The need for interdisciplinary studies of historic pandemics

What can we learn from historical pandemics? A systematic review of the literature

What are the insights from historical pandemics for policymaking today? We carry out a systematic review of the literature on the impact of pandemics that occurred since the Industrial Revolution and prior to Covid-19. Our literature searches were conducted between June 2020 and September 2023, with the final review encompassing 169 research papers selected for their relevance to understanding either the demographic or economic impact of pandemics. We include literature from across disciplines to maximise our knowledge base, finding many relevant articles in journals which would not normally be on the radar of social scientists. Our review identifies two gaps in the literature: (1) the need to study pandemics and their effects more collectively rather than looking at them in isolation; and (2) the need for more study of pandemics besides 1918 Spanish Influenza, especially milder pandemic episodes. These gaps are a consequence of academics working in silos, failing to draw on the skills and knowledge offered by other disciplines. Synthesising existing knowledge on pandemics in one place provides a basis upon which to identify the lessons in preparing for future catastrophic disease events.

Pandemics, past and present: The role of biological anthropology in interdisciplinary pandemic studies

Biological anthropologists are ideally suited for the study of pandemics given their strengths in human biology, health, culture, and behavior, yet pandemics have historically not been a major focus of research. The COVID‐19 pandemic has reinforced the need to understand pandemic causes and unequal consequences at multiple levels. Insights from past pandemics can strengthen the knowledge base and inform the study of current and future pandemics through an anthropological lens. In this paper, we discuss the distinctive social and epidemiological features of pandemics, as well as the ways in which biological anthropologists have previously studied infectious diseases, epidemics, and pandemics. We then review interdisciplinary research on three pandemics–1918 influenza, 2009 influenza, and COVID‐19–focusing on persistent social inequalities in morbidity and mortality related to sex and gender; race, ethnicity, and Indigeneity; and pre‐existing health and disability. Following this review of the current state of pandemic research on these topics, we conclude with a discussion of ways biological anthropologists can contribute to this field moving forward. Biological anthropologists can add rich historical and cross‐cultural depth to the study of pandemics, provide insights into the biosocial complexities of pandemics using the theory of syndemics, investigate the social and health impacts of stress and stigma, and address important methodological and ethical issues. As COVID‐19 is unlikely to be the last global pandemic, stronger involvement of biological anthropology in pandemic studies and public health policy and research is vital.

Flu-like pandemics and metaphor pre-covid: A corpus investigation

The use of metaphor in framing COVID-19 has already attracted considerable attention in both academic and public debate and we have seen extensive discussion of how this pandemic might be compared to past events, such as the so-called 'Spanish flu' of the 1910s. In this paper, we draw these two strands of metaphoric framing and historical comparison together by identifying the metaphorical framings of past influenza pandemics in media and political discourse in the UK over an extended period (1890-2009). The findings show remarkable continuity in the choice and proportion of conceptual metaphor across very different sociohistorical contexts. However, this does not correspond to entrenchment of the metaphors which continue to be creative and elaborated in many cases. In terms of variation over time, the analysis shows shifts in framing with greater focus on societal effects and reactions to influenza in later periods while the agency of the virus is reduced.

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

Severe mortality impact of the 1957 influenza pandemic in Chile

Introduction

Epidemiological studies of the 1957 influenza pandemic are scarce, particularly from lower‐income settings.

Methods

We analyzed the spatial–temporal mortality patterns of the 1957 influenza pandemic in Chile, including detailed age‐specific mortality data from a large city, and investigated risk factors for severe mortality impact across regions.

Results

Chile exhibited two waves of excess mortality in winter 1957 and 1959 with a cumulative excess mortality rate of 12 per 10 000, and a ~10‐fold mortality difference across provinces. High excess mortality rates were associated with high baseline mortality (R²=41.8%; P=.02), but not with latitude (P>.7). Excess mortality rates increased sharply with age. Transmissibility declined from R=1.4‐2.1 to R=1.2‐1.4 between the two pandemic waves.

Conclusions

The estimated A/H2N2 mortality burden in Chile is the highest on record for this pandemic—about three to five times as severe as that experienced in wealthier nations. The global impact of this pandemic may be substantially underestimated from previous studies based on high‐income countries.

Global Mortality Impact of the 1957-1959 Influenza Pandemic

BACKGROUND

Quantitative estimates of the global burden of the 1957 influenza pandemic are lacking. Here we fill this gap by modeling historical mortality statistics.

METHODS

We used annual rates of age- and cause-specific deaths to estimate pandemic-related mortality in excess of background levels in 39 countries in Europe, the Asia-Pacific region, and the Americas. We modeled the relationship between excess mortality and development indicators to extrapolate the global burden of the pandemic.

RESULTS

The pandemic-associated excess respiratory mortality rate was 1.9/10,000 population (95% confidence interval [CI], 1.2-2.6 cases/10,000 population) on average during 1957-1959. Excess mortality rates varied 70-fold across countries; Europe and Latin America experienced the lowest and highest rates, respectively. Excess mortality was delayed by 1-2 years in 18 countries (46%). Increases in the mortality rate relative to baseline were greatest in school-aged children and young adults, with no evidence that elderly population was spared from excess mortality. Development indicators were moderate predictors of excess mortality, explaining 35%-77% of the variance. Overall, we attribute 1.1 million excess deaths (95% CI, .7 million-1.5 million excess deaths) globally to the 1957-1959 pandemic.

CONCLUSIONS

The global mortality rate of the 1957-1959 influenza pandemic was moderate relative to that of the 1918 pandemic but was approximately 10-fold greater than that of the 2009 pandemic. The impact of the pandemic on mortality was delayed in several countries, pointing to a window of opportunity for vaccination in a future pandemic.

Death patterns during the 1918 influenza pandemic in Chile

Scarce information about the epidemiology of historical influenza pandemics in South America prevents complete understanding of pandemic patterns throughout the continent and across different climatic zones. To fill gaps with regard to spatiotemporal patterns of deaths associated with the 1918 influenza pandemic in Chile, we reviewed archival records. We found evidence that multiple pandemic waves at various times of the year and of varying intensities occurred during 1918-1921 and that influenza-related excess deaths peaked during July-August 1919. Pandemic-associated mortality rates were elevated for all age groups, including for adults >50 years of age; elevation from baseline was highest for young adults. Overall, the rate of excess deaths from the pandemic was estimated at 0.94% in Chile, similar to rates reported elsewhere in Latin America, but rates varied ≈10-fold across provinces. Patterns of death during the pandemic were affected by variation in host-specific susceptibility, population density, baseline death rate, and climate.

Years of life lost in the first wave of the 2009 influenza A(H1N1) pandemic in Hong Kong

The impact of influenza pandemics might be overestimated; the published studies of years of life lost (YLL) have typically ignored the presence of underlying chronic conditions or health risk behaviors in most deaths. We used data on deaths involving laboratory-confirmed 2009 influenza A(H1N1) virus infection that occurred between April 2009 and May 2010 in Hong Kong, China, to adjust for these underlying risk factors. Life expectancy was corrected with hazard-based modifications to the life tables. The excess hazards posed by underlying risk factors were added to the "baseline" age-specific hazards in the local life tables to reflect the life expectancy associated with each underlying risk factor. Of 72 deceased persons with laboratory-confirmed 2009 influenza A(H1N1) virus infection, 56% had underlying risk factors. We estimated that the 2009 pandemic was associated with 1,540 (95% confidence interval: 1,350, 1,630) YLL after adjustment for age and underlying risk factors. This figure is approximately 25% lower than the YLL estimate of 2,080 derived after adjustment for age but not for risk factors. Our analysis demonstrates the potential scale of bias in YLL estimation if underlying risk factors are ignored. The estimation of YLL with correction for underlying risk factors in addition to age could also provide a framework for similar calculations elsewhere.