1 A Global Immunological Observatory to meet a time of pandemics 2 3 4 Michael J. Mina1,2,*, C. Jessica E. Metcalf3,4,*+, Adrian B. McDermott4, Daniel C. Douek4, Jeremy 5 Farrar6, Bryan T. Grenfell3,4,5 6 7 1Center for Communicable Disease Dynamics, Departments of Epidemiology and of 8 Immunology & Infectious Diseases Harvard School of Public Health, United States 9 2Department of Pathology, Brigham and Women’s Hospital, Boston, United States 10 2Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United 11 States 12 3Woodrow Wilson School, Princeton University, Princeton, United States 13 4 Vaccine Research Center, National Institutes of Health, Bethesda, MD, United States 14 5Fogarty International Center, National Institutes of Health, Bethesda, MD, United States 15 6 Wellcome Trust, London, United Kingdom 16 17 *These two authors contributed equally 18 19 ABSTRACT 20 SARS-CoV-2 presents an unprecedented international challenge, but it will not be the last such 21 threat. Here, we argue that the world needs to be much better prepared to rapidly detect, define 22 and defeat future pandemics. We propose that a Global Immunological Observatory (GIO) and 23 associated developments in systems immunology, therapeutics and vaccine design should be at 24 the heart of this enterprise. 25 26 27 Early in a pandemic, clinical surveillance and pathogen sequencing are essential first steps, 28 bolstered by extensive testing to characterize and initiate control of the pandemic trajectory. But 29 this tracking of infection provides only part of the picture (Figure 1A). Specifically, it cannot tell 30 us the trajectory of susceptible individuals who, together with cases, determine the shape and 31 size of the pandemic; the other major group, recovered (potentially immune) individuals, who 32 drive the dynamics of herd immunity, is also not directly observable. Serological surveys can 33 characterize these key hidden variables; however, validation and interpretation are difficult 34 issues (especially for novel pathogens, as SARS-CoV-2 currently illustrates). Furthermore, 35 extensive universal and frequent population sampling for serology is not part of the routine 36 surveillance armory. GIO would address all of these gaps (Metcalf et al. 2017, 2016). 37 38 In theory, we have tremendous ability to deploy multiplex testing for immune responses to 39 pathogens (using techniques ranging from classic ELISAs to phage display approaches) as well 40 as pathogen presence (via genetic sequencing, antigen detection, etc). Further, these methods 41 work with an increasing variety of accessible sample types (saliva, blood spots, etc.) which are 42 minimally invasive. Yet, despite decades of technical progress in measurement of both immune 43 responses and pathogen presence (including SARS-CoV and MERS-CoV), which together 44 reveal the core processes driving pathogen transmission (Figure 1A), the global health 45 community was unable to identify and model local circulation of SARS-CoV-2 in a timely fashion 46 in almost any setting. 47 48 How could we better deploy and refine tools for sophisticated pathogen surveillance to better 49 meet likely future comparable threats (Metcalf et al. 2016)? Several problems have prevented 50 this in the current pandemic. First, a shortage of testing capacity and a paucity of historical and 51 contemporary samples to ground analyses combined to cripple our inferential capacity. More 52 fundamentally, despite huge recent progress in immunology, the immune system’s complexity 53 remains a barrier: a revolution in the infrastructure of immune surveillance and systems 54 immunology to generate new understanding and resultant techniques is required. A number of 55 innovations are in reach to step away from the status quo by building GIO, structured around 56 three core sample types: 57 58 1. Routinely collected seasonal and international surveillance samples to define the 59 baseline (e.g., clinical discard blood specimens from adults, or blood bank and 60 plasma donor samples, representative random sampling etc) and thus capture 61 anomalies reflecting immune responses to emerging threats. Ideally, this would 62 proceed in parallel with extensive pathogen detection and sequencing, see below. 63 2. Repeated samples from cohorts (ideally across the full age range, and including birth 64 cohorts) to characterize the mechanisms underlying the ontogeny and time-course of 65 immunity. This would be invaluable in the current crisis for teasing out immune 66 correlates of protection. 67 3. To anticipate zoonotic threats, a multi-species extension of GIO replicating these 68 surveillance streams in key reservoir species (notably bats), and associated at-risk 69 occupations, is an important extension (Daszak, Olival, and Li 2020). 70 71 Such samples are a necessary condition for GIO, however, they will not, in themselves, be 72 sufficient - a series of technological developments are also required: to define the core endemic 73 pathogen imprint on the individual and thus population level immune function, necessary to 74 enable identification of departures from it. Traditionally, ELISAs are the foundation of public 75 health immunological surveillance, although they largely remain limited in throughput, both in 76 the numbers of specimens tested and numbers of pathogen-specific antibodies detected. 77 Advances in highly multiplexed, comprehensive serological evaluations of known and potential 78 pathogen exposure (e.g., microarray chips, VirScan (Xu et al. 2015; Khan et al., n.d.)) are 79 increasingly available. Simultaneous epitope and T and B cell repertoire identification such as T- 80 scan (Kula et al. 2019), coupled with direct pathogen detection will provide a much more 81 exhaustive picture than currently available of pathogen exposure and immune response. 82 83 Critically, deployment of the observatory would have allowed us to rapidly achieve a preliminary 84 understanding of the dynamics of immunity - even a few weeks gained would have been 85 beneficial in the current crisis. For example, with GIO operational, ongoing evaluation of clinical 86 discard or blood donor specimens (e.g., using VirScan) would have enabled fast detection of 87 serological shifts away from baseline. In particular, we would have readily detected excess 88 cross-reactive antibodies for seasonal coronaviruses, SARS, MERS or any of an additional 89 number of animal coronaviruses already present in the VirScan library. The value of routine 90 measurements of such a broad diversity of antibodies (e.g. VirScan detects antibodies to 91 hundreds of thousands of potential pathogen epitopes with only a microliter of blood) is high 92 sensitivity to perturbations in the antibody repertoires including from zoonotic pathogens not 93 known to infect humans. Through GIO, routine evaluation of international longitudinal sample 94 sets would have been available to guide our understanding of attack-rate in vulnerable 95 populations and in children, sero-prevalence, acquisition of sero-positivity and how they map to 96 immunity, potentially vital weeks earlier. Further, even before the outbreak, many of the 97 fundamental immunological uncertainties revealed by the current pandemic could have been 98 addressed in ‘peacetime’: mass sampling coupled with technical refinements in measurements, 99 and immuno-epidemiological modeling would have shed important light on the potential for and 100 magnitude of cross-reactivity to existing coronaviruses, the role of cellular vs. humoral immunity, 101 the duration of immunity, and the potential for immune pathogenesis from autoimmunity to 102 antibody dependent enhancement. 103 104 Beyond immune based detection, ideally GIO would exist next to a similar Global Pathogen 105 Observatory (GPO) for a parallel effort surveilling and sequencing pathogens themselves, 106 potentially informed by the immunological context GIO reveals. In particular, GPO could build on 107 seminal previous efforts in pathogen discovery (eg, PREDICT, the Human Virome Project), 108 rooted in the disease ecosystem perspective of OneHealth (Daszak, Olival, and Li 2020; Carroll 109 et al. 2018). 110 111 112 From detection to control. 113 Of course, we must not simply observe a pandemic as it evolves. We must act in parallel and 114 we must translate our knowledge into directly reducing morbidity and mortality in all vulnerable 115 populations. GIO and GPO provide a unique lens for achieving this. The necessary technologies 116 fundamentally already exist. The time from release of the SARS-CoV-2 sequence to a needle in 117 an arm in the first Phase 1 vaccine trial was only 65 days 118 (https://www.clinicaltrials.gov/ct2/show/NCT04283461). This is a remarkable achievement made 119 possible through the recent development of easily manufactured, implementable and deployable 120 vaccine platforms, such as mRNA or DNA constructs. 121 122 Contemporaneously, permissive animal challenge models should be established to test the 123 vaccine and also to aid in the identification of neutralizing monoclonal antibodies (NAbs) which 124 could be used in short term prevention and as therapeutic agents. This endeavor should go 125 hand in hand with identification of NAbs in people who have recovered from the infection as well 126 as with detailed analysis of their T cell responses. Finally, and this all flows logically from the 127 initial serological signal and identification of the pathogen’s sequence and in vitro production of 128 its proteins, the establishment of assays to measure the breadth, affinity, receptor binding 129 competition analyses and neutralizing capacity of specific antibodies would be a critical part of 130 this process. Importantly, all these activities can and should be done in parallel. Indeed, with 131 GIO and GPO’s ‘peacetime’ sampling of reservoir species, a library of mRNA/DNA candidates 132 could be generated preemptively. More broadly, GIO could provide considerable insights into 133 other immune-related diseases: from cancer to autoimmune disorders. 134 135 We argue that a Global Immunological Observatory is essential for understanding and 136 combating future pandemics. The GIO/GPO infrastructure would bootstrap our ability to engage 137 with a series of fundamental questions associated with the other key processes in pandemics, 138 such as the transmission process — encompassing the biophysics of pathogen spread between 139 individuals, as well as the role of patterns of social mixing. To make full use of immunological 140 and pathogen observatories, cross fertilization between many fields will be required: spanning 141 systems immunology, virology and physical sciences, through to public health and social 142 science. Major progress in computational biology and disease modeling would also be enabled 143 by these rich and varied datasets. In particular, a key gap in pandemic and endemic disease 144 understanding is the cross-scale phylodynamic interaction between viral evolution and immune 145 kinetics in individuals and nonlinear epidemic dynamics at population and global scales 146 (Grenfell et al. 2004). Establishment of GIO/GPO would generate a step change in knowledge 147 at this key interface. 148 149 Many lessons will emerge from this pandemic, but we need to learn even more before the next 150 inevitable outbreaks occur. GIO and a larger pathogen enterprise will require significant annual 151 investments, but this will be trivial compared to the costs of future pandemics. Establishing 152 GIO/GPO would require solving a range of important practical issues, from sustainable funding, 153 governance, global equity, intellectual property and relationships with industry, but there are 154 existing models and clear paths to addressing all of these challenges (Figure 1B). Lessons for 155 infrastructure, funding and governance could also be learned from the virological and immune 156 phenotypes collected to inform strain selection for seasonal influenza vaccination coordinated 157 by the WHO (Morris et al. 2018). A variety of distributed models are possible for GIO/GPO, but 158 an essential condition is the transparent and effective governance of administrative, informatics 159 and laboratory cores. 160 161 Another field where understanding of spatio-temporal dynamics is crucial to addressing a deeply 162 applied problem is weather and climate science. The skill of weather forecasts was dramatically 163 improved by deployment of buoys measuring Sea Surface Temperature across the oceans of 164 the world (http://www.argo.ucsd.edu/About_Argo.html). Similarly, in the context of global health, 165 we have the means to deploy measurements crucial both to pandemic preparedness, and 166 endemic disease control, using the delicate and responsive sensor that is all of our (and 167 reservoir species’) immune systems. And as this pandemic has repeatedly shown, early warning 168 and rapid response can make dramatic differences that translate directly to immensely favorable 169 outcomes. Susceptibility and immunity have been the ‘dark matter’ of epidemic dynamics; GIO 170 could reveal them, to the considerable benefit of global health. 171 172 173 References

174 Carroll, Dennis, Peter Daszak, Nathan D. Wolfe, George F. Gao, Carlos M. Morel, Subhash 175 Morzaria, Ariel Pablos-Méndez, Oyewale Tomori, and Jonna A. K. Mazet. 2018. “The 176 Global Virome Project.” Science. 359:872–874. https://doi.org/10.1126/science.aap7463. 177 Daszak, Peter, Kevin J. Olival, and Hongying Li. 2020. “A Strategy to Prevent Future Epidemics 178 Similar to the 2019-nCoV Outbreak.” Biosafety and Health. 2:6–8. 179 Grenfell, Bryan T., Oliver G. Pybus, Julia R. Gog, James L. N. Wood, Janet M. Daly, Jenny A. 180 Mumford, and Edward C. Holmes. 2004. “Unifying the Epidemiological and Evolutionary 181 Dynamics of Pathogens.” Science 303:327–32. 182 Khan, Saahir, Rie Nakajima, Aarti Jain, Rafael Ramiro de Assis, Al Jasinskas, Joshua M. 183 Obiero, Oluwasanmi Adenaiye, et al. 2020. “Analysis of Serologic Cross-Reactivity 184 Between Common Human Coronaviruses and SARS-CoV-2 Using Coronavirus Antigen 185 Microarray.” bioRxiv https://doi.org/10.1101/2020.03.24.006544. 186 Kula, Tomasz, Mohammad H. Dezfulian, Charlotte I. Wang, Nouran S. Abdelfattah, Zachary C. 187 Hartman, Kai W. Wucherpfennig, Herbert Kim Lyerly, and Stephen J. Elledge. 2019. “T- 188 Scan: A Genome-Wide Method for the Systematic Discovery of T Cell Epitopes.” Cell 178: 189 1016–1028.e13. 190 Metcalf, C. Jessica E., Jeremy Farrar, Felicity T. Cutts, Nicole E. Basta, Andrea L. Graham, 191 Justin Lessler, Neil M. Ferguson, Donald S. Burke, and Bryan T. Grenfell. 2016. “Use of 192 Serological Surveys to Generate Key Insights into the Changing Global Landscape of 193 Infectious Disease.” The Lancet 388:728–730. 194 Metcalf, C. Jessica E., Michael J. Mina, Amy K. Winter, and Bryan T. Grenfell. 2017. 195 “Opportunities and Challenges of a World Serum Bank - Authors’ Reply.” The Lancet. 196 389:252 197 Morris, Dylan H., Katelyn M. Gostic, Simone Pompei, Trevor Bedford, Marta Łuksza, Richard A. 198 Neher, Bryan T. Grenfell, Michael Lässig, and John W. McCauley. 2018. “Predictive 199 Modeling of Influenza Shows the Promise of Applied Evolutionary Biology.” Trends in 200 Microbiology 26:102–118. 201 Xu, George J., Tomasz Kula, Qikai Xu, Mamie Z. Li, Suzanne D. Vernon, Thumbi Ndung’u, Kiat 202 Ruxrungtham, et al. 2015. “Viral Immunology. Comprehensive Serological Profiling of 203 Human Populations Using a Synthetic Human Virome.” Science 348:aaa0698. 204 205 Figure 1: 206 A) The epidemiological process (at its simplest) can be captured as a set of flows from 207 susceptibles (S) to infected individuals (I), which occurs at a rate defined by the numbers of 208 infected individuals and the rate at which they encounter susceptible individuals (a function of 209 human behavior) and then successfully transmit to them - these last two processes are here 210 captured by the parameter 훽. Infected individuals may then recover (entering the R class), and 211 may or may not then become susceptible again. Typical surveillance only captures the ‘I’ class, 212 innovations around GIO would provide a window on ‘S’ and ‘R’. 213 B) Addressing challenges to establishing GIO. 214 215 A) Serology as epidemiological ‘dark matter’ B) Addressing challenges to establishing GIO

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births Challenge: Addressed by: Funding, Distributed model, but with stable, sustainability multilateral, multi-year funding. A transparent, well governed administrative/ S I R transmission recovery informatics/laboratory core is essential. µ βI µ γ µ Global equity Strong focus in endemic countries. There are and ethics other models of global partnerships which could be looked for inspiration and Classic Unobserved Observed Unobserved governance - H3Africa, GISAID, GA4GH, EBI/ surveillance (cases, deaths) EMBO, CERN, GIP.

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