Download Infectious Diseases in Aging Populations: Unifying Statistical and Dynamical Approaches

The University of Tokyo - Princeton Strategic Partnership Application
Infectious Diseases in Aging Populations: Unifying Statistical and Dynamical Approaches
1. Introduction and Rationale
Demography has considerable implications for infectious disease transmission and risk across
populations. For example, it is well known that high birth rates lead to annual patterns of incidence due
to the numbers of individuals who are susceptible to disease being constantly replenished (e.g., measles
in the United States and United Kingdom during the post-World War II baby boom). However, many
countries – most notably Japan and South Korea – have passed through a rapid demographic transition
that is characterized by lower birth rates and lower death rates. Japan in the post-demographic
transition era has been revealed to be the world’s first “aging population”. The demographic features of
Japan and its implications for disease dynamics will be important for other countries to follow.
For rubella, a mild infection, decreasing birth rates in the population means that vaccine coverage levels
can be lower in order to achieve disease elimination. However, this increases the average age of
infection and can potentially also increase the subsequent risk of mothers infected with rubella giving
birth to children with congenital rubella syndrome, which causes significant morbidity. The severity of
disease and co-morbidities become increasingly more important in aging populations (e.g.,
pneumococcus outbreaks among the elderly in long-term care settings). As immunity against infectious
diseases such as measles or influenza is increasingly being derived from vaccination rather than prior
infection, it also becomes important to understand the short- and long-term implications of vaccinederived vs. naturally-acquired immunity (e.g., the idea of ‘original antigenic sin’ for influenza, where
humans produce antibodies of higher titer against the antigen that was their first childhood experience
of influenza; or the effects of vaccine-derived measles immunity in mothers on the duration and
strength of maternally-acquired measles immunity in newborns).
At the same time, geographic trends in demography and contact patterns are changing as younger
individuals move to large cities which maintain high population densities, and thus have higher rates of
interaction between those who are susceptible and those who are infectious (e.g., the ongoing epidemic
of hand, foot, and mouth disease in the Asia-Pacific region). Household structures are changing as
nuclear families become smaller and fewer generations of a family reside in the same household.
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Human movement within and between borders also has implications for risk of disease importation and
pandemic potential. In reality, both susceptibility and infectiousness may vary between individuals in
space and time. This translates to a need for more nuanced public health interventions against
infectious diseases that take population age structure and spatial structure into account. Additionally,
factors such as socio-economic status or access to health care may differentially contribute to the
relationship between demography and infectious disease dynamics.
We propose to form a new research exchange between UTokyo and Princeton to leverage our
complementary approaches to infectious diseases. In recent years, the UTokyo team has been trying to
improve epidemic forecasting using statistical approaches. Such forecasting techniques offer estimates
of epidemiological parameters such as the force of infection and the basic reproductive number of
infectious diseases in both outbreak and endemic contexts. Meanwhile, the Princeton team has been
developing mechanistic models to study the transmission dynamics of vaccine-preventable infectious
diseases. These models incorporate aspects of demography (e.g., birth rate, late-age survival rate, and
population size) as well as epidemiological parameters such as the basic reproductive number and
seasonality of diseases.
While this is a new partnership between our two groups, we would emphasize that statistical and
mathematical approaches to studying infectious diseases are entirely synergistic. We propose to use
data on demography and various infectious diseases from Japan and the United States (as well as from
other countries) to conduct multiple case studies in this area. Specifically, we plan to create an
integrated UTokyo-Princeton program focusing on the impact of demography on infectious disease
forecasting and dynamical modelling. Our deliverables will be a suite of jointly authored research
manuscripts as well as a set of review papers from our two proposed international workshops.
The findings from our research have important public health impact in designing effective disease
prevention and control strategies. Given that both groups have links to the greater global health
community (e.g., US Centers for Disease Control and Prevention, Japan National Institute of Infectious
Diseases), this collaboration seeks to address key health-related issues of the modern era in an impactful
manner. We believe that this collaboration comprises a world-class nexus of examining infectious
disease dynamics in aging populations that will sustain beyond the time frame of this grant. Our groups
share the value that science is best done via collaboration with frequent and meaningful exchanges.
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Our proposed UTokyo-Princeton program will bring together undergraduate students, graduate
students, post-doctoral researchers, and faculty members from our respective institutions. Our groups
also both place strong emphasis on providing our undergraduate and graduate students with ample
training to answer cutting-edge questions of global health importance.
2. Proposed Use of Funding
We aim to allocate half of the requested funding to build collaborations at all levels: by establishing
student (undergraduate and graduate) and faculty exchanges, and organizing international workshops to
present and share ideas about ongoing work. The remaining funds will be used to hire two postdoctoral/post-graduate researchers to lead the technical aspects of this joint project: one for data
acquisition and management, and another for software maintenance and programming.
Our proposed undergraduate student exchanges will be designed as summer internships to be made
available for students to study at the partner institution for up to two months. Calls for applications will
be sent out over email list-servs during the late winter and early spring. At the start of the internship,
graduate students, post-doctoral researchers, and/or faculty members from the host institution will
teach a short course introducing the visiting undergraduates to the research questions, analytical
methods, and data sets commonly used by researchers in the lab group. Visiting undergraduates will be
assigned a research mentor at the host institution for the duration of the internship and will receive
guidance on an independent research project. They will be expected to attend other seminars and
presentations as relevant during the course of the internship. Undergraduates will present their
research project at a poster presentation back at their home institution at the end of the summer. For
Princeton students, preference for this internship would be given to rising sophomores and juniors in
order to gauge potential ideas for a Junior Paper or the Senior Thesis. All visiting undergraduates will be
provided office space as well as assistance in securing local housing for the duration of their stay.
Our proposed graduate student exchanges will be designed as residencies (either in winter or summer)
to be made available for students to study at the partner institution for up to two months. Residencies
will be designed with student-specific needs, but their purpose would be to provide graduate students
with the opportunity to informally present findings from the aims relevant to this research
collaboration, as well as to obtain ample feedback from faculty members at the host institution. Visiting
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graduate students would be expected to be available for research-related meetings with students and
researchers at the host institution. All visiting graduate students will be provided office space as well as
assistance in securing local housing for the duration of their stay.
During the summer laboratory internships and residency programs, undergraduate and graduate
students visiting Japan will also have the opportunity to join the Summer Short Course of Infectious
Disease Modelling, organized by the UTokyo lead applicant, which is annually held for ten consecutive
days in early August at the Institute of Statistical Mathematics, Tokyo. In addition to attending lectures,
participants of the course will be assigned to a topical research subject for modelling an emerging
infectious disease epidemic, which will be done as a group exercise of 8-10 participants consisting of
UTokyo students, expert mathematicians, statisticians, computer scientists, epidemiologists, physicians,
veterinarians, and other health experts. The course will serve as an important opportunity to interact
with researchers and students from UTokyo with shared common interests in the global setting.
Our proposed faculty exchanges will be designed as residencies to be made available for faculty
members to visit the partner institution approximately one week. Graduate students and post-doctoral
researchers may accompany the visiting faculty member(s), schedules permitting. The faculty exchanges
are also designed to be flexible, but the visiting faculty will present informal seminar talks for the host
research team and greater university audience as relevant. This will serve as an opportunity for the
visiting and host faculty members to check in on progress towards research goals. Logistics for various
joint ventures will be set up (e.g., the summer internship programs as well as the international
workshops) during this time as well. All visiting faculty members will be provided office space. Staff
members at the host institution will arrange transportation and local accommodations.
Our proposed international workshops will be designed as an opportunity for members of the UTokyo
and Princeton partnership (as well as others) to gather and present on their research projects. External
speakers will also be invited, with a target final size of about thirty to forty participants (the Princeton
workshop will be of a larger scale, owing to our relative geographic proximity to European colleagues).
We plan to host one workshop at each institution over the three years. Each workshop will be held over
approximately three days, and will have an over-arching research theme. Members from the partner
institution will stay beyond the duration of the workshop to draft a joint review paper based on
discourse from the workshop for publication. Staff members at the host institution will arrange
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transportation and local accommodations. The Principal Investigators at UTokyo and Princeton have
considerable experience in organizing such large-scale workshops (e.g., 100+ workshops through the
NIH Research and Policy for Infectious Disease Dynamics (RAPIDD) program).
Additionally, we propose to hire two post-doctoral/post-graduate researchers to lead the technical
aspects of the research for this partnership. One researcher will focus on data acquisition and
management, and the other will focus on software development and programming. Experienced
researchers are essential because of the technical aspects of the joint work, and will be responsible for
coordinating across the groups to ensure synergy between the two major research threads. For the
researcher based at UTokyo, his or her salary will be derived entirely from this grant. For the researcher
based at Princeton, his or her salary will be partially funded by this grant with supplementary funding
leveraged from existing grants (see Grant Application Form). We intend to schedule monthly Skype calls
between faculty, researchers, and graduate students to share preliminary results and to obtain feedback
on the findings as well as the direction of the project.
We believe that this partnership funding is necessary – in addition to our external grants – because of
the scale of the exchanges across academic levels and the international workshops. Our hope is to
establish a fruitful research collaboration that will outlive the time frame of this grant, and we believe
that the exchange programs and workshops, are some of the best ways to forge long-term research and
teaching links between the two institutions.
3. Joint Research Goals
As countries (or parts of countries) undergo the demographic transition, they experience lower birth
rates and lower death rates in the population. For some infectious diseases, feedbacks between
incidence and the rate of infection mean that these lower birth rates (or similarly, higher vaccination
coverage) lead to an increase in the average age of infection. The changing landscapes of household
structure and population density or human movement patterns are important drivers of infection for
these and other diseases. In particular, the epidemic dynamics of vaccine-preventable diseases (e.g.,
rubella, measles, and influenza) have been insufficiently studied in the context of demographic
heterogeneities. We propose to study the multi-dimensional interactions between demography and
infectious disease under the following themes:
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(a) Investigating the inter-generational effects of demography and vaccination rates against
childhood infectious diseases
Rubella is a directly transmitted, completely immunizing, and generally very mild infection of children.
However, mothers infected with rubella during the first trimester of pregnancy may give birth to a child
with Congenital Rubella Syndrome (CRS), which can lead to deafness, blindness, congenital heart
disease, and other morbidities. There is a safe, effective and inexpensive vaccine for rubella that has
been in use in developed countries over the past few decades. Lower birth rates increase the average
age of rubella infection in the population (and can potentially increase the corresponding risk of CRS).
This occurs because there is less circulating rubella infection in the population, and therefore a woman
who is susceptible to rubella (e.g., never got the infection nor received the vaccine) may never become
infected until she reaches child-bearing age. Further complicating the issue is the fact that vaccination,
while reducing the overall burden of rubella in the population, monotonically increases the average age
of infection for the same reason.
Taken together, decreasing birth rates and increasing vaccination levels increase overall CRS risk in the
population. Moreover, heterogeneities in birth rates, population density, and vaccination rates (e.g.,
between women and men) exist both within and across countries that affect rubella dynamics. We
propose to study and disentangle the effects of demography and epidemiology using statistical and
mathematical models. One important case study of interest, which the UTokyo group has already begun
to explore, would be the late-age rubella outbreak in Japan in 2013 that produced 14,000 cases of
rubella (primarily among middle-aged men), and 31 cases of CRS in that year (1). We would
retrospectively analyze this outbreak by assessing historical and current birth rates (ideally aggregated
by different parts of the country) as well as historical and current rubella vaccination rates. On a global
level, rubella and other infections with an age-specific burden, such as mumps, pose a unique public
health challenge for policymakers because consideration of the introduction of the relevant vaccines
into a country’s immunization program requires an understanding of whether or not sufficiently high
vaccine coverage can be achieved in order to mitigate potential burdens associated with later age of
infection, namely CRS.
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Measles is another childhood infectious disease that is directly transmitted and completely immunizing,
and is more transmissible than rubella. Measles also has a safe, effective, and inexpensive vaccine that
confers long-term immunity, and this has fueled the promise of global measles elimination in our
lifetime. Measles infections are becoming increasingly rare in high-income countries, and population
immunity to measles is largely derived from the vaccine (contrasted to the pre-vaccination era where
population immunity to measles was naturally acquired, meaning it was derived from individuals having
had and survived the infection). Newborns are protected against measles via passively acquired
maternal antibodies against the infection, which wanes over the first year of life.
However, there are implications of vaccine-derived measles immunity in mothers on the duration and
strength of this maternally-acquired immunity among newborns: if this protection is due to vaccineacquired immunity in the mother, this has been shown to be associated with a lower concentration and
lower persistence of maternal antibodies, compared to naturally-acquired immunity in the mother (2).
We would propose to study this phenomenon using statistical and mathematical models and think
about its implications for adjusting the timing of measles-containing vaccine (currently given at 12
months of age in the Japanese and US immunization programs). This would also require consideration of
the decreasing birth rates, which could contribute to fewer infectious contacts between children
susceptible to and infectious with measles. Lastly, we propose to develop methodologies for forecasting
rubella and measles in both outbreak and endemic situations.
Citations:
1. 31 Japanese children born with rubella syndrome in 2013 | The Japan Times [cited 2015 Dec 9].
Available from: http://www.japantimes.co.jp/news/2014/01/14/national/31-japanese-childrenborn-with-rubella-syndrome-in-2013/.
2. Leuridan E, Van Damme P. Passive transmission and persistence of naturally acquired or
vaccine-induced maternal antibodies against measles in newborns. Vaccine. 2007 Aug
21;25(34):6296–304.
(b) Incorporating age and spatial structure into epidemic models of influenza and other regional-toglobal infections
Geographic trends in demography and contact patterns are rapidly changing. Younger individuals are
moving to cities which maintain high population densities, and thus have higher rates of interaction
between those who are susceptible to a disease and those who are infectious. Household structures are
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also changing as nuclear families become smaller and fewer generations of a family reside in the same
household, changing contact patterns by age. The ease of human movement and migration within and
between porous national borders also has implications for the risk of disease importation and pandemic
potential since the world is becoming increasingly more connected and travel times are being drastically
reduced. In reality, both susceptibility and infectiousness may vary between individuals in space and
time. This notion has introduced the ideas of the super-spreaders (e.g., individuals who come into
contact with many individuals) and the super-shedders (e.g., individuals who have high viral loads or
transmission potential) (1). For example, the global emergence of SARS in 2002-2003 was driven by a
single super-spreader from a hotel in Hong Kong who infected individuals that spread the disease to
other parts of Hong Kong, Singapore, Vietnam, Canada, and the United States.
We propose to study various ways that age and spatial structure affect infectious disease dynamics. A
common age-stratified contact matrix (“Who Acquires Infection From Whom”, or WAIFW matrix) used
by infectious disease modellers is the POLYMOD WAIFW, which assumes that contact patterns are
highly assortative with age as well as that there are increased levels of interactions between children
and their parents (2). However, as the populations go through the demographic transition, updated
matrices beyond the POLYMOD may likely be required to make more realistic assumptions about agedependent interactions.
Influenza is an infectious disease that can cause seasonal epidemics as well as pandemics, and
undergoes viral evolution via the changing phylogenies of viral surface molecules. This immune escape
translates to the need to revaccinate against influenza every few years to catch up on these immune
changes. The occasional influenza pandemics escape much of the previous population-level influenza
immunity, often competitively replace existing strains. The idea of ‘original antigenic sin’ for influenza,
where humans produce antibodies of higher titer against the antigen that was their first childhood
experience of influenza, becomes relevant in aging populations where segments of the population may
have differential immunity against strains. There are two types of influenza vaccines: the liveattenuated (nasal spray) and killed (flu shot). The killed vaccine does not boost levels of cytotoxic T
cells, which has negative implications for protection in pandemic influenza situations. The elderly are
not recommended to get the nasal spray due to their weaker immune systems and subsequent lower
likelihood of mounting an immune response post-vaccination. We propose to model this system using
statistical and mathematical models that incorporate waning immunity and strain replacement.
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We are also interested in addressing the emerging epidemic of hand, foot, and mouth disease (HFMD)
in the Asia-Pacific region (including Japan), potentially bringing in existing collaborators from China and
Vietnam (e.g., at the international workshops) to assess geographic heterogeneities in seasonality and
transmission. Another case study that we propose to do would be to investigate the effects of the
ongoing Syrian refugee crisis on vaccination rates and infectious disease risk (3). Outbreaks have
historically followed disruptions due to war, natural disasters (e.g., the eruptions of Mt. Pinatubo in
1991), and political crises (e.g., Haiti in the early 1990s). We propose to create maps of reductions in
vaccination rates and susceptibility to various infectious diseases, accounting for spatially
heterogeneous immigration, as well as to estimate demographic and epidemiological parameters and to
forecast epidemic sizes. Lastly, similarly as in the previous research aim, we propose to develop
methodologies for forecasting influenza and HFMD in both outbreak and endemic situations.
Citations:
1. Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual
variation on disease emergence. Nature. 2005 Nov 17;438(7066):355–9.
2. Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, et al. Social Contacts and Mixing
Patterns Relevant to the Spread of Infectious Diseases. PLoS Med. 2008 Mar 25;5(3):e74.
3. Sharara SL, Kanj SS. War and Infectious Diseases: Challenges of the Syrian Civil War. PLoS
Pathog. 2014 Nov 13 [cited 2015 Dec 10];10(11). Available from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231133/.
For both research aims, we intend to work in tandem with public health institutions (e.g., Japan NIID, US
CDC) to obtain relevant data and to share modelling results. Because we expect our findings to have
direct policy implications for disease prevention and control as well as access to care, we intend to
publish the suite of jointly authored research manuscripts as well as review papers from our two
proposed international workshops.
4. UTokyo Relevant Expertise
The UTokyo team is divided into two independent research groups, one at the Graduate School of
Medicine (PI: Hiroshi Nishiura) and the other at the Graduate School of Mathematical Sciences (PI:
Hisashi Inaba). Dr. Nishiura’s group in Medicine focuses on the statistical epidemiology of infectious
diseases, specifically in quantifying their transmission dynamics and natural history in real time. Dr.
Inaba’s group in Mathematics acts as a platform for training expert mathematicians with strong
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expertise in sound mathematical formulation of epidemic phenomena and analytical understanding of a
defined system.
The real-time modelling group excels at research studies on the statistical estimation of key
epidemiological parameters of novel emerging infectious diseases, including Ebola virus disease and
Middle East respiratory syndrome (MERS) (1,2), as well as real-time forecasting of epidemics (3). The
group aims to exploit mathematical models to characterize fundamental transmission dynamics and
natural history of diseases (left panel of the figure above), thereby identifying risk groups of
transmission and severe disease during the early stage of an epidemic. The group has also recently
focused on epidemic modelling of rubella that has caused an unprecedented epidemic from 2012-14 in a
highly vaccinated Japanese population. The mathematics group is known as an expert group that
specializes in both epidemiology and mathematical demography. The right panel of the figure above
shows the eradication threshold of vaccine-preventable disease as a function of the population growth
rate and the basic reproduction number (R 0 ) (4). The group is equipped with an expertise to describe
the existing phenomena in the most general sense and enables formulation so that all the associated
model parameters are expressed as measurable quantities.
A common thread in both groups is an understanding of the epidemiological dynamics underlying
empirically-observed information (5). In dealing with both the clarification of fundamental principles of
the epidemic process and quantitative application of various models to empirical data, UTokyo
researchers have emerged as leaders of the field from the Asian region.
Citations:
1. Nishiura H, Chowell G. Early transmission dynamics of Ebola virus disease (EVD), West Africa, March
to August 2014. Euro Surveill. 2014;19(36). pii: 20894.
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2. Mizumoto K, Endo A, Chowell G, Miyamatsu Y, Saitoh M, Nishiura H. Real-time characterization of
risks of death associated with the Middle East respiratory syndrome (MERS) in the Republic of
Korea, 2015. BMC Med 2015;13:228.
3. Nishiura H. Real-time forecasting of an epidemic using a discrete time stochastic model: a case study
of pandemic influenza (H1N1-2009). Biomed Eng Online 2011;10:15.
4. Inaba H, Nishiura H. The basic reproduction number of an infectious disease in a stable population:
The impact of population growth rate on the eradication threshold. Math Model Nat Phenom
2008;3:194-228.
5. Inaba H, Nishiura H. The state-reproduction number for a multistate class age structured epidemic
system and its application to the asymptomatic transmission model. Math Biosci 2008;216:77-89.
5. Princeton Relevant Expertise
Over the past few decades, the Princeton group has specialized in developing mechanistic models to
study the transmission dynamics of vaccine-preventable infectious diseases. Briefly, the underlying
structure of mechanistic models is created by separating a population into compartments, and tracking
the movement of individuals through these Susceptible, Infectious, and Recovered (SIR) classes (figure
below left): individuals are born with maternal immunity which wanes at some rate, after which a
proportion of individuals are vaccinated and effectively “recovered”; subsequently susceptible
individuals become infectious with a disease at the transmission rate, which may vary by space and
time; then, they then move to the recovered category at some rate, where they are and remain immune
(1,2).
These basic compartmental models, both in continuous- and discrete-time, are broadly applicable across
infectious diseases and contexts. Professor Metcalf and colleagues have also developed a discrete-time,
age-structured version of the SIR model, which employs a POLYMOD WAIFW (figure above right) for
specifying the strength of infectious interactions between age groups (3). We have also extensively
applied this model to study rubella/CRS dynamics and the kinetics of rubella vaccination on the country
level (4). We have also developed spatially-structured versions of the SIR model, which can account for
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differential timing and strength of epidemics by space. An application of this model by Professor Grenfell
showed that hierarchical waves of measles infection moved regionally from large cities to small towns in
England and Wales in the pre-vaccination era (5).
The Princeton group has also applied these models to study the implications of disease dynamics for
vaccination policy: we have mapped susceptibility to childhood infectious diseases across spatial
scales, and assessed the landscape of vaccination rates in various situations (6). We have also linked
demographic and epidemiological parameters to better understand susceptibility to childhood
infectious diseases across age classes, utilizing tools from the demography literature such as Lexis
diagrams to visualize vaccination coverage levels by age and the aging of cohorts. Lastly, our group has
considerable links to public health policymakers, having completed numerous consulting projects
related to the mathematical modelling of infectious disease and vaccination with WHO working groups,
Médecins Sans Frontières, and other notable organizations.
Citations:
1. Bjørnstad ON, Finkenstädt BF, Grenfell BT. Dynamics of measles epidemics: estimating scaling of
transmission rates using a time series SIR model. Ecol Monogr. 2002;72(2):169–84.
2. Grenfell BT, Bjørnstad ON, Finkenstädt BF. Dynamics of measles epidemics: scaling noise,
determinism, and predictability with the tsir model. Ecol Monogr. 2002 May 1;72(2):185–202.
3. Metcalf CJE, Lessler J, Klepac P, Morice A, Grenfell BT, Bjørnstad ON. Structured models of infectious
disease: inference with discrete data. Theor Popul Biol. 2012 Dec;82(4):275–82.
4. Metcalf CJE, Lessler J, Klepac P, Cutts F, Grenfell BT. Impact of birth rate, seasonality and
transmission rate on minimum levels of coverage needed for rubella vaccination. Epidemiol Infect.
2012 Dec;140(12):2290–301.
5. Grenfell BT, Bjørnstad ON, Kappey J. Travelling waves and spatial hierarchies in measles epidemics.
Nature. 2001 Dec 13;414(6865):716–23.
6. Takahashi S, Metcalf CJE, Ferrari MJ, Moss WJ, Truelove SA, Tatem AJ, et al. Reduced vaccination
and the risk of measles and other childhood infections post-Ebola. Science. 2015 Mar
13;347(6227):1240–2.
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