Early Epidemics Research Articles

Contextualising Edix Hill: First-Pandemic Plague and Britain

Abstract: The 2019 discovery of Yersinia pestis ancient DNA at Edix Hill in Cambridgeshire unquestionably confirms that plague was present in sixth-century Britain. Prior treatments of this evidence have decontextualised it from its British setting, considering it aside from the richly studied British archaeological and textual records, to detrimental effect. This article considers this new evidence in context, presenting a historiography of early medieval British epidemics and a summary of current scientific knowledge of the topic to contend that the discovery marks a significant shift in our understanding of early British plague and to clarify what palaeoscientific data can and cannot tell us about plague’s presence in and its impact on Britain. It then argues a new theory of the spread of plague based on recent developments in our understanding of British demographics, ecology and trade routes, contending that early first-pandemic Y. pestis was transmitted to Britain at least two distinct times via separate routes, with the first introduction potentially pre-dating the introduction of plague to Constantinople in 542 CE. The article then reflects on why British sources have been omitted from or misused in plague scholarship, arguing that this trend speaks to British history’s damaging insularity and to methodological issues within plague studies. It concludes by connecting this issue to wider geographical hierarchies within scholarship on Late Antiquity and suggests ways in which future plague scholarship can respond to these challenges.

Environmental DNA concentrations vary greatly across productive and degradative conditions, with implications for the precision of population estimates

Population size is an important metric to inform the conservation and management of species. For aquatic species, environmental DNA (eDNA) concentration has been suggested for non-invasively estimating population size. However, many biotic and abiotic factors simultaneously influence the production and degradation of eDNA which can alter the relationship between population size and eDNA concentration. We investigated the influence of temperature, salinity, and ranavirus infection on eDNA concentrations using tadpole mesocosms. Using linear regression models, we tested the influence of each experimental treatment on eDNA concentrations at three time points before and during epidemics. Prior to infection, elevated temperatures lowered eDNA concentrations, indicating that degradation was the driving force influencing eDNA concentrations. During early epidemics, no treatments strongly influenced eDNA concentrations and in late epidemics, productive forces dominated as ranavirus intensity and dead organisms increased eDNA concentrations. Finally, population size was only an important predictor of eDNA concentration in late epidemics and we observed high levels of variation between samples of replicate mesocosms. We demonstrate the complexities of several interacting factors influencing productive and degradative forces, variation in influences on eDNA concentration over short time spans, and examine the limitations of estimating population sizes from eDNA with precision in semi-natural conditions.

Emergence failure of early epidemics: A mathematical modeling approach.

Epidemic or pathogen emergence is the phenomenon by which a poorly transmissible pathogen finds its evolutionary pathway to become a mutant that can cause an epidemic. Many mathematical models of pathogen emergence rely on branching processes. Here, we discuss pathogen emergence using Markov chains, for a more tractable analysis, generalizing previous work by Kendall and Bartlett about disease invasion. We discuss the probability of emergence failure for early epidemics, when the number of infected individuals is small and the number of the susceptible individuals is virtually unlimited. Our formalism addresses both directly transmitted and vector-borne diseases, in the cases where the original pathogen is 1) one step-mutation away from the epidemic strain, and 2) undergoing a long chain of neutral mutations that do not change the epidemiology. We obtain analytic results for the probabilities of emergence failure and two features transcending the transmission mechanism. First, the reproduction number of the original pathogen is determinant for the probability of pathogen emergence, more important than the mutation rate or the transmissibility of the emerged pathogen. Second, the probability of mutation within infected individuals must be sufficiently high for the pathogen undergoing neutral mutations to start an epidemic, the mutation threshold depending again on the basic reproduction number of the original pathogen. Finally, we discuss the parameterization of models of pathogen emergence, using SARS-CoV1 as an example of zoonotic emergence and HIV as an example for the emergence of drug resistance. We also discuss assumptions of our models and implications for epidemiology.

Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa.

Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. Owing to inadequate public awareness, unestablished monitoring practices, limited testing and stigmas, there might exist extensive under-ascertainment of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (s.d. 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria). The mean overall report rate was estimated to be 5.37% (s.d. 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in São Tomé and Príncipe. An average of 5.46% (s.d. 6.4%) of all infected cases were severe cases and 66.74% (s.d. 17.28%) were asymptomatic ones. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance systems in these countries.

Healthcare providers as patients: COVID-19 experience.

There is compelling evidence for the psychological effects of the COVID-19 pandemic and earlier epidemics. However, fewer studies have examined the subjective meaning experience of healthcare providers who have survived COVID-19 as patients. This qualitative study aimed to understand further and describe the life experiences of healthcare providers who have survived COVID-19 as patients in Saudi Arabia. Data was collected using unstructured in-depth individual interviews among n = 10 healthcare providers from public hospitals in Saudi Arabia. Data were analyzed based on a phenomenological approach, which resulted in five themes: (i) physical and psychological signs and symptoms; (ii) self-healing, hiding pain, and family; (iii) fear of complications; (iv) disease stigma & long-term psychological outcomes; (v) emotional support, mental well-being & resignation. The overall synthesis showed that healthcare providers, as patients, experience the same difficulties and stressors as the general public. In some cases, these factors are even worse, as family members, colleagues, and employers develop a new type of stigma. Given the impact of social media and the flow of information of any type, more research is needed to examine the sources used to obtain information by the general public, whether these sources are reliable, and how the public can be taught to use only scientific data and not social data. Understanding the experience of healthcare providers as patients during the pandemic has allowed to look at the feelings and needs of people during illness from a new perspective. As expressed by participants, being a healthcare provider does not reduce the fear of the disease and does not mitigate its consequences in the form of stigmatization and isolation.

Screening of tomato (Solanum lycopersicon L.) genotypes by inducing systemic resistance against early blight disease caused by Alternaria solani

Early blight of tomato (Solanum lycopersicon L.) incited by Alternaria solani is highly destructive disease in the world. Environmental factors significantly impact early blight epidemics, leading to the loss of up to 78 per cent of tomato production. Twenty tomato genotypes were used in this study to identify the early blight resistant and susceptible genotypes selected to represent a range of reactions when screened under field conditions. The tomato plants were evaluated for early blight disease by using Per cent Disease Index (PDI). Pusa Uphar (20.18%) and Sankaranti (20.18%) showed resistance to early blight disease among the twenty genotypes. Anaka Kerala (61.25%), Arka Vikas (61.76%), Pusa Rohini (53.65%), Ashoka (50.60%) and Paiyur1 (56.08%) genotypes were found highly susceptible based on early blight disease intensity. Spore inoculation of A. solani was sprayed into tomato plants, it was discovered that the number of defense-inducing compounds viz., total phenols, peroxidase (PO), polyphenol oxidase (PPO) and phenylalanine ammonia lyase (PAL) has increased. Among the genotypes, Pusa Uphar (T2) and Sankaranti (T1) genotypes showed a high level of defense enzyme production. After tomato plants were exposed to pathogens through artificial inoculation, the activity of these defensive enzymes and compounds was highly induced in the resistant and sensitive germplasm than in the control.

Artificial intelligence and Machine Learning based Techniques in Analyzing the COVID-19 Gene Expression data: A Review

The novel Coronavirus associated with respiratory illness has become a new threat to human health as it is spreading very rapidly among the human population. Scientists and healthcare specialists throughout the world are still looking for a breakthrough technology to help combat the Covid-19 outbreak, despite the recent worldwide urgency. The use of Machine Learning (ML) and Artificial Intelligence (AI) in earlier epidemics has encouraged researchers by providing a fresh approach to combating the latest Coronavirus pandemic. This paper aims to comprehensively review the role of AI and ML for analysis of gene expressed data of COVID-19

Growth Recovery and COVID-19 Pandemic Model: Comparative Analysis for Selected Emerging Economies

The outburst of the COVID-19 pandemic and its rapid spread throughout the world in 2020 shed a new light on mathematic models describing the nature of epidemics. However, as the pandemic shocked economies to a much greater extent than earlier epidemics, the recovery potential of economies was emphasized and its inclusion in epidemic models is becoming more important. The present paper deals with the issues of modeling the recovery of economic systems that have undergone severe medical shocks, such as COVID-19. The proposed mathematical model considers the close relationship between the dynamics of pandemics and economic development. This distinguishes it from purely “medical” models, which are used exclusively to study the dynamics of the spread of the COVID-19 pandemic. Unlike standard SIR models, the present approach involves the introduction of the “vaccine” equation to the SIR model and introduces correction components that include the possibility of re-infection and other nuances such as the number of people at risk of infection (not sick with COVID but not vaccinated); sick with COVID; recovered; fully vaccinated (two doses) citizens; the rate of COVID infection; the rate of recovery of infected individuals; the vaccination coefficients, respectively, for those who have not been ill and recovered from COVID; the coefficient of revaccination; the COVID re-infection rate; and the population fluctuation coefficient, which takes into account the effect of population change as a result of births and deaths and due to the departure and return of citizens. The present model contains governance so that it not only generates scenario projections but also models specific governance measures as well to include the pandemic and restore economic growth. The model also adds management issues, so that it not only generates scenario forecasts but simultaneously models specific management measures as well, aiming to suppress the pandemic and restoring economic growth. The model was implemented on specific data on the dynamics of the spread of the COVID-19 pandemic in selected developing economies.

Planning for social distancing: How the legacy of historical epidemics shaped COVID-19's spread in Madrid.

This paper combines historical and contemporary sources to examine ‘epidemic urban planning’ from the first decades of the 20th century through to the present day. It considers how infamous early 20th-century epidemics triggered the development of several urban regulations that profoundly shaped the city’s future. To reduce the risk of contagion in bourgeois space, the city began displacing and spatially segregating the urban poor, leading to deprived neighbourhoods in the city’s suburbs. The social and urban structure of these deprived, ‘vulnerable’ neighbourhoods remains to this day. Madrid was also greatly impacted by the COVID-19 crisis, and the initial distribution of COVID geographies seemed to reflect these historical legacies. Epidemic-influenced segregation kept wealthy neighbourhoods relatively safe during the first waves of the COVID-19 pandemic, concentrating the disease in poorer areas.

Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain

In recent years, early blight epidemics have been frequently causing important yield loses in potato crop. This fungal disease develops quickly when weather conditions are favorable, forcing the use of fungicides by farmers. A Limia is one of the largest areas for potato production in Spain. Usually, early blight epidemics are controlled using pre-established schedule calendars. This strategy is expensive and can affect the environment of agricultural areas. Decision support systems are not currently in place to be used by farmers for managing early blight. Thus, the objective of this research was to evaluate different early blight forecasting models based on plant or/and pathogen requirements and weather conditions to check their suitability for predicting the first symptoms of early blight, which is necessary to determine the timings of the first fungicide application. For this, weather, phenology and symptomatology of disease were monitored throughout five crop seasons. The first early blight symptoms appeared starting the flowering stage, between 37 and 40 days after emergence of plants. The forecasting models that were based on plants offered the best results. Specifically, the Wang-Engel model, with 1.4 risk units and Growing Degree-Days (361 cumulative units) offeredthe best prediction. The pathogen-based models showed a conservative forecast, whereas the models that integrated both plant and pathogen features forecasted the first early blight attack markedly later.

Non-neutralizing antibodies: Deleterious or propitious during SARS-CoV-2 infection?

Antibody-dependent enhancement (ADE) is a complex phenomenon mediated by antibodies, frequently pre-existing non-neutralizing or sub-neutralizing antibodies. In the course of infectious diseases, ADE may be responsible for worsening the clinical course of the disease by increasing the virulence of pathogens (ADE of infection) or enhancing disease severity (ADE of disease). Here we reviewed the mechanisms thought to be behind the ADE phenomenon and its potential relationship with COVID-19 severity. Since the early COVID-19 epidemics, ADE has been mentioned as a possible mechanism involved in severe COVID-19 disease and, later, as a potential risk in the case of infection after vaccination. However, current data do not support its role in disease severity, both after infection and reinfection.

Characteristics of SARS-CoV-2 transmission in a medium-sized city with traditional communities during the early COVID-19 epidemic in China.

The nationwide COVID-19 epidemic ended in 2020, a few months after its outbreak in Wuhan, China at the end of 2019. Most COVID-19 cases occurred in Hubei Province, with a few local outbreaks in other provinces of China. A few studies have reported the early SARS-CoV-2 epidemics in several large cities or provinces of China. However, information regarding the early epidemics in small and medium-sized cities, where there are still traditionally large families and community culture is more strongly maintained and thus, transmission profiles may differ, is limited. In this study, we characterized 60 newly sequenced SARS-CoV-2 genomes from Anyang as a representative of small and medium-sized Chinese cities, compared them with more than 400 reference genomes from the early outbreak, and studied the SARS-CoV-2 transmission profiles. Genomic epidemiology revealed multiple SARS-CoV-2 introductions in Anyang and a large-scale expansion of the epidemic because of the large family size. Moreover, our study revealed two transmission patterns in a single outbreak, which were attributed to different social activities. We observed the complete dynamic process of single-nucleotide polymorphism development during community transmission and found that intrahost variant analysis was an effective approach to studying cluster infections. In summary, our study provided new SARS-CoV-2 transmission profiles representative of small and medium-sized Chinese cities as well as information on the evolution of SARS-CoV-2 strains during the early COVID-19 epidemic in China.

The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection.

Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the early epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the size of the epidemic over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the UK early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.

Cinematic Representation of Early Modern Women and Epidemics

Previous articleNext article No AccessCinematic Representation of Early Modern Women and EpidemicsQijun Han and Daniel R. CurtisQijun Han Search for more articles by this author and Daniel R. Curtis Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Early Modern Women: An Interdisciplinary Journal Volume 16, Number 1Fall 2021 Published for the Society for the Study of Early Modern Women and Gender Article DOIhttps://doi.org/10.1086/715752 Views: 57Total views on this site © 2021 Society for the Study of Early Modern Women and Gender. All rights reserved.PDF download Crossref reports no articles citing this article.

Yellow Fever’s Historical Lessons for COVID-19: International Interventions and Disease Control in Early Twentieth-century Ecuador and Guatemala

Throughout tropical urban Latin America, yellow fever wreaked havoc. Located at sea level, Guayaquil (Ecuador) and Puerto Barrios (Guatemala) were particularly susceptible to yellow fever; yet, Ecuadorians and Guatemalans enjoyed significant success in early twentieth-century campaigns against yellow fever. Reflecting international efforts that informed, collaborated with, and at times underwrote Latin American public health campaigns, the Rockefeller Foundation (RF) sent representatives to Guatemala and Ecuador in the mid-1910s to eradicate yellow fever. While those interventions enjoyed immediate success, the long-term effects were more ambiguous. By collaborating with RF, Ecuador had all but eradicated yellow fever by 1919. In Guatemala, however, a few months after RF declared Guatemala free of yellow fever, influenza struck, likely originating from US military camps in Guatemala that RF sought to shield from yellow fever. Analysis of early twentieth-century yellow fever epidemics and campaigns to arrest them sheds light on COVID-19 pandemic challenges. Even as knowledge of disease etiology was evolving in Ecuador and Guatemala, most leaders accepted or at least did not publicly reject scientific medicine. In contrast, beginning with the most powerful politicians and filtering down throughout federal, state, and municipal authorities, many US leaders rejected science crucial to the campaigns against COVID-19. Similarly, in a pattern that resonates with US residents rejecting precautionary measures against COVID-19 such as wearing masks and maintaining social distance, compliance with anti-yellow fever campaigns was not always forthcoming.

Parameter Space Exploration in Pedestrian Queue Design to Mitigate Infectious Disease Spread.

Reducing the interactions between pedestrians in crowded environments can potentially curb the spread of infectious diseases including COVID-19. The mixing of susceptible and infectious individuals in many high-density man-made environments such as waiting queues involves pedestrian movement, which is generally not taken into account in modeling studies of disease dynamics. In this paper, a social force-based pedestrian-dynamics approach is used to evaluate the contacts among proximate pedestrians which are then integrated with a stochastic epidemiological model to estimate the infectious disease spread in a localized outbreak. Practical application of such multiscale models to real-life scenarios can be limited by the uncertainty in human behavior, lack of data during early stage epidemics, and inherent stochasticity in the problem. We parametrize the sources of uncertainty and explore the associated parameter space using a novel high-efficiency parameter sweep algorithm. We show the effectiveness of a low-discrepancy sequence (LDS) parameter sweep in reducing the number of simulations required for effective parameter space exploration in this multiscale problem. The algorithms are applied to a model problem of infectious disease spread in a pedestrian queue similar to that at an airport security check point. We find that utilizing the low-discrepancy sequence-based parameter sweep, even for one component of the multiscale model, reduces the computational requirement by an order of magnitude.

Pandemics and education: A historical review.

Major pandemics have tremendous effects on society. They precipitated the early decline of the Western Roman Empire and helped spread Christianity. There are countless such examples of infectious diseases altering the course of history. The impact of epidemics on education however is less well documented. This present historical account of the past 800 years looks specifically at how some aspects of education were shaped from the early medieval epidemics such as leprosy and the Black Plague to the Spanish Flu and COVID-19. Leprosy changed religious education, and the Black Plague may have contributed to the rise of medical schools, hospitals, public health education, and led to the implementation of lazarettos and the quarantine. The smallpox epidemic helped usher in public health education for immunization, while the 1918 Spanish Flu precipitated the rise of education by correspondence, and recently COVID-19 has catapulted remote digital learning to the forefront of higher education.

HIV-2 diversity displays two clades within group A with distinct geographical distribution and evolution.

Genetic diversity of HIV-2 groups A and B has not yet been fully described, especially in a few Western Africa countries such as Ivory-Coast or Mali. We collected 444 pol, 152 vif, 129 env, and 74 LTR sequences from patients of the French ANRS CO5 HIV-2 cohort completed by 221 pol, 18 vif, 377 env, and 63 LTR unique sequences from public databases. We performed phylogenetic reconstructions and revealed two distinct lineages within HIV-2 group A, herein called A1 and A2, presenting non-negligible genetic distances and distinct geographic distributions as A1 is related to coastal Western African countries and A2 to inland Western countries. Estimated early diversification times for groups A and B in human populations were 1940 [95% higher probability densitiy: 1935–53] and 1961 [1952–70]. A1 experienced an early diversification in 1942 [1937–58] with two distinct early epidemics in Guinea-Bissau or Senegal, raising the possibility of group A emergence in those countries from an initial introduction from Ivory-Coast to Senegal, two former French colonies. Changes in effective population sizes over time revealed that A1 exponentially grew concomitantly to Guinea-Bissau independence war, but both A2 and B lineages experienced a latter growth, starting during the 80s economic crisis. This large HIV-2 genetic analysis provides the existence of two distinct subtypes within group A and new data about HIV-2 early spreading patterns and recent epidemiologic evolution for which data are scarce outside Guinea-Bissau.

Pandemic, Patents and Public Health

The COVID-19 pandemic has compelled a relook at public healthcare and the patent systems. It has brought to halt livelihoods with devastating consequences to people’s lives national economies. Global health security is at stake as there is a need to develop and deploy more vaccines, repurpose medicines and increase medical infrastructure support. Collaboration and collective response is imperative at international and national levels. IPR access is crucial in relation to public health. Many countries have issued new policies and enacted laws to make it easier for them to supply medicines to their population during the pandemic. Compulsory licensing has been used as an important mechanism to open up IP without the permission of patent holders. The present study analyses amendments to patent law and IP legislations that are effected from a cross country perspective during the pandemic time. It also examines international cooperation in the context of public health and IP under the TRIPS Agreement in view of the on-going consultation at the WTO. The study reveals differences in approaches to ‘governmental use’ of patents and access to know-how under the statutory framework. Improving the scope of use of products and process patents, suspension of patent term extension, consolidating the compulsory licensing mechanism, removal of inequity are the predominant aspects that are part of the amendments to patent law in the countries. Pandemics like COVID-19 need legislative initiatives to secure healthcare system access for all citizens. Healthcare access includes ready availability of basic vaccines, drugs, medical devices and medical infrastructure. There is often a clash between access to healthcare as a fundamental right on one hand and the need to award monopolies in the form of IP rights as an incentive for innovations from the pharmaceutical industry on the other. Hence, obstacles arise in decision-making to balance innovation incentives and ensuring rights to access healthcare. Judicial decision-making and public policy-making have been always at the centre stage in earlier epidemics and now in the current pandemic making it imperative for countries to protect the health interests of their citizens.

'I Think it Highly Necessary to Have it Done Before They Go Out into the World': Inoculation, Responsibility and Patterns of Familial Transmission of Smallpox

This paper examines two research streams. First, it will discuss some contemporary familial perspectives on smallpox inoculation in the eighteenth century. This is followed by a look at the level of provision of the practice in Oxfordshire and some of its contiguous counties. Second, the paper will present some findings on the nature of the transmission of smallpox during local early eighteenth century epidemics in Banbury, Oxfordshire and Aynho, Northamptonshire. Finally, the paper will put forward some conclusions which encompass these two streams.