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China is in the midst of a “significant” expansion of its nuclear capabilities and may have as many intercontinental ballistic missiles as the United States or Russia by 2030, according to a new global analysis of nuclear weapons.

Beijing is both growing and modernizing its nuclear arsenal, according to an annual report released Sunday by the Stockholm International Peace Research Institute (SIPRI), a group that tracks global security and arms control. China’s military stockpile grew from 410 nuclear warheads last year to 500 as of January, the report found.

Members of China’s strategic com­munity tend to dismiss such concerns. Likewise, U.S. studies of a potential war with China often exclude nuclear weapons from the analysis entirely, treating them as basically irrelevant to the course of a conflict. Asked about the issue in 2015, Dennis Blair, the former commander of U.S. forces in the Indo-Pacific, estimated the likelihood of a U.S.-Chinese nuclear crisis as “somewhere between nil and zero.”

This assurance is misguided. If deployed against China, the Pentagon’s preferred style of conventional warfare would be a potential recipe for nuclear escalation. Since the end of the Cold War, the United States’ signature approach to war has been simple: punch deep into enemy territory in order to rapidly knock out the opponent’s key military assets at minimal cost. But the Pentagon developed this formula in wars against Afghanistan, Iraq, Libya, and Serbia, none of which was a nuclear power.

Even accounts that seek to separate any necessary connection between American power and leadership and the perpetuation of Liberal International Order still tend to make arguments along similar lines, such that liberal institutions come to be seen as inherently adaptive, and hence resilient. On this view, even the very notion of the ‘Liberal International Order’ is misleading since it ‘. . .creates the impression we are referring to a static entity, rather than mutating and shifting ways of ordering regions and domains of world politics’.130 Once again, then, the Liberal International Order’s crisis ‘. . .articulates the very limits of the political’131 on the presumption that there is, ultimately, no ‘exit from liberalism’. Though, Cooley and Nexon, for example, concede that there might be ‘new ordering projects’ underway led in particular by China and Russia, these at most are argued to offer only ‘exits’ from ‘political liberalism’, while economic and institutional liberalism remain predominant.132

In the end we might come back to questions of what work crisis is doing in such debates, and how it orders the world in the process: not least in in terms of how the story of Liberal International Order is told in terms of crisis management, and the alternative conceptions of crisis that those narratives tend to either marginalise or exclude. Insofar as CLIO debates often tend to revolve around – and sometimes be resolved in terms of – the suitability of liberal institutions for managing the ‘violent storms of modernity’,133 wider and fundamental questions and issues open up as to how modernity is being understood in such conceptions. Those grappling with the implications of the ‘crisis of the Anthropocene’ for IR, by way of contrast, suggest that in the context of a ‘planetary emergency’ characterised by existential threats of climate change and species extinction, the underpinnings of modern IR are thrown into crisis precisely because the discipline has so often tended to assume a human/nature separation as both essential to the progress and flourishing of human societies and as a ‘boundary condition’ of the discipline as a field of knowledge.134 The conditions, ‘storms’, and ‘crises’ of ‘modernity’ are understood there in radically different terms. Questions and issues of race and racial hierarchy also tend to be either marginal to or absent from CLIO debates, with the notable exceptions to that highlighting the extent to which practices of Liberal International Ordering have been central to ‘embedding’ regimes of ‘racism and antiracism’ in ways that mainstream IR, not just scholarship on Liberal International Order, has been insufficiently attentive to.135 When situated in that wider context, and in the event that Crisis of the Liberal International Order debates might have continue to run on and recur, the proposition that the way to manage crisis is ‘more liberal order’ is at the very least worthy of being weighed up carefully: in terms of how ‘crisis’ is used to frame the problems of, and solutions assumed to endure within, practices of liberal international ordering.

‘It is likely that we are entering an age of pandemics,’ said Prof. Peter Piot, director of the UK’s London School of Hygiene and Tropical Medicine, who co-discovered the Ebola virus in 1976 and is now special advisor to the president of the European Commission on Covid-19.

He told the audience of researchers, policymakers and industry players that while he believes the risk of zoonoses – infectious diseases that cross from animals to humans – and pandemics will increase, the past year has shown the value of investing in research and innovation. He particularly singled out the value of basic research, with vaccines being developed in record time based on mRNA technology that has been decades in the making.

These exponential “super-spreading events”[2] should not be considered a ‘normal’ escalation from an endemic biological incident within a single country (i.e., ratcheted upward from a lesser level CBRNE incident), nor one where a pandemic expands beyond a single nation’s borders. One of the main constructs of emergency management is that it is designed to be jurisdictionally limited within a single nation. Emergency management is defensive and supports the homeland . While aid and advice can be provided from one nation to another, the incident is still managed within a single country or component subjurisdictions. Governmental missions which are predominately offensive, such as interventions, interdictions, interruptions, and isolations, are single-entity missions generally to be delegated to law enforcement entities (including Public Health Officers) domestically and the nation’s military and national defense agencies, internationally. In many countries, including the United States, military and national defense agencies were utilized domestically as part of these offensive missions.[3]

In the United States, the definition of what is an emergency, or a disaster, is very fluid. The Federal Emergency Management Agency (FEMA) has an incident typing system, where five is the lowest in terms of resources required and one is the highest, as shown in Figure 1:

The strange new reality of Covid-19 is forcing huge changes on all of us, some of which may become permanent features of how we live, work and organise our societies.

We are all being called upon to become more flexible, more adept with technology and more collaborative across borders and time zones.

“Lower the temperature,” Joe Biden tells us. Facing a country troubled by civil unrest while contending with a pandemic for nearly a year, it’s understandable the incoming president seeks unity.

But the goal of “lowering the temperature” illustrates a broader point: politics has gotten too hot because the stakes have become so high. The assault on the Capitol, encouraged by President Trump, inflamed public passions to a previously unimaginable degree. However, one should not be duped into thinking that Trump is the origin of divided politics.

Environmental policy in the United States is developed within a federal system — but what type of federalism do we experience today? There are a variety names that have defined environmental federalism over time: cooperative federalism, with clear lines of authority (and shared goals) and the more technical sounding conjoint federalism, with power sharing and overlapping roles (Welborn 1988). Newer terms suggest more contentious relationships: contested federalism (Conlan 2014) and disruptive federalism in which there are conflicts over goals, policy instability, and overt institutional and political conflicts (Fallon 2017).

The transition from the Obama to Trump administrations brought problems of environmental federalism into clear focus. My recent article with Daniel Fiorino highlights the four main challenges of environmental federalism and analyzes a continuum of control upon which policy issues fall. We conclude by discussing the ways in which the environment “needs” Washington, DC and warn of the risks of too much devolution.

We are needlessly allowing technology to take the world to the brink of disasters from accidental climate disruption, nuclear war, and pandemics— at the same time that we are allowing the means for controlling these technologies to erode. In effect, we are edging closer and closer to cliffs from which we have removed the guardrails. Fortunately, people are beginning to develop— or at least to think about— protections against some of these dangers.

A technology-based model of authoritarian government is being promoted and exported as a technological, economic, and political challenge to Western liberal democracies. The Internet and social media, conceived as vehicles for free exchange of information and platforms for untrammeled innovation, in some countries have become instruments of repression. At the same time, a systematic, worldwide campaign of misinformation and disinformation, spread via social and mass media, has deliberately sown distrust in the democratic process, in government, in international institutions, in science, in expertise of all kinds, and in the very idea that there is such a thing as truth. In the United States, the scientific consensus concerning climate change has been overwhelmed by misinformation and disinformation spread by political and business interests that it threatens, while public messaging regarding the CO VID-19 pandemic has been at best mixed. All this has made it difficult for people to understand the complex and dangerous new threats to the environment, to their health, to their security, and to democratic government.

Congress's power to entrust adjudication to non-Article III judges or tribunals is an enduring enigma. Article III provides that: "The judicial Power of the United States, shall be vested in one supreme Court, and in such inferior Courts as the Congress may from time to time ordain and establish." 1 If this Vesting Clause and the Article III, Section 2 enumeration of nine "Cases" and "Controversies" to which the "judicial Power shall extend" 2 are to mean something, there must be some limit Congress's power to assign final determinations in Article III-listed cases and controversies outside the judicial branch. But the nature of this limit remains one of the thorniest problems in modern constitutional and administrative law. The Supreme Court has developed a controversial "public rights doctrine" to define the constitutional scope of non-Article III adjudication: Congress has broad discretion to use non-Article III adjudicators in "public rights" cases and lacks similar discretion in "private rights" cases.

Concerns with individual liberty and federal-level separation of powers provide the two dominant themes in judicial opinions and scholarship relating to the public rights doctrine. 3 What has been overlooked in prior accounts is a third concern that was critical at the time of Article III's adoption: protecting the general primacy of state courts in deciding traditional categories of disputes between private parties outside the maritime context. In the wake of post-1930 federal legislation that has greatly expanded the scope of federally created rights and obligations, concern with the public rights doctrine has commonly focused on questions of separation of powers between the Article III courts and Congress's administrative creations. We highlight here, however, how the private rights side of the doctrine has operated to preserve pre-existing state judicial power by limiting federal tribunals for adjudicating cases and controversies between private parties. 4

This case raises a constitutional challenge to new post-grant patent proceedings. This Court has just granted certiorari to review statutory and discretionary matters within such proceedings. See Cuozzo Speed Techs., LLC v. Lee, No. 15-446 (cert. granted January 15, 2016). It stands to reason that the Court should, in addition, analyze constitutional validity. This Court may thus hear the two cases together, the one raising smaller issues (Cuozzo) alongside the one raising larger issues (this case). That would secure a more complete vetting of administrative agency authority.

Confusion over this Court’s Article III “public rights” jurisprudence reached its apex with this case. The lower court here endorsed a spectacular anomaly. Congress may now delegate authority to an administrative tribunal to determine the validity of private property rights amongst private litigants embroiled in a private federal dispute of a type with clear historical antecedents to 1789. Under the lower court’s decision, the “judicial power” is no longer reserved to Article III courts, unless a state law claim is in play. Under that reasoning, if Congress swoops in to regulate an area, the Separation of Powers question is decided in the negative at the moment Congress does regulate it, irrespective of whether it “originate[s] in a newly fashioned regulatory scheme or possess[es] a long line of common-law forebears.” Granfinanciera, SA v. Nordberg, 492 U.S. 33, 52 (1989) (citation omitted). Countless decisions of this Court direct otherwise.

I keep thinking about the comment that Rep. Mike Gallagher (R-Wis.) made at the first hearing, back on Feb. 28, of the House select committee on the Chinese Communist Party (CCP): “This is an existential struggle over what life will look like in the 21st century, and the most fundamental freedoms are at stake.” The committee chairman was right, but not in the way that he meant.

A leading China hawk, Gallagher claimed: “The CCP is laser-focused on its vision for the future — a world crowded with techno-totalitarian surveillance states where human rights are subordinate to the whims of the party.” While it’s true that the Chinese Communist Party has created a system of “techno-totalitarian surveillance” in China and is happy to sell its surveillance technology to other countries, there is scant evidence that it is conspiring to export its system of oppression globally. Gallagher’s vision is merely an update of the old Cold War paranoia about a supposed Soviet plot to take over the world, which missed the fact that Soviet leaders were driven primarily by defensive concerns about their own security.

Critical minerals undergird great power competition and war. These nonfuel minerals and mineral materials are vital to countries’ defense industrial bases, enabling the production of military platforms like tanks as well as munitions and artillery shells. Therefore, mineral supplies can help sustain military power, while mineral shortages can severely undermine it. For example, the Allied powers’ control of most of the world’s minerals before World War II proved instrumental in their eventual victory over the Axis powers.

Minerals remain important for military power today. Iron is used in steel, which is necessary for military components like ship hulls and tank armor. Copper is commonly used in munitions such as bullets and artillery shells. And lithium and other minerals have gained further relevance due to their use in new energy technologies, like high-capacity batteries. The U.S. Army aims to field an entirely electric non-tactical light-duty vehicle fleet by 2027, and the army’s climate strategy emphasizes installing battery storage systems and generating renewable electricity on its bases.

The Chinese Communist Party (CCP) has perfected the art of stealing U.S. technologies and taking control of entire markets. Their methods range from the well-known, like forced technology transfers or outright stealing technology, to more subtle methods like enlisting the U.S. government to destroy U.S. company patents, clearing the way for the CCP to dominate specific technologies, such as critical minerals processing.

Terves, LLC, based in Euclid, Ohio, invented new ways to make dissolvable magnesium materials for oil and gas drilling tools. These materials allow drillers to leave their tools underground, where they dissolve, saving the significant costs of retrieving tools from miles below ground. This innovation made Terves’s products highly sought after in oil and gas fracking industry. The CCP took note.

A new Cold War is upon us. Unlike the prior one between the democratic West and the Soviet Union, this time the U.S. and its allies do not have the advantage of superior technology with which to counter its adversary’s advantages.

Beijing learned a lesson from watching Moscow’s defeat at the end of the last century as the result of superior Western technological and economic power. Most importantly, it learned that it had to match or exceed the West in advanced technologies in order to win the approaching political, economic and military struggle.

The Patent Trial and Appeal Board (PTAB) effectively invalidates 84% of the patents it fully adjudicates. That fact comes directly from the U.S. Patent and Trademark Office’s (USPTO) PTAB Trial Statistics FY23 End of Year Outcome.

This astronomical PTAB invalidation rate has effectively shut down investment in early-stage startups, particularly in vital sectors that require patent protection, such as artificial intelligence, quantum computing, wireless technologies, cybersecurity, fintech, enterprise systems, next-generation communication systems like 5G and 6G, and much more.

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.

Pharma is mobilising substantial efforts to rise to the COVID-19 challenge at hand. However, we need to consider how pharmaceutical innovation for responding to emerging infectious diseases can best be enabled beyond the current crisis. Many public health threats (including those associated with other infectious diseases, bioterrorism agents and antimicrobial resistance) are urgently in need of pharmaceutical innovation, even if their impacts are not as visible to society as COVID-19 is in the immediate term. The pharmaceutical industry has responded to previous public health emergencies associated with infectious disease in recent times – for example those associated with Ebola and Zika outbreaks.11 However, it has done so to a lesser scale than for COVID-19 and with contributions from fewer companies. Similarly, levels of activity in response to the threat of antimicrobial resistance are still low.12 There are important policy questions as to whether – and how – industry could engage with such public health threats to an even greater extent under improved innovation conditions.

The COVID-19 pandemic is a game-changer among global public health threats. The risk to human life (both in terms of morbidity and quality of life), the economic risks, the epidemiology of the disease and speed of escalation have led to a crisis-response by many governments around the world. This has in turn influenced the immediate industry efforts. Many other infectious disease threats may not manifest as crises in the short term and in the same way as COVID-19, but they could nevertheless escalate. They are not considered to be crises from a short term perspective because they are contained to specific regions and affect fewer people at present – or are re-emerging (e.g. Ebola) – or their impacts have not yet materialised at a scale that would qualify as an immediate crisis (e.g. growing risks of antimicrobial resistance to some infectious pathogens). However, such diseases and issues are recognised as global threats that could become crises in the future.13

Inter partes review (IPR), a new pathway for challenging patents, is threatening the nature of competition in the pharmaceutical industry, drug innovation, and consumers’ access to life-improving drugs. Since its creation under the America Invents Act (AIA) in 2012,[1] this new administrative proceeding has produced noticeably anti-patent results. Whereas patents challenged in district court are invalidated in less than 40% of cases,[2] and patents challenged in the administrative predecessors of IPR were invalidated in less than one-third of cases, IPRs have resulted in patent invalidations in a shocking 70% of cases.[3] Moreover, the IPR process has been exploited by entities that would never be granted standing in traditional patent litigation—hedge funds betting against a company, then filing an IPR challenge in hopes of crashing the stock and profiting from the bet.[4]

Unfortunately, in recent decisions, courts have recognized the anti-patentee bias of IPR, yet punted to Congress the job of amending the provisions. In Cuozzo Speed Technologies v. Lee (Cuozzo) in June 2016, the U.S. Supreme Court found that an anti-patentee claim construction standard in IPR “increases the possibility that the examiner will find the claim too broad (and deny it),”[5] yet concluded that only Congress could mandate a specific standard.[6] Similarly, in Merck & Cie v. Gnosis in April 2016, the U.S. Court of Appeals for the Federal Circuit determined that an anti-patentee standard of review for IPR decisions “is seemingly inconsistent with the purpose and content of the AIA,”[7] yet decided that “the question is one for Congress.”[8] On the standing issue, the Patent Trial and Appeal Board (PTAB) concluded in 2015 that, under the AIA language created by Congress, hedge funds cannot be excluded from IPR proceedings.[9]

Specifically, regarding drugs, pharmaceuticals, and other medical devices, the AIA solidified the strength of already-existing corporations regarding the development and patenting of new drugs. Some have argued that the change from a first-to-invent to a first-to-file system will benefit larger businesses more than smaller ones,107Link to the text of the note as the bigger companies will already have set standards and either a relationship with an outside patent firm or in-house attorneys. Smaller ones, on the other hand, are unlikely to have these types of resources,108Link to the text of the note creating a better environment for patenting drugs for larger companies while smothering the smaller competitors that may attempt to enter the market. Because of this, the AIA's changes have largely been held as positive with some scientists holding a hopeful outlook for the future of medical patents; nonetheless, others grow worried as they recognize that challengers will experience even higher success rates in invalidating patents.109Link to the text of the note

The geographic expansion of what counts as prior art has made the invalidation of drug patents since the AIA much easier, stifling the invention of new drugs;110Link to the text of the note however, the focus on a date of filing, rather than a date of invention, set a more stringent standard for what is counted as prior art temporally, counterbalancing this geographic expansion effects on ease-of-invalidation.111Link to the text of the note While the AIA shortened the process for obtaining a patent on a pharmaceutical product or medical device, invalidating these patents both during the application and after the grant has become easier, making litigation over things like enforcement much risker since a defense can end up with one's patent being invalidated and lost. Since these changes affecting [*91] the patentability of pharmaceuticals came into effect, however, drug prices have remained largely the same when disregarding inflation; this is in part due to the strong influence of the insurance market here in the United States and the lack of regulation around drug prices. However, by using a government-supported patent enforcement entity as an incentive, it may be possible to reduce the burden on drug patentors while requiring them to reduce prices for end-users in turn. This will also have the effect of supporting smaller pharmaceutical companies that are attempting to reach the market but need that little bit of enforcement or licensing support that they currently lack.