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Imagination and remembrance: what role should historical epidemiology play in a world bewitched by mathematical modelling of COVID-19 and other epidemics?



Although every emerging infectious disease occurs in a unique context, the behaviour of previous pandemics offers an insight into the medium- and long-term outcomes of the current threat. Where an informative historical analogue exists, epidemiologists and policymakers should consider how the insights of the past can inform current forecasts and responses.
HPLS (2021) 43:81
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Imagination andremembrance: what role should
historical epidemiology play inaworld bewitched
bymathematical modelling ofCOVID‑19 andother
GeorgeS.Heriot1,4 · EuzebiuszJamrozik2,3,4
Received: 12 October 2020 / Accepted: 21 April 2021
© Springer Nature Switzerland AG 2021
Abstract Although every emerging infectious disease occurs in a unique con-
text, the behaviour of previous pandemics offers an insight into the medium- and
long-term outcomes of the current threat. Where an informative historical analogue
exists, epidemiologists and policymakers should consider how the insights of the
past can inform current forecasts and responses.
Keywords COVID 19· Epidemiology· Epidemiologic methods· History/
epidemiolgy· Models· Statistical
1 Text
The emergence of COVID-19 has seen an explosion of epidemiological models
seeking to characterise and forecast the course of the pandemic. The outputs of these
models have influenced policy decisions around the world despite extremely une-
ven forecasting performance of similar models of other recent emerging infectious
Topical Collection “Seeing Clearly Through COVID-19: Current and future questions for the history
and philosophy of the life sciences”, edited by G. Boniolo and L. Onaga.
* George S. Heriot
1 School ofPublic Health andPreventive Medicine, Monash University, 553 St Kilda Road,
Melbourne VIC 3004, Clayton, VIC, Australia
2 The Ethox Centre & Wellcome Centre forEthics andtheHumanities, Nuffield Department
ofPopulation Health, University ofOxford, Oxford, UK
3 Monash Bioethics Centre, Monash University, Clayton, VIC, Australia
4 Royal Melbourne Hospital Department ofMedicine, University ofMelbourne, Parkville, VIC,
G.S.Heriot, E.Jamrozik
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81 Page 2 of 5
diseases. Instead, one might look to data from past pandemics to inform current
risk assessments. Some view such analogies to events of the past as unreliable, rais-
ing the reductionist truism that every combination of disease and context is unique
(Peckham 2020). However, both epidemiological modelling of future scenarios and
analyses of historical data are liable to errors of inputs, assumptions and interpre-
tations; in this paper we argue that both techniques should be considered “wrong,
but useful” (Christley etal. 2013) and that greater awareness of historical data may
improve pandemic preparednessand responses.
The construction of an epidemiological model incorporates structural assump-
tions about the system under study and requires the assembly of input data describ-
ing the specific context and disease. Complex biological systems resist this simple
parametrisation and models of these systems necessarily involve simplifications
whose impact on the predictive skill of the model are difficult to quantify. In the
early phase of a new epidemic, the input conditions for these models are gleaned
from imperfect observations affected by, for example, ascertainment, time-based,
and reporting biases that undermine both their accuracy and precision. The sensitiv-
ity of these models to their input conditions, and the appropriateness and stability of
their structural assumptions, lends substantial uncertainty to any predictions made;
their extrapolation beyond the initial time, place, or pathogen is even less secure.
In contrast to mathematical models, the use of historical data for forecasting
contemporary epidemics does not require simplifying assumptions as to the mecha-
nisms of epidemic propagation or the sociogeographical structure of affected com-
munities. Instead, this approach relies on the comparability of the current disease
and context to previous diseases and contexts, similar to the analogue method used
for weather forecasting prior to the availability of sufficient reliabledata and com-
puting power. On the one hand, many features of the COVID-19 pandemic are inex-
tricably linked to contemporary circumstances and particular contexts. Local experi-
ences of epidemics among citizens, patients and clinicians will therefore vary. On
the other hand, although medical science has advanced considerably in recent cen-
turies, the spread of respiratory viruses between human hosts has changed little for
millennia. People are infected in the same way, suffer in the same way, and die in
the same way. Therefore, with respect to the transmission and sequelae of pandemic
viruses, twenty-first century human communities may bear greater resemblance to
communities in the eighteenth and nineteenth centuries than to an abstracted rep-
resentation within an epidemiological model. Moreover, epidemiological studies of
the variation of the expression of past pandemics in different communities may be
more informative for current pandemic responsesthan model simulations based on
combinations of uncertain abstract input variables.
Once a new pandemic appears to fit in the range of those observed before, the
behaviour and impact of previous pandemics should be considered rather than dis-
carded. Consultation of historical data reveals the significant similarities between
the respiratory viral pandemics of the last few centuries in general (Patterson 1986;
Valleron etal. 2010) and also the availability of reasonable analogues for the specific
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Imagination andremembrance: what role should historical… Page 3 of 5 81
epidemiological observations of COVID-19. The infectivity and severity of SARS-
CoV-2, whether assessed by statistical parameterisation (basic reproduction num-
ber1 and adjusted case or infection fatality ratios,2 respectively) or synoptic descrip-
tion (household attack rate,3 time to epidemic peak,4 and excess all-cause mortality
rates5), are well within the range described by respiratory viral pandemics of the last
few centuries (where the 1918–20 influenza is the clear outlier). The variation in
estimates for these parameters as they apply to COVID-19 is no narrower than those
calculated fromhistorical observations made at different locations during previous
Perhaps the best available historical analogue for COVID-19 is the 1889–91 pan-
demic of “la Grippe,” attributed either to an H3N8 influenza virus(Dowdle 1999) or
to the emergence of human coronavirus OC43 – now a globally endemic “common
cold” virus (Vijgen etal. 2005). This late nineteenth century pandemic has compel-
ling similarities with our current experience, both superficial (including the early
illness of a British prime minister, febrile media coverage, prominence of post-infec-
tious fatigue syndromes, and xenophobic or conspiratorial origin theories) and with
regard toits apparent epidemiological parameters.
Specific epidemiological correlates between the 1889–91 and 2020–21 pandem-
ics include the low morbidity among children, the lack of the shift in excess mortal-
ity to younger age groups usually seen with pandemic influenza, the magnitude and
distribution of peak excess mortality ratios in metropolitan settings, and the rapid-
ity of epidemic propagation within communities (Valleron etal. 2010; Campbell A.
and Morgan E. 2020; Nicoll etal. 2012; Nguyen-Van-Tam etal. 2003; Honigsbaum
2010; Smith 1995). While downscaling this synoptic analogy to make short-term
forecasts of COVID-19 activity in any given place 130years later is clearly foolish
(short-range forecasts from well-observed local data being very much the preserve
of computational modelling), the historical record may provide a richer and more
useful understanding of the range of medium- and long-term consequences of a pan-
demic of this epidemiological pattern on human societies than even the most com-
plex mathematical model.
Analogies to past pandemics can also provide an important check on the assump-
tions made during model construction. As an example, every established respiratory
pandemic of the last 130years has caused seasonal waves of infection and has culmi-
nated in viral endemicity. Despite this robust observation, initial models of COVID-
19 structurally excluded this possibility through the failure to incorporate seasonal
transmission effects, or either pre-existing or partial post-infection immunity to
infection. Although SARS-CoV-2 is a novel non-influenza pathogen, the strong
1 The number of new infections generated by each infectious individual in a given context assuming a
fully-susceptible population.
2 The proportion of (identified) individuals who die from infection, often adjusted for age or other fac-
3 The proportion of household contacts who contract infection from an index case.
4 The time between the first detected case and the highest daily incidence of infection in a population.
5 The difference in the total number of deaths during a pandemic as compared to a previous comparable
G.S.Heriot, E.Jamrozik
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81 Page 4 of 5
seasonal behaviour of closely-related endemic coronaviruses seems a more reliable
starting point than the assumption of an unprecedented weather-agnostic respiratory
pathogen causing permanentsterilising natural immunity. Recent COVID-19 mod-
els incorporating these minimal additional complications demonstrate the result-
ing deterministic chaos, highlighting both the limitations of current mathematical
approaches and the need to consider other sources of guidance for anything more
than short-term forecasts (Dalziel etal. 2016; Saad-Roy etal. 2020).Model extrapo-
lations suggesting that COVID-19 willhave consequences out of proportion to other
comparable respiratory pandemics should be viewed with suspicion rather than as a
sound counterfactual used to justify aspectsof the pandemic response.
While some degree of epistemic humility (Jones 2020) is prudent, the apparent
bias in favour of modelling techniques over analyses of historical data should be
discarded. Rather than relying only on mathematical models of the future, research-
ers and policymakers should consider how knowledge of the past might assist in
understanding the likely consequences of COVID-19 and future respiratory viral
Campbell A. & Morgan E. 2020. Comparisons of all-cause mortality between European countries and
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One, 8, e76277.
Dalziel, B. D., O. N. Bjørnstad, W. G. van Panhuis, D. S. Burke, C. J. Metcalf & B. T. Grenfell (2016)
Persistent chaos of measles epidemics in the prevaccination United States caused by a small change
in seasonal transmission patterns. PLoS Comput Biol, 12, e1004655.
Dowdle, W. (1999). Influenza A virus recycling revisited. Bulletin of the World Health Organization, 77,
Honigsbaum, M. (2010). The great dread: Cultural and psychological impacts and responses to the
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Jones, D. S. (2020). History in a crisis - Lessons for Covid-19. New England Journal of Medicine, 382,
Nguyen-Van-Tam, J. S., & Hampson, A. W. (2003). The epidemiology and clinical impact of pandemic
influenza. Vaccine, 21, 1762–1768
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps
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... 8 9 Respiratory pandemics of the past 130 years have been followed by annual seasonal waves fuelled by viral endemicity that typically continues until the next pandemic. 10 What goes down comes back up, and the difficulty in dating the end of a pandemic is reflected in the historical and epidemiological literature. Although many scholars describe the "Spanish flu" as occurring across three waves from "1918 to 1919," references to the "1918 to 1920" pandemic are also abundant, usually capturing what some call a "fourth wave." ...
... We did not yet know how far SARS-CoV-2 would spread. We also did not know at that time whether it would become an endemic virus that might face us for the rest of our livesalthough it should be noted that past pandemics typically became globally endemic, and that vaccines enabling elimination of respiratory viruses have proved difficult to develop in the past (Heriot and Jamrozik 2021). At the present time, however, consensus is forming that SARS-CoV-2 cannot be eradicated and, like other seasonal coronaviruses, is becoming an endemic virus, so that everyone stands to get infected at least once during their lifetime (Philips 2021;Veldhoen and Simas 2021). ...
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