Outbreak Stages Research Articles

The Effect of COVID-19 on Investment Decisions in Saudi Stock Market

In this study, the response to the COVID-19 global pandemic by the Saudi financial market is discussed. Using panel data and pooled Ordinary Least Squares (OLS), it is seen that the increase in COVID-19 verified cases has led to adverse reactions by the stock market. This can be determined through related stock market returns data from March 3rd, 2020 to December 9th, 2020, which shows that, during this period, stock market returns declined as numbers of registered cases grew. In addition, further observation reveals that stock markets took a more dynamic stance towards the rise in confirmed non-fatal cases as opposed to that of virus-related fatalities. Furthermore, it is noticed that when the degree of restriction of lockdown increases, the impact of Brent crude oil price return on Saudi stock market return decreases. Thus, the suggestion is that investors should not use Brent oil return as an indicator of Saudi stock return during a complete lockdown. Moreover, the rise of lockdown restrictions and the number of confirmed cases leads to more fluctuation in the Saudi stock market return. Overall, our findings indicate that financial markets are swiftly reacting to this destructive virus but this response differs according to different outbreak stages.

International Neurosurgery Activity after 2020- Silver Linings from the Covid-19 Pandemic & Lessons from the European Association of Neurosurgical Societies (EANS)

“Every cloud has a silver lining,” and indeed, every crisis provides opportunities for learning lessons and changing tactics. Nobody couldhave foreseen what happened because of the Covid-19 disease, from the unfolding outbreak stages to the epidemic, and now a globalpandemic; second and possibly third waves into 2021, and 2 million deaths and 98 million confirmed infections globally as of 25thJanuary 2021. The European Association of Neurosurgical Societies (EANS) canceled all major on-site activities in 2020; This included allthree cycles of our flagship event, the residential Training Courses for Residents in Neurosurgery, as well as various hands-on courses(table 1). However, amidst the chaos, suffering, and lockdown, and the uncertainties of traveling, hotel availabilities, and quarantines,the opportunity arose to convert some events to an online version. Several organizations and societies acted along the same lines, andthere has been an explosion of webinars and videoconferencing on a variety of platforms (1).

The Impact of Covid-19 on Smartphone Usage.

The outbreak of Covid-19 changed the world as well as human behavior. In this article, we study the impact of Covid-19 on smartphone usage. We gather smartphone usage records from a global data collection platform called Carat, including the usage of mobile users in North America from November 2019 to April 2020. We then conduct the first study on the differences in smartphone usage across the outbreak of Covid-19. We discover that Covid-19 leads to a decrease in users’ smartphone engagement and network switches, but an increase in WiFi usage. Also, its outbreak causes new typical diurnal patterns of both memory usage and WiFi usage. Additionally, we investigate the correlations between smartphone usage and daily confirmed cases of Covid-19. The results reveal that memory usage, WiFi usage, and network switches of smartphones have significant correlations, whose absolute values of Pearson coefficients are greater than 0.8. Moreover, smartphone usage behavior has the strongest correlation with the Covid-19 cases occurring after it, which exhibits the potential of inferring outbreak status. By conducting extensive experiments, we demonstrate that for the inference of outbreak stages, both Macro-F1 and Micro-F1 can achieve over 0.8. Our findings explore the values of smartphone usage data for fighting against the epidemic.

Epidemic and control of COVID-19 in Niger: quantitative analyses in a least developed country.

BackgroundThe COVID-19 pandemic is challenging the public health response system worldwide, especially in poverty-stricken, war-torn, and least developed countries (LDCs).MethodsWe characterized the epidemiological features and spread dynamics of COVID-19 in Niger, quantified the effective reproduction number (Rt), evaluated the impact of public health control measures, and estimated the disease burden.ResultsAs of 4 July 2020, COVID-19 has affected 29 communes of Niger with 1093 confirmed cases, among whom 741 (67.8%) were males. Of them 89 cases died, resulting in a case fatality rate (CFR) of 8.1%. Both attack rates and CFRs were increased with age (P < 0.0001). Health care workers accounted for 12.8% cases. Among the reported cases, 39.3% were isolated and treated at home, and 42.3% were asymptomatic. 74.6% cases were clustered in Niamey, the capital of Niger. The Rt fluctuated in correlation to control measures at different outbreak stages. After the authorities initiated the national response and implemented the strictest control measures, Rt quickly dropped to below the epidemic threshold (<1), and maintained low level afterward. The national disability-adjusted life years attributable to COVID-19 was 1267.38 years in total, of which years of life lost accounted for over 99.1%.ConclusionsClassic public health control measures such as prohibition of public gatherings, travelling ban, contact tracing, and isolation and quarantine at home, are proved to be effective to contain the outbreak in Niger, and provide guidance for controlling the ongoing COVID-19 pandemic in LDCs.

Study on Surface Characteristics of E-glass Fiber Reinforced Epoxy Resin Composites in Different Stages of Tracking

To study the variation of surface characteristics of glass fiber reinforced epoxy composite insulation materials during the development of tracking, this paper established an experimental platform for tracking under the inclined plate method and prepared samples of glass fiber reinforced epoxy resin. In this paper, according to the experimental discharge phenomenon, discharge repetition rate phase diagram and corrosion degree of materials, the process of tracking was divided into four stages: initiation, stability, development and outbreak stages. Scanning electron microscope was used to observe the change of micromorphology of samples in different stages of tracking. The content of elements in different stages of tracking was determined by energy dispersive spectrometer. The surface characteristic functional groups in different stages of tracking were measured by Fourier transform infrared spectroscopy. The results show that with the change of surface morphology and the formation of surface products during tracking, the content of C element in the spherical region of the material decreased first and then increased, and the content of O and Si increased first and then decreased. The epoxy group of the material was gradually decomposed. Carbonyl group was generated on the surface of the material, and then decomposed during the outbreak stages. In addition, the deterioration mechanism of thermal aging and tracking was quite different. Thermal aging provided convenient routes for the electron injection into the material during tracking, thus reducing the tracking and erosion resistance of the material.

Ideal guidance and procedures for donning and doffing personal protective equipment to be used by health workers during management of patients confirmed with COVID-19 or person under investigation

Highly infectious disease epidemics such as COVID-19 put healthcare workers at higher risk of infection than regular people as they come into more potential contact with contaminated respiratory droplets and so caution is warranted in the use of personal protective equipment (PPE) to lessen risk. It is not clear what kind of PPE best offers protection and the best way to remove PPE. PPE is used by Healthcare workers in the COVID-19 virus disease situation. Its use is an integral part of the strategy to combat COVID-19 and prevent its spread. Optimal PPE use is hard and thus healthcare workers may alter the delivery of care because of improper PPE use. Preventing contamination of PPE during donning and doffing therefore becomes crucial and requires improvisation. Donning and doffing of PPE should include adherence to protocols as well as COVID- 19 specific design of PPE and further research into the risks, benefits, and best practices of PPE use. Lastly, training of users must be stressed to minimize protocol deviations and in turn guarantee the best protection to HCWs. While ranked as the lowest in infection control hierarchy due to its effectiveness as compared to other measures and high cost, PPE is nonetheless extremely important and often overlooked aspect during early outbreak stages of a pandemic where neither drugs nor vaccines are available and access to them is limited. This paper sheds light on the donning and doffing protocols that should be used as well as discusses this often-overlooked aspect of the virus control measure.

Detecting the Coronavirus (COVID-19).

The COVID-19 pandemic has created huge damage to society and brought panic around the world. Such panic can be ascribed to the seemingly deceptive features of COVID-19: Compared to other deadly viral outbreaks, it has medium transmission and mortality rates. As a result, the severity of the causative coronavirus, SARS-CoV-2, was deeply underestimated by society at the beginning of the COVID-19 outbreak. Based on this, in this review, we define the viruses with features similar to those of SARS-CoV-2 as the Panic Zone viruses. To contain those viruses, accurate and fast diagnosis followed by effective isolation and treatment of patients are pivotal at the early stage of virus breakouts. This is especially true when there is no cure or vaccine available for a transmissible disease, which is the case for the current COVID-19 pandemic. As of July 2020, more than 100 kits for COVID-19 diagnosis on the market have been surveyed in this review, while emerging sensing techniques for SARS-CoV-2 are also discussed. It is of critical importance to rationally use these kits for efficient management and control of the Panic Zone viruses. Therefore, we discuss guidelines to select diagnostic kits at different outbreak stages of the Panic Zone viruses, SARS-CoV-2 in particular. While it is of utmost importance to use nucleic acid based detection kits with low false negativity (high sensitivity) at the early stage of an outbreak, the low false positivity (high specificity) gains importance at later stages of the outbreak. When society is set to reopen from the lockdown stage of the COVID-19 pandemic, it becomes critical to have immunoassay based kits with high specificity to identify people who can safely return to society after their recovery from SARS-CoV-2 infections. Finally, since a massive attack from a viral pandemic requires a massive defense from the whole society, we urge both government and the private sector to research and develop affordable and reliable point-of-care testing (POCT) kits, which can be used massively by the general public (and therefore called massive POCT) to contain Panic Zone viruses in the future.

Emergence of a Plant Pathogen in Europe Associated with Multiple Intercontinental Introductions.

Pathogen introductions have led to numerous disease outbreaks in naive regions of the globe. The plant pathogen Xylella fastidiosa has been associated with various recent epidemics in Europe affecting agricultural crops, such as almond, grapevine, and olive, but also endemic species occurring in natural forest landscapes and ornamental plants. We compared whole-genome sequences of X. fastidiosa subspecies multiplex from America and strains associated with recent outbreaks in southern Europe to infer their likely origins and paths of introduction within and between the two continents. Phylogenetic analyses indicated multiple introductions of X. fastidiosa subspecies multiplex into Italy, Spain, and France, most of which emerged from a clade with limited genetic diversity with a likely origin in California, USA. The limited genetic diversity observed in X. fastidiosa subspecies multiplex strains originating from California is likely due to the clade itself being an introduction from X. fastidiosa subspecies multiplex populations in the southeastern United States, where this subspecies is most likely endemic. Despite the genetic diversity found in some areas in Europe, there was no clear evidence of recombination occurring among introduced X. fastidiosa strains in Europe. Sequence type taxonomy, based on multilocus sequence typing (MLST), was shown, at least in one case, to not lead to monophyletic clades of this pathogen; whole-genome sequence data were more informative in resolving the history of introductions than MLST data. Although additional data are necessary to carefully tease out the paths of these recent dispersal events, our results indicate that whole-genome sequence data should be considered when developing management strategies for X. fastidiosa outbreaks.IMPORTANCEXylella fastidiosa is an economically important plant-pathogenic bacterium that has emerged as a pathogen of global importance associated with a devastating epidemic in olive trees in Italy associated with X. fastidiosa subspecies pauca and other outbreaks in Europe, such as X. fastidiosa subspecies fastidiosa and X. fastidiosa subspecies multiplex in Spain and X. fastidiosa subspecies multiplex in France. We present evidence of multiple introductions of X. fastidiosa subspecies multiplex, likely from the United States, into Spain, Italy, and France. These introductions illustrate the risks associated with the commercial trade of plant material at global scales and the need to develop effective policy to limit the likelihood of pathogen pollution into naive regions. Our study demonstrates the need to utilize whole-genome sequence data to study X. fastidiosa introductions at outbreak stages, since a limited number of genetic markers does not provide sufficient phylogenetic resolution to determine dispersal paths or relationships among strains that are of biological and quarantine relevance.

Ecological effects of Ulva prolifera green tide on bacterial community structure in Qingdao offshore environment.

Ulva prolifera green tide is a serious marine ecological problem in China's coastal areas, with recurrent outbreaks occurring annually during late spring and summer since 2007. Marine bacteria communities are closely linked with important ecological functions in coastal environments. Hence, the succession of bacterial community structures in seawater and sediments during the outbreak and extinction stages of U.prolifera green tide were studied in this paper. The results revealed that: (1) the outbreak of a green tide led to changes in the bacterial community structure and a reduction in the abundance and diversity of the bacterial community in the Qingdao offshore region. This change was more significant in sediments than in seawater. (2) The bacterial assemblage appeared to be dominated by members of Deltaproteobacteria, Bacteroidia and Flavobacteria in sediment, and reductions in the relative abundance of Flavobacteria and Alphaproteobacteria were also observed in seawater after the green tide outbreak. There was also a high similarity in species composition of bacteria in sediment (extinction stage) and seawater (outbreak stage). (3) The outbreak of the U.prolifera affected the abundance of functional bacterial communities in the offshore environment such as SRB (sulfate-reducing bacteria), CFB (Cytophaga-Flavobacter-Bacteroides), heterotrophic bacteria and the potentially algicidal bacteria Alteromonadaceae. Overall, U.prolifera affects aquatic ecosystem secondary production, biogenic geochemical cycles and the growth of other seaweeds in Qingdao offshore areas by driving variations in functional bacterial communities.

Prescription surveillance for early detection system of emerging and reemerging infectious disease outbreaks

Based on prescriptions filled at external pharmacies, prescription surveillance (PS) in Japan has been reporting the estimated numbers of influenza and varicella patients and people prescribed certain drugs since 2009. Every morning, this system estimates the numbers of patients from the numbers of prescriptions filled nationwide for neuraminidase inhibitors, anti-herpes virus drugs, antibiotic drugs, antipyretic analgesics, and multi-ingredient cold medications. Moreover, it can detect "unexplained" infectious diseases that are not explained as infectious diseases monitored by other surveillance systems. Such "unexplained" infectious diseases might be emerging and re-emerging infectious diseases including bioterrorism attacks, which are reportedly difficult to diagnose, at least in early outbreak stages. To ascertain the system's potential benefits, this study examined schemes to detect "unexplained" infectious diseases using PS information. The numbers of patients prescribed the respective drugs are first regressed on the known infectious diseases, time trends, and dummies for day-of-the-week, holidays, and days following a holiday. Known infectious diseases are defined as covered by the National Official Sentinel Surveillance for Infectious Diseases under the Infection Control Law. After the numbers of patients from PS are compared with the predicted numbers of patients, their probabilities of occurrence are calculated. We examined the system's prospective operation from January 2017 through July 2018. The criterion we used to define aberrations varied, from 0.01 to 10-7. For criteria of 0.01 and 10-7 we found 254 and 15 aberrations, respectively. We confirmed its feasibility and effectiveness.

EDNA detection of corallivorous seastar (Acanthaster cf. solaris) outbreaks on the Great Barrier Reef using digital droplet PCR

Coral loss through consumption by corallivorous crown-of-thorns seastars (CoTS, Acanthaster spp.) is a major contributor to the coral reef crisis in the Indo-Pacific region. The fourth wave of Acanthaster cf. solaris outbreaks since the 1960s started around 2010 on Australia’s Great Barrier Reef. Ecological monitoring failed to detect early outbreak stages, thus preventing timely intervention. Here, we develop a digital droplet PCR (ddPCR)-based method to detect environmental DNA (eDNA) of CoTS in 2-l water samples that can be compared with abundances of the species recorded by divers along 200-m2 transects. Aquarium tests demonstrated that eDNA was readily detectable and increases proportional to the biomass of CoTS (R2 = 0.99, p < 0.0001). Adaptation from a quantitative PCR technique developed for CoTS larvae (Doyle et al. in Marine Biology 164:176, 2017) to ddPCR improved the limit of quantification (LOQ) by a factor of 45. During field verification on 11 reefs, CoTS eDNA was detectable on all reefs suffering outbreaks. In contrast, CoTS eDNA was absent from ‘post-outbreak’ reefs after populations collapsed and from ‘pre-outbreak’ reefs. In linear models, CoTS densities explained a high amount of variance of eDNA concentrations, both for water samples taken at the depth of transects (R2 = 0.60, p < 0.0001) and on the sea surface (R2 = 0.46, p = 0.0004). The proportion of samples above LOQ was also correlated with CoTS densities, with a similar amount of variance explained as for the concentration (underwater R2 = 0.68, p < 0.0001; surface R2 = 0.49, p = 0.0004). We conclude that, after consideration of sampling locations and times, this method is promising for CoTS population monitoring and early detection of outbreaks and might supplement or replace traditional monitoring. Development of automated samplers and possibly on board PCR in the future will further improve early detection.

Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity

Mathematical models of cholera and waterborne disease vary widely in their structures, in terms of transmission pathways, loss of immunity, and a range of other features. These differences can affect model dynamics, with different models potentially yielding different predictions and parameter estimates from the same data. Given the increasing use of mathematical models to inform public health decision-making, it is important to assess model distinguishability (whether models can be distinguished based on fit to data) and inference robustness (whether inferences from the model are robust to realistic variations in model structure).In this paper, we examined the effects of uncertainty in model structure in the context of epidemic cholera, testing a range of models with differences in transmission and loss of immunity structure, based on known features of cholera epidemiology. We fit these models to simulated epidemic and long-term data, as well as data from the 2006 Angola epidemic. We evaluated model distinguishability based on fit to data, and whether the parameter values, model behavior, and forecasting ability can accurately be inferred from incidence data.In general, all models were able to successfully fit to all data sets, both real and simulated, regardless of whether the model generating the simulated data matched the fitted model. However, in the long-term data, the best model fits were achieved when the loss of immunity structures matched those of the model that simulated the data. Two parameters, one representing person-to-person transmission and the other representing the reporting rate, were accurately estimated across all models, while the remaining parameters showed broad variation across the different models and data sets. The basic reproduction number (R0) was often poorly estimated even using the correct model, due to practical unidentifiability issues in the waterborne transmission pathway which were consistent across all models. Forecasting efforts using noisy data were not successful early in the outbreaks, but once the epidemic peak had been achieved, most models were able to capture the downward incidence trajectory with similar accuracy. Forecasting from noise-free data was generally successful for all outbreak stages using any model.Our results suggest that we are unlikely to be able to infer mechanistic details from epidemic case data alone, underscoring the need for broader data collection, such as immunity/serology status, pathogen dose response curves, and environmental pathogen data. Nonetheless, with sufficient data, conclusions from forecasting and some parameter estimates were robust to variations in the model structure, and comparative modeling can help to determine how realistic variations in model structure may affect the conclusions drawn from models and data.

Principles underlying the epizootiology of viral hemorrhagic septicemia in Pacific herring and other fishes throughout the North Pacific Ocean

Although viral hemorrhagic septicemia virus (VHSV) typically occurs at low prevalence and intensity in natural populations of Pacific herring (Clupea pallasii) and other marine fishes in the Northeast Pacific Ocean, epizootics of the resulting disease (VHS) periodically occur, often in association with observed fish kills. Here we identify a list of principles, based on a combination of field studies, controlled laboratory experiments, and previously unpublished observations, that govern the epizootiology of VHS in Pacific herring. A thorough understanding of these principles provides the basis for identifying risk factors that predispose certain marine fish populations to VHS epizootics, including the lack of population resistance, presence of chronic viral carriers in a population, copious viral shedding by infected individuals, cool water temperatures, limited water circulation patterns, and gregarious host behavioral patterns. Further, these principles are used to define the epizootiological stages of the disease in Pacific herring, including the susceptible (where susceptible individuals predominate a school or subpopulation), enzootic (where infection prevalence and intensity are often below the limits of reasonable laboratory detection), disease amplification (where infection prevalence and intensity increase rapidly), outbreak (often accompanied by host mortalities with high virus loads and active shedding), recovery (in which the mortality rate and virus load decline owing to an active host immune response), and refractory stages (characterized by little or no susceptibility and where viral clearance occurs in most VHS survivors). In addition to providing a foundation for quantitatively assessing the potential risks of future VHS epizootics in Pacific herring, these principles provide insights into the epizootiology of VHS in other fish communities where susceptible species exist.

A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment

BackgroundSource identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens.MethodsAs a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study.ResultsHotspots – areas most likely to contain the actual source – were identified at early outbreak stages, based on the earliest 2–10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300–1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large.ConclusionsOur model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission.

Bark beetle effects on fuel profiles across a range of stand structures in Douglas‐fir forests of Greater Yellowstone

Consequences of bark beetle outbreaks for forest wildfire potential are receiving heightened attention, but little research has considered ecosystems with mixed-severity fire regimes. Such forests are widespread, variable in stand structure, and often fuel limited, suggesting that beetle outbreaks could substantially alter fire potentials. We studied canopy and surface fuels in interior Douglas-fir (Pseudotsuga menziesii v. glauca) forests in Greater Yellowstone, Wyoming, USA, to determine how fuel characteristics varied with time since outbreak of the Douglas-fir beetle (Dendroctonus pseudotsugae). We sampled five stands in each of four outbreak stages, validated for pre-outbreak similarity: green (undisturbed), red (1-3 yr), gray (4-14 yr), and silver (25-30 yr). General linear models were used to compare variation in fuel profiles associated with outbreak to variation associated with the range of stand structures (dense mesic forest to open xeric parkland) characteristic of interior Douglas-fir forest. Beetle outbreak killed 38-83% of basal area within stands, generating a mix of live trees and snags over several years. Canopy fuel load and bulk density began declining in the red stage via needle drop and decreased by approximately 50% by the silver stage. The dead portion of available canopy fuels peaked in the red stage at 41%. After accounting for background variation, there was little effect of beetle outbreak on surface fuels, with differences mainly in herbaceous biomass (50% greater in red stands) and coarse woody fuels (doubled in silver stands). Within-stand spatial heterogeneity of fuels increased with time since outbreak, and surface-to-crown continuity decreased and remained low because of slow/sparse regeneration. Collectively, results suggest reduced fire potentials in post-outbreak stands, particularly for crown fire after the red stage, although abundant coarse fuels in silver stands may increase burn residence time and heat release. Outbreak effects on fuels were comparable to background variation in stand structure. The net effect of beetle outbreak was to shift the structure of mnsic closed-canopy stands toward that of parklands, and to shift xeric parklands toward very sparse woodlands. This study highlights the importance of evaluating outbreak effects in the context of the wide structural variation inherent to many forest types in the absence of beetle disturbance.

Epidemics with general generation interval distributions

We study the spread of susceptible-infected-recovered (SIR) infectious diseases where an individual's infectiousness and probability of recovery depend on his/her “age” of infection. We focus first on early outbreak stages when stochastic effects dominate and show that epidemics tend to happen faster than deterministic calculations predict. If an outbreak is sufficiently large, stochastic effects are negligible and we modify the standard ordinary differential equation (ODE) model to accommodate age-of-infection effects. We avoid the use of partial differential equations which typically appear in related models. We introduce a “memoryless” ODE system which approximates the true solutions. Finally, we analyze the transition from the stochastic to the deterministic phase.