A scaling investigation of pattern in the spread of COVID-19: universality in real data and a predictive analytical description

We analyse the spread of COVID-19, a disease caused by a novel coronavirus, in various countries by proposing a model that exploits the scaling and other important concepts of statistical physics. Quite expectedly, for each of the considered countries, we observe that the spread at early times occurs exponentially fast. We show how the countries can be classified into groups, like universality classes in the literature of phase transitions, based on the rates of infections during late times. This method brings a new angle to the understanding of disease spread and is useful in obtaining a country-wise comparative picture of the effectiveness of lockdown-like social measures. Strong similarity, during both natural and lockdown periods, emerges in the spreads within countries having varying geographical locations, climatic conditions, population densities and economic parameters. We derive accurate mathematical forms for the corresponding scaling functions and show how the model can be used as a predictive tool, with instruction even for future waves, and, thus, as a guide for optimizing social measures and medical facilities. The model is expected to be of general relevance in the studies of epidemics.

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Spread of COVID-19: Investigation of universal features in real data

10.1101/2020.05.20.20107797 ◽

2020 ◽

Author(s):

Subir K. Das

Keyword(s):

Disease Model ◽

Finite Size ◽

Real Data ◽

Late Time ◽

Time Behavior ◽

General Applicability ◽

Number Of Patients ◽

Universality Classes ◽

Novel Coronavirus ◽

Natural Way

We present results on the existence of various common patterns in the growth of the total number of patients affected by COVID-19, a disease acquired through infection by a novel coronavirus, in different countries. For this purpose we propose a scaling model that can have general applicability in the understanding of real data of epidemics. This is analogous to the finite-size scaling, a technique used in the literature of phase transition to identify universality classes. In the disease model, the size of a system is proportional to the volume of the population, within a geographical region, that have been infected at the death of the epidemic or are eventually going to be infected when an epidemic ends. Outcome of our study, for COVID-19, via application of this model, suggests that in most of the countries, after the ‘onset’ of spread, the growths are described by rapid exponential function, for significantly long periods. In addition to accurately identifying this superuniversal feature, we point out that the model is helpful in grouping countries into universality classes, based on the late time behavior, characterized by physical distancing practices, in a natural way. This feature of the model can provide direct comparative understanding of the effectiveness of lockdown-like social measures adopted in different places.

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Potential of Selected Indian Herbs for COVID-19

Current Traditional Medicine ◽

10.2174/2215083806999201125122620 ◽

2020 ◽

Vol 06 ◽

Author(s):

Ravindra Verma ◽

Vaibhav Misra ◽

Dileep Tiwari ◽

Prakash S. Bisen

Keyword(s):

Risk Factors ◽

Withania Somnifera ◽

Epidemiologic Studies ◽

Climatic Conditions ◽

Tinospora Cordifolia ◽

Environmental Threats ◽

Scientific Databases ◽

First Case ◽

Ocimum Tenuiflorum ◽

Novel Coronavirus

Introduction: Many environmental risk factors are associated with some form of chronic inflammation. The spread of COVID-19 across the world has impacted every one of us. The first case of coronavirus was reported on 30 January 2020 in India originating from China. Study Area: India has a tremendous capacity to deal with the coronavirus outbreak because of its high immunity and climatic conditions. Maintaining social distancing and hand washing is not a sufficient step for preventing COVID-19. Indian system of traditional medicine has a potential worth to enhance immunity, which can resist a novel coronavirus. Material & Methods: A detailed study was carried out by analyzing national and international scientific databases (PubMed, SciFinder, ScienceDirect, Scopus, and Web of Science, Mendeley), thesis, and recognized books. Only Indian herbs with high immunity resistant power were analyzed. Epidemiologic studies with information on COVID-19 risk factors and precautions also considered for study purposes. Results: Some herbs like Ocimum tenuiflorum (Tulsi), Glycyrrhiza glabra (Liquorice), Curcuma domestica Vahl (Turmeric), Tinospora cordifolia (Giloy), Withania somnifera (Ashwagandha), Cinnamon (Dalchini), Shoot of Triticumaestivum Linn. (Wheatgrass), Andrographis paniculata (Kalmegh), can help in boosting immunity for COVID-19 disease. Discussion: Despite the shreds of evidence for the efficacy of these herbs in treating coronavirus induced infections; the proper dose with ideal timing for such interventions needs to verify in clinical trials. Researchers must have to take the privilege to explore the potential of herbs to reduce such epidemics of environmental threats.

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Modeling and Forecasting of COVID-19 Spreading by Delayed Stochastic Differential Equations

Axioms ◽

10.3390/axioms10010018 ◽

2021 ◽

Vol 10 (1) ◽

pp. 18

Author(s):

Marouane Mahrouf ◽

Adnane Boukhouima ◽

Houssine Zine ◽

El Mehdi Lotfi ◽

Delfim F. M. Torres ◽

...

Keyword(s):

Real Data ◽

Economic Damage ◽

Population Parameter ◽

The Novel ◽

First Case ◽

Epidemiological Trend ◽

Modeling And Forecasting ◽

Novel Coronavirus ◽

Parameter Values ◽

Stochastic Time

The novel coronavirus disease (COVID-19) pneumonia has posed a great threat to the world recent months by causing many deaths and enormous economic damage worldwide. The first case of COVID-19 in Morocco was reported on 2 March 2020, and the number of reported cases has increased day by day. In this work, we extend the well-known SIR compartmental model to deterministic and stochastic time-delayed models in order to predict the epidemiological trend of COVID-19 in Morocco and to assess the potential role of multiple preventive measures and strategies imposed by Moroccan authorities. The main features of the work include the well-posedness of the models and conditions under which the COVID-19 may become extinct or persist in the population. Parameter values have been estimated from real data and numerical simulations are presented for forecasting the COVID-19 spreading as well as verification of theoretical results.

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First Report of Choke Disease Caused by Epichloë typhina on Orchardgrass (Dactylis glomerata) in China

Plant Disease ◽

10.1094/pdis-93-6-0673b ◽

2009 ◽

Vol 93 (6) ◽

pp. 673-673 ◽

Cited By ~ 2

Author(s):

C. J. Li ◽

Z. F. Wang ◽

N. Chen ◽

Z. B. Nan

Keyword(s):

Dactylis Glomerata ◽

The United States ◽

Leaf Sheath ◽

Climatic Conditions ◽

Disease Spread ◽

Willamette Valley ◽

First Report ◽

Epichloë Typhina ◽

Pure Cultures ◽

Choke Disease

Orchardgrass or cocksfoot (Dactylis glomerata L.) has been widely cultivated as a forage crop in many provinces of China (1). It is also a native perennial forage grass, which grows at the edge of forests, shrubs, and mountainous grasslands in Xinjiang and Sichuan (2). In September of 2007, signs of choke disease on orchardgrass were observed in a native grassland under birch woodland near Altai City, Xinjiang, China. Stromata, which formed on culms of diseased grass, enclosing the inflorescence and leaf sheath, were 4.5 to 5.5 mm long, smooth or wrinkled, white and later becoming yellowish or yellow, tuberculate, dry, and covered with perithecia. Inflorescences surrounded by fungal stromata were choked and failed to mature, thus restricting seed production. Pure cultures were obtained by directly scraping stromata from the surface and incubating it on antibiotic potato dextrose agar (PDA). The colonies were cottony, white on the upper surface, and white to yellow on the reverse. The growth rate was 13 to 21 mm per week at 25°C on PDA. Conidia were hyaline, lunate to reniform, and measured 4.1 ± 0.5 × 2.2 ± 0.5 μm. They accumulated in small globose heads at the tips of conidiogenous cells and were produced singly on conidiophores of 13 to 33 μm long and 2.7 to 4.1 μm wide at the base. Internal transcribed spacer (ITS) sequence by BLAST search had 99% similarity with an Epichloë typhina isolate of orchardgrass in Spain (GenBank Accession No. AM262420.1). Cultural characteristics, microscopic examination, and phylogenetic analysis showed that this choke disease on D. glomerata was caused by the fungus E. typhina (Pers.) Tul. & C. Tul. as described by White (4). To our knowledge, this is the first report of E. typhina causing choke disease on orchardgrass in China. The pathogen has been identified in France, England, Germany, Sweden, Switzerland, and the United States (3,4) with the same symptoms as those reported here. In 1997, choke disease was found in 70% of the fields in the Willamette Valley of Oregon, with disease incidences ranging from 0.05 to 28%. It was predicted to increase and spread under the prevailing climatic conditions (3). This new disease report is to provide observational and diagnostic information to help with recognition and prevention of disease spread in orchardgrass cultivation regions of China. References: (1) X. R. Chao et al. Shandong Agric. Sci. 1:7, 2005. (2) S. X. Jia, ed. China Forage Plant Flora. China Agriculture Press, Beijing, 1987. (3) W. F. Pfender and S. C. Alderman. Plant Dis. 83:754, 1999. (4) J. W. White. Mycologia 85:444, 1993.

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Formulation and Solution of an Inverse Reliability Problem to Simulate the Dynamic Behavior of COVID-19 Pandemic

Trends in Computational and Applied Mathematics ◽

10.5540/tcam.2021.022.01.00091 ◽

2021 ◽

Vol 22 (1) ◽

pp. 91-107

Author(s):

F. S. Lobato ◽

G. M. Platt ◽

G. B. Libotte ◽

A. J. Silva Neto

Keyword(s):

Dynamic Behavior ◽

Differential Evolution Algorithm ◽

Iteration Process ◽

Real Data ◽

The Novel ◽

Design Variables ◽

Different Types ◽

Evolution Algorithm ◽

Novel Coronavirus ◽

Optimization Loop

Different types of mathematical models have been used to predict the dynamic behavior of the novel coronavirus (COVID-19). Many of them involve the formulation and solution of inverse problems. This kind of problem is generally carried out by considering the model, the vector of design variables, and system parameters as deterministic values. In this contribution, a methodology based on a double loop iteration process and devoted to evaluate the influence of uncertainties on inverse problem is evaluated. The inner optimization loop is used to find the solution associated with the highest probability value, and the outer loop is the regular optimization loop used to determine the vector of design variables. For this task, we use an inverse reliability approach and Differential Evolution algorithm. For illustration purposes, the proposed methodology is applied to estimate the parameters of SIRD (Susceptible-Infectious-Recovery-Dead) model associated with dynamic behavior of COVID-19 pandemic considering real data from China's epidemic and uncertainties in the basic reproduction number (R0). The obtained results demonstrate, as expected, that the increase of reliability implies the increase of the objective function value.

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Oral aspects related to SARS-CoV-2

10.30574/ijbpsa.2021.1.2.0038 ◽

2021 ◽

Vol 1 (2) ◽

pp. 153-160

Author(s):

Akram Belmehdi ◽

Saliha Chbicheb

Keyword(s):

Oral Cavity ◽

Diagnostic Value ◽

Disease Spread ◽

Health Crisis ◽

Rapid Spread ◽

The World ◽

Spanish Flu ◽

Taste Disorders ◽

Novel Coronavirus ◽

The Relationship

The pandemic of coronavirus disease (COVID-19) is considered as the biggest global health crisis for the world since the Spanish flu, also known as the 1918 flu pandemic. Driven by the SARS-CoV-2 novel coronavirus infection, the rapid spread of this disease and the related pneumonia COVID-19 are a challenge for healthcare systems in over the world, and it is a constantly evolving situation with new symptoms and prognostic factors. SARS-CoV-2 has lately been detected in infected patient’s oral cavity; the COVID-19 outbreak is an alert that all dental and other health professionals must be vigilant in defending against the infectious disease spread. This review summarizes an update from current medical literature about the relationship between oral cavity and coronavirus disease by presenting some oral aspects which was detected in infected patients such as the oral lesions related to this virus and its therapeutic protocol, taste disorders and also the diagnostic value of saliva for SARS-CoV-2.

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Predicting Ambulance Diverson

International Journal of Information Systems in the Service Sector ◽

10.4018/jisss.2010093001 ◽

2010 ◽

Vol 2 (1) ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Abey Kuruvilla ◽

Suraj M. Alexander

Keyword(s):

Kansas City ◽

Real Data ◽

The United States ◽

Time Of Day ◽

Multinomial Regression ◽

Predictive Tool ◽

Persistent Problem ◽

Cascading Effect ◽

The One ◽

Predictive Instrument

The high utilization level of emergency departments in hospitals across the United States has resulted in the serious and persistent problem of ambulance diversion. This problem is magnified by the cascading effect it has on neighboring hospitals, delays in emergency care, and the potential for patients’ clinical deterioration. We provide a predictive tool that would give advance warning to hospitals of the impending likelihood of diversion. We hope that with a predictive instrument, such as the one described in this article, hospitals can take preventive or mitigating actions. The proposed model, which uses logistic and multinomial regression, is evaluated using real data from the Emergency Management System (EM Systems) and 911 call data from Firstwatch® for the Metropolitan Ambulance Services Trust (MAST) of Kansas City, Missouri. The information in these systems that was significant in predicting diversion includes recent 911 calls, season, day of the week, and time of day. The model illustrates the feasibility of predicting the probability of impending diversion using available information. We strongly recommend that other locations, nationwide and abroad, develop and use similar models for predicting diversion.

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Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/9214159 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Fran Sérgio Lobato ◽

Gustavo Barbosa Libotte ◽

Gustavo Mendes Platt

Keyword(s):

Multiobjective Optimization ◽

Optimization Problem ◽

Search Algorithm ◽

Real Data ◽

The Novel ◽

Estimation Process ◽

Design Variables ◽

Novel Coronavirus ◽

Stochastic Fractal Search Algorithm ◽

Stochastic Fractal Search

Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.

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Citation Network Analysis of the Novel Coronavirus Disease 2019 (COVID-19)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17207690 ◽

2020 ◽

Vol 17 (20) ◽

pp. 7690 ◽

Cited By ~ 1

Author(s):

Clara Martinez-Perez ◽

Cristina Alvarez-Peregrina ◽

Cesar Villa-Collar ◽

Miguel Ángel Sánchez-Tena

Keyword(s):

Web Of Science ◽

Citation Network ◽

The United States ◽

Disease Spread ◽

The Novel ◽

Research Areas ◽

Citation Network Analysis ◽

Number Of Publications ◽

Novel Coronavirus ◽

The Relationship

Background: The first outbreaks of the new coronavirus disease, named COVID-19, occurred at the end of December 2019. This disease spread quickly around the world, with the United States, Brazil and Mexico being the countries the most severely affected. This study aims to analyze the relationship between different publications and their authors through citation networks, as well as to identify the research areas and determine which publication has been the most cited. Methods: The search for publications was carried out through the Web of Science database using terms such as “COVID-19” and “SARS-CoV-2” for the period between January and July 2020. The Citation Network Explorer software was used for publication analysis. Results: A total of 14,335 publications were found with 42,374 citations generated in the network, with June being the month with the largest number of publications. The most cited publication was “Clinical Characteristics of Coronavirus Disease 2019 in China” by Guan et al., published in April 2020. Nine groups comprising different research areas in this field, including clinical course, psychology, treatment and epidemiology, were found using the clustering functionality. Conclusions: The citation network offers an objective and comprehensive analysis of the main papers on COVID-19 and SARS-CoV-2.

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Extracting Data from Disparate Sources for Agent-Based Disease Spread Models

Epidemiology Research International ◽

10.1155/2012/716072 ◽

2012 ◽

Vol 2012 ◽

pp. 1-18 ◽

Cited By ~ 3

Author(s):

M. Laskowski ◽

B. C. P. Demianyk ◽

J. Benavides ◽

M. R. Friesen ◽

R. D. McLeod ◽

...

Keyword(s):

Demographic Data ◽

Population Level ◽

Real Data ◽

Smartphone Application ◽

Geographic Area ◽

Disease Spread ◽

Telecommunication Service ◽

Interaction Patterns ◽

Agent Based ◽

Contact Data

This paper presents a review and evaluation of real data sources relative to their role and applicability in an agent-based model (ABM) simulating respiratory infection spread a large geographic area. The ABM is a spatial-temporal model inclusive of behavior and interaction patterns between individual agents. The agent behaviours in the model (movements and interactions) are fed by census/demographic data, integrated with real data from a telecommunication service provider (cellular records), traffic survey data, as well as person-person contact data obtained via a custom 3G smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion and the role of data in calibrating and validating ABMs. The data become real-world inputs into susceptible-exposed-infected-recovered (SEIR) disease spread models and their variants, thereby building credible and nonintrusive models to qualitatively model public health interventions at the population level.

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