ArticlePDF Available

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

Authors:

Abstract

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.
Vol.:(0123456789)
HPLS (2021) 43:81
https://doi.org/10.1007/s40656-021-00422-6
1 3
NOTES & COMMENTS
Imagination andremembrance: what role should
historical epidemiology play inaworld bewitched
bymathematical modelling ofCOVID‑19 andother
epidemics?
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
george.heriot@mh.org.au
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,
Australia
G.S.Heriot, E.Jamrozik
1 3
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
1 3
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
pandemics.
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-
tors.
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
period.
G.S.Heriot, E.Jamrozik
1 3
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
pandemics.
References
Campbell A. & Morgan E. 2020. Comparisons of all-cause mortality between European countries and
regions: January to June 2020. ed. O. f. N. Statistics. United Kingdom.
Christley, R. M., M. Mort, B. Wynne, J. M. Wastling, A. L. Heathwaite, R. Pickup, Z. Austin & S. M.
Latham (2013) "Wrong, but useful": negotiating uncertainty in infectious disease modelling. PLoS
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,
820
Honigsbaum, M. (2010). The great dread: Cultural and psychological impacts and responses to the
Russian’influenza in the United Kingdom, 1889–1893. Social History of Medicine, 23(2), 299–319
Jones, D. S. (2020). History in a crisis - Lessons for Covid-19. New England Journal of Medicine, 382,
1681–1683
Nguyen-Van-Tam, J. S., & Hampson, A. W. (2003). The epidemiology and clinical impact of pandemic
influenza. Vaccine, 21, 1762–1768
Nicoll, A., Ciancio, B., Chavarrias, V. L., Mølbak, K., Pebody, R., Pedzinski, B., Penttinen, P., van der
Sande, M., Snacken, R., & Van Kerkhove, M. (2012). Influenza-related deaths-available methods for
estimating numbers and detecting patterns for seasonal and pandemic influenza in Europe. Eurosur-
veillance, 17, 20162
Patterson, K. D. 1986. Pandemic influenza, 1700–1900: a study in historical epidemiology. Rowman &
Littlefield Pub Incorporated.
Peckham, R. (2020). COVID-19 and the anti-lessons of history. Lancet, 395, 850–852
Saad-Roy, C. M., Wagner, C. E., Baker, R. E., Morris, S. E., Farrar, J., Graham, A. L., Levin, S. A., Mina,
M. J., Metcalf, C. J. E., & Grenfell, B. T. (2020). Immune life history, vaccination and the dynamics
of SARS-CoV-2 over the next 5 years. Science, 370, 811–818.
Smith, F. B. (1995). The Russian influenza in the United Kingdom, 1889–1894. Social History of Medi-
cine, 8, 55–73
Valleron, A. J., Cori, A., Valtat, S., Meurisse, S., Carrat, F., & Boëlle, P. Y. (2010). Transmissibility and
geographic spread of the 1889 influenza pandemic. Proceedings of the National Academy of Sci-
ences of the USA, 107, 8778–8781
1 3
Imagination andremembrance: what role should historical… Page 5 of 5 81
Vijgen, L., Keyaerts, E., Moës, E., Thoelen, I., Wollants, E., Lemey, P., Vandamme, A.-M., & Van Ranst,
M. (2005). Complete genomic sequence of human coronavirus OC43: Molecular clock analy-
sis suggests a relatively recent zoonotic coronavirus transmission event. Journal of Virology, 79,
1595–1604
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps
and institutional affiliations.
... 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). ...
Article
Full-text available
Moralization is a social-psychological process through which morally neutral issues take on moral significance. Often linked to health and disease, moralization may sometimes lead to good outcomes; yet moralization is often detrimental to individuals and to society as a whole. It is therefore important to be able to identify when moralization is inappropriate. In this paper, we offer a systematic normative approach to the evaluation of moralization. We introduce and develop the concept of ‘mismoralization’, which is when moralization is metaethically unjustified. In order to identify mismoralization, we argue that one must engage in metaethical analysis of moralization processes while paying close attention to the relevant facts. We briefly discuss one historical example (tuberculosis) and two contemporary cases related to COVID-19 (infection and vaccination status) that we contend to have been mismoralized in public health. We propose a remedy of de-moralization that begins by identifying mismoralization and that proceeds by neutralizing inapt moral content. De-moralization calls for epistemic and moral humility. It should lead us to pull away from our tendency to moralize—as individuals and as social groups—whenever and wherever moralization is unjustified.
Article
Full-text available
The global response to the recent coronavirus pandemic has revealed an ethical crisis in public health. This article analyses key pandemic public health policies in light of widely accepted ethical principles: the need for evidence, the least restrictive/harmful alternative, proportionality, equity, reciprocity, due legal process, and transparency. Many policies would be considered unacceptable according to pre-pandemic norms of public health ethics. There are thus significant opportunities to develop more ethical responses to future pandemics. This paper serves as the introduction to this Special Issue of Monash Bioethics Review and provides background for the other articles in this collection.
Article
Full-text available
Imperfect future immunity Humans are infected by several seasonal and cross-reacting coronaviruses. None provokes fully protective immunity, and repeat infections are the norm. Vaccines tend to be less efficient than natural infections at provoking immunity, and there are risks of adverse cross-reactions. Saad-Roy et al. used a series of simple models for a variety of immune scenarios to envisage immunological futures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with and without vaccines. The model outcomes show that our imperfect knowledge about the imperfect coronavirus immune landscape can give rise to diverging scenarios ranging from recurring severe epidemics to elimination. It is critical that we accurately characterize immune responses to SARS-CoV-2 for translation into managing disease control. Science , this issue p. 811
Article
Full-text available
Epidemics of infectious diseases often occur in predictable limit cycles. Theory suggests these cycles can be disrupted by high amplitude seasonal fluctuations in transmission rates, resulting in deterministic chaos. However, persistent deterministic chaos has never been observed, in part because sufficiently large oscillations in transmission rates are uncommon. Where they do occur, the resulting deep epidemic troughs break the chain of transmission, leading to epidemic extinction, even in large cities. Here we demonstrate a new path to locally persistent chaotic epidemics via subtle shifts in seasonal patterns of transmission, rather than through high-amplitude fluctuations in transmission rates. We base our analysis on a comparison of measles incidence in 80 major cities in the prevaccination era United States and United Kingdom. Unlike the regular limit cycles seen in the UK, measles cycles in US cities consistently exhibit spontaneous shifts in epidemic periodicity resulting in chaotic patterns. We show that these patterns were driven by small systematic differences between countries in the duration of the summer period of low transmission. This example demonstrates empirically that small perturbations in disease transmission patterns can fundamentally alter the regularity and spatiotemporal coherence of epidemics.
Article
Full-text available
For infectious disease dynamical models to inform policy for containment of infectious diseases the models must be able to predict; however, it is well recognised that such prediction will never be perfect. Nevertheless, the consensus is that although models are uncertain, some may yet inform effective action. This assumes that the quality of a model can be ascertained in order to evaluate sufficiently model uncertainties, and to decide whether or not, or in what ways or under what conditions, the model should be 'used'. We examined uncertainty in modelling, utilising a range of data: interviews with scientists, policy-makers and advisors, and analysis of policy documents, scientific publications and reports of major inquiries into key livestock epidemics. We show that the discourse of uncertainty in infectious disease models is multi-layered, flexible, contingent, embedded in context and plays a critical role in negotiating model credibility. We argue that usability and stability of a model is an outcome of the negotiation that occurs within the networks and discourses surrounding it. This negotiation employs a range of discursive devices that renders uncertainty in infectious disease modelling a plastic quality that is amenable to 'interpretive flexibility'. The utility of models in the face of uncertainty is a function of this flexibility, the negotiation this allows, and the contexts in which model outputs are framed and interpreted in the decision making process. We contend that rather than being based predominantly on beliefs about quality, the usefulness and authority of a model may at times be primarily based on its functional status within the broad social and political environment in which it acts.
Article
Full-text available
This article examines the impact of the 1889-93 'Russian' influenza on late Victorian society and culture. Using medical officer of health and national and local newspaper reports, and the poetry and memoirs of prominent survivors, I argue that the rapid progress of the influenza across Europe and the morbidity of leading politicians and other members of the British establishment occasioned widespread 'dread' and in some cases panic. This dread of influenza was fuelled by the high mortality rate in northern towns such as Sheffield, as well as by the disease's association with pneumonia, neurasthenia, psychosis and suicide. However, the key factor was the growth of mass circulation newspapers and the way that the influenza drew on fin de siecle cultural anxieties about urbanisation and the increasing speed of modern life.
Article
Full-text available
Two methodologies are used for describing and estimating influenza-related mortality: Individual-based methods, which use death certification and laboratory diagnosis and predominately determine patterns and risk factors for mortality, and population-based methods, which use statistical and modelling techniques to estimate numbers of premature deaths. The total numbers of deaths generated from the two methods cannot be compared. The former are prone to underestimation, especially when identifying influenza-related deaths in older people. The latter are cruder and have to allow for confounding factors, notably other seasonal infections and climate effects. There is no routine system estimating overall European influenza-related premature mortality, apart from a pilot system EuroMOMO. It is not possible at present to estimate the overall influenza mortality due to the 2009 influenza pandemic in Europe, and the totals based on individual deaths are a minimum estimate. However, the pattern of mortality differed considerably between the 2009 pandemic in Europe and the interpandemic period 1970 to 2008, with pandemic deaths in 2009 occurring in younger and healthier persons. Common methods should be agreed to estimate influenza-related mortality at national level in Europe, and individual surveillance should be instituted for influenza-related deaths in key groups such as pregnant women and children.
Article
Full-text available
Until now, mortality and spreading mechanisms of influenza pandemics have been studied only for the 1918, 1957, and 1968 pandemics; none have concerned the 19th century. Herein, we examined the 1889 "Russian" pandemic. Clinical attack rates were retrieved for 408 geographic entities in 14 European countries and in the United States. Case fatality ratios were estimated from datasets in the French, British and German armies, and morbidity and mortality records of Swiss cities. Weekly all-cause mortality was analyzed in 96 European and American cities. The pandemic spread rapidly, taking only 4 months to circumnavigate the planet, peaking in the United States 70 days after the original peak in St. Petersburg. The median and interquartile range of clinical attack rates was 60% (45-70%). The case fatality ratios ranged from 0.1% to 0.28%, which is comparable to those of 1957 and 1968, and 10-fold lower than in 1918. The median basic reproduction number (R(0)) was 2.1, which is comparable to the values found for the other pandemics, despite the different viruses and contact networks. R(0) values varied widely from one city to another, and only a small minority of those values was within the range in which modelers' mitigation scenarios predicted effectiveness. The 1889 and 1918 R(0) correlated for the subset of cities for which both values were available. Social and geographic factors probably shape the local R(0) , and they could be identified to design optimal mitigation scenarios tailored to each city.
Article
The pandemic of severe influenza known in western Europe as the Russian flu, with its associated infections, caused extensive morbidity and high general mortality. In the United Kingdom, as elsewhere, sufferers and their doctors were hard put to explain the visitation and resorted to analogies with physical or bacteriological phenomena, or recalled older beliefs in extra-terrestrial forces. The outcomes were more disturbing than was appreciated at the time, or since. The Russian influenza and its sequelae might well have had a crucial part in creating the 'spirit of the 1890s'.