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Anthropogenic-driven changes to the hydroclimate will impact on water resources. For example, rainfall changes will affect crop yields and food production; snow and ice, which melt to feed major river systems, are at risk with warming. More than one-sixth of the global population rely on melt-fed river catchments, and this resource is at risk as climate change is likely to cause greater seasonal variability — higher winter flows, because of earlier melt, and reduced summer and autumn flows. Additionally, higher temperatures, and resultant increased biological activity, could lead to decreased water quality. All these changes could occur alongside increased populations and economic development, creating greater demand for a diminishing resource.

The potential for water resource scarcity to lead to conflict, particularly between nations, is the question Michael T. Klare of Hampshire College, Amherst, USA, considers by examining the available research. While within-country water conflicts have been considered and identified as climate change risk, international conflict has received less attention. Globally, many nations share river catchments, and these may not be covered by an agreed cooperative management framework. Conflict between nations over water could trigger secondary engagement from additional nations in an attempt to resolve the conflict.

The Global IP Center and its members believe that strong intellectual property rights are integral to driving the innovation and creativity necessary to create jobs, save lives, advance economic growth and development around the world, and generate breakthrough solutions to global challenges such as climate change.

Our Nation's Founders recognized the link between strong IP rights and innovation more than 200 years ago and explicitly gave Congress the power to protect IP rights in the constitution. As a result, America has led the world in innovation for generations.

Vladimir Putin's losses in Ukraine and the rebellion of the Wagner Group have increased the chances that the Russian president will lash out and expand the 17-month-old conflict. But World War III may not be what you expect. The current paradigm of escalating nuclear conflict was articulated 60 years ago by physicist Herman Kahn, founder of the Hudson institute, but other technologies have come a long way since then. Conventional guns, bombs, missiles or troops may not figure in World War III at all. Biological and computer viruses are likely to be the weapon of choice. Covid wasn't a deliberate attack, but it quickly and successfully damaged the American economy. Any nation thinking of using a deadly virus as a weapon of war would first need to immunize its own people, perhaps under the guise of a flu vaccination. Long-term population-level immunity would require the virus be sufficiently optimized, before release, to reduce the probability of further mutation. The novel coronavirus was sufficiently optimized so that no serious mutations occurred for nine months. The Delta variant appeared in India in October 2020. A weaponized virus would also need to incorporate an immune suppression gene -- Covid had ORF8 -- that reduces early symptoms, facilitating spread by asymptomatic carriers. For a covert attack to be successful, the virus would need to be released not in the country of origin but in the target country, perhaps near a biological facility so the world would falsely conclude it came as a leak from a surreptitious domestic program. Recall that early Covid panic came from Italy's inability to care for all of its infected patients. Thus, for maximum disruption, the second thrust of any aggression might be a cyber attack on hospitals, perhaps disguised as ransomware. Again, the trick would be to make it seem as if the attack were originating outside the aggressor's country. In other contexts this is called a "false flag" operation. The target country might not even recognize it as part of a two-front, synergistic attack of biological and computer viruses. Ransomware could simultaneously target energy grids, power plants, factories, refineries, trains, airlines, shipping, banking, water supplies, sewage-treatment plants and more. But hospitals would be the most salient targets. Avoiding obvious military targets would enhance the illusion that World War III hadn't begun. The attacker or attackers might falsely claim their own systems are also under siege. Misdirection can be more effective than a smoke screen. This isn't some far-fetched disaster scenario cooked up by Hollywood screenwriters. Biological and cyber viruses have been, in a sense, field tested. The great value to the attacker of a two-pronged biological and cyber attack is the possibility of achieving destructive goals while keeping the whole operation covert.

Piers & Andrew, 08-01-2017, “Existential Risk and Cost-Effective Biosecurity,” Health Security, 15(4), PubMed.

In the decades to come, advanced bioweapons could threaten human existence. Although the probability of human extinction from bioweapons may be low, the expected value of reducing the risk could still be large, since such risks jeopardize the existence of all future generations. We provide an overview of biotechnological extinction risk, make some rough initial estimates for how severe the risks might be, and compare the cost-effectiveness of reducing these extinction-level risks with existing biosecurity work. We find that reducing human extinction risk can be more cost-effective than reducing smaller-scale risks, even when using conservative estimates. This suggests that the risks are not low enough to ignore and that more ought to be done to prevent the worst-case scenarios. How worthwhile is it spending resources to study and mitigate the chance of human extinction from biological risks? The risks of such a catastrophe are presumably low, so a skeptic might argue that addressing such risks would be a waste of scarce resources. In this article, we investigate this position using a cost-effectiveness approach and ultimately conclude that the expected value of reducing these risks is large, especially since such risks jeopardize the existence of all future human lives. Historically, disease events have been responsible for the greatest death tolls on humanity. The 1918 flu was responsible for more than 50 million deaths,1 while smallpox killed perhaps 10 times that many in the 20th century alone.2 The Black Death was responsible for killing over 25% of the European population,3 while other pandemics, such as the plague of Justinian, are thought to have killed 25 million in the 6th century—constituting over 10% of the world's population at the time.4 It is an open question whether a future pandemic could result in outright human extinction or the irreversible collapse of civilization. A skeptic would have many good reasons to think that existential risk from disease is unlikely. Such a disease would need to spread worldwide to remote populations, overcome rare genetic resistances, and evade detection, cures, and countermeasures. Even evolution itself may work in humanity's favor: Virulence and transmission is often a trade-off, and so evolutionary pressures could push against maximally lethal wild-type pathogens.5,6 While these arguments point to a very small risk of human extinction, they do not rule the possibility out entirely. Although rare, there are recorded instances of species going extinct due to disease—primarily in amphibians, but also in 1 mammalian species of rat on Christmas Island.7,8 There are also historical examples of large human populations being almost entirely wiped out by disease, especially when multiple diseases were simultaneously introduced into a population without immunity. The most striking examples of total population collapse include native American tribes exposed to European diseases, such as the Massachusett (86% loss of population), Quiripi-Unquachog (95% loss of population), and the Western Abenaki (which suffered a staggering 98% loss of population).9 In the modern context, no single disease currently exists that combines the worst-case levels of transmissibility, lethality, resistance to countermeasures, and global reach. But many diseases are proof of principle that each worst-case attribute can be realized independently. For example, some diseases exhibit nearly a 100% case fatality ratio in the absence of treatment, such as rabies or septicemic plague. Other diseases have a track record of spreading to virtually every human community worldwide, such as the 1918 flu,10 and seroprevalence studies indicate that other pathogens, such as chickenpox and HSV-1, can successfully reach over 95% of a population.11,12 Under optimal virulence theory, natural evolution would be an unlikely source for pathogens with the highest possible levels of transmissibility, virulence, and global reach. But advances in biotechnology might allow the creation of diseases that combine such traits. Recent controversy has already emerged over a number of scientific experiments that resulted in viruses with enhanced transmissibility, lethality, and/or the ability to overcome therapeutics.13-17 Other experiments demonstrated that mousepox could be modified to have a 100% case fatality rate and render a vaccine ineffective.18 In addition to transmissibility and lethality, studies have shown that other disease traits, such as incubation time, environmental survival, and available vectors, could be modified as well.19-21 Although these experiments had scientific merit and were not conducted with malicious intent, their implications are still worrying. This is especially true given that there is also a long historical track record of state-run bioweapon research applying cutting-edge science and technology to design agents not previously seen in nature. The Soviet bioweapons program developed agents with traits such as enhanced virulence, resistance to therapies, greater environmental resilience, increased difficulty to diagnose or treat, and which caused unexpected disease presentations and outcomes.22 Delivery capabilities have also been subject to the cutting edge of technical development, with Canadian, US, and UK bioweapon efforts playing a critical role in developing the discipline of aerobiology.23,24 While there is no evidence of state-run bioweapons programs directly attempting to develop or deploy bioweapons that would pose an existential risk, the logic of deterrence and mutually assured destruction could create such incentives in more unstable political environments or following a breakdown of the Biological Weapons Convention.25 The possibility of a war between great powers could also increase the pressure to use such weapons—during the World Wars, bioweapons were used across multiple continents, with Germany targeting animals in WWI,26 and Japan using plague to cause an epidemic in China during WWII.27 

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.

Biodisaster X could result in catastrophic human and economic consequences. Yet to date, no studies have examined the potential role of information technologies in preventing and mitigating Biodisaster X, especially in the context of advanced and emerging technologies such as AI and 6G. To bridge the research gap, in this study, we sought to explore 2 fundamental research questions that could considerably enrich the literature: (1) what Biodisaster X might entail and (2) solutions that use AI and emerging 6G technologies to help monitor and manage Biodisaster X threats. In the following sections, we will detail our findings pivoting on these 2 research objectives, as well as practical and powerful strategies that have the potential to effectively control and contain Biodisaster X threats. Biodisaster X: What is in a Name? A disaster can be defined as “a serious disruption of the functioning of a community or society involving widespread human, material, economic, or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources” [47]. Based on the contributing causes, disasters are usually categorized as natural (eg, earthquakes, infectious disease-inducing epidemics, or pandemics of natural origin) and anthropogenic (eg, armed conflicts, nuclear accidents, or the release of pathogenic genetically modified organisms from laboratory settings). In the context of this study, biodisasters are defined as disasters that occur as a result of infectious pathogens with bioweapon potential, which are unleashed by state or nonstate actors accidentally and intentionally (eg, the Japanese government’s controversial decision to dump Fukushima’s contaminated water into the boundless and borderless ocean shared by all life forms on earth, including humans and sharks [48]). In the context of biodisasters, a state actor often takes the form of a nation that deliberately and systematically designs and develops infectious pathogens with its national interest in mind. In contrast, a nonstate actor is an individual or group acting independently to obtain or manufacture a pathogen either owing to misguidance or malice. Of note, although existing multilateral agreements prohibit the production and use of bioweapons by state actors (termed biowarfare) [49], the presence of signed agreements does not imply that accidental or intentional development and release of pathogens by state actors will not occur. The concept of “bioterrorism,” defined as the deliberate release of pathogens that could cause illnesses and deaths in society, is not the focus of this study because “bioterrorism” entails both deliberation and malice (eg, to elicit terror to the public) [50]; antecedents may not necessarily apply to Biodisaster X threats. Insights from behavioral science [51-53] and evidence regarding individual-caused mass casualty events (eg, indiscriminate mass shootings) [54-56] suggest that individual actors’ behaviors, potentially leading to the onset of Biodisaster X, may or may not include conscious deliberation to harm. In other words, while it is possible that individual actors’ malicious actions might cause some biodisasters, it is also possible that some individual-caused biodisasters are accidental. Furthermore, the term bioterrorism is limited, in that “terror” is the main outcome. We believe that for Biodisaster X, which could upend lives, livelihoods, and economies, “disaster” is a more appropriate description that sheds light on the scale and severity of its consequences and is more diverse than “terror.” Drawing insight from real-world examples, similar to the prevalent ransomware hacks, it is possible that state or individual actors could develop and utilize infectious pathogens as “ransomgens” for financial gain rather than merely aiming to generate terror in society. Therefore, under the current research context, we adopted the term “biodisaster” instead of “bioterrorism.” Furthermore, considering that various studies have discussed approaches to address state actor–initiated biodisasters [57-61], this study focuses on biodisasters that are infectious in nature, caused by individual actors, and can result in catastrophic human and economic consequences. Biodisaster X vs Disease X The risk of biodisasters, such as Biodisaster X, is increasing in likelihood: advances in technology, particularly the availability and maturity of biotechnology, have grown considerably in recent years. Inadvertently, these advances may resemble those of Oppenheimer [62] in facilitating the release of destructive factors. One example of the misuse of biotechnology is a microbiologist, vaccinologist, and senior biodefense researcher who worked at the United States Army Medical Research Institute of Infectious Diseases, who allegedly engineered the 2001 anthrax attacks [63-65]. While the scale of the 2001 anthrax attacks was minor, it demonstrated how easily biodisasters can occur and how unprepared society was for these events. As seen in the lack of adequate preparation and coherent responses to infectious disease–induced pandemics, including COVID-19 [66-69], Biodisaster X’s effects may be compounded to the same, if not greater, degree by incompetence across international, national, and regional agencies and organizations. The concept of Biodisaster X can be best understood in contrast with Disease X. In terms of similarities, both Biodisaster X and Disease X are driven by pathogens unknown to humans and have the potential to cause crippling effects on society. Furthermore, based on previous inadequacies in response to emergency events including pandemics [66-74], the world at large may be ill-prepared for both Biodisaster X and Disease X. In terms of unique attributes, compared to Disease X, Biodisaster X is more likely to have the following characteristics: (1) having a pathogen directly affiliated to a laboratory; (2) having distinctive and engineered attributes tailored by the capabilities and intentions of the developer; and (3) the origin, development, and history can be definitively ascertained upon identification of the developer, which is not possible for naturally occurring pathogens (eg, the 1918 influenza pandemic), where there is always uncertainty regarding the origin and evolutionary history of the disaster [75-77]. The Imperative of Preparing for Biodisaster X Some of the deadliest pandemics—the most recent ones ranging from AIDS, severe acute respiratory syndrome, Middle East respiratory syndrome, Ebola, and COVID-19—all have zoonotic origins [78]. Studies have further shown that for viruses that can transmit from animals to humans, especially those that can infect a diverse range of host species, the transmission speeds are substantially amplified once human-to-human transmission is established, and the diseases can quickly evolve into global pandemics [79]. Consequently, once a pathogen is transmissible within a population, there is a low access threshold: an individual actor can “obtain” these deadly pathogens without the need for advanced laboratory skills or extensive financial resources. However, costs to physical and mental health may reveal a counternarrative. Based on available evidence, it is difficult to determine whether an individual can be a malicious “patient zero”; an individual who intentionally contracts a novel virus intending to cause infectious disease outbreaks in a society [80]. It is not impossible to purposely study and capture known or unknown deadly pathogens that can trigger infectious diseases; microbial surveys are commonly conducted to identify novel pathogens before they pose a threat to public health [81-84]. In theory, there could be individual actors, with adequate knowledge or experience (similar to the microbiologist allegedly behind the 2011 anthrax attacks [63-65]), who may take the same actions but with different motives, ranging from scientific curiosity to ill-guided intentions. Considering the rich biodiversity of wildlife, along with the large number of “missing viruses” and “missing zoonoses” that remain unidentified [85], close contacts with latent deadly pathogens are nearly impossible to control, which in turn, renders it challenging to locate or identify individual actors who might utilize them. Advances in synthetic biology may further compound the situation, especially considering the scholarly endeavors using pathogens in laboratory settings, which could amount to the level of real-world pandemics (eg, laboratory-cultured viruses such as smallpox [86-88]). The likelihood of Biodisaster X increases in proportion to these factors. Overall, considering the species diversity of wildlife, the unknown factors related to the scale and severity of viruses in animals, which have the latent potential to infect humans, and the varying degrees of competency of community health centers in detecting infectious disease outbreaks in a bottom-up manner, it could be tremendously difficult for health experts and government officials to monitor potentially emerging Biodisaster X threats. However, not all hope is lost. Technology-based solutions, especially those utilizing AI and 6G technologies, can help address these issues. The Need for Advanced Technology Solutions for Monitoring and Managing Biodisaster X The Need for Technology-Based Solutions Once Biodisaster X becomes a reality, human contact will drive transmission and become the primary fuel for exacerbating infections and deaths caused by the disaster. As seen during the COVID-19 pandemic, owing to virus spread and subsequent public health policies (eg, lockdowns), many critical societal functions could be substantially disrupted. The potential to control and contain human and economic consequences of Biodisaster X, such as the functionality of the health care systems (eg, infected health care professionals) [89-91], may also become critically undermined. In these circumstances, technology-based solutions could be the key to addressing these crises, as they are different from conventional solutions; they are not highly dependent on physical interactions and transportation. Overall, technology-based solutions require limited human resources (eg, with the ability to operate without human input), can be delivered independent of physical human contact (eg, web-based and remote deployment), and are immune to infectious diseases (eg, can function in contaminated environments). Furthermore, technology-based solutions are less vulnerable to issues ranging from physical fatigue to mental health burdens, which are health challenges that frontline workers often face amid emergency events. The Need for Advanced Technologies To effectively predict, control, and manage Biodisaster X, which is an event with a low probability (ie, difficult to detect preemptively) and a high impact (ie, difficult to control and contain), advanced technologies are needed. While many emerging technologies can address the dangers and damages associated with Biodisaster X [92,93], 2 families of advanced technology-based solutions show particular promise, namely AI techniques and 6G technologies. Unique Capabilities of AI AI is generally considered synonymous with “thinking machines” [94], or techniques that can facilitate “a computer to do things which, when done by people, are said to involve intelligence” [95]. With AI technologies, machines can identify patterns too intricate for humans to identify and process quickly. AI techniques are widely used in areas such as natural language processing, speech recognition, machine vision, targeted marketing, and health care, including efforts to combat COVID-19 [96-99]. While technologies such as virtual reality, smart sensors, drones, and robotics could play a positive role in supporting health care professionals to cope with the pandemic [100-102], AI technologies are arguably most instrumental in addressing some of the most prominent issues health experts and government officials are faced with, ranging from pandemic surveillance to COVID-19 drug and vaccine development [103-106]. AI and machine learning techniques are particularly valuable in their ability to identify trends and patterns across large amounts of data promptly and cost-effectively; for example, in identifying or searching for specific patterns. With natural language processing, for instance, data can be extracted retrospectively from clinical records or prospectively in real time and statistically processed for insights, which, in turn, can supplement existing structured data to enrich actionable information [86]. During the COVID-19 pandemic, natural language processing models have been used to analyze publicly available information such as tweets, tweet timestamps, and geolocation data, to identify and map potential COVID-19 cases cost-effectively, without utilizing testing devices or other medical resources that involve health care professional [107]. Overall, most, if not all, AI techniques are irreplaceable in regard to administering complex tasks such as extracting useful information from large data sets. Moreover, with the continuously increasing speed of its technological advancements and applications, AI technologies are often utilized as core components in other emerging technologies [108]. Smart sensors that perform advanced tasks, such as effectively identifying and recognizing captured motions and images, often need to integrate deep learning technologies (a subgroup of AI) [109-111]. These combined insights suggest that AI techniques have great potential in monitoring and managing Biodisaster X threats.[

Roko’s Basilisk exists at the horizon where philosophical thought experiment blurs into urban legend. The Basilisk made its first appearance on the discussion board LessWrong, a gathering point for highly analytical sorts interested in optimizing their thinking, their lives, and the world through mathematics and rationality. LessWrong’s founder, Eliezer Yudkowsky, is a significant figure in techno-futurism; his research institute, the Machine Intelligence Research Institute, which funds and promotes research around the advancement of artificial intelligence, has been boosted and funded by high-profile techies like Peter Thiel and Ray Kurzweil, and Yudkowsky is a prominent contributor to academic discussions of technological ethics and decision theory. What you are about to read may sound strange and even crazy, but some very influential and wealthy scientists and techies believe it.

One day, LessWrong user Roko postulated a thought experiment: What if, in the future, a somewhat malevolent AI were to come about and punish those who did not do its bidding? What if there were a way (and I will explain how) for this AI to punish people today who are not helping it come into existence later? In that case, weren’t the readers of LessWrong right then being given the choice of either helping that evil AI come into existence or being condemned to suffer?

The objective of patent rights is to foster innovation and economic growth. However, to date, there is little robust evidence that patents “work”.

Here, we applied a difference-in-differences strategy to study the impact of changes in patent rights within panels of up to 54 manufacturing industries in up to 72 countries between 1981-2000. We found that stronger patent rights were associated with faster growth among more patent-intensive industries.

The report recognizes that innovation—the process through which new ideas are generated and successfully introduced in the marketplace—is a primary driver of U.S. economic growth and national 2 competitiveness.1 U.S. companies’ use of patents, copyright, and trade secrecy to protect their creations, and trademarks to distinguish their goods and services from those of competitors represent important supports for innovation, enabling firms to capture market share, which contributes to growth in our economy. The granting and protection of intellectual property rights is vital to promoting innovation and creativity and is an essential element of our free enterprise, market-based system. Patents, trademarks, and copyrights are the principal means used to establish ownership of inventions and creative ideas in their various forms, providing a legal foundation to generate tangible benefits from innovation for companies, workers, and consumers. Without this framework, the creators of intellectual property would tend to lose the economic fruits of their own work, thereby undermining the incentives to undertake the investments necessary to develop the IP in the first place.2 Moreover, without IP protection, the inventor who had invested time and money in developing the new product or service (sunk costs) would always be at a disadvantage to the new firm that could just copy and market the product without having to recoup any sunk costs or pay the higher salaries required by those with the creative talents and skills. As a result, the benefits associated with American ingenuity would tend to more easily flow outside the United States. The report finds that IP is used everywhere in the economy, and IP rights support innovation and creativity in virtually every U.S. industry. While IP rights play a large role in generating economic growth, too little attention has been given to identifying which industries produce or use significant amounts of IP and rely most intensively on these rights. The report was written to give policy makers and the public more information about the impacts of IP protection in the U.S. economy on which to base sound policy.

In recent years, a great many studies of the real-world impact of patenting on innovation and economic growth (many available for free on ssrn.com) point to its beneficial effects. Arrow (1962), Griliches (1963), Schmookler (1966), Kitch (1977), Reinganum (1981), Tirole (1988), Klemperer (1990), Romer (1990), Giulbert and Shapiro (1990), Grossman and Helpman (1991), Aghion and Howitt (1992), Scotchmer (1999), and Gallini (2002) all found that patents foster ex ante innovation — meaning, they induce people to invent because of the prospect of reward.

Invention, it has been shown, is driven primarily not by genius or happenstance but rather by markets and the expectation of the profit that can be gained by securing the patent rights to new technologies. Zorina Khan of Bowdoin College and the late Kenneth Sokoloff at UCLA found that among the “great inventors” of the 19th century, “their patterns of patenting were procyclical [and] responded to expected profit opportunities.” And as Khan noted elsewhere, “Ordinary people [are] stimulated by higher perceived returns or demand-side incentives to make long-term commitments to inventive activity.”

In the event that AI-generated inventions are patentable, there still remains the question of who would own these patents. An AI should not be able to own a patent, not only because AI cannot legally own property, but because it would be a bad idea. The AI would not be motivated by a patent, the AI would not obtain any direct benefit from ownership, and the AI would not be able to effectively enforce a patent. This presents a number of obvious options for patent ownership such as the AI’s owner, developer, or user. The developer, user, and owner may be the same person or persons, or they may be different entities.

Ownership rights to AI-generated inventions should vest in an AI’s owner because it would be most consistent with the way personal property (including both AI and patents) is treated. If you own a machine that produces property then you own that property - whether a loaf of bread or valuable data which you choose to protect as a trade secret (leaving aside more complex cases in which multiple parties are involved or you did not have rights to the machine’s input [whether baking soda or another party’s data]). AI owner patent ownership could be taken as a starting point, although parties should be able to contract around this default, and as AI-generated inventions become more common, negotiations over these inventions may become a standard part of contract negotiations. So long as property entitlements are clearly allocated, these parties can ultimately work out the most efficient allocation of rights between themselves. However, a default ownership rule is still necessary to minimize overall transaction costs.

A full decoupling of the Chinese and Western economies could be a costly proposition. The International Monetary Fund estimates that it could shave around 7 percent off global GDP in the longer term, a loss equivalent to $7.4 trillion or about the size of the French and German economies combined.

The IMF also predicts that developing economies would be hit hardest if Washington and Beijing were to cut economic ties. But what these aggregate figures miss is that is that some emerging economies will be winners from decoupling—or, as its somewhat milder form is called today, de-risking. In fact, some emerging countries are fast positioning themselves to benefit as China and the West rewire their supply chains—for example, by serving as industrial and trade hubs between both sides or by playing one side against the other for investment support.

For decades, numerous U.S. administrations have taken steps to decelerate China’s technological development and economic growth.

It hasn’t worked.

Second, although U.S.-China bilateral trade and investment ties have stagnated, the two countries are still embedded in an extensive web of complex interdependence across all aspects of their economies and broader societies, which extends to countries around the world. Interconnectivity has created certain vulnerabilities, such as overdependence for critical goods and the potential transfer of dual-use technologies, but it also has created extensive economic and national security benefits that raise the costs of conflict and the value of continued interaction to both sides. Understandably, neither side is keen to abandon such ties. The fact that other countries are deeply connected to the United States and China further raises the costs to Washington and Beijing from taking precipitous actions. Interdependence is no panacea, but it is a structural feature shaping the choices of all parties.

The credible military deterrents that the United States and China both maintain against the other form the first source of stability. China’s relative power has risen dramatically in recent years, and the United States and others take as credible Chinese threats to use force in the Taiwan Strait if Taiwan were to cross any of Beijing’s red lines. Conversely, the U.S. credibility to provide defensive support to Taiwan and its allies has been strengthened through the development of the Quad and AUKUS, improved bilateral relations with countries around China’s periphery, and its support to Ukraine. Equally important, although there are “green” and “blue” wings in Taiwan’s political spectrum regarding their ideal preferences for cross-strait outcomes, in the context of realistic constraints, the vast majority of the population—and the political parties—are one or another shade of turquoise. In sum, the astronomical costs of an outbreak of hostilities have created a widely shared preference in favor of maintaining the status quo.

The trade war between the United States and China started when the U.S. levied new tariffs in 2018. But decades of tension over technology, security, and global influence have shaped the current state of relations between the world’s two largest economies.

Two recent studies by Professor Kim Ruhl of the University of Wisconsin-Madison suggest that focusing on the trade war instead of the longer-term tensions misses the bigger picture.

Amid growing tensions between the two countries, trade expert William Reinsch of the Centre for Strategic and International Studies says: "Last year's decline in U.S.-China trade does appear to be a sign that both economies are moving away from each other"

"But if you look at the increased imports from Southeast Asia into the United States, it appears that a good part of that increase is coming from Chinese companies that have either moved production or are simply moving their products through third countries in order to circumvent tariffs or other restrictions."

Whether all this amounts to a full-on trade war between the U.S. and China is a question of semantics. Tang says businesses are constantly shifting strategy in response to geopolitical tensions and changing trade dynamics. “This kind of supply chain restructuring has evolved along with tariffs and trade barriers,” he says. “It’s happened throughout history.”

In any case, don’t expect the move toward “decoupling” to sever the U.S.-China trade relationship entirely. “Indirect trade throughout China, behind the scenes, will not disappear,” Tang says. When it comes to sheer manufacturing capacity, efficiency and scale, “no other countries right now can do what China can do.” Even those manufacturers taking advantage of production fleeing China will continue to rely heavily on the nation for essential parts and raw materials.

Under President Biden, Washington has taken the most serious steps yet toward weakening China’s play for economic dominance. He has signed legislation that could lead to the ban of China-owned social media giant TikTok; retained some $360 billion worth of tariffs as well as many sanctions applied by Trump on Chinese individuals associated with human rights abuses in Xinjiang and Hong Kong; introduced unprecedented export controls that restrict Beijing’s ability to obtain advanced technology; and banned some U.S. investment in sensitive technologies that lawmakers fear could be used to aid China’s growing military. He has also quadrupled tariffs on electric vehicles made in China, tripled those on steel and aluminum, and doubled the duty on semiconductors. Meanwhile, several U.S. governors have signed laws preventing state pensions from investing in equities controlled by the Chinese state.

What lies ahead for U.S.-China trade?

The “quiet” trade war that has been underway between the U.S. and China for a few years now is getting louder.

A recent series of punitive actions by both countries is making their ongoing dispute “more explicit,” says Christopher S. Tang, distinguished professor and Edward W. Carter Chair in business administration at the UCLA Anderson School of Management.