Context: Enabling access to data is critical for experts to analyse and provide evidence-based knowledge to guide decision making. The article highlights how data played an important role in decision-making in dealing with the Covid-19 pandemic. 

Importance of data in decision making

• The COVID-19 pandemic has highlighted globally how important data is to governments in decision-making.
• Epidemiological data is of paramount significance for targeting and implementing control measures for public health in a timely manner
• Such data was used effectively in the evidence-based response and decision-making in countries like South Korea.
• Modern response to pandemics has focused on exploiting all the available data to inform policy action in real time.

How data analysis helped during pandemic

• Data analysis has revealed the need for continuous and repeated tracking of case numbers, fatalities and recoveries.
• The epidemiological concept of flattening the curve and its predictions are results of data analysis and modelling.
• Understanding testing adequacy or lack thereof allows us to measure our preparedness, prognostic versus diagnostic ability, and shape our responses to identify, manage, and care for new cases.
• Epidemic outbreak data like case data, medical and treatment data can be used to understand disease pathogenesis and severity.
• Genome sequencing surveillance helps identify and track viral genome sequence variants in real time and the evolution of the virus.
• The concept of open access to various data enables models to improve forecast and study the spread of the disease.’

Integration and analysis of multiple datatypes

• The integration and analysis of multiple heterogeneous datatypes eventually would yield a holistic picture.
• This helps guide policy decisions for control and management of public health.
• When genome surveillance data is correlated with the magnitude of cases and their outcomes, then we can understand the transmissibility or infectivity of the virus.
• Geographical mapping of prevalence of mutants allows us to understand viral spread and explain recoveries or deaths in a specific area.
• The roll out of vaccinations can shape viral evolution and drug-treatment strategies.
• Surveillance through studying genome sequencing of the virus, coupled to other epidemiological data allows us to identify these connections.

Challenges

• Part of the challenge lies in the standardisation of data collection, curation, annotation and the integration of data analytics pipelines for outbreak analytics.

Suggestion

• Ensuring data availability and quality under operational constraints is critical.
• The use of data standards instils consistency, reduces errors and enables transparency.
• Embedded in the idea of data sharing lies the concept of data security and confidentiality.
• Concerns of privacy and security calls for a systemic infrastructure with built-in safeguards to ensure data encryption while preserving anonymity and ensuring privacy.
• As our dependence on data-based decisions becomes more and more critical, an urgent charter for standardised digital health data in India is required.

Road Ahead

• Rational and scientific methods necessitate data without which neither can we have information, nor knowledge or wisdom. Data sharing, and transparency and timely dissemination of data are critical to overcome the pandemic.