Modelling and assessing the effects of medical resources on transmission of novel coronavirus (COVID-19) in Wuhan, China

Clinical characteristics and factors for serious outcomes among outpatients infected with the Omicron subvariant BF.7

To evaluate clinical characteristics and identify risk factors associated with severe outcomes in outpatients infected with the Omicron subvariant BF.7, data were collected from outpatients diagnosed with Corona Virus Disease 2019 from December 19, 2022 to January 5, 2023. Clinical characteristics were analyzed using descriptive statistics. Univariate and multivariate logistic regression analyses were conducted to identify factors associated with serious outcomes. Variables with a p < 0.10 in the univariate analysis were included in the multivariate model. Our study analyzed 770 patients, of whom 380 (49.4%) were male, with a median age of 59. The most common symptoms reported were cough (71.2%), fever (64.7%), and sore throat (37.7%). Fever lasted an average of 5.93 ± 3.37 days for the general population and 10.64 ± 7.12 days for impaired-immunity patients. Most cases were mild (68.7%), followed by moderate (27.1%). Severe cases accounted for 2.2%, with 0.5% critically ill. Serious outcomes occurred in 4.2% of cases, with 11 deaths during follow-up. Underlying-diseases patients had a higher rate of serious outcomes. Factors associated with serious outcomes included receiving a three-dose vaccination (odds ratio [OR] = 0.324, 95% confidence interval [CI]: 0.113-0.932, p = 0.037), male gender (OR = 2.890, 95% CI: 1.107-7.548, p = 0.030), age (OR = 1.060, 95% CI: 1.024-1.097, p = 0.001), and chest tightness or dyspnea at the time of visit (OR = 4.861, 95% CI: 2.054-11.507, p < 0.001). Our study found that cough, fever, and sore throat were the most common symptoms reported by patients. Receiving a three-dose vaccination was protective, while male gender, age, and chest tightness or dyspnea were identified as risk factors for serious outcomes.

The modeling and analysis of the COVID-19 pandemic with vaccination and isolation: a case study of Italy

The global spread of COVID-19 has not been effectively controlled. It poses a significant threat to public health and global economic development. This paper uses a mathematical model with vaccination and isolation treatment to study the transmission dynamics of COVID-19. In this paper, some basic properties of the model are analyzed. The control reproduction number of the model is calculated and the stability of the disease-free and endemic equilibria is analyzed. The parameters of the model are obtained by fitting the number of cases that were detected as positive for the virus, dead, and recovered between January 20 and June 20, 2021, in Italy. We found that vaccination better controlled the number of symptomatic infections. A sensitivity analysis of the control reproduction number has been performed. Numerical simulations demonstrate that reducing the contact rate of the population and increasing the isolation rate of the population are effective non-pharmaceutical control measures. We found that if the isolation rate of the population is reduced, a short-term decrease in the number of isolated individuals can lead to the disease not being controlled at a later stage. The analysis and simulations in this paper may provide some helpful suggestions for preventing and controlling COVID-19.

Influence Mechanism Analysis of the Spatial Evolution of Inter-Provincial Population Flow in China Based on Epidemic Prevention and Control

Based on big data on migration from the Baidu Map platform, this paper divides China's epidemic prevention and control efforts into four stages. Then, the characteristics and spatial patterns of daily population flows are studied by social network analysis. Subsequently, the exponential random graph model is used to investigate the influence of dynamic characteristics of changes in the spatial structure of the interprovincial population flow network during the postepidemic period. The spatial structure of the population flow network before, during, and after the epidemic shows significantly different characteristics, with epidemic prevention and control measures playing a significant role in restricting population flows. Interprovincial population flows have a certain degree of transmissibility, but two-way flows are not obvious. In addition, for regions with a larger resident population and a higher unemployment rate, a larger population tends to flow out. For regions with higher per capita GDP, the secondary and tertiary industries account for a relatively larger proportion, and the public environment is better. The more attractive a region is to the population, the higher is the tendency towards population inflows. Moreover, the level of medical care and epidemic prevention and control have become the main influencing factors of population movement.

The impact of multi-level interventions on the second-wave SARS-CoV-2 transmission in China

Background

A re-emergence of COVID-19 occurred in the northeast of China in early 2021. Different levels of non-pharmaceutical interventions, from mass testing to city-level lockdown, were implemented to contain the transmission of SARS-CoV-2. Our study is aimed to evaluate the impact of multi-level control measures on the second-wave SARS-CoV-2 transmission in the most affected cities in China.

Methods

Five cities with over 100 reported COVID-19 cases within one month from Dec 2020 to Feb 2021 were included in our analysis. We fitted the exponential growth model to estimate basic reproduction number (R₀), and used a Bayesian approach to assess the dynamics of the time-varying reproduction number (R_t). We fitted linear regression lines on R_t estimates for comparing the decline rates of R_t across cities, and the slopes were tested by analysis of covariance. The effect of non-pharmaceutical interventions (NPIs) was quantified by relative R_t reduction and statistically compared by analysis of variance.

Results

A total of 2,609 COVID-19 cases were analyzed in this study. We estimated that R₀ all exceeded 1, with the highest value of 3.63 (1.36, 8.53) in Haerbin and the lowest value of 2.45 (1.44, 3.98) in Shijiazhuang. Downward trends of R_t were found in all cities, and the starting time of R_t < 1 was around the 12th day of the first local COVID-19 cases. Statistical tests on regression slopes of R_t and effect of NPIs both showed no significant difference across five cities (P = 0.126 and 0.157).

Conclusion

Timely implemented NPIs could control the transmission of SARS-CoV-2 with low-intensity measures for places where population immunity has not been established.

Numerical Simulation to Predict COVID-19 Cases in Punjab

Coronavirus disease 2019 is a novel disease caused by a newly identified virus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). India recorded its first case of COVID-19 on 30 January 2020. This work is an attempt to calculate the number of COVID-19 cases in Punjab by solving a partial differential equation using the modified cubic B-spline function and differential quadrature method. The real data of COVID-19 cases and Google Community Mobility Reports of Punjab districts were used to verify the numerical simulation of the model. The Google mobility data reflect the changes in social behavior in real time and therefore are an important factor in analyzing the spread of COVID-19 and the corresponding precautionary measures. To investigate the cross-border transmission of COVID-19 between the 23 districts of Punjab with an analysis of human activities as a factor, the 23 districts were divided into five regions. This paper is aimed at demonstrating the predictive ability of the model.

The Impact of Lockdown, Patient Classification, and the Large-Scale Case Screening on the Spread of the Coronavirus Disease 2019 (COVID-19) in Hubei

The coronavirus disease (COVID-19) which emerged in Wuhan, China, in December 2019, is widely controlled now in China. However, the global epidemic is still severe. To study and comment on Hubei's approaches for responding to the disease, the paper considered some factors such as suspected cases (part of them are influenza patients or common pneumonia patients, etc.), quarantine, patient classification (three types), clinically diagnosed cases, and lockdown of Wuhan and Hubei. After that, the paper established an SELIHR model based on the surveillance data of Hubei published by the Hubei Health Commission from 10 January 2020 to 30 April 2020 and used the fminsearch optimization method to estimate the optimal parameters of the model. We obtained the basic reproduction number ℛ 0 = 3.1571 from 10 to 22 January. ℛ 0 was calculated as 2.0471 from 23 to 27 January. From 28 January to 30 April, ℛ 0 = 1.5014. Through analysis, it is not hard to find that the patients without classification during the period of confirmed cases will result in the cumulative number of cases in Hubei to increase. In addition, regarding the lockdown measures implemented by Hubei during the epidemic, our simulations also show that if the lockdown time of either Hubei or Wuhan is advanced, it will effectively curb the spread of the epidemic. If the lockdown measures are not taken, the total cumulative number of cases will increase substantially. From the results of the study, it can be concluded that the lockdown, patient classification, and the large-scale case screening are essential to slow the spread of COVID-19, which can provide references for other countries or regions.

An extended state observer based U-model control of the COVID-19

The coronavirus disease 2019 (COVID-19) is a new, rapidly spreading and evolving pandemic around the world. The COVID-19 has seriously affected people's health or even threaten people's life. In order to contain the spread of the pandemic and minimize its impact on economy, the tried-and-true control theory is utilized. Firstly, the control problem is clarified. Then, by combining advantages of the U-model control and the extended state observer (ESO), an extended state observer-based U-model control (ESOUC) is proposed to generate a population restriction policy. Closed-loop stability of the regulation system is also proved Two examples are considered, and numerical simulation results show that the ESOUC can suppress the COVID-19 faster than the linear active disturbance rejection control, which benefits controlling the infectious disease and the economic recovery. The ESOUC may provide a feasible non-pharmaceutical intervention in the control of the COVID-19.

Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges

Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.

What matters: non-pharmaceutical interventions for COVID-19 in Europe

OBJECTIVES

The purpose of this study is to describe the situation of COVID-19 in European countries and to identify important factors related to prevention and control.

METHODS

We obtained data from World Health Statistics 2020 and the Institute for Health Metrics and Evaluation (IHME). We calculated the Rt values of 51 countries in Europe under different prevention and control measures. We used lasso regression to screen factors associated with morbidity and mortality. For the selected variables, we used quantile regression to analyse the relevant influencing factors in countries with different levels of morbidity or mortality.

RESULTS

The government has a great influence on the change in Rt value through prevention and control measures. The most important factors for personal and group prevention and control are the mobility index, testing, the closure of educational facilities, restrictions on large-scale gatherings, and commercial restrictions. The number of ICU beds and doctors in medical resources are also key factors. Basic sanitation facilities, such as the proportion of safe drinking water, also have an impact on the COVID-19 epidemic.

CONCLUSIONS

We described the current status of COVID-19 in European countries. Our findings demonstrated key factors in individual and group prevention measures.

Modeling and dynamic analysis of novel coronavirus pneumonia (COVID-19) in China

Although novel coronavirus pneumonia (COVID-19) was widely spread in mainland China in early 2020, it was soon controlled. To study the impact of government interventions on the spread of disease during epidemics, a differential equation system is established to simulate the process of virus propagation in this paper. We first analyze its basic properties, basic reproduction number R 0 and existence of equilibria. Then we prove that the disease-free equilibrium (DFE) is Globally Asymptotically Stable when R 0 is less than 1. Through the analysis of the daily epidemic data from January 10, 2020 to March 11, 2020, combined with the implementation of the national epidemic policy, we divide the whole process into three stages: the first stage (natural state), the second stage (isolation state), the third stage (isolation, detection and treatment). By using the weighted nonlinear least square method to fit the data of three stages, the parameters are obtained, and three basic reproduction numbers are calculated, which are:R 01 = 2.6735 ,R 02 = 0.85077 ,R 03 = 0.18249 . Sensitivity analysis of threshold parameters and corresponding graphical results were also performed to examine the relative importance of various model parameters to the spread and prevalence of COVID-19. Finally, we simulate the trend of three stages and verify the theory of Global Asymptotic Stability of DFE. The conclusion of this paper proves theoretically that the Chinese government's epidemic prevention measures are effective in the fight against the spread of COVID-19. This study can not only provide a reference for research methods to simulate COVID-19 transmission in other countries or regions, but also provide recommendations on COVID-19 prevention measures for them.

Mathematical modeling of intervention and low medical resource availability with delays: Applications to COVID-19 outbreaks in Spain and Italy

Infectious diseases have been one of the major causes of human mortality, and mathematical models have been playing significant roles in understanding the spread mechanism and controlling contagious diseases. In this paper, we propose a delayed SEIR epidemic model with intervention strategies and recovery under the low availability of resources. Non-delayed and delayed models both possess two equilibria: the disease-free equilibrium and the endemic equilibrium. When the basic reproduction number $ R_0 = 1 $, the non-delayed system undergoes a transcritical bifurcation. For the delayed system, we incorporate two important time delays: $ \tau_1 $ represents the latent period of the intervention strategies, and $ \tau_2 $ represents the period for curing the infected individuals. Time delays change the system dynamics via Hopf-bifurcation and oscillations. The direction and stability of delay induced Hopf-bifurcation are established using normal form theory and center manifold theorem. Furthermore, we rigorously prove that local Hopf bifurcation implies global Hopf bifurcation. Stability switching curves and crossing directions are analyzed on the two delay parameter plane, which allows both delays varying simultaneously. Numerical results demonstrate that by increasing the intervention strength, the infection level decays; by increasing the limitation of treatment, the infection level increases. Our quantitative observations can be useful for exploring the relative importance of intervention and medical resources. As a timing application, we parameterize the model for COVID-19 in Spain and Italy. With strict intervention policies, the infection numbers would have been greatly reduced in the early phase of COVID-19 in Spain and Italy. We also show that reducing the time delays in intervention and recovery would have decreased the total number of cases in the early phase of COVID-19 in Spain and Italy. Our work highlights the necessity to consider the time delays in intervention and recovery in an epidemic model.

Transmission dynamics of COVID-19 in Nepal: Mathematical model uncovering effective controls

While most of the countries around the globe are combating the pandemic of COVID-19, the level of its impact is quite variable among different countries. In particular, the data from Nepal, a developing country having an open border provision with highly COVID-19 affected country India, has shown a biphasic pattern of epidemic, a controlled phase (until July 21, 2020) followed by an outgrown phase (after July 21, 2020). To uncover the effective strategies implemented during the controlled phase, we develop a mathematical model that is able to describe the data from both phases of COVID-19 dynamics in Nepal. Using our best parameter estimates with 95% confidence interval, we found that during the controlled phase most of the recorded cases were imported from outside the country with a small number generated from the local transmission, consistent with the data. Our model predicts that these successful strategies were able to maintain the reproduction number at around 0.21 during the controlled phase, preventing 442,640 cases of COVID-19 and saving more than 1,200 lives in Nepal. However, during the outgrown phase, when the strategies such as border screening and quarantine, lockdown, and detection and isolation, were altered, the reproduction number raised to 1.8, resulting in exponentially growing cases of COVID-19. We further used our model to predict the long-term dynamics of COVID-19 in Nepal and found that without any interventions the current trend may result in about 18.76 million cases (10.70 million detected and 8.06 million undetected) and 89 thousand deaths in Nepal by the end of 2021. Finally, using our predictive model, we evaluated the effects of various control strategies on the long-term outcome of this epidemics and identified ideal strategies to curb the epidemic in Nepal.

Quantifying compliance with COVID-19 mitigation policies in the US: A mathematical modeling study

The outbreak of COVID-19 disrupts the life of many people in the world. In response to this global pandemic, various institutions across the globe had soon issued their prevention guidelines. Governments in the US had also implemented social distancing policies. However, those policies, which were designed to slow the spread of COVID-19, and its compliance, have varied across the states, which led to spatial and temporal heterogeneity in COVID-19 spread. This paper aims to propose a spatio-temporal model for quantifying compliance with the US COVID-19 mitigation policies at a regional level. To achieve this goal, a specific partial differential equation (PDE) is developed and validated with short-term predictions. The proposed model describes the combined effects of transboundary spread among state clusters in the US and human mobilities on the transmission of COVID-19. The model can help inform policymakers as they decide how to react to future outbreaks.

Global stability of a SEIR epidemic model with infectious force in latent period and infected period under discontinuous treatment strategy

We consider a SEIR epidemic model with infectious force in latent period and infected period under discontinuous treatment. The treatment rate has at most a finite number of jump discontinuities in every compact interval. By using Lyapunov theory for discontinuous differential equations and other techniques on non-smooth analysis, the basic reproductive number [Formula: see text] is proved to be a sharp threshold value which completely determines the dynamics of the model. If [Formula: see text], then there exists a disease-free equilibrium which is globally stable. If [Formula: see text], the disease-free equilibrium becomes unstable and there exists an endemic equilibrium which is globally stable. We discuss that the disease will die out in a finite time which is impossible for the corresponding SEIR model with continuous treatment. Furthermore, the numerical simulations indicate that strengthening treatment measure after infective individuals reach some level is beneficial to disease control.

The basic reproduction number of SARS-CoV-2 in Wuhan is about to die out, how about the rest of the World?

The virologically confirmed cases of a new coronavirus disease (COVID-19) in the world are rapidly increasing, leading epidemiologists and mathematicians to construct transmission models that aim to predict the future course of the current pandemic. The transmissibility of a virus is measured by the basic reproduction number ( R0 ), which measures the average number of new cases generated per typical infectious case. This review highlights the articles reporting rigorous estimates and determinants of COVID-19 R0 for the most affected areas. Moreover, the mean of all estimated R0 with median and interquartile range is calculated. According to these articles, the basic reproduction number of the virus epicentre Wuhan has now declined below the important threshold value of 1.0 since the disease emerged. Ongoing modelling will inform the transmission rates seen in the new epicentres outside of China, including Italy, Iran and South Korea.