29 Jan 2021
How are data driving the response for the ongoing COVID-19 pandemic?
By Priyanka Pillai, APPRISE Health Informatics Specialist
How do data support preparedness toward epidemics and pandemics? How do data inform the potential severity and spread of an outbreak?
Past infectious disease outbreaks have demonstrated several challenges associated with rapid aggregation, integration, and sharing of data to inform a response during an outbreak. The ongoing pandemic response has demonstrated the value of timely data collection and sharing and the usage of data for decision-making.
Role of data in pandemic preparedness and response
The 2015 Zika virus outbreak and 2013–16 Ebola virus outbreak highlighted the importance of public health emergency research in accelerating response measures. However, these public health emergency scenarios clearly demonstrated the challenges associated with rapid sharing of data and dissemination of research findings to inform the response. There is a vast global capacity to implement infectious disease data-sharing systems, yet the timeliness of collecting and sharing data are currently major roadblocks. The WHO statement on data sharing during public health emergencies clearly summarizes the need for timely sharing of preliminary results and research data.1 There is also strong support for recognizing open research data as a key component for emergency preparedness and response.
Public health and research response to the ongoing pandemic clearly articulates how data can be turned into information, knowledge, and wisdom by applying the right context, meaning, and insights. The infectious diseases data ecosystem comprises valuable data from a wide range of sources like clinical settings, primary care, diagnostic laboratories, public health surveillance systems, clinical research, emergency departments, epidemiology studies, and multi-omics studies.2 The knowledge and wisdom distilled from this diverse data ecosystem not only supports better preparedness for an outbreak but also bolsters response during a pandemic.
Historically, pneumonia has been the predominant indicator of critical illness associated with influenza pandemics. Patients with life-threatening pneumonia are treated at intensive care units (ICUs); thus, doing clinical research in ICUs is an important component of both pandemic preparedness as well as response. The clinical settings are at the forefront of combating epidemics and pandemics. The early warning signals also emerge in primary care and ICU settings.
Timely and accurate diagnostics are fundamental to understanding, measuring, and mitigating the burden of infectious diseases. The testing capacities should be rapid, cost efficient, reliable, sustainable, and available nationally to people. Ongoing research that evaluates and applies new diagnostics effectively to understand the features of diseases is a critical component of both preparedness and response. Next-generation sequencing (NGS) methods such as whole-genome sequencing (WGS) are important techniques for rapidly detecting pathogens and indentifying transmission pathways. It is the fastest way to understand the genetic features of a pathogen and also to understand the spread.
Epidemiology-based surveillance systems are essential because the impact assessment, based on early estimates of transmissibility and severity, cannot rely solely on observations based on clinical data. Individuals with mild infections are unlikely to present for treatment or be hospitalized, so those mild infections should be identified through enhanced case finding among contacts of the first few people presenting as cases. Community-based studies are also essential to determine the relative number of severe cases.
The data collected during an outbreak are used to build an evidence base for informing and implementing a response. The evidence base needs supporting tools and systems to develop estimation algorithms that will assess the early characteristics of the outbreak. The outbreak analysis data will be used to assess the risk, spatiotemporal spread, genetic diversity of the virus, clinical characteristics, disease burden, and prediction of epidemic peak timing, informing strategic public health objectives and appropriate deployment of front-line responders.
Challenges associated with using, re-using data for preparedness toward and response during an outbreak
Research data stored in siloed proprietary systems are often not standardized, making it difficult to collate and share information.3 Technical challenges in sharing data include lack of harmonization in surveillance systems, varying data quality, incompatible databases, differences in vocabulary, and inadequate data collection protocols.3 Therefore, it can be difficult to collate and share data across such barriers during a pandemic. There is a lack of consensus around the minimum informative dataset required for notifying a public health emergency and for planning response.
Due to understandable sensitivity around the handling of personal information, health data require robust privacy and security policies. Health data can be misused as a determinant to evaluate competency for work, mental health conditions, sexual health, etc. There is also limited clarity on what is ethical and what are legal requirements for data collection and usage. The bundled consent for secondary usage of data can be problematic due to a lack of clarity on the purposes for which the data may be used in the future.
Lack of harmonization around health data regulations across sectors and jurisdictions can significantly slow down the sharing of data during an infectious disease emergency. One of the biggest fears around sharing not only infectious diseases data but also health data in general is the risk to patient privacy posed by the secondary use of health information.3 There are serious concerns regarding the accidental release of sensitive personal data, misinterpretation of data, unintended consequences of sharing data, and possible negligence by data handlers who fail to comply with regulations.
Research during infectious disease emergencies can be intensive, and data sharing requires additional time and effort. There are negative perceptions around disclosure of key findings and sharing of pre-publication data during infectious disease emergency research. A significant challenge is the well-known “publish or perish” culture that impacts on public health surveillance where sharing of data can be perceived as a lost opportunity for academic gain. Motivational barriers may arise from lack of incentives to share data as the appropriate credit may not be given.
Public health units and hospitals and their associated ethical bodies work on different timelines that may also lead to the delay in approval processes. The reluctance of data custodians to release data for use within the jurisdiction and/or sector may cause further delays for accessing information during infectious disease emergency. There are also international barriers to sharing information, as some international privacy regulations may not be consistent with local regulations.
How do we address some of those challenges?
It is essential to facilitate harmonization by establishing consensus around a standardized vocabulary and structure (definition, variables, and formats) for datasets. The focus should be on implementing widely used existing standards instead of developing new standards. Data standardization also ensures the data are clean and consistent, saving precious data pre-processing time during an infectious disease emergency. The FAIR Data Principles4 framework promotes best practices in the collection, use, and re-use of data (Table 1).
Priyanka’s original piece appeared in Patterns, a Cell Press Journal.