COVI-19 surge preparedness with AI, genomic surveillance

The novel coronavirus disease (COVID-19) crisis has significantly redefined the humanitarian emergency paradigm and changed the understanding of disaster management in several ways.

• New and emerging variants of SARS-CoV-2 virus continue to pose a threat to the health of populations across the globe.
• Counting the unique and observable changes in the sequences available until January 2022 shows more than 6,000 mutations have accumulated in the spike gene of the virus.
• Initial studies claimed SARS-CoV-2 to be a fast-mutating virus which may make the virus fitter over time.
• More recent studies have estimated moderate substitution rates of the whole genome at 0.00067 and the spike gene at 0.00081 substitutions per site per year, respectively.
• A preprint claimed that the fitness of the SARS-CoV-2 virus is increasing because of the natural phenomenon of purifying selection of the spike protein.

How management of COVID is difficult than other disasters?

• Not limited to a geography: The crisis is not limited by a geographic area or a cluster or physically defined areas in which the disaster occurred — as in an earthquake, flood or cyclone.
• Unimaginable transmission: Effects of the disaster are so microscopic and invisible that one can easily underestimate its virulence or potency, as it happened in the early days of the pandemic. 

• Earlier epidemics like SARS (Severe Acute Respiratory Syndrome) and those due to bird flu and Ebola had a relatively lower geographical influence, but the speed of transmission and virulence of COVID-19 has posed an entirely new challenge.
• Threatened globalization: To mitigate the impacts of COVID-19, the process of globalisation, travel and access was strictly restricted. 

The changing nature of disasters

• Increasing unpredictability: With the nature of disasters changing constantly, they can surprise humanity by their unpredictability and speed of onset, despite access to the most advanced and sophisticated information and early warning systems.

• In recent disasters, the inability to predict the incidence of mudslides or the amount of water to be held or released in dams during heavy rains — whether in Mumbai, Kerala or Chennai in recent years.
• The ferocity of volcanic discharges recently in the Philippines and New Zealand surprised many scientists and earthquakes continue to surprise us with their relative unpredictability.
• The ability of disaster management authorities to reasonably predict or anticipate would be put to test in the days to come.
• Inability to anticipate impact: One of the issues that came to the forefront in the COVID-19 crisis in India was the seeming inability of governments to anticipate the impact of the suddenness of the lockdown on migrant labourers in various parts of the country.

How to manage disasters in future?

• Quick response: The speed of response would need to be gauged not only how quickly we enforced physical distancing and lockdowns, but also in the speed and reach of preventive messaging.
• Quick procurement: The speed of response is often linked to the ability to procure materials in a timely and cost-effective manner in every disaster.

• Notable among the countries that responded quickly have been Taiwan and Hong Kong which could therefore contain the infection levels quickly.
• Smart response: There is also an urgent need to be “smart” in responses.
• Strategic and tactical responses: The government should not lose sight of our strategic and tactical responses while implementing steps to mitigate the crisis.
• Good Governance: Good governance, responsive administration and active coordination should be non-negotiable features of a dynamic process that is driven by transparency and accountability on the part of public officials.

Where utilisation of genomic can help?

• There is crucial need to use genomic features to predict surges of cases.
• Consortia and open-data initiatives across the globe, such as the Indian SARS-CoV-2 Genomics Consortium (INSACOG) and GISAID have been instrumental for identification of new variants.
• However, most of the inferences from genomic surveillance have so far been retrospective in nature — explaining the past rather than predictive of the future.

AI & Disaster Management

• AI or Artificial Intelligence mimics human intelligence and processes by computer systems procedures.
• AI has the potential to speed up our understanding of natural hazards, analysing large volumes of data (and images) from different sources and improve proactive rather than reactive actions for disaster risk reduction (DRR).
• It can improve readiness and lessen the human and infrastructure costs of major events when they do occur.
• AI and machine learning can help public safety officials refine strategies over time, getting smarter about planning and response.
• AI can be used to analyze event data for patterns, identify current at-risk areas and populations, and model future needs, based on population growth, development, and climate change, among other variables.
• Government leaders can use these insights to craft policies that reduce the impact of disasters on communities, like planning new buildings in less vulnerable areas.

Way forward

Effective learnings from the covid-19 hopefully would result in a lot more of preventive disaster management plans and strategies being implemented across the country in the future —

• flood-proofing areas prone to annual flooding
• creating infrastructure for community disaster response plans
• drought-proofing arid areas by implementing ever-greening strategies combining

• decentralised, community-based water management
• appropriate agriculture choices
• agro-forestry with active encouragement by local, central and state governments