An engineering model of the COVID-19 trajectory to predict the success of isolation initiatives.

Much media and societal attention is today focused on how to best control the spread of coronavirus (COVID-19). Every day brings us new data, and policy makers are implementing different strategies in different countries to manage the impact of COVID-19. To respond to the first 'wave' of infection, several countries, including the UK, opted for isolation/lockdown initiatives, with different degrees of rigour. Data showed that these initiatives have yielded the expected results in terms of containing the rapid trajectory of the virus. When this article was first prepared (April 2020), the affected societies were wondering when the isolation/lockdown initiatives should be lifted. While detailed epidemiological, economic as well as social studies would be required to answer this question completely, here we employ a simple engineering model. Albeit simple, the model is capable of reproducing the main features of the data reported in the literature concerning the COVID-19 trajectory in different countries, including the increase in cases in countries following the initially successful isolation/lockdown initiatives. Keeping in mind the simplicity of the model, we attempt to draw some conclusions, which seem to suggest that a decrease in the number of infected individuals after the initiation of isolation/lockdown initiatives does not necessarily guarantee that the virus trajectory is under control. Within the limit of this model, it would seem that rigid isolation/lockdown initiatives for the medium term would lead to achieving the desired control over the spread of the virus. This observation seems consistent with the 2020 summer months, during which the COVID-19 trajectory seemed to be almost under control across most European countries. Consistent with the results from our simple model, winter 2020 data show that the virus trajectory was again on the rise. Because the optimal solution will achieve control over the spread of the virus while minimising negative societal impacts due to isolation/lockdown, which include but are not limited to economic and mental health aspects, the engineering model presented here is not sufficient to provide the desired answer. However, the model seems to suggest that to keep the COVID-19 trajectory under control, a series of short-to-medium term isolation measures should be put in place until one or more of the following scenarios is achieved: a cure has been developed and has become accessible to the population at large; a vaccine has been developed, tested and distributed to large portions of the population; a sufficiently large portion of the population has developed resistance to the COVID-19 virus; or the virus itself has become less aggressive. It is somewhat remarkable that an engineering model, despite all its approximations, provides suggestions consistent with advanced epidemiological models developed by several experts in the field. The model proposed here is however not expected to be able to capture the emergence of variants of the virus, which seem to be responsible for significant outbreaks, notably in India, in the spring of 2021, it cannot describe the effectiveness of vaccine strategies, as it does not differentiate among different age groups within the population, nor does it allow us to consider the duration of the immunity achieved after infection or vaccination.

Research on the propagation model of COVID-19 based on virus dynamics

At the end of 2019, a new kind of coronavirus spread in Wuhan city of Hubei province and other places, seriously endangering people's health. Scientific prediction of the spread trend of the novel coronavirus makes a big difference in epidemic prevention, treatment, and relevant health decisions. The COVID-19 transmission model based on virus dynamics was established. We propose a data-driven dynamic modeling method for infectious disease transmission, which is a sparse identification method for nonlinear dynamic models. Sparse regression and parameter identification are used to accurately find the control equation from the potential dynamic models to simulate the dynamic transmission process of the novel coronavirus in Wuhan at the end of 2019. Through the experiment, we get the results that the model can well describe the spread of COVID-19 in Wuhan and also prove that the model is practical and can be extended to the prediction of related epidemic situations. © 2023 SPIE.

Micromodel and Analysis of COVID-19 Based on Hypergraph

In view of the fact that the existing propagation models ignore the influence of different fields and different virus variants on individual infection, and the classical propagation models only describe the macroscopic situation of virus transmission, which cannot be specific to individual cases, this paper proposes 67ya microscopic virus propagation model based on hypergraph (HC-SIRS). Firstly, the concept of hypergraph is used to divide different fields of individuals into corresponding hyperedges. Based on different contact probabilities of each hyperedge, the contact probability matrix is formed to relate the contact between individuals. The individual infection probability of micro-virus propagation model based on hypergraph is deduced, and the corresponding differential equation is established. Secondly, the basic regeneration number and its characteristics of the model are derived. The upper bound of the basic regeneration number of the model is less than or equal to that of the classical SIRS model, indicating that the virus is more difficult to spread in this model. In fact, the different fields people live in and the different personal constitutions have a certain impact on the spread of the virus. The model is more comprehensive, so it is more suitable for simulating the spread of the virus in theory. Finally, the COVID-19 data of Diamond Princess and two cities in China are used for simulation experiments, and the mean absolute error(MAE) is used as the evaluation standard. The results showed that HC-SIRS could well simulate the spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Construction and Simulation Analysis of Epidemic Propagation Model Based on COVID-19 Characteristics.

This paper proposes the epidemic propagation model SEAIHR to elucidate the propagation mechanism of the Corona Virus Disease of 2019 (COVID-19). Based on the analysis of the propagation characteristics of COVID-19, the hospitalization isolation state and recessive healing state are introduced. The home morbidity state is introduced to consider the self-healing of asymptomatic infected populations, the early isolation of close contractors, and the impact of epidemic prevention and control measures. In this paper, by using the real epidemic data combined with the changes in parameters in different epidemic stages, multiple model simulation comparative tests were conducted. The experimental results showed that the fitting and prediction accuracy of the SEAIHR model was significantly better than the classical epidemic propagation model, and the fitting error was 34.4-72.8% lower than that of the classical model in the early and middle stages of the epidemic.

Bridging the Gap Between Domain Models and Computational Models: A Case Study of COVID-19

Novel coronavirus pneumonia (COVID-19) has broken out and spread rapidly in many countries and regions around the world. Since the outbreak, many researchers have proposed propagation models of COVID-19, among which the mainstream computational epidemiology model requires the establishment of a corresponding artificial society model for computational experiments. However, such models tightly coupled domain knowledge about epidemics with computational models and have low reusability. On this basis, we take COVID-19 as our research object and propose a hierarchical modeling framework for epidemic transmission, which describes how to decouple and dock domain models and computational models. This framework consists of three levels: individual capability model and virus model at the individual level, organizational structure and interaction mechanisms between individuals at the organizational level, and intervention model and environmental model design at the social level. The experimental results show that this is an effective hierarchical framework modeling approach for studying transmission mechanisms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Construction of SCUIR Propagation Model Based on Time-Varying Parameters

The novel coronavirus is a new type of virus, and its transmission characteristics are different from the previous virus. Based on the SEIR transmission model, this paper redefines the latent state as close contacts state, introduces an asymptomatic infection state, and considers the influence of time on the state transition parameters in the model, proposing a new transmission model. The experimental results show that the fitting accuracy of the model has significantly improved. Compared with the traditional model, the fitting error was reduced by 8.3%-47.6%. Also, this study uses the US epidemic data as the training set to predict the development of the US epidemic, and the forecast results show that the US epidemic cannot be quickly controlled in a short time. However, the number of active cases will usher in a rapid decline after August 2021.

Time-lag rumor propagation model and rumor-refuting strategy of SEIRD under COVID-19

With the increasing popularity of the Internet and the spread of COVID-19, epidemic-related rumors have attracted significant attention, allowing them to brew quickly and pose extremely negative social impacts. It is of great significance to investigate the propagation process of online rumors and offer tentative strategies to curb it. Based on the traditional susceptible, infected, recovered (SIR) model of online rumor propagation, groups of potential and die-hard rumor believers were introduced in this paper, establishing an authoritative rumor-refuting mechanism. Meanwhile, this paper considered factors such as the time-lag effect of rumor refutation from the nonauthoritative and authoritative institutions and the impact of the popularizing rate of higher education on the propagation and refutation of rumors. As a result of the process, the SEIRD (susceptible, exposed, infected, recovered, die-hard-infected) rumor propagation model was established to study how the proportion of the susceptible, exposed, infected, recovered, and die-hard-infected varies under different popularizing rates of higher education, the presence or absence of the authoritative rumor-refuting institutions, and the time-lag effect of rumor refutation. Finally, the model's effectiveness was verified via experimental simulation, which provided a reference for controlling the spread of online rumor propagation. In addition, the paper proposed a rumor-refuting coefficient to measure the rumor-refuting ability of the nonauthoritative and authoritative institutions. The results show that (1) increasing popularizing rate of higher education significantly slows down the rumor propagation and reduces the rumor propagation peak;(2) refuting the rumors based on the authoritative institutions is decisive for the ultimate elimination of rumors;and (3) eliminating the time-lag effect in refuting rumors facilitates slowing down the propagation of the online rumors. Therefore, the paper puts forward a feasible strategy to eliminate the time-lag effect of online rumor refutation in the future. Copyright ©2022 Chinese Journal of Engineering. All rights reserved.

Construction and Research of SCUIR Propagation Model Based on Time-varying Parameters

Novel coronavirus is a new type of virus, and its transmission characteristics are different from previous virus. Infected people not only have an incubation period, but also a large number of asymptomatic infections. Based on the classic model SEIR, this study redefines the latent state as close contact state, introduces an asymptomatic state of infection, and the influence of time on the state transition parameters in the model is considered, proposed a new transmission model which includes five types of states: susceptible state, close contact state, asymptomatic infection state, infected state, and removed state. The model uses the actual epidemic data of Hubei Province to conduct experiments, and uses RMSE and MAPE as evaluation indicators to compare the experimental results. The results show that the fitting accuracy of the SCUIR model has been significantly improved. Compared with the traditional model, the fitting error is reduced by 8.3%~47.6%, and hidden data that is difficult to count in the epidemic can be calculated, which further characterizes the mechanism of epidemic transmission. © 2022, The Editorial Department of Complex Systems and Complexity Science. All right reserved.

Spatial and Temporal Transmission Differences between SARS and COVID-19 and Analysis of Influence Factors

The outbreaks of SARS and COVID-19 have had a serious impact on public health, social economy and so on in China, in order to reveal the common law and difference characteristics of space-time transmission of respiratory infectious diseases and the reasons behind them, using space-time statistical methods, systematically analyzed and compared the difference characteristics of space-time transmission between SARS and COVID-19, and combined with the transmission characteristics of the virus itself and temperature, traffic and other factors to analyze the causes. The study shows that, ① SARS experiences two stages, the rising period-flat phase, and the COVID-19 experiences three stages, the rising period-sharp rise-slow up period. ② In the mode of spatial transmission, the transmission intensity and range of COVID-19 is greater than that of SARS, and the overall connectivity of COVID-19 is greater and the provinces are more closely related to the outbreak of the virus. Both SARS and COVID-19 transmission have obvious spatial aggregation characteristics. They are based on proximity propagation and long-range leaps, and SARS has a secondary communication center, and COVID-19 diffusion center has not been relocated. ③ In the direction of space communication, SARS is centered in Beijing, Hong Kong and Guangdong, the direction of spatial communication is stronger, and COVID-19 is only spread outwards with Hubei as the center. ④ In terms of spatial transmission speed, the spread time of the first case in each province of SARS is relatively large, and the spread time of the first case in each province of COVID-19 is roughly divided by Hu Huanyong Line, showing a phenomenon of "fast in the east and slow in the west", and the spread time span is relatively short. ⑤ R0 is the main reason for the difference between the spatial transmission range of SARS and COVID-19 and the speed of spatial transmission. The temperature suitability of SARS and COVID-19 viruses is different, but spatial aggregation transmission and adjacent area transmission are occurring in areas with similar temperatures. Besides the virus transmission capacity and temperature impact, traffic is the main reason affecting SARS and COVID-19 space long-range leap transmission, and the spatial transmission speed of both is negatively related to the density of the road network. 2021, Science Press. All right reserved.

The role of proactive behavior on COVID-19 infordemic in the Chinese Sina-Microblog: a modeling study.

In order to avoid forming an information cocoon, the information propagation of COVID-19 is usually created through the action of "proactive search", an important behavior other than "reactive follow". This behavior has been largely ignored in modeling information dynamics. Here, we propose to fill in this gap by proposing a proactive-reactive susceptible-discussing-immune (PR-SFI) model to describe the patterns of co-propagation on social networks. This model is based on the forwarding quantity and takes into account both proactive search and reactive follow behaviors. The PR-SFI model is parameterized by data fitting using real data of COVID-19 related topics in the Chinese Sina-Microblog, and the model is calibrated and validated using the prediction accuracy of the accumulated forwarding users. Our sensitivity analysis and numerical experiments provide insights about optimal strategies for public health emergency information dissemination.

Propagation Model of Panic Buying Under the Sudden Epidemic.

The sudden outbreak of COVID-19 at the end of 2019 has had a huge impact on people's lives all over the world, and the overwhelmingly negative information about the epidemic has made people panic for the future. This kind of panic spreads and develops through online social networks, and further spreads to the offline environment, which triggers panic buying behavior and has a serious impact on social stability. In order to quantitatively study this behavior, a two-layer propagation model of panic buying behavior under the sudden epidemic is constructed. The model first analyzes the formation process of individual panic from a micro perspective, and then combines the Susceptible-Infected-Recovered (SIR) Model to simulate the spread of group behavior. Then, through simulation experiments, the main factors affecting the spread of panic buying behavior are discussed. The experimental results show that: (1) the dissipating speed of individual panics is related to the number of interactions and there is a threshold. When the number of individuals involved in interacting is equal to this threshold, the panic of the group dissipates the fastest, while the dissipation speed is slower when it is far from the threshold; (2) The reasonable external information release time will affect the occurrence of the second panic buying, meaning providing information about the availability of supplies when an escalation of epidemic is announced will help prevent a second panic buying. In addition, when the first panic buying is about to end, if the scale of the second panic buying is to be suppressed, it is better to release positive information after the end of the first panic buying, rather than ahead of the end; and (3) Higher conformity among people escalates panic, resulting in panic buying. Finally, two cases are used to verify the effectiveness and feasibility of the proposed model.

A time-delay COVID-19 propagation model considering supply chain transmission and hierarchical quarantine rate.

In this manuscript, we investigate a novel Susceptible-Exposed-Infected-Quarantined-Recovered (SEIQR) COVID-19 propagation model with two delays, and we also consider supply chain transmission and hierarchical quarantine rate in this model. Firstly, we analyze the existence of an equilibrium, including a virus-free equilibrium and a virus-existence equilibrium. Then local stability and the occurrence of Hopf bifurcation have been researched by thinking of time delay as the bifurcation parameter. Besides, we calculate direction and stability of the Hopf bifurcation. Finally, we carry out some numerical simulations to prove the validity of theoretical results.