Why it matters: The COVID-19 pandemic has had a profoundly negative impact on global health outcomes and is projected to cost the global economy $12.5 trillion through 2024.14 Pandemics stretch the limited resources of national healthcare systems and increase clinical backlogs,15 leading to more illness and death in both the short term (as surgeries and critical care are neglected) and long term (as fewer ailments are caught early when available treatments are more effective). Pandemic prediction and intelligence suffer from an inherent tension: there’s limited individual benefit to mobilizing early against a pandemic. However, the collective benefit is potentially enormous. Given this large downstream impact, governments have invested heavily in capabilities to predict infectious diseases at a population level before they spread so they can better direct public health resources. Making progress: National health systems have made progress in standardizing diagnosis reporting. Yet, accuracy and transparency vary by country, and – more importantly – diagnosis reporting is a lagging indicator. Disease monitoring shouldn’t just describe disease spread once it’s under way; monitoring must anticipate and guide institutions on the best way to act quickly, decisively and effectively. In addition to overcoming data issues, such as availability and interoperability, researchers have focused on three areas for spotting leading indicators: 1) the pathogen and its unique properties, 2) the population and its movement, and 3) the environment and how it can affect the speed at which pandemics progress. As climate change accelerates, the importance of tracking vectors and predicting infectious disease spread will increase. Analysts can model pathogen spread using data from animal samples, but collection at scale is often very difficult. Companies such as Concentric, a subsidiary of Ginkgo Bioworks, have successfully used natural language processing – which renders human text and speech understandable to computers – to track pathogen spread in human populations by scraping local news for reports of respiratory and gastrointestinal-related illnesses. When surveying the environment, companies such as Ginkgo Bioworks and BlueDot have demonstrated positive results using mobility and climate data to understand and predict mosquito movement patterns to help forecast the spread of mosquito-borne diseases. Using AI to sound the alarm (and even recommend interventions) is critical, but governments must commit the funds, sustain their willpower and focus on the agility to act when the next pandemic or epidemic arrives. The potential of AI to serve as an early warning system can complement and enhance the outcomes of traditional mathematical modelling and algorithmic forecasting. For example, AI can identify and predict climate-related health risks by collecting multiple layers of inputs, such as temperature trends, meteorological events, population density, vector habitat suitability, and flood- and droughtprone zones. These can be communicated to healthcare providers and communities as an early warning to ensure the supply reliability of medication, hospital equipment and beds and that important preventative measures are undertaken to minimize the event’s effect. Stakeholder roles: Governments are increasingly focused on predicting and controlling the spread of infectious diseases. While this has been a long-standing priority for low- and middle-income countries, it is also of increasing interest for highincome ones, especially in light of the COVID-19 pandemic. Public health agencies should lead in directing resources, but they must partner closely with technology companies, NGOs, providers and other community-based organizations to ensure AI-generated insights lead to timely interventions. Life sciences and payers must have sufficient, timely insights to help focus resources on outbreak prevention, but they should not be seen as primary actors in predicting or intervening against outbreaks. Public-private matrix for infectious disease intelligence F I GUR E 5 COVID-19 cost to global economy through 2024 $12.5 trillion Note: Connectors defined as NGOs, advocacy and patient groups, distributors, group purchasing organizations, investors and other organizations in healthcare. Use case/ stakeholders Government Connectors Technology Life sciences/ medtech Providers Payers Infectious disease intelligence Relatively more supportive role Relatively more central role 1.2 Use case for acceleration #2: infectious disease intelligence Scaling Smart Solutions with AI in Health: Unlocking Impact on High-Potential Use Cases 15
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