With a 24-hour news cycle and social media prevalence, we are being inundated with crisis after crisis. The latest example is with the new 2019-nCov coronavirus. Media and researchers across the globe are chasing the disease as it spreads. Similar to the 2002 Severe Acute Respiratory Syndrome (SARS) and the 2012 Middle East Respiratory Syndrome (MERS), we are witnessing a similar media phenomenon. Current efforts to control an outbreak have limited effectiveness due to the reactive nature of the available response options. What is required are options that get us ahead of the threat to identify those pathogens most likely to “leap” from animals to humans, ideally to prevent outbreaks in the first place.
These three outbreaks all carry the same characteristics: they are all coronaviruses and zoonotic, meaning they are transmitted between animals to people. These recent diseases originated in bats that in turn infected other animals, such as monkeys (as was the case with Ebola). As human society continues to encroach on and have greater interactions with the animal world, we can expect zoonotic outbreaks to continue with varying lethality.
To counter diseases, policymakers must make informed decisions, especially where to deploy limited medical assets and what countermeasures to take such as travel bans. Although less sensational as cruise ship quarantines, policymakers need to understand the effects of all the actions they take on the population and the disease. To make such informed decisions, policymakers rely upon data modeling to project the spread of the disease. However, even utilizing supercomputers such as those at the Los Alamos National Lab, the accuracy and effectiveness of the models can vary due to constantly changing inputs such as the movement patterns of those potentially infected and government actions such as the underreporting of cases. Even with the best and most accurate data, a complicated model can take days or even weeks to process, and this does not include the weeks of work modelers spend writing code and equations for the computer to utilize.
Disease surveillance using models to project outcomes is still the key in evaluating the effectiveness of control and preventive measures taken by governments. However, in a rapidly changing situation with potentially infected people traveling by air, land, and sea it is almost impossible to keep up with the disease. Even in the U.S. and cities with robust heath care systems projecting the spread of the disease is difficult, especially when there is not a rapid and real-time method of testing for the disease. We have already seen cases in the U.S. where patients were released but later identified as actually being infected. In other parts of the world, surveillance and limiting the spread of the disease is even more problematic due to the lack of a modern medical system and infrastructure. Additionally, once a disease is detected, the most countries can do is to try to limit its spread or develop a vaccine. In some cases, vaccines can take 12–18 months to develop and would be available only well after the disease would run its course naturally.
A more proactive solution is to prevent outbreaks in the first place through the early identification of dangerous pathogens from animals before they become threats to humans. According to the U.S. Agency for International Development (USAID), nearly 75 percent of all new, emerging or re-emerging diseases affecting humans at the beginning of the 21 stcentury were zoonotic. A novel approach to counter novel diseases is for governments to invest in programs that focus at the source of the disease. One such program is USAID’s Emerging Pandemic Threats (EPT) program, which strengthens capacities in developing countries. A reasonable approach is to expend resources for disease detection in those areas of the world where people have close contact with wildlife and “hotspots” for disease activity such as central Africa, South and Southeast Asia and Latin America. There are four projects within the EPT program, operating in 20 countries -predict, prevent, identify, and respond. “Predict”, has estimated that there are over 1.6 million unknown viral diseases in animal populations, of which 700,000 have the potential to infect and cause disease in humans.
Much like we are doing with modeling and artificial intelligence (AI) to project the spread of disease, advanced computing can be utilized to analyze animal viruses and allow us to intervene to prevent disease spillover in areas with high-risk animal-human interfaces. For example, modeling with the assistance of AI could help identify which of the 700,000 animal viruses has the greatest probability of causing diseases in humans. Following upon USAID’s EPT program is the Global Virome Project designed to identify, characterize and catalogue these viruses in an attempt to get ahead of potential threats by developing countermeasures such as vaccines in advance of future pandemic events. Some may balk at the cost of the program that could cost as much as U.S. $1.2 billion. However, to put this in perspective a single outbreak in one year, the SARS virus, cost the world economy about U.S. $40 billion. According to a study by the World Bank, a severe pandemic could cause economic losses equal to nearly 5 percent of global GDP, or more than U.S. $3 trillion. As it stands, the 2019-nCov coronavirus will end up costing the world economy over U.S. $280 billion in the first quarter alone of this year. Given these figures, the U.S. $1.2 billion to fund the Global Virome Project that attempts to get ahead of the threat seems like a reasonable investment to make.
Originally published at https://www.e3federal.com on February 25, 2020.