serology in a time of pandemic: consequences for ... · 100% of introductions cause onward...
TRANSCRIPT
C. Jessica E. Metcalf
Serology in a time of pandemic: consequences for population dynamics
Every emergence event has contained surprises
-H1N1 did not attack older individuals-MERS has not gone on to cause widespread outbreaks-Ebola was not thought likely to spread so far or so fast-Zika was not thought to be pathogenic-SARS-CoV-2 case burden (infection?) is low in children
2005 2010 2015
SARS 2003
H1N1 2009
Ebola 2014
MERS2013
Zika2015
2020
Emergence time line
SARS-CoV-2 2019
Metcalf & Lessler 2017 Science
Every emergence event has contained surprises
-H1N1 did not attack older individuals-MERS has not gone on to cause widespread outbreaks-Ebola was not thought likely to spread so far or so fast-Zika was not thought to be pathogenic-SARS-CoV-2 case burden (infection?) is low in children
2005 2010 2015
SARS 2003
H1N1 2009
Ebola 2014
MERS2013
Zika2015
2020
Emergence time line
SARS-CoV-2 2019
Metcalf & Lessler 2017 Science
Aims
1. Describing transmission Early outbreak growth The role of heterogeneities
2. The longer term: How herd immunity emerges
3. Susceptibility as ‘immune dark matter’ Serology for model calibrationSerology to understand immunity
4. Serology to meet future pandemics
5. Conclusions: the use case
Two key quantities: R0, here = 2Serial interval:
initial case
1. Describing transmission
Two key quantities: R0, here = 2Serial interval:
1. Describing transmission
Two key quantities: R0, here = 2Serial interval:
1. Describing transmission
Two key quantities: R0, here = 2Serial interval:
… … … … … … … … …… … … … … …
1. Describing transmission
Two key quantities: R0, here = 2Serial interval:
… … … … … … … … …… … … … … …
With a serial interval of ~ 1 week and an R0 of 2, cases double approximately every week (R0 estimate: ~ 2 - 3)
1. Describing transmission
Two key quantities: R0, here = 2Serial interval:
… … … … … … … … …… … … … … …
https://coronavirus.jhu.edu/map.html
Feb 01 Mar 01 Apr 01
1100
10000
Deaths
Feb 01 Mar 01 Apr 01
1100
10000
Cases
New JerseyCaliforniaWashingtonNew York
Exponential growth
1. Describing transmission
Two key quantities: R0, here = 2Serial interval:
… … … … … … … … …… … … … … …
https://coronavirus.jhu.edu/map.html
Feb 01 Mar 01 Apr 01
1100
10000
Deaths
Feb 01 Mar 01 Apr 01
1100
10000
Cases
New JerseyCaliforniaWashingtonNew York
Exponential growth
Cases: under-report infections. How many people could still be infected?
1. Describing transmission
Not ‘over-dispersed’
60% causes 80%
100% of introductions cause onward transmission
1. Describing transmission: superspreading events
Not ‘over-dispersed’
60% causes 80%
‘Over-dispersed’
20% causes 80%
100% of introductions cause onward transmission
40% of introductions cause onward transmission
Superspreading events: with R0 = 3, on average, 1 person infects 3 others, but most people infect no one, and a few infect very many
outbreaks are more explosive, but also more prone to extinction.
1. Describing transmission: superspreading events
Not ‘over-dispersed’
60% causes 80%
‘Over-dispersed’
20% causes 80%
100% of introductions cause onward transmission
40% of introductions cause onward transmission
https://hopkinsidd.github.io/nCoV-Sandbox/DispersionExploration.html
Map observed (165 introductions; 84 local cases)
Re, # secondary cases per case
prop
ortio
n in
fect
ing
80%
1. Describing transmission: superspreading events
Aims
1. Describing transmission Early outbreak growth The role of heterogeneities
2. The longer term: How herd immunity emerges
3. Susceptibility as ‘immune dark matter’ Serology for model calibrationSerology to understand immunity
4. Serology to meet future pandemics
5. Conclusions: the use case
recovered
infected
‘herd immunity’
out$time
RE
0.5
1.0
1.5
2.0
2.5
3.0
0 5 10 150.0
0.2
0.4
0.6
0.8
1.0
Proportion
Susceptible
Infected
Recovered
R0 = 3
Pro
porti
on
Time (weeks)
susceptible
infected
Metcalf et al. 2015 Trends in Immunology
2. The longer term
recovered
infected
‘herd immunity’
out$time
RE
0.5
1.0
1.5
2.0
2.5
3.0
0 5 10 150.0
0.2
0.4
0.6
0.8
1.0
Proportion
Susceptible
Infected
Recovered
R0 = 3
Pro
porti
on
Time (weeks)
susceptible
infected recovered
Metcalf et al. 2015 Trends in Immunology
2. The longer term
recovered
infected
susceptible
‘herd immunity’
indirectly protected
out$time
RE
0.5
1.0
1.5
2.0
2.5
3.0
0 5 10 150.0
0.2
0.4
0.6
0.8
1.0
Proportion
Susceptible
Infected
Recovered
R0 = 3
Pro
porti
on
recovered
Metcalf et al. 2015 Trends in Immunology
2. The longer term
recovered
infected
susceptible
‘herd immunity’
indirectly protected
out$time
RE
0.5
1.0
1.5
2.0
2.5
3.0
0 5 10 150.0
0.2
0.4
0.6
0.8
1.0
Proportion
Susceptible
Infected
Recovered
R0 = 3
Pro
porti
on
Metcalf et al. 2015 Trends in Immunology
2. The longer term
recovered
infected
susceptible
‘herd immunity’
indirectly protected
out$time
RE
0.5
1.0
1.5
2.0
2.5
3.0
0 5 10 150.0
0.2
0.4
0.6
0.8
1.0
Proportion
Susceptible
Infected
Recovered
R0 = 3
Pro
porti
on
Metcalf et al. 2015 Trends in Immunology
pc = 1 − 1/R0
critical threshold for vaccination
2. The longer term
recovered
infected
susceptible
‘herd immunity’
indirectly protected
out$time
RE
0.5
1.0
1.5
2.0
2.5
3.0
0 5 10 150.0
0.2
0.4
0.6
0.8
1.0
Proportion
Susceptible
Infected
Recovered
R0 = 3
Pro
porti
on
Metcalf et al. 2015 Trends in Immunology
pc = 1 − 1/R0
critical threshold for vaccination
2. The longer term
• 1/R0 is conservative (heterogeneities)
• The downslope is long
Aims
1. Describing transmission Early outbreak growth The role of heterogeneities
2. The longer term: How herd immunity emerges
3. Susceptibility as ‘immune dark matter’ Serology for model calibrationSerology to understand immunity
4. Serology to meet future pandemics
5. Conclusions: the use case
Cases and deaths:
Susceptible
Infected
Recovered
3. Susceptibility as immune ‘dark matter’
Cases and deaths:Provide an incomplete window onto the epidemic
Susceptible
Infected
Recovered
Viewpoint
www.thelancet.com Published online April 5, 2016 http://dx.doi.org/10.1016/S0140-6736(16)30164-7 1
Use of serological surveys to generate key insights into the changing global landscape of infectious diseaseC Jessica E Metcalf, Jeremy Farrar, Felicity T Cutts, Nicole E Basta, Andrea L Graham, Justin Lessler, Neil M Ferguson, Donald S Burke, Bryan T Grenfell
A central conundrum in the study of infectious disease dynamics is to defi ne the landscape of population immunity. The proportion of individuals protected against a specifi c pathogen determines the timing and scale of outbreaks, and the pace of evolution for infections that can evade prevailing humoral immunity. Serological surveys provide the most direct measurement to defi ne the immunity landscape for many infectious diseases, yet this methodology remains underexploited. To address this gap, we propose a World Serology Bank and associated major methodological developments in serological testing, study design, and quantitative analysis, which could drive a step change in our understanding and optimum control of infectious diseases.
Epidemic dynamics result from an interaction between the contagious spread of infection, the resulting depletion of population susceptibility, and its replenishment via births, immigration, or waning immunity. Under-standing this interaction is key to assess the eff ect of vaccination, which artifi cially reduces the number of people susceptible to infection.
Researchers mainly observe infection dynamics and the eff ect of population (or herd) immunity in limiting spread via surveillance of clinically apparent cases of infection or deaths. This method has led to some powerful insights;1 however, even in the simplest instances in which subclinical infection is uncommon, cases only provide information about the dynamics of infection. Susceptibility and immunity are hidden variables. For infections that people can be completely immunised against, such as measles, susceptible reconstruction can be used to estimate immune profi les,2 but infection prevalence and vaccination coverage records are frequently inadequate to capture key social and geographical heterogeneities. Additionally, inference is weakened if the risk of infection is low.
Serological surveys (usually used to quantify the proportion of people positive for a specifi c antibody or, better yet, the titre or concentrations of an antibody) are potentially the most direct and informative technique available to infer the dynamics of a population’s susceptibility and level of immunity. However, the use of current serological tests varies greatly depending on type of pathogens and there are major methodological gaps in some areas for some pathogens and tests. In terms of use of current serological methods, infections can be classifi ed into four broad groups (appendix). The fi rst group contains acute immunising, antigenically stable pathogens (eg, measles, rubella, and smallpox) for which serology provides a strong signal of lifetime
protection and a clear marker of past infection (or vaccination).
The second group contains immunising, but antigenically variable pathogens (eg, infl uenza, invasive bacterial diseases, and dengue). Despite complexities (appendix), serology in some cases can provide powerful evidence, both for vaccine formulation and pandemic planning.3,4 A serum bank would have been extremely useful in interpretation of the unusual profi le of susceptibility associated with age in the 2009 infl uenza pandemic.3 For these fi rst two groups, if suitable serum banks existed, the deployment of current serological tests could have helped to clarify the association between serological profi les and protective immunity.
The third group includes infections for which infection-induced antibodies are not thought to be protective, such as tuberculosis in which the targets of the immune response vary with stage of infection; malaria, whereby infected erythrocytes generate several antibodies whose individual importance has not been fully elucidated (and might indicate exposure rather than protection5); and HIV. Although antibodies might not be representative of immunity against a pathogen, they do show current or previous infection.
Finally, the last broad grouping consists of infections that do not lead to reliably sustained, measurable antibody responses or for which presence of specifi c antibodies do not correlate with protection from future infection. These include many enteric infections and the human papillomavirus. In these cases, serological data can nonetheless be valuable to assess a population’s coverage of vaccine programmes if vaccination leads to long-lasting antibody responses.
In the context of public health, for immunisation against group one infections, vaccination programmes aim to protect vaccinated individuals and indirectly protect unvaccinated individuals by maintaining high population immunity.1 If a valid correlate of immunity is measurable in sera, serological surveys could be used to identify population subgroups in which immunity is low, or even to identify individuals in whom immunity has waned, and directly inform targeted vaccination strategies (appendix).
Household surveys are a major source of data for vaccination coverage in low-income countries.6 The recent extension of eff orts to measure biomarkers for infections such as HIV (eg, the Demographic Health Surveys) could provide infrastructure for sera collection for an expanded range of infections, thus leveraging an existing platform. For many infections, however, to
Published OnlineApril 5, 2016http://dx.doi.org/10.1016/S0140-6736(16)30164-7
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA (C J E Metcalf PhD, A L Graham PhD, Prof B T Grenfell PhD); Fogarty International Center, National Institute of Health, Bethesda, MD, USA (C J E Metcalf, N E Basta PhD, A L Graham, Prof B T Grenfell); Wellcome Trust, Gibbs Building, London, UK (J Farrar MD); London School of Hygiene & Tropical Medicine, London, UK (Prof F T Cutts MD);
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA (N E Basta); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA (J Lessler PhD); Department of Medicine, School of Public Health, Imperial College London, London, UK (Prof N M Ferguson DPhil); and Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA (Prof D S Burke MD)
Correspondence to:Dr C Jessica E Metcalf, Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, [email protected]
See Online for appendix
For more on the Demographic Health Surverys see http://www.dhsprogram.com/
3. Susceptibility as immune ‘dark matter’
Cases and deaths:Provide an incomplete window onto the epidemic
Susceptible
Infected
Recovered
Viewpoint
www.thelancet.com Published online April 5, 2016 http://dx.doi.org/10.1016/S0140-6736(16)30164-7 1
Use of serological surveys to generate key insights into the changing global landscape of infectious diseaseC Jessica E Metcalf, Jeremy Farrar, Felicity T Cutts, Nicole E Basta, Andrea L Graham, Justin Lessler, Neil M Ferguson, Donald S Burke, Bryan T Grenfell
A central conundrum in the study of infectious disease dynamics is to defi ne the landscape of population immunity. The proportion of individuals protected against a specifi c pathogen determines the timing and scale of outbreaks, and the pace of evolution for infections that can evade prevailing humoral immunity. Serological surveys provide the most direct measurement to defi ne the immunity landscape for many infectious diseases, yet this methodology remains underexploited. To address this gap, we propose a World Serology Bank and associated major methodological developments in serological testing, study design, and quantitative analysis, which could drive a step change in our understanding and optimum control of infectious diseases.
Epidemic dynamics result from an interaction between the contagious spread of infection, the resulting depletion of population susceptibility, and its replenishment via births, immigration, or waning immunity. Under-standing this interaction is key to assess the eff ect of vaccination, which artifi cially reduces the number of people susceptible to infection.
Researchers mainly observe infection dynamics and the eff ect of population (or herd) immunity in limiting spread via surveillance of clinically apparent cases of infection or deaths. This method has led to some powerful insights;1 however, even in the simplest instances in which subclinical infection is uncommon, cases only provide information about the dynamics of infection. Susceptibility and immunity are hidden variables. For infections that people can be completely immunised against, such as measles, susceptible reconstruction can be used to estimate immune profi les,2 but infection prevalence and vaccination coverage records are frequently inadequate to capture key social and geographical heterogeneities. Additionally, inference is weakened if the risk of infection is low.
Serological surveys (usually used to quantify the proportion of people positive for a specifi c antibody or, better yet, the titre or concentrations of an antibody) are potentially the most direct and informative technique available to infer the dynamics of a population’s susceptibility and level of immunity. However, the use of current serological tests varies greatly depending on type of pathogens and there are major methodological gaps in some areas for some pathogens and tests. In terms of use of current serological methods, infections can be classifi ed into four broad groups (appendix). The fi rst group contains acute immunising, antigenically stable pathogens (eg, measles, rubella, and smallpox) for which serology provides a strong signal of lifetime
protection and a clear marker of past infection (or vaccination).
The second group contains immunising, but antigenically variable pathogens (eg, infl uenza, invasive bacterial diseases, and dengue). Despite complexities (appendix), serology in some cases can provide powerful evidence, both for vaccine formulation and pandemic planning.3,4 A serum bank would have been extremely useful in interpretation of the unusual profi le of susceptibility associated with age in the 2009 infl uenza pandemic.3 For these fi rst two groups, if suitable serum banks existed, the deployment of current serological tests could have helped to clarify the association between serological profi les and protective immunity.
The third group includes infections for which infection-induced antibodies are not thought to be protective, such as tuberculosis in which the targets of the immune response vary with stage of infection; malaria, whereby infected erythrocytes generate several antibodies whose individual importance has not been fully elucidated (and might indicate exposure rather than protection5); and HIV. Although antibodies might not be representative of immunity against a pathogen, they do show current or previous infection.
Finally, the last broad grouping consists of infections that do not lead to reliably sustained, measurable antibody responses or for which presence of specifi c antibodies do not correlate with protection from future infection. These include many enteric infections and the human papillomavirus. In these cases, serological data can nonetheless be valuable to assess a population’s coverage of vaccine programmes if vaccination leads to long-lasting antibody responses.
In the context of public health, for immunisation against group one infections, vaccination programmes aim to protect vaccinated individuals and indirectly protect unvaccinated individuals by maintaining high population immunity.1 If a valid correlate of immunity is measurable in sera, serological surveys could be used to identify population subgroups in which immunity is low, or even to identify individuals in whom immunity has waned, and directly inform targeted vaccination strategies (appendix).
Household surveys are a major source of data for vaccination coverage in low-income countries.6 The recent extension of eff orts to measure biomarkers for infections such as HIV (eg, the Demographic Health Surveys) could provide infrastructure for sera collection for an expanded range of infections, thus leveraging an existing platform. For many infections, however, to
Published OnlineApril 5, 2016http://dx.doi.org/10.1016/S0140-6736(16)30164-7
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA (C J E Metcalf PhD, A L Graham PhD, Prof B T Grenfell PhD); Fogarty International Center, National Institute of Health, Bethesda, MD, USA (C J E Metcalf, N E Basta PhD, A L Graham, Prof B T Grenfell); Wellcome Trust, Gibbs Building, London, UK (J Farrar MD); London School of Hygiene & Tropical Medicine, London, UK (Prof F T Cutts MD);
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA (N E Basta); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA (J Lessler PhD); Department of Medicine, School of Public Health, Imperial College London, London, UK (Prof N M Ferguson DPhil); and Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA (Prof D S Burke MD)
Correspondence to:Dr C Jessica E Metcalf, Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, [email protected]
See Online for appendix
For more on the Demographic Health Surverys see http://www.dhsprogram.com/
Serology:Measurement of antibodies can complete the picture
3. Susceptibility as immune ‘dark matter’
0 2 4 6 8 10 12
0.00
0.05
0.10
0.15
0 2 4 6 8 10 12
0.0
0.2
0.4
0.6
0.8
1.0
Pro
porti
on s
usce
ptib
le
infected
3. Susceptibility as immune ‘dark matter’
0 2 4 6 8 10 12
0.00
0.05
0.10
0.15
0 2 4 6 8 10 12
0.0
0.2
0.4
0.6
0.8
1.0
Pro
porti
on s
usce
ptib
le
reported cases (1 in 5)
infected
3. Susceptibility as immune ‘dark matter’
0 2 4 6 8 10 12
0.00
0.05
0.10
0.15
0 2 4 6 8 10 12
0.0
0.2
0.4
0.6
0.8
1.0
Pro
porti
on s
usce
ptib
le
reported cases (1 in 5)
infected susceptible
3. Susceptibility as immune ‘dark matter’
0 2 4 6 8 10 12
0.00
0.05
0.10
0.15
0 2 4 6 8 10 12
0.0
0.2
0.4
0.6
0.8
1.0
Pro
porti
on s
usce
ptib
le
reported cases (1 in 5)
infected susceptible
span data early in the outbreak….
3. Susceptibility as immune ‘dark matter’
0 2 4 6 8 10 12
0.00
0.05
0.10
0.15
observedfittedtrue
0 2 4 6 8 10 12
0.0
0.2
0.4
0.6
0.8
1.0
Pro
porti
on s
usce
ptib
le
fittedtrue
infected
fit 1
susceptible
fit 1
span data
Estimated reporting of 3 in 5 infections, estimated transmission accurate, but duration infection shorter, and later start.
early in the outbreak….
3. Susceptibility as immune ‘dark matter’
span data
fit 1
susceptible
fit 1
fit 2
fit 2
0 2 4 6 8 10 12
0.00
0.05
0.10
0.15
observedfittedtrue
0 2 4 6 8 10 12
0.0
0.2
0.4
0.6
0.8
1.0
Pro
porti
on s
usce
ptib
le
fittedtrue
infected
early in the outbreak….
Estimated reporting of 1 in 10 infections, estimated transmission of 2 x the truth, duration infection longer, and earlier start.
Estimated reporting of 3 in 5 infections, estimated transmission accurate, but duration infection shorter, and later start.
3. Susceptibility as immune ‘dark matter’
span data
fit 1
susceptible
fit 1
fit 2
fit 2
0 2 4 6 8 10 12
0.00
0.05
0.10
0.15
observedfittedtrue
0 2 4 6 8 10 12
0.0
0.2
0.4
0.6
0.8
1.0
Pro
porti
on s
usce
ptib
le
fittedtrue
infected
Susceptibility integrates over the past, and thus provides much more information than cases.
Estimated reporting of 1 in 10 infections, estimated transmission of 2 x the truth, duration infection longer, and earlier start.
Estimated reporting of 3 in 5 infections, estimated transmission accurate, but duration infection shorter, and later start.
https://labmetcalf.shinyapps.io/serol1/
3. Susceptibility as immune ‘dark matter’
Susceptible
Infected
Recovered
https://science.sciencemag.org/content/early/2020/04/14/science.abb5793?rss=1
What will 'seropositive' mean?
‘Recovereds’ may not be susceptible to re-infection for at least a season, based on:
-Experimental evidence (re-infection fails)-Indirect math models (signatures of immunity)-Profile of immunity following COVID-19 infection
3. Susceptibility as immune ‘dark matter’
Susceptible
Infected
Recovered
Testing health care workers, other essential professions could contribute to moving back to normality.
What will 'seropositive' mean?
‘Recovereds’ may not be susceptible to re-infection for at least a season, based on:
-Experimental evidence (re-infection fails)-Indirect math models (signatures of immunity)-Profile of immunity following COVID-19 infection
https://science.sciencemag.org/content/early/2020/04/14/science.abb5793?rss=1
3. Susceptibility as immune ‘dark matter’
Aims
1. Describing transmission Early outbreak growth The role of heterogeneities
2. The longer term: How herd immunity emerges
3. Susceptibility as ‘immune dark matter’ Serology for model calibrationSerology to understand immunity
4. Serology to meet future pandemics
5. Conclusions: the use case
Prognostic serology
Measles:Cross-sectional annual age serology was used to trigger a vaccination campaign in England (Babad et al. 1995)
A convenience sample predicted high outbreak risk in Madagascar (Winter et al. 2018); the outbreak then sadly occurred (2018-2019)
H1N1: “Representative serological surveys stand out as a critical source of data with which to reduce uncertainty around policy choices […]” (Kerkove et al. 2010)
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000275
4. Serology to meet future pandemics?
Prognostic serology
Measles:Cross-sectional annual age serology was used to trigger a vaccination campaign in England (Babad et al. 1995)
A convenience sample predicted high outbreak risk in Madagascar (Winter et al. 2018); the outbreak then sadly occurred (2018-2019)
H1N1: “Representative serological surveys stand out as a critical source of data with which to reduce uncertainty around policy choices […]” (Kerkove et al. 2010)
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000275
4. Serology to meet future pandemics?
A Global Immunological Observatory
Data MethodsLaboratory techniques Statistical approachesViewpoint
www.thelancet.com Published online April 5, 2016 http://dx.doi.org/10.1016/S0140-6736(16)30164-7 1
Use of serological surveys to generate key insights into the changing global landscape of infectious diseaseC Jessica E Metcalf, Jeremy Farrar, Felicity T Cutts, Nicole E Basta, Andrea L Graham, Justin Lessler, Neil M Ferguson, Donald S Burke, Bryan T Grenfell
A central conundrum in the study of infectious disease dynamics is to defi ne the landscape of population immunity. The proportion of individuals protected against a specifi c pathogen determines the timing and scale of outbreaks, and the pace of evolution for infections that can evade prevailing humoral immunity. Serological surveys provide the most direct measurement to defi ne the immunity landscape for many infectious diseases, yet this methodology remains underexploited. To address this gap, we propose a World Serology Bank and associated major methodological developments in serological testing, study design, and quantitative analysis, which could drive a step change in our understanding and optimum control of infectious diseases.
Epidemic dynamics result from an interaction between the contagious spread of infection, the resulting depletion of population susceptibility, and its replenishment via births, immigration, or waning immunity. Under-standing this interaction is key to assess the eff ect of vaccination, which artifi cially reduces the number of people susceptible to infection.
Researchers mainly observe infection dynamics and the eff ect of population (or herd) immunity in limiting spread via surveillance of clinically apparent cases of infection or deaths. This method has led to some powerful insights;1 however, even in the simplest instances in which subclinical infection is uncommon, cases only provide information about the dynamics of infection. Susceptibility and immunity are hidden variables. For infections that people can be completely immunised against, such as measles, susceptible reconstruction can be used to estimate immune profi les,2 but infection prevalence and vaccination coverage records are frequently inadequate to capture key social and geographical heterogeneities. Additionally, inference is weakened if the risk of infection is low.
Serological surveys (usually used to quantify the proportion of people positive for a specifi c antibody or, better yet, the titre or concentrations of an antibody) are potentially the most direct and informative technique available to infer the dynamics of a population’s susceptibility and level of immunity. However, the use of current serological tests varies greatly depending on type of pathogens and there are major methodological gaps in some areas for some pathogens and tests. In terms of use of current serological methods, infections can be classifi ed into four broad groups (appendix). The fi rst group contains acute immunising, antigenically stable pathogens (eg, measles, rubella, and smallpox) for which serology provides a strong signal of lifetime
protection and a clear marker of past infection (or vaccination).
The second group contains immunising, but antigenically variable pathogens (eg, infl uenza, invasive bacterial diseases, and dengue). Despite complexities (appendix), serology in some cases can provide powerful evidence, both for vaccine formulation and pandemic planning.3,4 A serum bank would have been extremely useful in interpretation of the unusual profi le of susceptibility associated with age in the 2009 infl uenza pandemic.3 For these fi rst two groups, if suitable serum banks existed, the deployment of current serological tests could have helped to clarify the association between serological profi les and protective immunity.
The third group includes infections for which infection-induced antibodies are not thought to be protective, such as tuberculosis in which the targets of the immune response vary with stage of infection; malaria, whereby infected erythrocytes generate several antibodies whose individual importance has not been fully elucidated (and might indicate exposure rather than protection5); and HIV. Although antibodies might not be representative of immunity against a pathogen, they do show current or previous infection.
Finally, the last broad grouping consists of infections that do not lead to reliably sustained, measurable antibody responses or for which presence of specifi c antibodies do not correlate with protection from future infection. These include many enteric infections and the human papillomavirus. In these cases, serological data can nonetheless be valuable to assess a population’s coverage of vaccine programmes if vaccination leads to long-lasting antibody responses.
In the context of public health, for immunisation against group one infections, vaccination programmes aim to protect vaccinated individuals and indirectly protect unvaccinated individuals by maintaining high population immunity.1 If a valid correlate of immunity is measurable in sera, serological surveys could be used to identify population subgroups in which immunity is low, or even to identify individuals in whom immunity has waned, and directly inform targeted vaccination strategies (appendix).
Household surveys are a major source of data for vaccination coverage in low-income countries.6 The recent extension of eff orts to measure biomarkers for infections such as HIV (eg, the Demographic Health Surveys) could provide infrastructure for sera collection for an expanded range of infections, thus leveraging an existing platform. For many infections, however, to
Published OnlineApril 5, 2016http://dx.doi.org/10.1016/S0140-6736(16)30164-7
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA (C J E Metcalf PhD, A L Graham PhD, Prof B T Grenfell PhD); Fogarty International Center, National Institute of Health, Bethesda, MD, USA (C J E Metcalf, N E Basta PhD, A L Graham, Prof B T Grenfell); Wellcome Trust, Gibbs Building, London, UK (J Farrar MD); London School of Hygiene & Tropical Medicine, London, UK (Prof F T Cutts MD);
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA (N E Basta); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA (J Lessler PhD); Department of Medicine, School of Public Health, Imperial College London, London, UK (Prof N M Ferguson DPhil); and Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA (Prof D S Burke MD)
Correspondence to:Dr C Jessica E Metcalf, Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, [email protected]
See Online for appendix
For more on the Demographic Health Surverys see http://www.dhsprogram.com/
Convenience samples Longitudinal age serology
4. Serology to meet future pandemics?
Aims
1. Describing transmission Early outbreak growth The role of heterogeneities
2. The longer term: How herd immunity emerges
3. Susceptibility as ‘immune dark matter’ Serology for model calibrationSerology to understand immunity
4. Serology to meet future pandemics
5. Conclusions: the use case
• Proportion seronegative at one time-point tells us ‘how many people are left to infect’: where are we on the epidemic trajectory?
• Longitudinal samples that track individuals’ status through time will help establish: what does seropositivity ‘mean’ for immunity?
• Serology patterns over age and through time will help demystify the role of children in transmission (linked to core question of school closures) and establish risks to older populations via transmission (models + information on contact patterns will be required).
• Larger sampling (including internationally!) should clarify transmission and burden effects (from socio-economic, to co-morbidities, to genetics).
• Serology in tandem with the step down of Non-Pharmaceutical Interventions could establish the costs and benefits of different interventions.
5. Serology: the use case
• Proportion seronegative at one time-point tells us ‘how many people are left to infect’: where are we on the epidemic trajectory?
• Longitudinal samples that track individuals’ status through time will help establish: what does seropositivity ‘mean’ for immunity?
• Serology patterns over age and through time will help demystify the role of children in transmission (linked to core question of school closures) and establish risks to older populations via transmission (models + information on contact patterns will be required).
• Larger sampling (including internationally!) should clarify transmission and burden effects (from socio-economic, to co-morbidities, to genetics).
• Serology in tandem with the step down of Non-Pharmaceutical Interventions could establish the costs and benefits of different interventions.
A
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age
of c
onta
ct
0
2
4
6
8
10+
Regular day
Wuh
an
A
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
0
2
4
6
8
10+
OutbreakB
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
−20246810+
DifferenceC
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
Age
of c
onta
ct
0
2
4
6
8
10+
Shan
ghai
D
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
0
2
4
6
8
10+
E
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
−20246810+
F
All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which was not peer-reviewed) is.https://doi.org/10.1101/2020.03.19.20039107doi: medRxiv preprint
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age
of c
onta
ct
0
2
4
6
8
10+
Regular day
Wuh
an
A
05
1015202530354045505560
65+0 5 10 15 20 25 30 35 40 45 50 55 60 65+
0
2
4
6
8
10+
OutbreakB
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
−20246810+
DifferenceC
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
Age
of c
onta
ct
0
2
4
6
8
10+
Shan
ghai
D
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
0
2
4
6
8
10+
E
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
−20246810+
F
All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which was not peer-reviewed) is.https://doi.org/10.1101/2020.03.19.20039107doi: medRxiv preprint
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age
of c
onta
ct
0
2
4
6
8
10+
Regular day
Wuh
an
A
05
1015202530354045505560
65+0 5 10 15 20 25 30 35 40 45 50 55 60 65+
0
2
4
6
8
10+
OutbreakB
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
−20246810+
DifferenceC
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
Age
of c
onta
ct
0
2
4
6
8
10+
Shan
ghai
D
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
0
2
4
6
8
10+
E
05
1015202530354045505560
65+
0 5 10 15 20 25 30 35 40 45 50 55 60 65+
Age of participant
−20246810+
F
All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (which was not peer-reviewed) is.https://doi.org/10.1101/2020.03.19.20039107doi: medRxiv preprint
Diary studies in Wuhan
5. Serology: the use case
• Proportion seronegative at one time-point tells us ‘how many people are left to infect’: where are we on the epidemic trajectory?
• Longitudinal samples that track individuals’ status through time will help establish: what does seropositivity ‘mean’ for immunity?
• Serology patterns over age and through time will help demystify the role of children in transmission (linked to core question of school closures) and establish risks to older populations via transmission (models + information on contact patterns will be required).
• Larger sampling (including internationally!) should clarify transmission and burden effects (from socio-economic, to co-morbidities, to genetics).
• Serology in tandem with the step down of Non-Pharmaceutical Interventions could establish the costs and benefits of different interventions.
5. Serology: the use case
Thank you!
C. Jessica E. [email protected]
Illustrating model calibration:https://labmetcalf.shinyapps.io/serol1/
Literature overview: https://larremorelab.github.io/covid19testgroup
Combining models and serological data: https://dash.harvard.edu/handle/1/42659939
Resources