Developing robust epidemiological models Enormous amounts of diverse types of data are being collected during the COVID-19 pandemic. Modern computational power, advances in machine learning and artificial intelligence, and mechanistic understanding of immunology and epidemiology can be brought together to analyze these data and thus learn how viruses spread and the relative efficacy of mitigation strategies in ways that have not been possible heretofore. This knowledge will help create robust epidemiological models. Rigorous statistical analyses of the data obtained during the next pandemic using the types of testing devices described above will inform these improved epidemiological models, which can then make reliable projections to guide data-driven public health policy. In this regard, it will be especially important to combine epidemiological and economic data and models to assess the relative merits of mitigation strategies and suggest public health measures that can optimally balance public health needs with economic ones.
Vaccines SARS-CoV-2 is not mutating much as of now, but the challenges for vaccine design are much greater for pandemics caused by rapidly mutating viruses. By bringing together the life, physical, engineering, and medical sciences, we can create an approach for rational design of vaccines against highly mutable viruses. Sophisticated computational approaches can be applied to massive sets of data on virus sequences and structures and coupled to clinical data on disease progression. This approach can enable us to rapidly identify targets on the virus that a vaccine-induced immune response should hit in order to negate the virus’s ability to mutate. These targets are regions of the virus’s proteins where mutations are not tolerated because the mutant viruses are unviable. This knowledge can inform the design of vaccines that elicit immune responses against the mutationally vulnerable regions. Such a vaccine could protect against diverse strains of the mutating virus. This is because the targets that the vaccine-induced immune response would hit would corner the virus between a rock and a hard place—being killed by the immune responses or committing suicide by evolving mutations that evade the response. Such an approach could potentially result in universal vaccines against all coronaviruses, different influenza strains, HIV, and highly mutable viruses that may cause future pandemics. Successful development of such an influenza vaccine, for example, would require only one injection (or maybe a few, depending on the duration of protection) of the flu vaccine in your lifetime. This vaccine would confer protection against seasonally variant influenza strains and those that may cause a pandemic.
It is also possible that the knowledge gained from analyses of sequences, structures, and disease pathogenesis, combined with global virus surveillance capabilities enabled by the diagnostic devices noted earlier, may allow us to anticipate pandemic-causing viruses that may evolve in the future. This capability will allow the design of potential vaccines in advance of the emergence of a dangerous virus.
The vaccines being developed in response to the COVID-19 pandemic will provide us with important lessons regarding the efficacy of novel vaccine constructs, such as those that use RNA or DNA. Further progress in novel vaccine delivery modalities can lead to more efficient immunization strategies.
Social scientists, doctors, ethicists, scientists, engineers, and national leaders across the world will have to work together to clearly communicate the benefits of vaccination to society and describe why the associated risk is low. Without such programs, the development of sophisticated vaccine technologies will be for naught.
Antiviral therapeutics Therapies that can cure disease are game-changing. The rapid development of antiviral therapies tailored to the most vulnerable replication mechanisms of a new pandemic-causing virus can be achieved by leveraging strengths in artificial intelligence, bioengineering, and basic biology. Specifically, combining high-throughput screening of drugs and their viral targets with novel machine-learning approaches can enable rapid discovery, design, and development of new antiviral drugs. Artificial intelligence–based drug design augmented by lessons learned from past failed and successful drugs and new discoveries in virology can lead to designing bespoke anti-viral drugs tailored to a specific virus. Taken together, these technologies will enhance our arsenal of potential antiviral therapies.
Manufacturing Formulation and manufacturing of billions of doses of new biologic materials, either vaccines or therapeutics, usually takes many months because it is a formidable challenge. We are only beginning to understand how to develop flexible manufacturing methods that will enable us to rapidly make new products that are safe to administer to humans. We need advanced manufacturing approaches and compatible regulatory policies to enable large-scale manufacturing of vaccines and therapies to begin shortly after successful clinical trials. Absent this ability, during the COVID-19 pandemic, governments and philanthropists were investing in manufacturing infrastructure before vaccines were even in the late stages of clinical trials, thereby taking on significant financial risk. Lessons learned during the manufacturing of vaccines for COVID-19 will be helpful in shaping the future. The COVID-19 experience will also help us to optimally time and stage clinical trials for vaccines. We will also have to learn how to rapidly develop the infrastructure to make billions of doses of a vaccine, and mobilize the equipment and materials needed to store, transport, and deploy these vaccines.
Safer living spaces, workplaces, and hospitals Person-to-person transmission is an important factor determining the spread of infectious diseases. A fundamental understanding of transmission modalities and mechanisms, and their link to both host physiology and environments, is required to inform the design and retrofitting of housing, workplaces, and hospital environments to minimize transmission. This can be achieved by bringing together approaches from fluid dynamics, aerosol science, optical sensing, signal processing, virology, and monitoring of infected persons.
It is critical to recognize that our vision is not entirely “futuristic.” The technologies described here are likely within reach with adequate investments to advance science and engineering. But getting there requires recognizing a fundamental barrier: many of the needed advances are not in the interest of individual companies who are uncertain whether investing in making these goods will be profitable absent another pandemic. A coordinated program of public-private-academic partnerships is required to undertake the necessary research, development, and manufacturing.
Governments must play a leadership role by signaling that they will support the purchase of pandemic preparation materials and by providing the support for basic research and development required to push forward the technological frontier. Academic institutions must provide leadership in answering the questions that broadly advance our scientific and technological knowledge and in working with private companies to turn those answers into reality. Private companies must respond to the signals coming from government and the knowledge coming out of academia with a coherent and productive plan for creating the goods that we need. Finally, private philanthropists and foundations can provide rapid and flexible funds to help establish the backbone of such an initiative.
It is is important to realize that students and young scientists join faculty members in universities to carry out the research that leads to new discoveries and inventions, such as the ones we envisage above. Therefore, investments in this research will necessarily result in the education of a generation of leaders who can create a more pandemic-resilient world. In the United States, after the launch of Sputnik, a generation of young people was inspired to pursue careers in science and engineering. Many societal benefits accrued from their work. Perhaps the same will happen after the COVID-19 pandemic.
History also teaches us that pursuing the goals we have articulated above will yield a high return to the world economy. In the United States, government support of research and development during and in the decades after World War II created the modern American economy—and enriched a generation of workers. There continues to be outsized returns to investments in basic science. As just one example, th
e US government’s investment of $3 billion in the Human Genome Project has created 280,000 jobs, and the genomics sector pays more than $6 billion a year in US taxes. Investing in a pandemic-resilient future will ensure that the world creates more well-paid jobs while also advancing healthcare. This will require coordination between governments, private and academic sectors, and the healthcare delivery system. We anticipate that some of the scientific and technological advances that will emerge will become essential tools for healthcare unrelated to pandemics.
We hope that the development of new approaches to anticipate, prepare, and combat future pandemics will be pursued by all nations in cooperation. As the COVID-19 pandemic, and many that have come before, make clear, viruses do not discriminate between different peoples or respect national borders and walls. The human race is connected by our shared history of combating the same viruses, and we need to build the shields that will protect us all from these enemies in the future.
We know with certainty that there will be another pandemic. Whether it will be a further mutation of SARS-CoV-2 or some new pathogen, and when it will happen, is unclear. What is clear is that unless we make far-sighted investments in science, technology, and human capital, we will suffer needless deaths and economic catastrophe again. Let’s make the investments required to realize the future that we describe here by the time we face our next pandemic. We hope that the readers of this book from across the spectrum of society will play an important role in the debates that are happening, and will happen, to see how best to win the future. Indeed, this was the sole purpose for writing this book.
Acknowledgments
We are indebted to Dr. Philip Stork for painstakingly drawing all the illustrations in this book. We have benefited from discussions with Abul Abbas, Paul Allen, Kristian Andersen, David Baltimore, Dan Barouch, Deepta Bhattacharya, Sara Cutler, Tony DeFranco, Michael Diamond, Yonatan Grad, Jonathan Gruber, Clifford Lowell, David Masopust, Bernhardt Trout, and Emil Unanue. Some aspects of the views expressed in the epilogue were based on discussions that led to an opinion piece written by AC and Jonathan Gruber. However, none of those noted above bear responsibility for the material presented in this book. We are also grateful to several others who commented on sections of this book. Detailed descriptions of the historical material presented in chapters 1 and 2 have been provided by many others in the past. We have especially learned from, and been inspired by, Arthur Silverstein’s beautiful book, A History of Immunology. Finally, we express our sincere gratitude to our families, which put up with us while we worked at our “day jobs” and on this book during the lockdowns necessitated by the COVID-19 pandemic in the spring and summer of 2020.
Arup K. Chakraborty
Lexington, Massachusetts
Andrey S. Shaw
San Francisco, California
Suggested Reading
Chapters 1 and 2
Silverstein, A. M. A History of Immunology. San Diego: Academic Press, 1989.
Chapter 3
Andersen, K. G., A. Rambaut, W. I. Lipkin, E. C. Holmes, and R. F. Garry. “The Proximal Origin of SARS-CoV-2.” Nature Medicine 26, no. 4 (2020): 450–452.
Lodish, H., Berk, A., Zipursky, S. L., Matsudaira, P., D. Baltimore, and J. Darnell. Molecular Cell Biology. 4th ed. New York: W. H. Freeman, 2000.
Lowen, A. C. “Constraints, Drivers, and Implications of Influenza A Virus Reassortment.” Annual Reviews of Virology 4, no. 105 (2017): 105–121.
Chapter 4
Abbas, A. K., A. H. Lichtman, and S. Pillai. Basic Immunology: Functions and Disorders of the Immune System. 6th ed. Philadelphia: Elsevier, 2020.
Murphy, K., and C. Weaver. Janeway’s Immunobiology. 9th ed. London: Garland Science, 2016.
Chapter 5
Ferretti, L., C. Wymant, M. Kendall, L. Zhao, A. Nurtay, L. Abeler-Dörner, M. Parker, D. Bonsall, and C. Fraser. “Quantifying SARS-CoV-2 Transmission Suggests Epidemic Control with Digital Contact Tracing.” Science 368 (2020): eabb6936.
Hatchett, R. J., C. E. Mercher, and M. Lipsitch. “Public Health Interventions and Epidemic Intensity during the 1918 Influenza Pandemic.” Proceedings of the National Academy of Sciences USA 104, no. 7582 (2007): 7582–7587.
Haushofer, J., and C. J. E. Metcalf. “Which Interventions Work Best in a Pandemic.” Science 368, no. 1063 (2020): 1063–1065.
Kissler, S. M., C. Tedijanto, E. Goldstein, Y. Grad, and M. Lipsitch. “Projecting the Transmission Dynamics of SARS-CoV-2 through the Postpandemic Period.” Science 368, no. 860 (2020): 860–868.
Metcalf, C. J. E., D. H. Morris, and S. Park. “Mathematical Models to Guide Pandemic Response.” Science 369, no. 6502 (2020): 368–369.
Chapter 6
Flint, S. Jane, Vincent R. Racaniello, Glenn F. Rall, Anna Marie Skalka, and Lynn W. Enquist. Principles of Virology. 4th ed. Hoboken, NJ: Wiley, 2015.
Li, G., and E. De Clercq. “Therapeutic Options for the 2019 Novel Coronavirus (2019-nCoV).” Nature Reviews Drug Discovery 19, no. 149 (2020): 149–150.
Yin, W., C. Mao, X. Luan, D.-D. Shen, Q. Shen, H. Su, et al. “Structural Basis for Inhibition of the RNA-Dependent RNA Polymerase from SARS-CoV-2 by Remdesivir.” Science 368, no. 1499 (2020): 1499–1504.
Chapter 7
Bloom, S., and I. Geesink. “A Brief History of Polio Vaccines.” Science 288, no. 5471 (2000): 1593–1594.
Haynes, B. F., and D. R. Burton. “Developing an HIV Vaccine.” Science 355, no. 6330 (2017): 1129–1130.
Hedrick, S. M. “The Imperative to Vaccinate.” Journal of Pediatrics 201 (2018): 259–263.
Juskewitch, J. E., B. A. Carmen, B. A. Tapia, and A. J. Windebank. “Lessons from the Salk Polio Vaccine: Methods for and Risks of Rapid Translation.” Clinical and Translational Science 3 (2010): 182–185.
Plotkin, S. “History of Vaccination.” Proceedings of the National Academy of Sciences (USA) 111, no. 34 (2014): 12283–12287.
Sabin, A. B., and L. R. Boulger. “History of Sabin Attenuated Poliovirus Oral Live Vaccine Strains.” Journal of Biological Standardization 1, no. 2 (1973): 115–118.
Index
Page numbers followed by an “f” indicate figures.
ACE2 (angiotensin-converting enzyme 2), 45, 54, 140f
SARS-CoV-2 and, 45, 54, 72–73, 99, 138, 140
spike proteins and, 45, 54, 72, 138, 140
Acquired immunity. See Adaptive immunity
Ad26 vector, 173, 174
Adaptive immunity, 73, 98, 100, 154, 155. See also Antibodies; T cells
B cells and, 93, 98, 154, 155
innate immunity and, 72, 93–96, 98, 156, 160
T cells and, 85, 87–90, 91f, 92–93
vaccination and, 94
Adjuvants, immunologic, 160–164
AIDS (acquired immunodeficiency syndrome), 62–63. See also HIV
Al-Razi, Bekr Mohammed ibn Zakariya, 3
Amino acids and proteins, 42–43, 49
“Animalcules,” 20
under a microscope, 20–22
Anthrax, 22, 23, 31
Koch, Robert, and, 22–23
Anthrax attacks of 2001, 23
Anthrax vaccine, 30–32
Antibodies, 74–79, 80f–81f, 172
are not the only way to retain memory of past infections, 85 (see also Immunological memory)
B cells and, 77, 79, 85, 99, 101, 155
against HIV, 81, 82, 177–178
against SARS-CoV-2, 82, 84, 139, 140
spike proteins and, 77–79, 139, 140, 155, 159, 161, 174, 175, 177
T cells and, 89, 99, 155
Antibody (Ab) testing, 79, 81–82, 83f, 84. See also COVID-19 testing
Antitoxin, 71
Antiviral drugs. See also HIV: drug treatment
blocking assembly of virus, 145–147
blocking release of virus, 147–148
blocking replication, 136, 137, 142–143, 144f, 145, 148, 149
blocking viral entry, 139–142
combination therapies, 148–149
/>
future of, 152
life cycle of viruses defines targets for, 137–139, 138f
Asia, 116–117. See also China
Asymptomatic infections, 98, 113, 116, 118, 128, 146, 168
Attenuated vaccines, 30–31, 156–159, 169, 171, 172. See also Variolation
Bacteria
grown in culture, 23
under a microscope, 20–22
Baltimore, David, 48
Barouch, Dan, 173, 174
Basic reproductive number (R0)
the concept of R0, 106–109
R0 is not an absolute number, 109–111
Bats, 30
coronaviruses in, 55–58
B cell receptor (BCR), 77, 79, 98
B cells (B lymphocytes), 77, 85
adaptive immune system and, 93, 98, 154, 155
antibodies and, 77, 79, 85, 99, 101, 155
DNA and, 77
lymph nodes and, 77, 79
mutation, selection, and evolution of, 78–79
spike proteins and, 77, 79, 88, 98
T cells and, 88, 89, 99
vaccines and, 101, 155, 159
viruses and, 77–79, 85, 88, 93, 98–101, 154, 155
Beutler, Bruce, 95
Bjorkman, Pamela, 90
Booster shots, 159, 176
Viruses, Pandemics, and Immunity Page 16