Throughout human history, there have been pandemics, and pandemics can be caused by a wide variety of infectious agents. In 165 A.D., the Antonine Plague caused 2,000 deaths per day in Rome and killed one quarter of the people that became infected with smallpox-like illness (Littman and Littman 1973). In 541 A.D., the Justinian Plague caused 5,000 deaths per day in Constantinople, killing an estimated 25 million people globally (Scott and Duncan 2001). The Black Death killed an estimated 100 million people over 7 years (Ziegler 2013). In 1918, the Spanish flu (H1N1) killed roughly 100 million people and infected 500 million people while affecting working age people (15–54 year olds) the most severely (Johnson and Mueller 2002). Although the 2009 H1N1 influenza virus did not have the high levels of mortality observed in the previously mentioned pandemics, the pandemic affected working age adults the most severely similarly to the 1918 Spanish flu (20–59 year olds; Viboud et al. 2010). Although the scientific community does not have a crystal ball to predict when the next pandemic will occur, history is likely to repeat itself.
Concerns over climate and environmental change, limited natural resources, and a population expected to reach as much as 13 billion by 2050 highlight several of the challenges that are increasingly the likelihood of the next pandemic (Pimentel et al. 2010). Beyond history repeating itself, the world is rapidly changing, making a severe pandemic increasingly likely (Suk and Semenza 2011). Increasing population (especially in urban areas) and increasing pollution of food, water, air, and soil by chemicals and infectious diseases are causing a rapid increase in the prevalence of disease and human mortality (Pimentel et al. 2010; Murray and Lopez 1997; Pimentel and Pimentel 2007).
Climate change will cause different ecological interactions; thus, zoonotic diseases will likely emerge in new transitional ecological zones (Harvell et al. 2002; Patz et al. 2005). While old diseases will re-emerge in the developed world, their effects will be most detrimental in the third world (Patz et al. 2004). From influenza to HIV, the urbanization of the global population will increase the rate at which zoonotic and anthropogenic diseases are transmitted (Sclar et al. 2005). High population density and greater ease of global transportation will increase the frequency and intensity of disease cycles and increase the demand for limited public health resources (Alirol et al. 2011). Complicating matters further, 925 million humans are currently malnourished worldwide, which increases the probability of disease infection (FAO 2010). An increasing population will cause the competition for water, energy, and food resources to intensify. Furthermore, climate change will increase ecological interactions likely contributing to growing disease emergence risk; possible reductions in crop productivity due to changing weather patterns, increasing plant pathogens, and pests; and armed conflicts where potable water, natural resources for manufacturing and energy, and nutritious foods are less readily available or extremely scarce. By increasing the variety and transmissibility of infectious agents, and by increasing the stress on food production systems, these challenges will complicate the response to a pandemic in the future.
One of the greatest challenges in pandemic planning is developing systems (e.g., food, water, and energy production) that are resilient enough to continue functioning during a severe pandemic. Unfortunately, there are no simple solutions to tackle problems of this magnitude and complexity. The combination of multiple interdependent systems and worker absenteeism creates a potentially fragile situation during a pandemic due to the critical interdependencies between multiple systems (Fig. 1). Worker absenteeism can place significant stress on product manufacturing, energy production, and transportation systems (Hessel 2009; Osterholm 2005). The global food system depends on these systems, as do most other vital systems in modern society. Without a healthy workforce, supply chains operate below optimal capacity or shut down altogether. Sick employees, changes in demand, or inventory shortages can all affect a broad spectrum of supply chains, including supplies needed to combat the pandemic (Kumar and Chandra 2010). For example, everything created and used in modern medicine is reliant on fossil fuel and electricity systems in some fashion (Osterholm and Kelley 2009). There is currently an inadequate amount of medical supplies that are vital to pandemic preparedness and response (Adalja et al. 2012), and this problem is likely to be exacerbated during pandemic response.
A fundamental property of interdependent networks is that failure or degradation in one system may cause the failure of other dependent systems (Buldyrev et al. 2010). There are multiple examples of real-world cascading failures, especially in systems that have single points of failure. The most pervasive cascading system failure is the electric system blackout (Dobson et al. 2007). On August 14, 2003, a series of failures on the US and Canadian Northeastern Power grid caused 55 million people to go without electricity causing sewage systems to overflow, rail service to retard, gas stations to shut down, communications systems to fail, food to spoil, and food processing and distribution to come to a halt (Lin et al. 2011; Hines et al. 2009). This real-world example, and many others, demonstrates the reliance of the food and agriculture system on other interdependent systems. In the case of a pandemic, worker absenteeism may cause multiple points of failure within the food and agriculture system itself or in the interdependent systems that the food and agriculture system relies upon to function.
For these reasons, private industry is starting to have greater interest in resilience (Meuwissen et al. 2010), but private companies in the food system are still unprepared for disruptions to the supply chain (Nikou and Selamat 2013). Typical food supply chains are large, vertically integrated, and owned by multinational public and private corporations with a high degree of product diversity (Roth et al. 2008). More than 80 % of food is delivered through the global supply chain with a major focus on low cost and high efficiency. Due to the small profit margins across the majority of the food industry, pressure to reduce cost has led to the consolidation of food companies, and now, only a few companies control most of the volume of food products in the global food system (e.g., Archer Daniels Midland, Cargill, Kraft, Nestle, PepsiCo, Unilever, and Walmart). The economies of scale created by these companies have created major barriers for new competitors. The dependence on large multinational private food companies for domestic and international food security is a difficult challenge for food system resiliency leaving limited options to government policy makers, especially during a pandemic.
The food system’s dependence on the transportation system creates a major vulnerability. The transportation system can shut down during pandemics, causing the movement of vital cargo to halt (Luke and Rodrigue 2008). The food system has become increasingly dependent on transportation to deliver its products over long distances. On average, food travels 1,300 miles from “farm to fork” (Zsidisin and Ritchie 2009).
The global food system, with its broad array of perishable products, functions in a just-in-time economy where food inventories are intentionally kept at such low levels that food arrives just in time for consumption. This is the source of much of the increased efficiency in the food system. Since inventories are kept very low, there is vulnerability to unanticipated variations in flow. Increasing stocks of food costs money and decreases profits; therefore, agricultural businesses are reluctant to build food security resilience via stockpiling (Beck et al. 2006). Modern society heavily depends on the timely delivery of goods (McKinnon 2006), not only for delivery of food to retail distribution but also for delivery of agricultural inputs to farms (e.g., seeds, animal food, fertilizer) and the delivery of farm products to processors, packagers, spot markets, and exporters.
Two case studies examined the impact of interruption in transportation on food supply. First, in 1979, truck drivers in the UK went on strike for a few days, and because food inventories were high, the worker absenteeism did not affect local food availability (McKinnon 2006). In 2000, truck owner operators in the UK blocked major roads and fuel distribution depots for 3 days. If the blockade had lasted 1 day more, food retailers in the UK would have run out of food. The volume of retail traffic dropped to 10–12 % below average and the national industrial output decreased by 10 %. This experience demonstrates that relatively minor disruptions on transportation can cause large problems if they persist. McKinnon (2006) also simulated the effect of a total loss of trucking and found that bread would be gone within 2 days from supermarkets. Recently, worker absenteeism caused by the largest outbreak of Ebola virus disease shut down food production and food supply chains in Western Africa (FAO 2014). As of November 2014, the World Food Program estimated that 460,000 additional individuals became food insecure in Liberia, Sierra Leone, and Guinea as a result of production and trade reductions (FAO 2014, 2015). These real-world events and simulations highlight the fragile nature of the food system and the important relationship between food and transportation systems.
Unfortunately, the consolidation of retail distribution could increase the consequences of a pandemic (Peck 2006). Portions of supply chains that are dense, complex, or critical are more vulnerable to disruptions (Lederman et al. 2009). The USA’s food system’s critical points are in the middle of the supply chain. This creates a bottleneck effect where there are a large number of farmers and producers, and a large number of consumers, but there are not many processing and packaging plants in the middle of the supply chain (Burger et al. 2010). The reliance on these choke points creates vulnerability where a disruption to the food system’s workforce at processing plants, packaging plants, and distribution centers could disrupt the entire food supply chain.
Another potential problem is the USA’s reliance on imported food. Of the food consumed in the USA, 10–15 % is imported (McDonald 2013). If a localized outbreak were to affect worker absenteeism abroad, then the food supply chain in other countries would likely be disrupted causing a reduction in the amount of food imported into the USA. Companies are currently unprepared for this possibility and rely on international borders that remain open to transport, which may not be the case during a pandemic (Meuwissen et al. 2010).
Consumers do not generally store large amounts of food (Sennebogen 2011), in part because a large number live in cities without much personal storage space. For example, the average home size is 1,895 square feet in Los Angeles, 1,417 square feet in Chicago, and 1,124 square feet in New York City (Calin 2012; U.S. Census 2014). Currently, 50 % of people worldwide live in cities, and this percentage is expected to rise to 60 % by 2030 (National Intelligence Council 2013). This will likely exacerbate the problem of small amounts of individual food storage, especially during events that cause disruptions to the food supply chain. Another cause of small individual stores of food is poverty. During the 2002–2004 SARS outbreak in Asia, most people had very little food stored at home (Lederman et al. 2009). The combination of the food supply chain disruption due to the SARS outbreak and the minimal individual stores of food created a situation where many people had difficulty obtaining food. A similar situation could be caused by a wide variety of infectious agents (Brown 2009).
Though it is not possible to know whether there will be a severe pandemic in any given year, highly pathogenic airborne viruses like pandemic influenza can spread rapidly around the world. A severe pandemic would likely have multiple waves of infection, each lasting 2–3 months, and reaching infection rates of 30 % or more (DHS 2006; FFIEC 2007; OSHA 2007). In independent studies, it was determined that a pandemic could last for up to 18–24 months, with three waves each lasting up to 3 months (Hickson et al. 2008; Staples 2006).
One way that a pandemic would indirectly impact the food supply chain is by altering consumer behavior. Pandemics create uncertainty and volatility in consumer demand, making it particularly difficult to maintain food inventories in a just in time economy (Vo and Thiel 2006). In a study of the effect of a disaster on behavior, the most frequent response is to stockpile supplies, food, and water (Kohn et al. 2012). This rush to buy food would quickly raise demand on a weakened food production and transportation system, which would likely lead to more shortages. The most common food items to be stockpiled by consumers are bottled water, milk, and canned food. Even food retailers panic purchase (Peck 2006).
Another major impact of a severe pandemic is on the workforce, affecting food system output at every step of production. The significance of transportation for the food system is not simply a matter of transporting food from one step of the supply chain to another. Other systems and supply chains, on which the food system depends, like water, electricity, and transportation, are also vulnerable to disruption due to labor shortages (Beck et al. 2006). Absenteeism was found to be a major source of potential vulnerability in the coal supply chain during a severe pandemic in the USA (Kelley and Osterholm 2008). The greatest impact projected by absenteeism along the coal supply chain was in transportation of coal stocks, which over the course of a severe pandemic could lead to power shortages (Kelley and Osterholm 2008).
The National Infrastructure Simulation and Analysis Center created a model to evaluate the potential impacts of a pandemic on numerous sectors of the USA’s economy. NISAC claimed that the food system is vulnerable to disruptions but could not withstand a labor shortage of over 10 % for a few months. NISAC also found that many aspects of the food system are labor intensive (i.e., transportation, wholesale, processing, and farming) and estimated that a 25 % reduction in labor would cause a 49 % reduction in food production. Their analysis concluded that with a 10 % reduction in labor all elements would remain operational, though there would be major shortages. However, the absenteeism rate of 10 % in the NISAC study was highly optimistic. Absenteeism in a severe pandemic could be as much as 20–40 % (DHS 2006; FFIEC 2007; OSHA 2007). Furthermore, one of NISAC’s analyses examined the effect of worker absenteeism on a regional milk supply chain and found that although milk production facilities did not shut down with a 25 % reduction in labor, there was a 49 % reduction in milk production—a worrisome result.
Despite the direct effects of worker absenteeism on the food production process, worker absenteeism can affect food systems indirectly. A loss of transportation can interrupt waste removal. In a survey (Peck 2006), one retail distributor stated:
… food production operations would cease within 36 h if (production) waste could not be disposed of. The food system should be viewed as a pipeline. The supplier at one end, and consumers at the other—there is little capacity to stop the pipeline in mid-flow.
This suggests that a high rate of worker absenteeism in the waste disposal system could bring food production to a halt.
Modeling is one way to explore how the resilience to withstand pandemics can be built into food systems. Hickson et al. (2008) researched several different aspects of Manitoba’s resiliency: population, nutritional needs, nutrition of food being consumed, the food system (i.e., inventories, transportation), and possible mitigation. They simulated a food delivery system with 35 % absenteeism due to pandemic and found that some regions of Manitoba could have food shortages due to transportation shortfalls. The Hickson et al. (2008) research was performed to identify potential solutions to mitigate food shortages during a pandemic, as opposed to identifying root causes of their food system’s failure.
Payan (2013) created an agent-based model to examine the effects of worker absenteeism on milk supply. The model incorporates a “bullwhip effect,” in which the variation in the purchasing orders is amplified as orders move closer to the source of production. The model assumed no changes to inputs or outputs during a pandemic (an unrealistic assumption). However, the model assumed that every part of the milk production process would be affected by labor shortages except for retail. Lastly, the model assumed about a 10 % slack in processing and transportation. The simulation counted the number of days in which demand was not met. The model found that (1) the greatest amount of disruption to the supply chain was in the middle of milk supply chain; (2) the least amount of disruption was in the retail sector; (3) that the greatest variability in demand was at the farm level; and (4) that the inventory decreased nearing the consumer. From this analysis, Payan (2013) concluded that the oscillating behavior of the milk inventory reflected the high impact of a pandemic on the milk supply chain.