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China’s quest for 5G dominance has played out within a complex technological and geopolitical landscape.108 Indeed, different countries have their own security concerns and considerations, but not all share American assessments of the severity of these risks. Insofar as American policymakers see China as a great power rival and strategic competitor, allowing Chinese companies to play a key role in American critical infrastructure, or that of U.S. allies and partners, presents grave threats that are untenable and unacceptable for the United States, not only espionage but also outright subversion of this critical infrastructure.109 Yet Huawei has continued to expand its global presence, and the U.S. government has yet to present a viable and attractive alternative to working with Huawei. Many countries may have sunk costs and be “locked in” already to this choice based on earlier decisions, which raises concerns about not only security but also fair competition.110 However, it is encouraging to see emerging consensus among like-minded countries about potential principles and shared approaches to 5G security, particularly through the progress of a recent conference on 5G security in Prague.111 The age of 5G will present new risks and novel threats of disruption or exploitation. 5G involves far more than just new and faster wireless networks; it will be a vital component of future critical infrastructure. Consequently, the cybersecurity of 5G networks could prove uniquely challenging, considering the high levels of complexity and much greater potential for damage in the case of an attack. Not only the confidentiality of data on 5G networks but also questions of integrity and assurance will become urgent challenges. Whereas most cyberattacks to date have involved only data theft, an attack against future 5G networks could cause massive damage that might threaten public safety and critical industries in future smart cities.112 The often subpar security of IoT devices, of which there are an estimated 20 billion globally and growing, also presents serious reasons for concern. A high proportion of devices on the U.S. market have been made in China by companies with very poor track records on security.113 While vulnerabilities have been and remain a major concern in the telecom industry for 3G and 4G, the stakes will be even higher for securing 5G networks at all stages of their life cycles.114 In some cases, supply chains could be weaponized deliberately by adversaries that may prefer to “win without fighting.”115 The exclusion of high-risk vendors is an important measure to mitigate risk but does not constitute a complete solution. 5G must be designed and implemented with a holistic approach to security in mind from the start. The development of secure networks must entail more than simply excluding high-risk vendors, requiring rigorous, ongoing testing and screening. Indeed, careful scrutiny should be extended to all aspects of the production, construction, and management of these networks, involving screening of the security of all vendors and carriers. If an end-to-end approach to security is effectively implemented, 5G could prove more secure than our existing networks and critical infrastructure, but the consequences of insecurity would be far graver. In public debates on 5G security, the call and search for a “smoking gun” has been problematic. This framing of the issue has often distracted policymakers from thinking about the greater challenge of mitigating vulnerabilities that tend to be pervasive. Bugs can be just as problematic as backdoors. It is inherently challenging to differentiate an accidental vulnerability from one that is deliberately introduced. The primary difference is intent, which cannot be discerned from code alone. It is encouraging that the 3GPP’s SA3 working group is focusing on security, seeking to ensure that such security concerns will shape the development of standards.116 However, industry and government are just starting to grapple with the full range of issues in play. Given the gravity of these security challenges, the apparent centrality of Chinese companies in the global development of 5G has raised intense concerns. There is a very real risk that vulnerabilities in networks, whether the result of poor security practices or deliberate introduction of backdoors, could be weaponized for leverage or coercive purposes, particularly in a crisis or conflict scenario. Considering China’s history of IP theft and cyberespionage, there is also a real risk such networks could be exploited for purposes of espionage.117 As a Chinese company, Huawei also would be subject to a number of legal demands, regulatory requirements, and mechanisms of coercion that are often ambiguous and expansive.118 Regardless of whether Huawei’s leadership may wish to disregard an order from the Chinese government, China lacks an independent judiciary system for company leaders to plead their case against the government, as Apple did in the United States when it fought an FBI order to unlock an iPhone. Huawei’s claims that it would “say no” to the Chinese government are not credible without indications of the company’s actual ability to do so. Even if Huawei is given the full benefit of the doubt, despite its history and apparent involvement with the Chinese military and intelligence organizations, Huawei’s products and services have been assessed to be highly insecure, with a much greater prevalence of vulnerabilities relative to their primary competitors.119 Moreover, there are reasons to question whether knowledge of any bugs in its equipment could be shared more readily with China’s Ministry of State Security (MSS). This risk may be heightened given the influence of MSS in China’s vulnerabilities database, not to mention Huawei’s historical and continued linkages to the Chinese People’s Liberation Army, including military intelligence.120 For the United States, these risks and security concerns are inextricable from today’s geopolitical exigencies, insofar as the U.S.-China rivalry encompasses scenarios for which there is a nonzero probability of conflict, including over Taiwan. Consistently, Chinese military writings have highlighted the potential for cyberattacks on critical infrastructure as a prelude to outright warfare.121 The presence of equipment from high-risk vendors, such as Huawei, even in rural telecoms is concerning, considering that some of these networks are near military bases, which raises risks of espionage or exploitation. 5G security presents a global challenge that will demand creative and cooperative solutions. Huawei will likely remain a major player in 5G in a number of countries, including some U.S. allies and partners, that believe the benefits of partnering with it outweigh the risks. Although a criteria-based calculation of risk provides compelling arguments for exclusion of such highly risky players, many nations could still continue current collaborations with Huawei in ways that exacerbate global risks to this emergent ecosystem. Even if the United States were to succeed in fully securing its own 5G networks, U.S. data and entities may remain reliant, including for military and commercial activities, upon overseas digital infrastructure that could prove highly vulnerable.The presence of Huawei’s equipment in the critical infrastructure of U.S. allies and partners, whose support or location as a staging ground the U.S. military might require to fulfill its treaty obligations in the event of a crisis or conflict, also creates new risks, to an extent that could undermine U.S. capabilities for command and control and power projection. As Dan Coats, had warned during his time as director of national intelligence (DNI), “U.S. data will increasingly flow across foreign-produced equipment and foreign-controlled networks, raising the risk of foreign access and denial of service.”122

Forced to choose sides, governments may face economic or diplomatic consequences, as proxy disputes play out in control over economic or geographic resources. The deepening of geopolitical fault lines and the lack of viable middle power alternatives make it harder for countries to cultivate connective tissue with a diverse set of partner countries based on mutual values and maximizing efficiencies. Instead, networks will become thick in some directions and non-existent in others. The COVID-19 crisis has amplified this dynamic, as digital interactions represent a “huge loss in efficiency for diplomacy” compared with face-to-face discussions.23 With some alliances weakening, diplomatic relationships will become more unstable at points where superpower tectonic plates meet or withdraw At the same time, without superpower referees or middle power enforcement, global norms may no longer govern state behaviour. Some governments will thus see the solidification of rival blocs as an opportunity to engage in regional posturing, which will have destabilizing effects.24 Across societies, domestic discord and economic crises will increase the risk of autocracy, with corresponding censorship, surveillance, restriction of movement and abrogation of rights.25 Economic crises will also amplify the challenges for middle powers as they navigate geopolitical competition. ASEAN countries, for example, had offered a potential new manufacturing base as the United States and China decouple, but the pandemic has left these countries strapped for cash to invest in the necessary infrastructure and productive capacity.26 Economic fallout is pushing many countries to debt distress (see Chapter 1, Global Risks 2021). While G20 countries are supporting debt restructure for poorer nations,27 larger economies too may be at risk of default in the longer term;28 this would leave them further stranded—and unable to exercise leadership—on the global stage. Multilateral meltdown Middle power weaknesses will be reinforced in weakened institutions, which may translate to more uncertainty and lagging progress on shared global challenges such as climate change, health, poverty reduction and technology governance. In the absence of strong regulating institutions, the Arctic and space represent new realms for potential conflict as the superpowers and middle powers alike compete to extract resources and secure strategic advantage.29 If the global superpowers continue to accumulate economic, military and technological power in a zero-sum playing field, some middle powers could increasingly fall behind. Without cooperation or access to important innovations, middle powers will struggle to define solutions to the world’s problems. In the long term, GRPS respondents forecasted “weapons of mass destruction” and “state collapse” as the two top critical threats: in the absence of strong institutions or clear rules, clashes— such as those in Nagorno-Karabakh or the Galwan Valley—may more frequently flare into full-fledged interstate conflicts,30 which is particularly worrisome where unresolved tensions among nuclear powers are concerned. These conflicts may lead to state collapse, with weakened middle powers less willing or less able to step in to find a peaceful solution.

So suddenly, the world is being left on its own. And what we're seeing is the world is not on autopilot. It doesn't self-organize. And that's the most worrisome thing going forward that this pandemic is both a reflection, but also an accelerant of that because the United States has not rallied the world to meet the challenge posed by this pandemic. And instead, we've pulled back we become much more national, kind of an island power, the rest of the world, it's on its own, it's not doing very well. And this is this is the reason I'm so worried that a lot of the trends that were in place, including them on America, much less involved in the world, nationalism, populism, the breakdown of international collaboration, all those things were in train before this pandemic, and now we're seeing them double down.

MEGHNA: So is the you're saying the world is not self-organizing right now, perhaps, at this moment? And at the same time, it doesn't? Is it feeling the loss of the centrality of American leadership? Because, I think we could argue that the way that, for example, international leaders do talk about the United States right now, at times, they're not necessarily feeling the loss of American influence.

Innovation, in particular, technology-based innovation, is the key driver for both economic competitiveness and national security. Other nations, with interests adverse to the United States, recognize this fact. In an increasingly interconnected world, nation states seek to accumulate innovation prowess, and hence economic strength, as a key element of their geopolitical power. Especially savvy nation states also pursue such ends as a mechanism to influence or diminish the national security and geopolitical power of the United States. There is no need to inflict upon the world the carnage of war if one’s geopolitical aims can be achieved via alternative competitive means. Several authors suggest China’s long-term ambitions include unseating the United States as the world’s economic and political leader.1 More compelling than opinions, several United States (“U.S.”) government and private studies document a systematic and coordinated effort by China to achieve technical and economic dominance through misappropriation of U.S. technology.2 These efforts are additionally supported by a companion effort to weaken international economic institutions and norms designed to protect U.S. intellectual property and free trade.3 The Chinese tactics include illegal means, and sophisticated use of legal means, to misappropriate U.S. technology and weaken the U.S. innovation infrastructure including: a) Leveraging the open university and laboratory ecosystem via direct sponsorship and engagement of Chinese nationals;4 b) Devaluing U.S. positions in patents and technology platforms;5 and c) Accessing private sector U.S. technology through acquisitions and ownership stakes in existing firms, funding of high-tech start-ups, and forced joint ventures and other contractual agreements as a prerequisite for entering the Chinese market.6 This particular form of competitive strategy targeting the innovation ecosystem in the United States is labeled by the Authors as “Innovation Warfare,”7 and it is defined as an executable competitive strategy: a) Reflecting an innovation, intellectual property, and technology strategy articulated and executed by the state (e.g. China); b) Using illegal means, political means, and legal economic activities—of the type previously residing solely in the province of commercial enterprise, to achieve the state’s objectives; c) Employing these economic and innovation activities to achieve both economic geopolitical power and to enhance military capabilities; and d) Functioning as a military, national security, and defense doctrine not solely as a reflection of the state’s economic policy goals nor commercial competition in the ordinary course. Innovation Warfare does not just threaten American jobs and economic prosperity. By simultaneously co-opting and weakening the innovation capabilities of the United States, China seeks to advance its rise to world power. China’s prosecution of Innovation Warfare not only encompasses a rejection of a rules-based international order, but also poses an existential threat. A world where China dominates the technology landscape is not just about who earns the profits or prevails in an abstract geopolitical fight. According to the National Security Strategy of the United States of America (“National Security Strategy”), China pursues a world in which economies are less free, less fair, and less likely to respect human dignity and freedoms.8 China’s Innovation Warfare activities risk the type of economic and geopolitical aggressions that were a root cause of two World Wars.

Is a camera an abstract idea? Does a camera merely “exist[] in thought or as an idea [while] not having a physical or concrete existence?”1 That would seem an odd way to define an object with a defined shape that can be held in your hand and can give you a concussion if it is thrown at your head. Abstract ideas, on the whole, are concussion-proof.

A separate but related inquiry: is a process for manufacturing automobile driveshafts a law of nature? A few examples of laws of nature include Newton’s law of gravity, the ideal gas law, and the law of supply and demand.2 These are laws of nature because they describe “an observable law relating to natural phenomena.”3 Essentially, they are laws of nature because they are “statements of the uniformities or regularities of the world” and because “the natural world ‘obeys’” them.4 So, is a purportedly better way to manufacture a car part really a statement of the uniformity in the world? Or rather, does the process merely observe and provide a practical application of a law of nature?

Not hundred per cent of the dialogue has been frozen, fortunately. Certain informal, mostly offthe-record, meetings of US and Russian experts on cyber agenda continue taking place, both through Track 2 and Track 1.5. One of the most intellectually stimulating meetings, with frank exchanges, took place in Vienna in December 2018. The report produced after the meeting stressed “the significant risk […] that cyber-attacks could conceivably lead to a military escalation that may further trigger a nuclear weapons exchange, a fact that became more explicit with the adoption of the current Nuclear Posture Review. This issue gets complicated given that third parties may have the capabilities to invoke a cyber conflict between Russia and the United States. Whether a country or a non-state actor, they could put the two countries on the verge of an armed conflict by attacking critical infrastructure of either of them and making it look as if the aggressor were the other one”[22]. However, one should have no illusion: such informal meetings may be fully fruitful only when their reports and policy recommendations are utilized by the governments. And for that, a warmer climate in bilateral relations is a must. So far, we see exactly the opposite: mercury falling to freezing levels. Risk of cyber clashes growing into a chaotic global cyber war has been emphasized by the UN Secretary-General Antonio Guterres in his Agenda for Disarmament: “Malicious acts in cyberspace are contributing to diminishing trust among States… States should implement the recommendations elaborated under the auspices of the General Assembly, which aim at building international confidence and greater responsibility in the use of cyberspace.[23]” However, as the members of the US-Russian Track 1.5 working group on strategic stability recently concluded, “without a constructive dialogue on cyber issues between the United States and Russia, the world would most likely fail to agree on any norms of responsible behavior of states in cyber space”[24]. Do we really have to survive a cyber equivalent of the Cuban Missile Crisis to realize the importance of achieving some kind of agreement on cyber issues, and on the broader agenda of international information security?[25] Or is that kind of talk plain old alarmism? I don’t want to sound a fatalist, but I am even less keen on sounding like an ostrich that’s buried its head in the sand. We cannot ignore the obvious: whether the world’s most powerful actors like it or not, the world is sliding to another major crisis like the one in 1962. The cyber war is already raging. There are no rules of engagement in that war. The uncertainty is high. The spiral of tension is getting out of control. The cyber arms race is gaining momentum. And there are no guarantees that the next crisis will be controllable, or that it will result in a catharsis as far as international information security regulation is concerned. There’s no telling what will happen once the cyber genie is out of the bottle.

Today, innovation within cybersecurity is critical. The rise in remote working and cloud computing, among other factors, continues to expose vulnerabilities that cyber attackers are quick to exploit. As cyber threats become more sophisticated, cybersecurity prevention measures must evolve as well.

Cybercrime Magazine expects global cybercrime costs to grow by 15% per year, reaching $10.5 trillion USD annually by 2025. Although large enterprises often dominate the headlines when it comes to cybercrime, Verizon’s latest Data Breach Investigations Report highlights that even very small organizations are just as attractive to criminals. In many ways they can be more appealing targets and the cost of a cyber attack can, and has, made small and medium-sized businesses (SMBs) bankrupt.

Although there may at first glance appear to be no close linkage between weapons of mass destruction and food, in the twenty-first century with world resources of food, land and water under growing stress, nothing can be ruled out. Indeed, chemical weapons have frequently been deployed in the Syrian civil war, which had drought, agricultural failure and hunger among its early drivers. And nuclear conflict remains a distinct possibility in South Asia and the Middle East, especially, as these regions are already stressed in terms of food, land and water, and their nuclear firepower or access to nuclear materials is multiplying. It remains an open question whether panicking regimes in Russia, the USA or even France would be ruthless enough to deploy atomic weapons in an attempt to quell invasion by tens of millions of desperate refugees, fleeing famine and climate chaos in their own homelands – but the possibility ought not to be ignored. That nuclear war is at least a possible outcome of food and climate crises was first flagged in the report The Age of Consequences by Kurt Campbell and the US-based Centre for Strategic and International Studies, which stated ‘it is clear that even nuclear war cannot be excluded as a political consequence of global warming’. 15 Food insecurity is therefore a driver in the preconditions for the use of nuclear weapons, whether limited or unlimited. A global famine is a likely outcome of limited use of nuclear weapons by any country or countries – and would be unavoidable in the event of an unlimited nuclear war between America and Russia, making it unwinnable for either. And that, as the mute hands of the ‘Doomsday Clock’ so eloquently admonish, is also the most likely scenario for the premature termination of the human species. Such a grim scenario can be alleviated by two measures: the voluntary banning by the whole of humanity of nuclear weapons, their technology, materials and stocks – and by a global effort to secure food against future insecurity by diverting the funds now wasted on nuclear armaments into building the sustainable food and water systems of the future (see Chapters 8 and 9).

Sustainable agriculture aims to meet the food and fiber demands of society in an environmentally sound manner that does not harm the natural ecosystem or processes. The integration of a healthy environment and economic profitability with social and economic equality is essential to develop sustainable climate-resilient agriculture. Biotechnology has emerged as a promising tool in crop improvement, offering the potential to increase yield, biotic and abiotic resistance, and nutrient-rich crops.

The term “sustainable” derives from “sustain,” which means to maintain, uphold, and endure. Sustainable agricultural development attempts to meet society’s food and fiber demands with long-term solutions for current and future generations. The primary requirement for sustainable agriculture is to merge a healthy environment and economic profitability with social and economic equality and to develop climate-resilient agriculture. To attain this goal, sustainable agricultural scientists must use multidisciplinary approaches that combine biology, engineering, chemistry, economics, and community development. The 1990 Farm Bill (The Food, Agriculture, Conservation, and Trade (FACT) Act of 1990) defines sustainable agriculture as an integrated system of plant and animal production practices having a site-specific application that will, over the long term, fulfill the (a) food and fiber needs; (b) enhance environmental quality by judicial use of natural resources; (c) make the most efficient use of on-farm resources by integrating regenerative approaches; (d) improve farm economics; and (e) enhance the quality of life for farmers and society as a whole [1–3].

Several epidemics, such as the Black Death and the Spanish flu, have threatened human life throughout history; however, it is unclear if humans will remain safe from the sudden and fast spread of epidemic diseases. Moreover, the transmission characteristics of epidemics remain undiscovered. In this study, we present the results of an epidemic simulation experiment revealing the relationship between epidemic parameters and pandemic risk. To analyze the time-dependent risk and impact of epidemics, we considered two parameters for infectious diseases: the recovery time from infection and the transmission rate of the disease. Based on the epidemic simulation, we identified two important aspects of human safety with regard to the threat of a pandemic. First, humans should be safe if the fatality rate is below 100%. Second, even when the fatality rate is 100%, humans would be safe if the average degree of human social networks is below a threshold value. Nevertheless, certain diseases can potentially infect all nodes in the human social networks, and these diseases cause a pandemic when the average degree is larger than the threshold value. These results indicated that certain infectious diseases lead to human extinction and can be prevented by minimizing human contact. 1. Introduction The emergence of a pandemic is one of the various scenarios frequently discussed as a human extinction event, and it is listed as one of the global catastrophic risks in studies regarding the future [1,2,3]. In particular, several pandemics, such as the Black Death [4,5], Spanish flu [6], and those caused by smallpox [7], severe acute respiratory syndrome (SARS) [8], and Ebola [9], have affected a large population throughout history. The risk of pandemics increases with an increase in population mobility between cities, nations, and continents, thereby threatening humankind [10,11,12]. It is essential to analyze the epidemic spread in society to minimize the damage from epidemic disasters; however, extinctive epidemic spreading experiments have limitations in real-world situations, as they predict stochastic effects on the spread without considering the structure of human society. Network-based approaches have been proposed to overcome these limitations and perform epidemic spreading simulations by considering the network structure of numerous real-world connections [13,14,15]. These methods use various models of epidemic spreading, such as the susceptible–infectious–susceptible (SIS) [16,17,18], susceptible–infectious–recovered (SIR) [19,20,21], and Watts threshold models [22]. While these methods are mathematically convenient, they are epidemiologically unrealistic for various infections because they require exponentially distributed incubation and infectious periods [23,24,25]. Moreover, previous epidemic studies did not perform quantitative assessment of the pandemic risk depending on the network connectivity in individuals and fatality rate of various diseases [26]. In the present study, we applied an SIR epidemic model to a scale-free network with Monte Carlo simulation to identify the quantitative relationship between infectious diseases and human existence. Our fundamental hypothesis states that when the epidemic spreads to all nodes of the network and the fatality rate is 100%, it can increase the pandemic risk. To address this, we initially constructed a scale-free network to simulate a society. Moreover, for the epidemic spreading simulation, an SIR model was applied to the network to describe the immune state of an individual after infection. From the simulation study, we found that the mean degree of a scale-free network was an essential factor in determining whether epidemics threaten humans. This approach provides important insights into epidemic spreading analysis by investigating the relationship between epidemic and scale-free network parameters. Furthermore, it highlights the necessity of determining information flow during an epidemic. 2. Materials and Methods We designed an epidemic simulation process to identify the relationship between pandemic risk and network parameters. This study was performed in four steps (Figure 1): (i) generating a scale-free network model to reflect real-world conditions; (ii) applying an SIR model to the scale-free network for epidemic spreading simulations; (iii) adapting the Monte Carlo method to reflect the stochastic process in the node status of the SIR model; and (iv) iteratively performing simulation for every parameter set and analyzing the results. We have provided the source code and sample results of epidemic simulation in Supplementary Materials.

Figure 1. Overview of epidemic simulation process based on the Monte Carlo method. (A) We generated scale-free networks for a fixed population (N = 1,000,000) and various node degrees (k = 2, 5, 7, and 10). (B) Epidemic spreading was simulated by applying a susceptible–infectious–recovered (SIR) model to the scale-free network. We set the epidemic parameters, β and γd. β represents the spreading rate of epidemics, and γd is the reciprocal of γ and reflects the time interval between infection and recovery. Randomly, 0.05% of nodes were initially infected. (C) We adapted the Monte Carlo method to determine the status of the transition from the infection node to immunization node. Repeated simulations were performed until a steady state was achieved. (D) For every parameter set, 10,000 simulations were performed. 2.1. Network Generation Based on a Scale-Free Model We constructed a network model for the epidemic spreading simulation (Figure 1). The nodes and edges of the network represent people in the society and their physical contacts, respectively. We used a scale-free network model, which follows the preferential attachment property observed in numerous real-world networks, such as social networks, physical systems, and economic networks [27,28,29]. In the scale-free network, when a node is added to the network, its likelihood of connecting to existing nodes increases with an increase in the node’s degree. Hub nodes, which lead to fast and vast spreading of epidemics, exist. Two characteristic parameters, including N and k, affect the form of scale-free networks. The parameter N denotes all nodes in the network. In the real world, N indicates the whole population size. The parameter k is the average degree of the network, which determines the degree of the newly attached node for each step during network generation. Following the characteristics of the network model, we generated scale-free networks representing human contacts for epidemic spread. The scale-free network was generated by the Barabasi–Albert graph distribution, in which the network is constructed from a cycle graph with three vertices, followed by the addition of k edges at each construction step [30]. The k edges are randomly attached to the vertex based on the degree distribution of the vertex. After network generation, we investigated the degree distribution properties of the network (Figure 2). The results indicate that the degree distributions have similar tendency for networks with varying number of nodes and edges. This study constructed scale-free networks with the largest number of nodes considering computational complexity (N = 1,000,000). [FIGURE 2 OMITTED] Figure 2. Degree distribution of the scale-free network. We analyzed the degree distribution of the network based on the number of nodes (N) and mean degree (k). 2.2. Epidemic Spreading Based on the SIR Model For the epidemic spreading simulations, we applied an SIR model to the generated scale-free network. The classical SIR model can be expressed by the following nonlinear differential equations [21]: [EQUATIONS OMITTED] where S, I, and R represent susceptible, infected, and recovered compartments, respectively, in the whole population. S represents people who have not been infected yet but can be infected in future. I represents infected people who can spread the epidemic to susceptible people through physical contact. R denotes people who have recovered or died from the epidemic and who no longer participate in the epidemic spreading process. The sum of the S, I, and R values represents the whole population size N. Epidemics have two parameters in the SIR model, transmission rate (β) and recovery rate (γ), which arise from the basic reproduction number R0 (Figure 1B). The basic reproduction number is the number of infections caused by one infective node [31,32,33]. If the R0 is more than 1, the infection can spread in a population, whereas if R0 is less than 1, the infection cannot spread. We express the basic reproduction number as R0 = β/γ, where β represents the spreading rate of epidemics between infective nodes and adjacent susceptible nodes and γ represents the probability of recovery from infection [34]. We mainly used γd, which is the reciprocal of γ and reflects the time interval between infection and recovery. 2.3. Investigation of Epidemic Status Based on the Monte Carlo Method The epidemic simulation was performed for a time series event by constructing epidemic status matrix (z) to represent the status of the nth node at time step t. For each node, the value of epidemic status matrix at time step t can be 0, 1, or 2, indicating that a node is susceptible, infective, or recovered, respectively. We initially (t = 0) set every value of epidemic status matrix to 0 because all nodes are susceptible before the epidemic spreads. At the initial infection stage, randomly selected 0.05% of nodes were infected. At every time period, we performed immunization and observed the infection stages (Figure 3). [FIGURE 3 OMITTED] At the immunization stage, we identified infective nodes and determined whether these nodes would be recovered in the next time step. To calculate the transition probability of infected and recovered phenomena, the Monte Carlo method was applied [35,36]. When infection and recovery parameters are provided, it is possible to investigate whether a node transitions from an epidemic state to another state. To accomplish this, we compared the method revealing the change in each population in every compartment over time (Figure 4). [FIGURE 4 OMITTED] The final steady state of the epidemic spreading simulation model indicates the total number of casualties of the epidemic who either are dead or have recovered from the disease. Infective nodes at time t (zn [t] = 1) are transformed to recovered nodes at time t + 1 (zn [t + 1] = 2) when 1/γd is larger than a random real number between 0 and 1. We determined whether the neighbor nodes of the infection node would be infected by identifying susceptible nodes adjacent to the infective nodes at time t (zn [t] = 0, with the adjacent infective node) (Figure 5). When β is larger than a random real number between 0 and 1, a susceptible node becomes an infective node at time t + 1 (zn [t + 1] = 1); this scenario represents epidemic spread. For each time step, we recorded the number of susceptible, infective, and recovered nodes during epidemic spread. [FIGURE 5 OMITTED] 2.4. Simulation Parameters We carried out simulation trials for various mean degrees of networks (k = 2, 5, 7, and 10). Each network considered the following epidemic parameters: β ranges from 0.05 to 0.95 and γd ranges from 1 to 10. The Monte Carlo model was repeatedly simulated to observe saturation of the recovery process. Considering that the simulation pipeline contains random processes such as initial infection and Monte Carlo trials, we performed the simulation iteratively until the status of nodes remained unchanged. After simulation, time series data from every simulation were interpolated in the time domain. The fatality rate determines the ratio of deceased and recovered individuals in the final population [37,38,39]. If the fatality rate is below 100%, the recovered population contains both dead and recovered individuals. Such a situation does not always cause a pandemic. In this simulation, we assumed a 100% fatality rate. To accomplish this, we enumerated the recovered nodes as dead for considering the pandemic risk. 3. Results Through our method, we obtained epidemic spreading data with various network and epidemic parameter sets. In the present study, we focused on the case where the epidemic infects all nodes and defined this phenomenon as “extinctive spread”. Diseases causing extinctive spread are potential candidates of high pandemic risk. In the real world, extinctive spreading indicates that the disease will infect every person in the society. From the simulation data, we calculated the extinctive spread score by dividing the total number of simulation trials by the number of extinctive spread cases. Thereafter, we identified that the number of extinctive spread cases is mainly influenced by spreading speed, which is determined by β, γd, and k (Figure 6). [FIGURE 6 OMITTED] The extinctive spread region (brown area in Figure 6) is expanded as the value of mean degree of network (k) is increased, thereby indicating that the area of extinctive spread becomes noticeably wider in a dense network than in a sparse network. Thus, the more contact between people, the higher the risk of epidemics. Moreover, high γd and high β cause extinctive spread across a large region, indicating that the high spreading rate and short time interval between infection and recovery are risk factors of epidemic diseases. In contrast, the infective nodes recover before they transmit the disease to their neighbors in low β and low γd scenarios, thus disconnecting the network and preventing extinctive spread. This occurs because the infective nodes need more time to transmit the disease in low β and high γd scenarios. Therefore, the disease begins to subside due to a lack of new infective nodes. Furthermore, we investigated the range of β and γd for existing epidemics of the common cold [40,41] and fatal diseases, namely, cholera [42,43], Marburg [44,45], Ebola (Congo and Uganda) [46,47,48,49], SARS [50], and MERS [51] (Table 1). We selected diseases with relatively well-known epidemic parameters, such as average duration of infection and basic number of reproductions from previous studies. Transmission rates were calculated using the mean duration of infectious periods and basic reproduction numbers of the epidemics. Different studies reveal multiple values of infectious period and transmission rate for some of these diseases; we considered these values separately [40,41,42,43,46,47,48,49]. For example, the infectious period of a common cold is from 3 to 7 days and that of Ebola is 6.5 days. Next, we placed the possible regions of these epidemics as a disease band for various k values (colored lines in Figure 6). When k > 5, fatal diseases have an opportunity to cause a pandemic. Even when k = 5, diseases such as cholera and Ebola (Congo) can be threatening in regions of low γd and high, thus demonstrating that the knowledge of network parameters of the society and the characteristics of epidemic diseases can aid in quantifying the risk of epidemics. [TABLE 1 OMITTED] 4. Discussion Many previous studies have made stochastic SIR models to analyze the dynamics or stability of epidemic diseases. They investigated the distribution of susceptible, infected, and removed populations for specific epidemic disease spreading, such as cholera, SARS, Marburg, and MERS, based on mathematical modelling [52,53,54,55]. However, they did not conduct a quantitative assessment of pandemic risk taking into account physical contact between people. To solve this limitation, we performed epidemic spreading simulations by applying an SIR model to scale-free networks with Monte Carlo simulation. In the simulation, we consider various connectivity and disease characteristics on scale-free networks. For each network and epidemic parameter set, the probability of extinctive spread was calculated. The results revealed that certain infectious diseases can lead to extinction. Moreover, even if the disease band extends over the extinctive spread regions, it does not indicate that human extinction results from the disease, as the fatality rate is below 100%; however, in the case of 100% fatality, the disease can cause a human extinction event. The risk of infectious disease is influenced by the network structure. A dense network has a higher risk of spreading infectious disease than a sparse network, as we observed in the extinctive spreading maps. According to our results, when the average degree of human social networks is below the risk threshold, i.e., less than 4 in this study, human society is safe from an extinctive outbreak based on our knowledge regarding the epidemic parameters of the infectious disease. Nevertheless, in other cases, human extinction is possible. For example, if the population is 1,000,000 and there are 4 or more instances of physical contact between people, human extinction events may occur, depending on the fatality rate of the epidemics. Hence, physical contact between people is closely related to an extinction event of infectious diseases. Eventually, from a public health perspective, lowering the average contact level of society is an appropriate way to increase the robustness of strategies against the occurrence of extinction. In the real world, reducing network density can be accomplished by epidemic prevention activity, such as isolation and quarantine treatment. This action prevents epidemic risk to the society, thereby avoiding human extinction. Additional considerations may improve our analysis. First, large population size and various proportions of initial infective nodes were not considered in the experiments. We have confirmed that the result was consistent when the proportion of initial infective nodes was 0.05% of the total population; however, this can vary depending on the distinct proportion of initial infective nodes in a different population. To achieve robust results, we need to perform additional experiments for various parameters; however, we could not address this issue due to computational complexity. Second, we did not consider numerous known epidemic diseases. We calculated the transmission rates of epidemic diseases using the known infectious periods and reproduction numbers of the epidemics from evidence in the literature. In the present study, we only considered five epidemic diseases, since the information on infectious periods and reproduction numbers of diseases was mostly unavailable for other epidemic diseases. Third, this study only considers the SIR model on scale-free networks in epidemic simulation. Since the dynamics of epidemic diseases can be varied in different models or networks, it is important to experiment in various simulation environments to confirm the robustness of the results. Nevertheless, these limitations can be considered in future experiments or using improved computational methods. With these further improvements, our approach can be used as a computational tool to analyze the risk of epidemic diseases. 5. Conclusions In this study, we analyzed the risk of epidemic diseases by creating an epidemic simulation on a scale-free network. Based on the simulation results for various epidemic parameters, we confirmed that certain infectious diseases can lead to extinction and can be prevented by minimizing human contact. We believe that identifying potential candidate diseases that may lead to human extinction is crucial in addressing epidemic prevention activities such as quarantine.

Biotech companies are crucial to and at the forefront of the development of transformative and innovative new treatments that result in better patient outcomes. In fact, according to an industry report by Ernst and Young from 2022, small to midcap biotech companies are responsible for a growing number and share of FDA approvals.(1) To thrive, biotech companies must be able to navigate complex and high-risk, high-reward situations.

Advancements in the exciting field of gene therapy, as an example, are largely driven by the biotech industry, and thanks to the innovative new medicines being discovered and developed by biotech companies, patients with genetic disorders now have more and better treatment options.2-4 Biotech companies are helping to uncover underlying causes of diseases, better understand how to slow disease progression, and maybe even find cures for certain conditions. But with high reward comes high risk.

Intellectual property rights can encourage small and large companies in the biotech industry to enter into partnerships, facilitating the exchange of information. Smaller research startups typically lack the resources to meet the regulatory requirements associated with bringing a product to market, let alone cover the often extensive costs of manufacturing, marketing, and distributing a new product. Without intellectual property protection, other restrictions, or alternative mechanisms to reduce the risks of expropriation, many of these products would be less likely to make it to market. Even when alternative mechanisms or restrictions can function to mitigate expropriation risks at certain stages of the transaction, awareness of intellectual property rights may act as a further deterrence to would-be expropriators.

The United States has a long tradition of technological innovation, and U.S. inventors and innovators continue to drive the creation of new companies, jobs, and economic growth. Secure intellectual property (IP) rights are essential to spur this innovation. Critically, patents transform ideas into property that can be assigned and protected, allowing small businesses to attract investment and collaborate with other firms with the assurance that their ideas will not be stolen. For this reason, the community of inventors, entrepreneurs, and start-ups repeatedly emphasize the importance of secure IP rights in providing them a path to market entry, growth, and fair participation.

However, there are actors who abuse the patent system to eliminate and extort competitors, stifling innovation by preying on start-ups, small businesses, and inventors that often do not have the resources to pursue expensive and time-consuming litigation. Some of the so-called Big Tech firms are known to infringe on patents and rely on large and well-compensated legal teams to drag out litigation and avoid penalties, or simply swallow fines as a cost of doing business. Other observers of the patent system decry “patent trolls,” who purportedly weaponize overly broad patents to extort legitimate businesses by initiating questionable lawsuits.

The evidence that this contradicts long-standing U.S. patent doctrine is made clear by explaining how the courts’ application of the Alice-Mayo framework today would result in the invalidation of the very first patent that was issued in the U.S. under the first Patent Act of 1790. The first U.S. patent issued was to Samuel Hopkins in 1790 for his discovery of a new method of making potash.62 His novel method comprised well-known steps at the time, such as burning and dissolving ash. Hopkins’ claimed discovery was in the timing and order of the steps in the process.63

If Hopkins’ patent were challenged today and the courts applied their now-established methodology in construing it under the Alice-Mayo framework, they would first find that the elements of timing one’s steps in a process and using heat are abstract ideas, laws of nature, or natural phenomena—and thus ineligible for patent protection. Under the second step of the Alice-Mayo framework, a court would find that Hopkins did not add anything “inventive” to the steps beyond timing and heating contributions, all of which were well-known and conventional at his time. Thus, it is an entirely logical application of the Alice-Mayo framework today to find the first U.S. patent for a process of making potash to be ineligible for patent protection.

Among the many changes in the patent system wrought by the Supreme Court in the past decade were four decisions between 2010 and 2014 on patent eligibility doctrine under § 101.13 These four decisions culminated in the “Alice-Mayo framework”—a new two-factor inquiry that courts and the U.S. Patent & Trademark Office (USPTO) have applied since 2014 in assessing whether an invention or discovery qualifies under § 101 as an invention or discovery that is patentable under the Patent Act even if it is new, useful, and fully disclosed in the patent.14

The Alice-Mayo framework does not apply the statutory text of § 101, but rather the judicially created exclusionary rule prohibiting patents issuing on abstract ideas, laws of nature, or physical phenomena. The two-factor inquiry is: (1) determine whether the patent claims an abstract idea, natural phenomenon, or law of nature that are ineligible for patent protection, and, if so, (2) determine if the patent claims something more that transforms it into a patent-eligible application (an “inventive concept”) of one of these ineligible categories or is this additional activity in the claim merely well-understood, routine, and conventional activity in the art.

However, there are some reasons to think that naturally occurring pathogens are unlikely to cause human extinction. Firstly, Homo sapiens have been around for 200,000 years and the Homo genus for around six million years without being exterminated by an infectious disease, which is evidence that the base rate of extinction-risk natural pathogens is low.82 Indeed, past disease outbreaks have not come close to rendering humans extinct. Although bodies were piled high in the streets across Europe during the Black Death,83 human extinction was never a serious possibility, and some economists even argue that it was a boon for the European economy.84 Secondly, infectious disease has only contributed to the extinction of a small minority of animal species.85 The only confirmed case of a mammalian species extinction being caused by an infectious disease is a type of rat native only to Christmas Island. Having said that, the context may be importantly different for modern day humans, so it is unclear whether the risk is increasing or decreasing. On the one hand, due to globalisation, the world is more interconnected making it easier for pathogens to spread. On the other hand, interconnectedness could also increase immunity by increasing exposure to lower virulence strains between subpopulations.87 Moreover, advancements in medicine and sanitation limit the potential damage an outbreak might do.

THE PERFECT STORM Will these inequities eventually lead to a species-wide downfall? It's not easy to calculate the likelihood that, say, the climate emergency will kill us all, says Luke Kemp, a research affiliate at the Center for the Study of Existential Risk at the University of Cambridge. But it's probably not realistic to consider risks individually anyway, he says. "When we look at the history of things like mass extinctions and societal collapses, it's never just one thing that happens," Kemp says. "If you're trying to rely on a single silver bullet to kill everyone in a single event, you have to write sci-fi." The end of humanity is far more likely to be brought about by multiple factors, Kemp says—a pileup of disasters. Although apocalyptic movies often turn to viruses, bacteria and fungi to wipe out huge swathes of the population, a pandemic alone probably won't drive humanity to extinction simply because the immune system is a broad and effective defense, says Amesh Adalja, an infectious disease physician and senior scholar at the Johns Hopkins Center for Health Security. A pandemic could be devastating and lead to severe upheaval—the Black Death killed 30 to 50 percent of Europe's population—but it's unlikely that a pathogen would kill all of humanity, Adalja says. "Yes, an infectious disease could kill a lot of people," he says, "but then you're going to have a group [of people] that are resilient to it and survive." Humans also have tools to fight back against a pathogen, from medical treatments to vaccines to the social-distancing measures that became familiar worldwide during the COVID pandemic, Adalja says. There is one example of a mammalian species that may have been entirely eliminated by an infectious disease, he says: the Christmas Island rat (Rattus macleari), also called Maclear's rat, an endemic island species that may have gone extinct because of the introduction of a parasite. "We are not helpless like the Christmas Island rat, which couldn't get away from that island," Adalja says. "We have the ability to change our fate."

If infectious disease contributes to the downfall of humanity, it will probably be as just one piece of a larger puzzle. Imagine a planet pushed to upheaval by sea-level rise and disruption to agriculture from climate change. The humans of this climate-ravaged world attempt a geoengineering solution that goes wrong. The situation worsens. Resources are scarce, and a bunch of countries have nuclear weapons. Oh, by the way, the mosquitoes that carry yellow fever range as far north as Canada in this scenario. It's not hard to see how the human population could decline and disappear in the face of an arsenal of challenges, according to Kemp. Worst-case scenarios are understudied, Kemp says. In climate science, for example, there is a lot of research into what the world might look like at two or three degrees Celsius warmer than the preindustrial average but very little looking at what an increase of five or six degrees C might look like. This is partly because scientists have a hard time predicting the effects of that much warming and partly because climate scientists feel pressure from politicians not to appear alarmist, Kemp says. Models of future worst-case scenarios also tend to do an inadequate job of predicting the cascading effects of a disaster. "The general field of existential risk is relatively new, nascent and just understudied," Kemp says. There are questions as to how much humans should worry about something as big picture as extinction. While some see the question as pressing—controversial tech billionaires such as Elon Musk and Peter Thiel have funded organizations dedicated to studying the risks of transformative technologies—others argue that today's problems are urgent enough. Already humans are heating the globe, overexploiting and destroying nature, using land and water unsustainably, and creating chemicals that are harmful to all life, often in service to the globally well-off, says Sarah Cornell, who studies global sustainability at the Stockholm Resilience Center at Stockholm University. "Today's reality is that some human beings are undermining or even destroying living conditions of many, many other people," Cornell says. "From a human-scale perspective, this is an existential crisis already, not a risk somewhere up ahead."

Pricing Realities The populists tell us that prices are rising way too fast because of “corporate greed.” The Kaiser Family Foundation rails that “Prices Increased Faster Than Inflation for Half of all Drugs Covered by Medicare in 2020.”23 But of course that means that prices increased less than inflation for the other half. Three professors of medicine at Harvard agree, claiming that drug prices are “skyrocketing.”24 To be sure, there are cases where individuals confront large and sudden medical bills: One study found that nearly 40 percent of commercially insured individuals incurred half of their annual out-of-pocket drug spending on one purchase, and 26 percent incurred 90 percent of their annual healthcare spending in only one or two encounters.25 (This is why policies such as “smoothing” Medicare beneficiaries’ out-of-pocket costs over the course of a year are often warranted.)26 Yet overall, the data shows that American consumers’ out-of-pocket expenditures on drugs over the past two decades have grown much less than their overall healthcare and health insurance expenditures. Furthermore, the distinction between net prices and list prices is critical because while list prices may be on the rise, the share of expenditures being paid to drug manufacturers in recent years is lower than before. For instance, Andrew Brownlee at the Berkeley Research Group found that the share of revenues accruing to drug manufacturers for all drugs decreased by over 17 percentage points from 2013 to 2020, from 66.8 percent to 49.5 percent, while the share going to intermediaries (wholesalers, pharmacies, Pharmacy Benefit Managers (PBMs), and insurance companies) increased from 33.2 to 50.5 percent.27 In other words, less than half of every dollar Americans spend on drugs actually goes to the companies developing and making them. Overall, Brownlee found that brand manufacturers retain just 37 percent of total spending on all prescription medicines.28 Similarly, researchers at the University of Southern California examined the “net price” of insulin and found that list prices did increase between 2014 and 2018, but that the share of insulin drug sales flowing to manufacturers decreased, with more than half of insulin expenditures going to intermediaries by 2018.29 Indeed, there has been a 140 percent increase in insulin list prices over the past eight years, but net prices actually declined by 41 percent, casting a new light on Angelis and colleagues’ argument that “old and common drugs” like insulin “have seen inexplicable price increases.”30 Meanwhile, the Biden administration has used the assertion that America’s biopharmaceutical industry is excessively concentrated to justify policies such as expanding Bayh-Dole Act march-in rights to control the price of resulting products. For instance, the administration palpitates that America’s 25 largest pharmaceutical companies command around 70 percent of industry revenues. Yet that is hardly evidence of an overconcentrated market. To see one that truly is, just look at the pharmacy benefit managers (PBM) market, where just four companies—Express Scripts, OptumRx, Prime Therapeutics, and Kaiser Pharmacy—commanded 68 percent of the revenue in 2021. That’s a concentrated market; America’s pharmaceutical sector is not. Thus, as noted, America’s PBM market is both substantially more concentrated and profitable than America’s pharmaceutical sector, yet it’s investing a scant fraction of its revenues back into R&D, let alone creating innovative products that substantially benefit quality and longevity of life.

On March 2, 2023, the authors convened a group of experts from academia, industry, government, and nonprofits to explore technology options for increasing the resilience in the supply of generic prescription drugs. The workshop was convened at the Massachusetts Institute of Technology with the help of the National Network for Critical Technology Assessment and financial support from the Alfred P. Sloan Foundation. This report, authored by the organizers, summarizes the discussion. Because the workshop was not open to the public, this summary maintains the anonymity of the speakers and does not attribute comments to individuals. Motivation for the workshop Assured high quality and consistent availability of medicines is a cornerstone of patient care, yet U.S. prescription drug supply chains are highly vulnerable to disruptions. If drug shortages result, they may place patients’ health and even their lives at risk through treatment delays, rationing, increased likelihood of medication errors, and the substitution of possibly inferior alternatives. Drug shortages result when supply chains cannot withstand demand or supply shocks. Recent demand shocks included a post-COVID increase respiratory illnesses driving up demand for amoxicillin or an increase in the off-label use for certain diabetes drugs like Ozempic. But shortages occur more frequently when supply disruptions – whether caused by manufacturing problems, natural disasters, or delays in obtaining ingredients – are significant enough that available inventories or scaling available manufacturing sites does not suffice. Historically, manufacturing quality problems with older generic (off patent) medicines have been the most common cause of shortages drug shortages. To ensure that medicines are available when and where they are needed, numerous reports from government, industry, and academia have called for greater supply chain resilience where supply chains can either absorb or recover readily from demand shocks and supply shocks, both unexpected (like natural disasters) and preventable (like manufacturing quality). Advanced manufacturing technologies (AMTs) such as continuous manufacturing, are among oft-cited solutions to resolve or mitigate challenges in pharmaceutical manufacturing supply resilience. Proponents of these technologies posit that AMTs enhance pharmaceutical manufacturing quality, speed, flexibility, and cost-effectiveness. The Food and Drug Administration (FDA) has specifically called out continuous manufacturing as a technology that can help prevent drug shortages caused by product quality and manufacturing problems. Despite the promise of AMTs to address drug manufacturing quality and shortages, AMTs have been slow to emerge, especially among generic drugs. To date, there are a handful of branded drugs approved with continuous manufacturing but no generic drugs. Even conferences and workshops are missing engagement from generic manufacturers. To explore the application of AMTs in the generics manufacturing context, this workshop convened experts in the fields of material chemistry, manufacturing, economics, and regulatory science. Participants explored the following questions: What are the key failure modes for pharmaceutical manufacturing and how do they differ for brands and generics? What are the AMT options to overcome those failure modes? What are the barriers for generic drug manufacturers to adopt those technologies? What are practical next steps that government, industry, or academia could take? Main takeaways The main takeaways from the workshop can be summarized as follows: The cost of AMTs outweighs the return on investment that generic manufacturers might expect relative to other options. Throughout the workshop, current and former generic business leaders spoke about the economics of the generic drug industry and the constraints they present in adoption of new technologies. Demonstrations using continuous manufacturing for generics suggest that currently any production cost-efficiencies do not suffice when those products compete with low-cost foreign producers. AMTs, continuous manufacturing in particular, do not align with the fast turnover, large portfolio nature of generic drug manufacturing. The existing evidence on the value of AMT technologies applies to single, continually produced product. This, however, does not translate well to generic drug manufacturing where the unstable nature of demand can lead to 20-30 products run on a single line over a course of a year, leading to frequent switchovers. Developing a platform approach and better documenting the return on investment would be important for driving adoption of AMT technologies among mainstream generic manufacturers. Current direction for reducing regulatory barriers is insufficient. Business and government leaders acknowledged regulatory constraints and uncertainty that make it costly to change manufacturing technology and processes. On the one hand is the work-in-progress nature of FDA’s regulations regarding the use of AMTs, with guidances and pilots in the works. On the other hand is the nature of the generics business, with a large installed base of products and questions about how to address technology adoption for product portfolios, not just individual products. While FDA is making process on the former, the latter remains unaddressed. A full range of technologies, not just AMTs, should be considered when addressing generic prescription drug supply chain resilience. Although the workshop did not cover the full spectrum of technologies that could help address supply chain resilience for generics, participants argued that basic technologies such as record digitization might provide a better return on investment from a social perspective. The role of technology and its type (AMT vs low-tech) should match the specific vulnerabilities in supply chains. Workshop participants identified medical countermeasures, antibacterials, and pharmaceuticals with narrow therapeutic index (NTI) as most likely, preliminary use cases for continuous manufacturing. These preliminary use cases should be assessed by the U.S. government as part of a broader effort to evaluate the return on investment on a broad range of interventions, whether technological or not.

Given the lack of private incentives, U.S. government must spur AMT innovation and subsidize adoption of AMTs to generic drug supply. The government can alter the current business case for adopting AMT. To account for the variability in demand that makes continuous manufacturing unattractive for multi-product lines, participants recommended investments in developing a platform approach in such technologies. To address the large upfront costs, participants pointed to semiconductor manufacturing incentives in the CHIPS & Science Act as an example. Government drug supply chain resilience efforts should include prioritization of supply chains. Given the lack of private incentives, the role of U.S. government is critical. But the size of the generic industry and the chemical industry that supports it is immense. To best identify where and how to engage in support of drug supply chain resilience, U.S. government must take a systematic look on which supply chains are essential and which of these are vulnerable.1

Normal Profits Start with profits. The left argues that there is plenty of money for the industry to develop drugs, even if its revenues are significantly lowered as a result of price controls or reduced intellectual property protection. They rationalize this assertion with claims of excess profits and wasted spending. U.S. Rep. Katie Porter (D-CA), deputy chair of the Congressional Progressive Caucus, issued a scathing report in 2021 titled “Killer Profits,” attempting to make the case that the industry makes too much money.5 Two years earlier, the Center for American Progress made the same claim that “Big Pharma Reaps Profits.”6 More recently, the self-described “democratic socialist” Senator Bernie Sanders (VT) has claimed that “Greedy pharma firms rip off Americans.”7 The reality is that, when adjusting for risk and comparing it to other industries, pharmaceutical firms’ profits are not excessive, nor are drug prices driving up overall healthcare expenditures. Researchers Sood, Mulligan, and Zhong compared excess profits of pharmaceutical companies and S&P 500 firms.8 They defined excess profit as “higher than expected profits given the risk associated with their investments” and found that pharmaceutical companies’ excess profits were actually 1.7 percentage points lower than the 3.6 percent average among S&P 500 firms.9 By contrast, researchers found that the excess profits of middlemen in the U.S. biopharmaceutical supply chain—insurers, pharmacy benefit managers, and retailers—averaged 5.9 percent from 2013 to 2018. Moreover, an analysis in PLosONE found that pharmaceutical “returns were substantially lower than [those of ] the other eight health care industries.”10 Despite today’s rhetoric, prescription medicines have accounted for only about 14 percent of U.S. healthcare spending in recent years, and that share is expected to remain stable going forward.11 Similarly, the research firm Altarum found in a 2020 report that the retail pharma share will likely remain stable in the 9 percent range through most of this decade, with non-retail expenditures also roughly stable in the 4.5 to 4.9 percent range. (Fig. 1.) This puts the United States right in line with other OECD nations.12 Others, such as Dean Baker, attack the industry for spending too little money on R&D for drug development and too much on stock buybacks and marketing. In arguing that the industry wastes money on marketing, Baker notes that industry marketing costs are comparable to research expenditures, and that if the industry just reduced advertising, it could reduce drug prices. This sentiment is no doubt fueled by pharma’s extensive use of television advertising. Yet, over the last 10 years, the annualized return on the Standard & Poor’s (S&P) Biotech Index averaged 7.95 percent and the Pharmaceutical Select Index averaged 3.29 percent, both lower than the S&P 500 index return of 12.49 percent, which undercuts the argument that stock buybacks are being used to “prioritize short term financial returns,” as one article contended.13 When it comes to marketing, the charge is just as specious. An analysis in JAMA Network, which included all promotional activities, physician education, advertising, and unbranded disease awareness campaigns as “medical marketing,” puts the total of these activities (many of which have significant value to patients) at one-third of R&D expenditures.15 When looking just at pharmaceutical advertising, total spending reached was $6.58 billion in 2020, a small fraction of the $122 billion the industry invested in R&D in the United States that year.16 Opponents of large drug companies also claim that the companies are not investing in R&D. The progressive think tank Roosevelt Institute has argued that, “as prices have skyrocketed over the last few decades, these same companies’ investments in research and development have failed to match this same pace.”17 In reality, from 2012 to 2016, drug sales increased $5.8 billion a year, while R&D actually increased $6.8 billion a year.18 Drug companies in America are highly R&D intensive and have become even more so, with their R&D-to-sales ratio increasing from 11 percent in 2006 to 20 percent in 2018.19 The ratio for the top 20 U.S. drug companies increased from 15 percent in 2006 to 23.6 percent.20 Further, while drug revenues increased 56 percent from 2006 to 2018 (in nominal dollars), R&D increased by 85 percent, and it is the largest firms, not the smallest, that are the most R&D intensive.21 The non-partisan Congressional Budget Office reports that “In 2019, the pharmaceutical industry spent $83 billion dollars on R&D. Adjusted for inflation, that amount is about 10 times what the industry spent per year in the 1980s.”22 In fact, America’s pharmaceutical industry is the most R&D-intensive industry in the world, investing over 20 percent of its sales into R&D each year, which accounts for 18 percent of the total U.S. business R&D investment.