Systematic description of COVID-19 pandemic using exact SIR solutions and Gumbel distributions.

An epidemiological study is carried out in several countries analyzing the first wave of the COVID-19 pandemic using the SIR model and Gumbel distribution. The equations of the SIR model are solved exactly using the proper time as a parameter. The physical time is obtained by integration of the inverse of the infected function over proper time. Some properties of the solutions of the SIR model are studied such as time scaling and the asymmetry, which allows to obtain the basic reproduction number from the data. Approximations to the solutions of the SIR model are studied using Gumbel distributions by least squares fit or by adjusting the maximum of the infected function. Finally, the parameters of the SIR model and the Gumbel function are extracted from the death data and compared for the different countries. It is found that ten of the selected countries are very well described by the solutions of the SIR model, with a basic reproduction number between 3 and 8.

SIR model for the spread of COVID-19: A case study

In this article, we study the spread pattern of the epidemic of COVID-19 disease from the point of view of mathematical modeling. Considering that this virus follows the basic rules of epidemic disease transmission, we use the SIR model to show the spread process of this disease in Iran. Then we estimate the primary reproduction number (R0) of COVID-19 in Iran by matching an epidemic model with the data of reported cases. © 2022

Analysis, predicting, and controlling the COVID-19 pandemic in Iraq through SIR model

Using the standard SIR model with three unknown biological parameters, the COVID-19 pandemic in Iraq has been studied. The least squares method and real data on confirmed infections, deaths, and recoveries over a long time (455 days) were used to estimate these parameters. In this regards, first, we find the basic reproductive number R0 is 0.9422661124 which indicates and predicts that the COVID-19 pandemic in Iraq will gradually subside until it is eradicated permanently with time. Additionally, we develop an optimal vaccination strategy with the goal of reducing COVID-19 infections and preventing their spread in Iraq, thereby putting a clear picture of control this pandemic.

Testing our way out of pandemics

Objectives: In the face of pandemics, a viable global strategy, beyond relying on the fast discovery of a vaccine or a cure, is needed. We study quantitatively the feasibility and effectiveness of mass testing to contain an epidemic. We also explore the implications of various smart testing strategies to decrease the needed testing rates. Methods: We use a modified SIR model with testing and extend the model to incorporate mobility patterns in a densely populated area. Results: For a pandemic like COVID-19, model simulations show that the rate of testing needed to squash the curve within a month varies between 20–30 percent of the population randomly tested daily to less than 5 percent, combining periodic and group testing. We also show that mobility restrictions can enhance the efficacy of testing. Scale could be as important as accuracy in testing, implying that an epidemiological rather than clinical approach for the approval of tests is needed. The estimated cost of testing is dwarfed by its return, mitigating the economic fallout of the pandemic. Conclusions: Without a vaccine or a cure, mass testing is the only viable and less costly strategy to indefinitely "squash the curve” while allowing for major economic activities to resume. Planning and executing a testing strategy is necessary and urgent as an insurance policy against future pandemics. It should be considered as an investment to build a testing and isolation infrastructure, which should be maintained as part of the pandemic preparedness. © 2022

Modeling of Computer Virus Propagation with Fuzzy Parameters

Typically, a computer has infectivity as soon as it is infected. It is a reality that no antivirus programming can identify and eliminate all kinds of viruses, suggesting that infections would persevere on the Internet. To understand the dynamics of the virus propagation in a better way, a computer virus spread model with fuzzy parameters is presented in this work. It is assumed that all infected computers do not have the same contribution to the virus transmission process and each computer has a different degree of infectivity, which depends on the quantity of virus. Considering this, the parameters beta and gamma being functions of the computer virus load, are considered fuzzy numbers. Using fuzzy theory helps us understand the spread of computer viruses more realistically as these parameters have fixed values in classical models. The essential features of the model, like reproduction number and equilibrium analysis, are discussed in fuzzy senses. Moreover, with fuzziness, two numerical methods, the forward Euler technique, and a nonstandard finite difference (NSFD) scheme, respectively, are developed and analyzed. In the evidence of the numerical simulations, the proposed NSFD method preserves the main features of the dynamic system. It can be considered a reliable tool to predict such types of solutions.

Predicting COVID-19 Pandemic Endpoint in Some Sub-Saharan African and European Countries

In this study, a novel modified SIR model is presented with two control measures to predict the endpoint of COVID-19, in top three sub-Saharan African countries (South Africa, Ethiopia, and Kenya) including Ghana and top four European countries (France, Germany, UK, and Italy). The reproduction number's sensitivity indices with regard to the model parameters were explicitly derived and then numerically evaluated. Numerical simulations of the suggested optimal control schemes in general showed a continuous result of decline at different anticipated extinction timelines. Another interesting observation was that in the simulation of sub-Saharan African dynamics, it was observed that the use of personal protective equipment was more effective than the use of vaccination, whereas in Europe, the use of vaccination was more effective than personal protective equipment. From the simulations, the conclusion is that COVID-19 will end before the 3rd year in Ghana, before the 6th year in Kenya, and before the 9th year in both Ethiopia and South Africa. © 2023 Saviour Worlanyo Akuamoah et al.

COVID-19, traffic demand, and activity restriction in China: A national assessment.

The global COVID pandemic of 2020, affected travel patterns across the world. The level of impact was influenced not only by the virus itself, but also by the nature, extent, and duration of governmental restriction on commerce and personal activity to limit its spread. This paper focuses on the interaction between COVID-19 transmission and traffic volume and further explores the impact of traffic control policies on the interaction. Roadway traffic volume was used to quantify and assess the Chinese response to the pandemic; specifically, the relationship between government restrictions, travel activity, and COVID-19 progression across 29 provinces. Space and time distributions of traffic volume across China during the first half of 2020, were used to quantity the response and recovery of travel during the critical initial onset period of the virus. Most revealing of these trends were the impact of the Chinese restriction policies on both travel and the virus as well as the relationship of traffic trends during the closure period with the speed and extent of the recovery "bounce" across individual provinces based on location, economic activity, and restriction policy. These suggest that the most significant and rapid declines in traffic volume during the restriction period resulted in the most pronounced returns to normal (or more) demand levels. Based on these trends a Susceptible Infection Recovery model was created to simulate a range of outbreak and restriction policies to examine the relationship between COVID-19 spread and traffic volume in China.

Risk assessment of imported COVID-19 in China: A modelling study in Sichuan Province.

The importation of COVID-19 cases in China is due to the returning of Chinese citizens abroad, where the majority of cases stand. This study aimed to evaluate the risk of importing COVID-19 into the Sichuan Province of China and conduct a short-term risk prediction assessment and analysis. Data on COVID-19 cases in each country and Sichuan were collected, as well as visitors to Sichuan, population, area, and medical resources in each city in Sichuan province. According to different control strategies of entry aviation and quarantine control, we built models of epidemic transmission to estimate the risk for imported COVID-19 cases in 21 cities of Sichuan. Within 140 days of the policy change's implementation, the number of susceptible, infected, and recovered people in all cities followed the same pattern over time: (1) the number of susceptible people declined slowly at first, then accelerated to reach a stable value; (2) the number of infections gradually increased to a peak, then decreased; and (3) the number of recovered patients gradually increased to a stable value. Under the four different scenarios, there were no significant differences between the risk peaks because the social distance did not change. However, the peak time would be delayed due to the implementation of flight control and nucleic acid detection measures. The improvement of foreign epidemics (reduction of attenuation factors) all delayed the arrival of the peak risk value in Chengdu by about 20 days; however, the size of the peak value did not change significantly. The improvement of nucleic acid detection accuracy delayed the arrival of the peak risk value in Chengdu, but the size of the peak value did not change significantly. Therefore, flight control and the improvement of nucleic acid detection accuracy and overseas epidemic situations have positively affected the prevention and control of the epidemic in Sichuan.

Effect of Infection Hubs in District-Based Network Epidemic Spread Model †

A network model of epidemic spread accounting for inhomogeneous population district division is investigated. Motivated by the COVID-19 pandemic, we analyze the effects of infection development in the area, for example, of a city divided into several population districts. The districts are characterized by a certain intensity of contact inside and with inter-district communication that can be generally controlled by the authorities. Specifically, we consider the effect of the central district, which is the hub of infection. We investigate how the interaction strength influences the city's level of epidemic development. We obtained that the final infected amount in the district rises with an increasing degree of connection with the hub. However, the model situation was not limited by the first outbreak but included the subsequent waves of infection. We obtained that the appearance and disappearance of subsequent waves of infection essentially depended on the intensity of communication with the infected hub. Our results suggest the mechanism where stricter communication policy can negatively affect the subsequent infection waves. © 2023 by the authors.

Prediction of the Epidemic Trend of Novel Coronavirus Pneumonia Based on SIR Model

This paper collects real-time epidemic data released by the World Health Organization and various Internet authorities, predict the development of the epidemic through the classical model (SIR model) in the field of communication disease, bring historical data into the model, verify the parameters of the model and establish a new model, compare multiple sets of data, obtain the system that is closest to the real data, and speculate on the development direction and turning point of the subsequent NEW CROWN epidemic. The use of scientific and technical means to reason and analyze the overall situation of the new crown epidemic situation provides a solid backing for the prevention and control of the epidemic. © 2022 IEEE.

Asymptotic analysis of the SIR model and the Gompertz distribution

The SIR (Susceptible–Infected–Removed) is one of the simplest models for epidemic outbreaks. The present paper derives a novel, simple, analytical asymptotic solution for the I-variable, which is valid on the entire real line. Connections with the Gompertz and Gumbel distributions are also demonstrated. The approach is applied to the ongoing coronavirus disease 2019 (COVID-19) pandemic in four European countries — Belgium, Italy, Sweden, and Bulgaria. The reported raw incidence data from the outbreaks in 2020–2021 have been fitted using constrained least squares. It is demonstrated that the asymptotic solution can be used successfully for parametric estimation either in stand-alone mode or as a preliminary step in the parametric estimation using numerical inversion of the exact parametric solution. [ FROM AUTHOR]

Spatio-temporal evolution of the COVID-19 across African countries.

The aim of this study is to make a comparative study on the reproduction number R 0 computed at the beginning of each wave for African countries and to understand the reasons for the disparities between them. The study covers the two first years of the COVID-19 pandemic and for 30 African countries. It links pandemic variables, reproduction number R 0, demographic variable, median age of the population, economic variables, GDP and CHE per capita, and climatic variables, mean temperature at the beginning of each waves. The results show that the diffusion of COVID-19 in Africa was heterogeneous even between geographical proximal countries. The difference of the basic reproduction number R 0 values is very large between countries and is significantly correlated with economic and climatic variables GDP and temperature and to a less extent with the mean age of the population.

Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".