Some fractal thoughts about the COVID-19 infection outbreak

Role cognition of assigned nurses supporting Hubei Province in the fight against COVID-19 in China: a hermeneutic phenomenological study

Aims

During the COVID-19 epidemic, nurses played a crucial role in clinical treatment. As a special group, front-line nurses, especially those assigned to support Hubei Province in the fight against COVID-19 between February and April 2020, brought diverse experiences from different provinces in China in taking care of COVID-19 patients and role cognition. Therefore, our purpose is to explore the real coping experience and role cognition of front-line nurses during the novel coronavirus outbreak to provide relevant experience references for society and managers in the face of such major public health emergencies in the future.

Design

This qualitative study was performed using the phenomenological hermeneutics method.

Method

This is a qualitative phenomenological study. Semi-structured in-depth interviews were used to collect data. The interviewees were 53 front-line nurses who assisted and supported the fight against COVID-19 in Hubei Province during the COVID-19 epidemic. Data were collected through individual online and telephone interviews using a semi-structured interview during March 2020. The COREQ guidance was used to report this study.

Results

The findings revealed that front-line nurses assisting in the fight against COVID-19 developed a context-specific role cognition of their work and contribution to society. The qualitative analysis of the data revealed 15 sub-categories and 5 main categories. These five themes represented the different roles identified by nurses. The roles included expectations, conflicts, adaptation, emotions, and flow of blessing. Belief in getting better, a sense of honor, and training could help them to reduce feelings of conflict in this role and adapt more quickly.

Discussion

This article discusses the real coping experience and role cognition of front-line nurses during the novel coronavirus epidemic. It provides relevant experience references for society and managers to face similar major public health emergencies in the future. This study makes a significant contribution to the literature because it demonstrates how non-local nurses sent to Hubei to work perceived their roles as part of a larger narrative of patriotism, duty, solidarity, and hope.

Mental health impacts of nurses caring for patients with COVID-19 in Peru: Fear of contagion, generalized anxiety, and physical-cognitive fatigue

The health crisis caused by COVID-19 has resulted in the physical and emotional deterioration of health personnel, especially nurses, whose emotional state is affected by the high risk of contagion, the high demands of health services, and the exhausting working hours. The objective of this research was to determine the relationship between fear, anxiety, and fatigue of nurses caring for patients with COVID-19 in a second level public hospital in Peru. This study presents a quantitative approach and correlational level, cross-sectional, and non-experimental design. The sample consisted of 145 nurses who attended patients with COVID-19 in health care areas. The results show a significant relationship between fear of contagion and physical-cognitive fatigue (p < 0.001; r = 317) and a significant relationship between generalized anxiety and physical-cognitive fatigue (p < 0.001; r = 480). It is concluded that in this context, both fear of contagion and generalized anxiety are related to physical-cognitive fatigue.

Seascape origin of Richards growth

First proposed as an empirical rule over half a century ago, the Richards growth equation has been frequently invoked in population modeling and pandemic forecasting. Central to this model is the advent of a fractional exponent γ, typically fitted to the data. While various motivations for this nonanalytical form have been proposed, it is still considered foremost an empirical fitting procedure. Here, we find that Richards-like growth laws emerge naturally from generic analytical growth rules in a distributed population, upon inclusion of (i) migration (spatial diffusion) among different locales, and (ii) stochasticity in the growth rate, also known as "seascape noise." The latter leads to a wide (power law) distribution in local population number that, while smoothened through the former, can still result in a fractional growth law for the overall population. This justification of the Richards growth law thus provides a testable connection to the distribution of constituents of the population.

Agent-based analysis of contagion events according to sourcing locations

The first human infected with the Covid-19 virus was traced to a seafood market in Wuhan, China. Research shows that there are comparable types of viruses found in different and mutually distant areas. This raises several questions: what if the virus originated in another location? How will future waves of epidemics behave if they originate from different locations with a smaller/larger population than Wuhan? To explore these questions, we implement an agent-based model within fractal cities. Cities radiate gravitational social attraction based on their Zipfian population. The probability and predictability of contagion events are analyzed by examining fractal dimensions and lacunarity. Results show that weak gravitational forces of small locations help dissipate infections across country quicker if the pathogen had originated from that location. Gravitational forces of large cities help contain infections within them if they are the starting locations for the pathogen. Greater connectedness and symmetry allow for a more predictable epidemic outcome since there are no obstructions to spreading. To test our hypothesis, we implement datasets from two countries, Sierra Leone and Liberia, and two diseases, Ebola and Covid-19, and obtain the same results.

Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Simple Summary

A modified SIR model was applied to provide COVID-19 pandemic analysis and predictions for Gulf Cooperation Council countries, as well as representative countries in Europe and New York City. We estimated reported, infected, and unreported cases from cumulative reported cases and simulated data. We also estimated the basic reproduction rates at different phases of the pandemic. Outputs show that the modified SIR model fits very well with the outcome of the COVID-19 pandemic for the studied countries and could be generalized to other countries. The model prediction emphasizes the value of significant interventions in public health in regulating the epidemic taking into account that a constant fraction of the infected cases remain unreported during the pandemic. We report and analyze the effectiveness of preventive/intervention measures applied to the overall community to curb the severity of the pandemic. Our model could be used to support public health authorities with respect to post-outbreak reopening decisions, highlighting effective measures that need to be maintained, eased, or implemented to support safe reopening strategies in the GCC countries.

Abstract

Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19

COVID-19 is a novel coronavirus affecting all the world since December last year. Up to date, the spread of the outbreak continues to complicate our lives, and therefore, several research efforts from many scientific areas are proposed. Among them, mathematical models are an excellent way to understand and predict the epidemic outbreaks evolution to some extent. Due to the COVID-19 may be modeled as a non-Markovian process that follows power-law scaling features, we present a fractional-order SIRD (Susceptible-Infected-Recovered-Dead) model based on the Caputo derivative for incorporating the memory effects (long and short) in the outbreak progress. Additionally, we analyze the experimental time series of 23 countries using fractal formalism. Like previous works, we identify that the COVID-19 evolution shows various power-law exponents (no just a single one) and share some universality among geographical regions. Hence, we incorporate numerous memory indexes in the proposed model, i.e., distinct fractional-orders defined by a time-dependent function that permits us to set specific memory contributions during the evolution. This allows controlling the memory effects of more early states, e.g., before and after a quarantine decree, which could be less relevant than the contribution of more recent ones on the current state of the SIRD system. We also prove our model with Italy's real data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.

A stretched logistic equation for pandemic spreading

In this brief work we present a novel approach to the logistic dynamics of populations and epidemic spreading that can take into account of the complex nature of such a process in several real situations, where due to different agents the dynamics is no longer characterized by a single characteristic timescale, but conversely by a distribution of time scales, rendered via a time-dependent growth rate. In detail, a differential equation containing a power-law time dependent growth rate is proposed, whose solution, named Stretched Logistic Function, provides a modified version of the usual logistic function. The model equation is inspired by and applied to the recent spreading on COVID-19 disease in Italy, showing how the real dynamics of infection spreading is characterized by a time dependent dynamics. A speculative discussion of the Stretched Logistic Function in relation to diffusion processes is attempted.

On the temporal spreading of the SARS-CoV-2

The behaviour of SARS-CoV-2 virus is certainly one of the most challenging in contemporary world. Although the mathematical modelling of the virus has made relevant contributions, the unpredictable behaviour of the virus is still not fully understood. To identify some aspects of the virus elusive behaviour, we focused on the temporal characteristics of its course. We have analysed the latency trends the virus has realized worldwide, the outbreak of the hot spots, and the decreasing trends of the pandemic. We found that the spatio-temporal pandemic dynamics shows a complex behaviour. As with physical systems, these changes in the pandemic's course, which we have called transitional stages of contagion, highlight shared characteristics in many countries. The main results of this work is that the pandemic progression rhythms have been clearly identified for each country, providing the processes and the stages at which the virus develops, thus giving important information on the activation of containment and control measures.