In 1918 an unknown infectious agent spread around the world infecting over one-third of the general population and killing almost 50 million people. Many countries were at war, the First World War. ...Since Spain was a neutral country and Spanish press could report about the infection without censorship, this condition is commonly remembered as "Spanish influenza". This review examines several aspects during the 1918 influenza pandemic to bring out evidences which might be useful to imagine the possible magnitude of the present coronavirus disease 2019 (COVID-19).
In the first part of this review we will examine the origin of the SARS-Coronavirus-2 and 1918 Spanish Influenza Virus and the role played by host and environment in its diffusion. We will also include in our analysis an evaluation of different approaches utilized to restrain the spread of pandemic and to treat infected patients. In the second part, we will try to imagine the magnitude of the present COVID-19 pandemic and the possible measures able to restrain in the present environment its spread.
Several factors characterize the outcome in a viral pandemic infection. They include the complete knowledge of the virus, the complete knowledge of the host and of the environment where the host lives and the pandemic develops.
By comparing the situation seen in 1918 with the current one, we are now in a more favourable position. The experience of the past teaches us that their success is linked to a rapid, constant and lasting application. Then, rather than coercion, awareness of the need to observe such prevention measures works better.
Long-term sequelae of coronavirus disease 2019 (COVID-19) due to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are now recognized. However, there is still a lack of ...consensus regarding the terminology for this emerging chronic clinical syndrome, which includes long COVID, chronic COVID syndrome, post-COVID-19 syndrome, post-acute COVID-19, and long-hauler COVID-19. In this review, I will use the term "long COVID". A review of the medical history and epidemiology of past pandemics and epidemics in modern literature review identifies common long-term post-infectious disorders, with the common finding of altered cognition. In the brain, the cerebral hypoxia induced by SARS-CoV-2 infection may be caused by mitochondrial dysfunction, resulting in "brain fog". Historically, the common symptom of altered cognition has been reported during earlier pandemics, which include the influenza pandemics of 1889 and 1892 (Russian flu), the Spanish flu pandemic (1918-1919), encephalitis lethargica, diphtheria, and myalgic encephalomyelitis (chronic fatigue syndrome or post-viral fatigue syndrome). There are similarities between chronic fatigue syndrome and the "brain fog" described in long COVID. During past viral epidemics and pandemics, a commonality of neural targets may have increased viral survival by conformational matching. The neurological and psychiatric sequelae of SARS-CoV-2 infection, or long COVID, may have emerged from neural effects that have emerged from an invertebrate and vertebrate virosphere. This review aims to present a historical overview of infections and disorders associated with neurological and psychiatric sequelae that have shown similarities with long COVID.
Abstract
The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in ...British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.
Throughout history, society has dealt with several devastating pandemics. Our objective is to analyze society's coping mechanisms to deal with pandemic-related stress in history congruent with the ...values of the time. For that purpose, we have carefully selected some of the most significant pandemics based on their impact and the available psychosocial literature. After a brief introduction, society's coping tools are reviewed and analyzed for the Antonine Plague, the second bubonic plague, the third cholera pandemic, the Spanish flu, the HIV pandemic, and the COVID-19 pandemic. Despite occurring at different times in history, parallels can be established in the study of society's psychological reactions among different pandemics. Magical thinking, political skepticism, fake accusations, and discrimination of minorities are recurrent reactions in society among different pandemics in history.
The impact of socio-demographic factors and baseline health on the mortality burden of seasonal and pandemic influenza remains debated. Here we analyzed the spatial-temporal mortality patterns of the ...1918 influenza pandemic in Spain, one of the countries of Europe that experienced the highest mortality burden.
We analyzed monthly death rates from respiratory diseases and all-causes across 49 provinces of Spain, including the Canary and Balearic Islands, during the period January-1915 to June-1919. We estimated the influenza-related excess death rates and risk of death relative to baseline mortality by pandemic wave and province. We then explored the association between pandemic excess mortality rates and health and socio-demographic factors, which included population size and age structure, population density, infant mortality rates, baseline death rates, and urbanization.
Our analysis revealed high geographic heterogeneity in pandemic mortality impact. We identified 3 pandemic waves of varying timing and intensity covering the period from Jan-1918 to Jun-1919, with the highest pandemic-related excess mortality rates occurring during the months of October-November 1918 across all Spanish provinces. Cumulative excess mortality rates followed a south-north gradient after controlling for demographic factors, with the North experiencing highest excess mortality rates. A model that included latitude, population density, and the proportion of children living in provinces explained about 40% of the geographic variability in cumulative excess death rates during 1918-19, but different factors explained mortality variation in each wave.
A substantial fraction of the variability in excess mortality rates across Spanish provinces remained unexplained, which suggests that other unidentified factors such as comorbidities, climate and background immunity may have affected the 1918-19 pandemic mortality rates. Further archeo-epidemiological research should concentrate on identifying settings with combined availability of local historical mortality records and information on the prevalence of underlying risk factors, or patient-level clinical data, to further clarify the drivers of 1918 pandemic influenza mortality.
History has informed much of the social and political response to the coronavirus disease 2019 (COVID-19) pandemic, most notably in decisions about having people shelter in place and donning masks, ...as well as when and how to ease quarantine restrictions. When confronting the uncertainty of controlling the spread of a novel disease, these actions rely on information about the management of past pandemics to estimate their potential effectiveness as public health interventions. For example, during the current COVID-19 pandemic, public health decision makers in San Francisco reflected on the 1918-1919 influenza epidemic in the city, which offered evidence of the risks of relaxing measures too soon, after an apparent flattening of the curve, offering insight into the limits of public tolerance for social interventions and the political and economic pressures to return to normal.1 In addition, history exposes long-term structural inequities that create barriers to health care access, foster community distrust in public health ordinances, and result in worse health outcomes for vulnerable populations (eg, people who are low income, African American, and older) compared with non-vulnerable populations (eg, people who are high income, White, and younger). In this commentary, we examine the role that historical investigation plays in rationalizing public health interventions and helping to understand the public response to pandemic controls.
COVID-19 has been disturbing human society with an intensity never seen since the Influenza epidemic (Spanish flu). COVID-19 and Influenza are both respiratory viruses and, in this study, we explore ...the relations of COVID-19 and Influenza with atmospheric variables and socio-economic conditions for tropical and subtropical climates in Brazil. Atmospheric variables, mobility, socio-economic conditions and population information were analyzed using a generalized additive model for daily COVID-19 cases from March 1st to May 15th, 2020, and for daily Influenza hospitalizations (2017–2019) in Brazilian states representing tropical and subtropical climates. Our results indicate that temperature combined with humidity are risk factors for COVID-19 and Influenza in both climate regimes, and the minimum temperature was also a risk factor for subtropical climate. Social distancing is a risk factor for COVID-19 in all regions. For Influenza and COVID-19, the highest Relative Risks (RR) generally occurred in 3 days (lag = 3). Altogether among the studied regions, the most important risk factor is the Human Development Index (HDI), with a mean RR of 1.2492 (95% CI: 1.0926–1.6706) for COVID-19, followed by the elderly fraction for both diseases. The risk factor associated with socio-economic inequalities for Influenza is probably smoothed by Influenza vaccination, which is offered free of charge to the entire Brazilian population. Finally, the findings of this study call attention to the influence of socio-economic inequalities on human health.
•The Temperature combined with humidity are risk factors for COVID-19 and Influenza.•Social-economic inequalities are the most important risk factors for COVID-19.•COVID-19 may have seasonality similar to that of Influenza.•The highest RR occurred usually in 3 days (lag = 3) for the entire set of variables.
•During the Spanish flu, there was no rise in emergency psychiatric admissions.•During the Covid-19 pandemic, there was also no rise in emergency psychiatric admissions.•During the Covid-19 pandemic, ...the most significant decline was observed for the affective disorders group.•During the Covid-19 pandemic, the smallest decline was observed for the psychotic disorders group.
The last pandemic comparable to the current COVID-19 pandemic was the Spanish flu. Using the admission record books for the years 1917 and 1918 and electronic health records for the years 2019 and 2020, we extracted the relevant data and explored how they affected the numbers of emergency psychiatric admissions. The general trend in both pandemics was that they did not cause a rise in psychiatric admissions, findings which go along with reports around Europe. The causes for these similarities are complex but provide an interesting perspective as to why there is no concurrent rise in emergency psychiatric admissions.
Background
Whether morbidity from the 1918‐19 influenza pandemic discriminated by socioeconomic status has remained a subject of debate for 100 years. In lack of data to study this issue, the recent ...literature has hypothesized that morbidity was “socially neutral.”
Objectives
To study the associations between influenza‐like illness (ILI) and socioeconomic status (SES), gender, and wave during the 1918‐19 influenza pandemic.
Methods
Availability of incidence data on the 1918‐19 pandemic is scarce, in particular for waves other than the “fall wave” October‐December 1918. Here, an overlooked survey from Bergen, Norway (n = 10 633), is used to study differences in probabilities of ILI and ILI probability ratios by apartment size as a measure of SES and gender for 3 waves including the waves prior to and after the “fall wave.”
Results
Socioeconomic status was negatively associated with ILI in the first wave, but positively associated in the second wave. At all SES levels, men had the highest ILI in the summer, while women had the highest ILI in the fall. There were no SES or gender differences in ILI in the winter of 1919.
Conclusions
For the first time, it is documented a crossover in the role of socioeconomic status in 1918 pandemic morbidity. The poor came down with influenza first, while the rich with less exposure in the first wave had the highest morbidity in the second wave. The study suggests that the socioeconomically disadvantaged should be prioritized if vaccines are of limited availability in a future pandemic.
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