Identifying, Tracking, and Mitigating Future Outbreaks

Arvind Kothandaraman is the managing director of specialty diagnostics at PerkinElmer. His primary interest is in equipping clinical laboratories with the tools needed to meet their technical and operational goals. Prior to PerkinElmer, he held positions at Thermo Fisher Scientific, BioReference Laboratories (an OPKO Health Company), and Advanced Analytical Technologies (now part of Agilent Technologies).

It’s been two and half years since the initial SARS-CoV-2 outbreak, but we’re not completely out of the woods yet. As mask mandates and other restrictions are lifted and normal daily life starts to resume, it’s important that we continue to take steps to monitor potential variants of SARS-CoV-2 and other infectious diseases very closely. One such avenue is a global surveillance model that can be implemented through a three-pronged approach: outbreak monitoring, an early warning system, and intervention and containment measures.

1. Outbreak monitoring

First, a zoonotic spillover tracking mechanism would enable outbreak monitoring at the source of global hotspots, which is important as most human infectious diseases (60–75 percent) are derived from pathogens that originally circulated in nonhuman animal species. Multifaceted specimen collection and processing strategies need to be deployed, including analysis of food content, respiratory swabs, surface swabs, sputum, urine, and wastewater. Additionally, a multilayered technology strategy, including next-generation sequencing (NGS), real-time polymerase chain reaction (RT-PCR), mass spectrometry, and infrared spectroscopy platforms, can be used to take a multi-omics approach for promptly tackling outbreaks. One such organization that is employing these strategies is the Oklahoma Pandemic Center for Innovation and Excellence (OPCIE), which is focused on the entire ecosystem (i.e., humans, animals, and the environment) to study the COVID-19 pandemic and prepare for emerging ones. The state’s public health lab, located within OPCIE, uses state-of-the-art automated technology to monitor, research, and address public health concerns before they arise.

2. Early warning system

The second component of a global surveillance model is an early warning system that would identify and characterize progressively dangerous pathogens based on their ability to survive in human hosts, their transmission between humans, and their progress toward a pathogenic state. For example, in a study conducted between 2015 and 2017 in rural Southern China, 17 percent of the 1,596 participants reported symptoms consistent with SARS in the year prior, which were associated with human–animal contact, highlighting the risk of zoonotic spillover in that region. Early warning protocols allow such patterns to be captured, communicated, and acted upon in a timely manner.

3. Intervention and containment measures

Finally, a preselected set of intervention and containment measures would enable the prompt identification of at-risk populations in future pandemics. Existing infrastructure, such as the Global Influenza Surveillance and Response System or FluNet, can be leveraged to monitor and share data, and send alerts about potential pathogens. Rapid disease characterization could then be used to drive recommendations for intervention. Similarly, the U.S. Centers for Disease Control and Prevention launched the Center for Forecasting and Outbreak Analytics, which uses data, modeling, and analytics to enable timely and effective decision-making to improve outbreak response.

The COVID-19 pandemic has taught us that it’s crucial to be proactive rather than reactive in response to global outbreaks. A global surveillance model would create an openly shared resource enabling the rapid and appropriate communication of accurate, up-to-date information to ensure appropriate measures can be taken without delay. In an increasingly collaborative world, that type of model would benefit the global population by minimizing the risk of an outbreak escalating into the next pandemic.