How ownership rights over microorganisms affect infectious disease control and innovation: A root-cause analysis of barriers to data sharing as experienced by key stakeholders.

Genetic information of pathogens is an essential input for infectious disease control, public health and for research. Efficiency in preventing and responding to global outbreaks relies on timely access to such information. Still, ownership barriers stand in the way of timely sharing of genetic data from pathogens, frustrating efficient public health responses and ultimately the potential use of such resources in innovations. Under a One Health approach, stakeholders, their interests and ownership issues are manifold and need to be investigated. We interviewed key actors from governmental and non-governmental bodies to identify overlapping and conflicting interests, and the overall challenges for sharing pathogen data, to provide essential inputs to the further development of political and practical strategies for improved data sharing practices

How ownership rights over microorganisms affect infectious disease control and innovation: A root-cause analysis of barriers to data sharing as experienced by key stakeholders

Background Genetic information of pathogens is an essential input for infectious disease control, public health and for research. Efficiency in preventing and responding to global outbreaks relies on timely access to such information. Still, ownership barriers stand in the way of timely sharing of genetic data from pathogens, frustrating efficient public health responses and ultimately the potential use of such resources in innovations. Under a One Health approach, stakeholders, their interests and ownership issues are manifold and need to be investigated. We interviewed key actors from governmental and non-governmental bodies to identify overlapping and conflicting interests, and the overall challenges for sharing pathogen data, to provide essential inputs to the further development of political and practical strategies for improved data sharing practices. Methods & findings To identify and prioritize barriers, 52 Key Opinion Leaders were interviewed. A root-cause analysis was performed to identify causal relations between barriers. Finally, barriers were mapped to the innovation cycle reflecting how they affect the range of surveillance, innovation, and sharing activities. Four main barrier categories were found: compliance to regulations, negative consequences, self-interest, and insufficient incentives for compliance. When grouped in sectors (research institutes, public health organizations, supra-national organizations and industry) stakeholders appear to have similar interests, more than when grouped in domains (human, veterinary and food). Considering the innovation process, most of barriers could be mapped to the initial stages of the innovation cycle as sampling and sequencing phases. These are stages of primary importance to outbreak control and public health response. A minority of barriers applied to later stages in the innovation cycle, which are of more importance to product development. Conclusion Overall, barriers are complex and entangled, due to the diversity of causal factors and their crosscutting features. Therefore, barriers must be addressed in a comprehensive and integrated manner. Stakeholders have different interests highlighting the diversity in motivations for sharing pathogen data: prioritization of public health, basic research, economic welfare and/or innovative capacity. Broad inter-sectorial discussions should start with the alignment of these interests within sectors. The improved sharing of pathogen data, especially in upstream phases of the innovation process, will generate substantial public hea

The COMPARE Data Hubs

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats, often lead to data not being shared or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders

Blockchain-facilitated sharing to advance outbreak R&D.

Timely and widespread dissemination of resources and information related to pathogenic threats plays a critical role in outbreak recognition, research, containment, and mitigation, as stakeholders from government, public health (PH), industry, and academia seek to implement interventions and develop vaccines, diagnostics, and drugs. But there are persistent barriers to sharing and cooperative research and development (R&D) in the context of epidemics, rooted in a lack of trust in confidentiality and reciprocity, ambiguity over resource ownership, and conflicting public, private, and academic incentives. Here, we suggest how recent advances in blockchain and related technologies can enable decentralized mechanisms to help break down these systemic and largely nontechnological barriers. These mechanisms resolve scalability, energy consumption, and security concerns of early blockchain models and may be applied to underpin and interconnect, rather than supersede or conflict with existing, well-established systems and practices for storing, sharing, and governing resources

Root-cause analysis of the four barriers classified under the category “self-interest” (C).

<p>Columns represent barriers, causes and root causes and rows represent the causal argumentation of the KOLs. The barriers are color-coded and numbered following the thematic classification in barriers types as the following: compliance to regulations (A, #1–7 are light grey), negative consequences (B, #8–10 are dark grey), self-interest (C, #11–14 are white) and insufficient incentives for compliance (D, #15 is black).</p

Root-cause analysis of the barrier classified under the category “insufficient incentives for compliance” (D).

<p>Columns represent barriers, causes and root causes and rows representing the causal argumentation of the KOLs. The barriers are color-coded and numbered following the thematic classification in barriers types as the following: compliance to regulations (A, #1–7 are light grey), negative consequences (B, #8–10 are dark grey), self-interest (C, #11–14 are white) and insufficient incentives for compliance (D, #15 is black).</p

The valorization & technology transfer cycle depicts the steps in the innovation process for infectious diseases control: From disease emergence to development and marketing of medical countermeasures [16].

<p>The upstream phases (steps 1–7) in the cycle overlap with infectious disease surveillance and initial outbreak response, while downstream phases (steps 8–14) overlap with product development as medical countermeasures to control infectious diseases.</p

The commercial sector (CS) differentiated substantially from the other stakeholder groups in the quantification of the mentioned barriers.

<p>The order generated by the frequency of occurrence as mentioned by the national surveillance centers (NSC) is taken as a reference, to which the behavior of the curves representing the commercial sector (CS), research institutes (RI), and supranational organizations (SO) is compared. Barriers are numbered according to the root-cause analysis (Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195885#pone.0195885.g003" target="_blank">3</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195885#pone.0195885.g006" target="_blank">6</a>) and the asterisk identifies barriers that persist in the downstream phases of the innovation cycle (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195885#pone.0195885.g007" target="_blank">Fig 7</a>).</p