, and poultry density in combination with environmental, climatic, and socio-economic risk factors. Species distribution modeling was used to produce high-resolution (cell size: 500m x 500m) maps for Avian Influenza (AI) suitability in each of the four North American migratory flyways (NAMF). Results reveal that AI suitability is heterogeneously distributed throughout the US with higher suitability in specific zones of the Midwest and coastal areas. The resultant suitability maps adequately predicted most of the HPAI outbreak areas during the 2014-2015 epidemic in the US (i.e. 89% of HPAI outbreaks were located in areas identified as highly suitable for LPAI). Results are potentially useful for poultry producers and stakeholders in designing risk-based surveillance, outreach and intervention strategies to better prevent and control future HPAI outbreaks in the US

Disease Surveillance Networks Initiative Asia: Final Evaluation

The DSN Initiative was launched in 2007 under the new strategy of the Rockefeller Foundation. The initiative intends:[1] To improve human resources for disease surveillance in developing countries, thus bolstering national capacity to monitor, report, and respond to outbreaks;[2] To support regional networks to promote collaboration in disease surveillance and response across countries; and[3] To build bridges between regional and global monitoring effortsThe purpose of the DSN evaluation in the Mekong region was twofold:[1]To inform the work and strategy of the Foundation, its grantees, and the broader field of disease surveillance, based on the experience of DSN investments in the Mekong region. More specifically, the evaluation will inform future directions and strategies for current areas of DSN Initiative work, particularly in Asia, and will highlight potential new areas of work and strategy; and[2] To provide accountability to the Rockefeller Foundation's board, staff, and stakeholders for the DSN funds spent in the Mekong region

Modelling the species jump: towards assessing the risk of human infection from novel avian influenzas

The scientific understanding of the driving factors behind zoonotic and pandemic influenzas is hampered by complex interactions between viruses, animal hosts and humans. This complexity makes identifying influenza viruses of high zoonotic or pandemic risk, before they emerge from animal populations, extremely difficult and uncertain. As a first step towards assessing zoonotic risk of Influenza, we demonstrate a risk assessment framework to assess the relative likelihood of influenza A viruses, circulating in animal populations, making the species jump into humans. The intention is that such a risk assessment framework could assist decisionmakers to compare multiple influenza viruses for zoonotic potential and hence to develop appropriate strain-specific control measures. It also provides a first step towards showing proof of principle for an eventual pandemic risk model. We show that the spatial and temporal epidemiology is as important in assessing the risk of an influenza A species jump as understanding the innate molecular capability of the virus.We also demonstrate data deficiencies that need to be addressed in order to consistently combine both epidemiological and molecular virology data into a risk assessment framework

Disease Surveillance Networks Initiative Africa: Final Evaluation

The overall objective of the Foundation's Disease Surveillance Networks (DSN) Initiative is to strengthen technical capacity at the country level for disease surveillance and to bolster response to outbreaks through the sharing of technical information and expertise. It supports formalizing collaboration, information sharing and best practices among established networks as well as trans-national, interdisciplinary and multi-sectoral efforts, and is experienced in developing and fostering innovative partnerships. In order to more effectively address disease threats, the DSN has four key outcome areas:(1) forming and sustaining trans-boundary DSN;(2) strengthening and applying technical and communication skills by local experts and institutions;(3) increasing access and use of improved tools and methods on information sharing, reporting and monitoring; and(4) emphasizing One Health and transdisciplinary approaches to policy and practice at global, regional and local levels