Real Epidemic Data Research Articles

A new model for COVID-19 in the post-pandemic era

Coronavirus disease 2019 (COVID-19) in the early days of the pandemic had significant differences in propagation and contact modes from those in the post-pandemic era. In order to capture the real dynamic behavior of COVID-19 propagation in the post-pandemic era, this study takes into account groups with the awareness of self-protection (including taking self-quarantine measures), as well as with loss of immunity, and establishes a new SLEIRS (Susceptible, Low-risk, Asymptomatic infected, Infected and Recovered) epidemic model with births and deaths on the basis of an SEIR model through adding compartment for low-risk groups. For the proposed model, we proved the existence of equilibrium points, identified the stability condition of equilibrium points as well as the basic regeneration number, and verified the proposed theoretical results with numerical simulations. Furthermore, the analysis of the impact of parameters on disease transmission has revealed that detecting the asymptomatic infected is a good measure to prevent and control the disease transmission. More practically, we used the particle swarm optimization (PSO) algorithm to estimate the model parameters based on the real epidemic data, and we then applied the model with estimated parameters to make predictions for the half-a-month epidemic in 2022. Results show the prediction and the estimated parameters are basically consistent with the practical situation, indicating that the proposed model has good capability in short-term prediction for COVID-19 in the post-pandemic.

Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case.

The development of an epidemic always exhibits multiwave oscillation owing to various anthropogenic sources of transmission. Particularly in populated areas, the large-scaled human mobility led to the transmission of the virus faster and more complex. The accurate prediction of the spread of infectious diseases remains a problem. To solve this problem, we propose a new method called the multi-source dynamic ensemble prediction (MDEP) method that incorporates a modified susceptible-exposed-infected-removed (SEIR) model to improve the accuracy of the prediction result. The modified SEIR model is based on the compartment model, which is suitable for local-scale and confined spaces, where human mobility on a large scale is not considered. Moreover, compartmental models cannot be used to predict multiwave epidemics. The proposed MDEP method can remedy defects in the compartment model. In this study, multi-source prediction was made on the development of coronavirus disease 2019 (COVID-19) and dynamically assembled to obtain the final integrated result. We used the real epidemic data of COVID-19 in three cities in China: Beijing, Lanzhou, and Beihai. Epidemiological data were collected from 17 April, 2022 to 12 August, 2022. Compared to the one-wave modified SEIR model, the MDEP method can depict the multiwave development of COVID-19. The MDEP method was applied to predict the number of cumulative cases of recent COVID-19 outbreaks in the aforementioned cities in China. The average accuracy rates in Beijing, Lanzhou, and Beihai were 89.15%, 91.74%, and 94.97%, respectively. The MDEP method improved the prediction accuracy of COVID-19. With further application to other infectious diseases, the MDEP method will provide accurate predictions of infectious diseases and aid governments make appropriate directives.

Compartmental modelling in epidemic diseases: a comparison between SIR model with constant and time-dependent parameters

The compartmental modelling is one of the most widely used techniques in investigating the dynamics of infectious diseases. This modelling technique usually treats model parameters as constant. However, the parameters associated with infectious diseases randomly change following the changes in the conditions of disease transmission. As a result, the estimated parameters are often found over or under-determined by direct problems when some conditions change and the forecasting using direct problems often goes wrong. In this study, we estimate the model parameters over different time intervals by means of the inverse problem method and then solve the forward problem using these estimated parameters to compare them with the real epidemic data. We apply the method to estimate the parameters corresponding to Nipah virus, Measles and COVID-19 in the context of Bangladesh. The results suggest that the method helps to gain improved insights into epidemic scenarios corresponding to smaller time intervals. The results of the direct problem are found to fall apart fairly quickly from the real epidemic data as the length of the interval used in the inverse problem method increased.

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.

Dynamic survival analysis for non-Markovian epidemic models.

We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method’s versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.

Is the Omicron variant of SARS-CoV-2 coming to an end?

Is the Omicron variant of SARS-CoV-2 coming to an end?

The spatiotemporal transmission dynamics of COVID-19 among multiple regions: a modeling study in Chinese provinces.

Current explosive outbreak of COVID-19 around the world is a complex spatiotemporal process with hidden interactions between viruses and humans. This study aims at clarifying the transmission patterns and the driving mechanism that contributed to the COVID-19 prevalence across the provinces of China. Thus, a new dynamical transmission model is established by an ordinary differential system. The model takes into account the hidden circulation of COVID-19 virus among/within humans, which incorporates the spatial diffusion of infection by parameterizing human mobility. Theoretical analysis indicates that the basic reproduction number is a unique epidemic threshold, which can unite infectivity in each region by human mobility and can totally determine whether COVID-19 proceeds among multiple regions. By validating the model with real epidemic data in China, it is found that (1) if without any intervention, COVID-19 would overrun China within three months, resulting in more than 1.1 billion clinical infections and 0.2 billion subclinical infections; (2) high frequency of human mobility can trigger COVID-19 diffusion across each province in China, no matter where the initial infection locates; (3) travel restrictions and other non-pharmaceutical interventions must be implemented simultaneously for disease control; and (4) infection sites in central and east (rather than west and northeast) of China would easily stimulate quick diffusion of COVID-19 in the whole country.Supplementary InformationThe online version supplementary material available at 10.1007/s11071-021-07001-1.

Criticality in epidemic spread: An application in the case of COVID19 infected population.

Recently, it has been successfully shown that the temporal evolution of the fraction of COVID-19 infected people possesses the same dynamics as the ones demonstrated by a self-organizing diffusion model over a lattice, in the frame of universality. In this brief, the relevant emerging dynamics are further investigated. Evidence that this nonlinear model demonstrates critical dynamics is scrutinized within the frame of the physics of critical phenomena. Additionally, the concept of criticality over the infected population fraction in epidemics (or a pandemic) is introduced and its importance is discussed, highlighting the emergence of the critical slowdown phenomenon. A simple method is proposed for estimating how far away a population is from this "singular" state, by utilizing the theory of critical phenomena. Finally, a dynamic approach applying the self-organized diffusion model is proposed, resulting in more accurate simulations, which can verify the effectiveness of restrictive measures. All the above are supported by real epidemic data case studies.

Trajectory Linkage and Spreader Centrality for Social Epidemic Networks

Constructing social networks for real epidemic cases is very challenging. Many mathematical models have been proposed to model such networks using simulation models, such as susceptible-infected (SI), susceptible-infected-recovered (SIR), and susceptible-exposed-infectious-removed (SEIR). Nonetheless, social network analyses can fail to capture real conditions, as such models are constructed based on many assumptions. Furthermore, unlike standard online social networks (OSNs), a social epidemic network requires different treatment from both model construction and social network analysis perspectives, especially for the detection of superspreaders. To address these issues, we propose a trajectory linkage method to automatically discover social networks from historical patient-trajectory data, wherein relations among patients are determined by spatial proximity and time-windows. Moreover, we introduce a novel spreader centrality measure that is devised to identify superspreaders in a social epidemic network. Extensive experiments were performed using real epidemic data. The results revealed that trajectory linkage can obtain a denser social network model than is possible by only incorporating patient data (the “who is infected by whom” relationship). By performing a social network analysis, the trajectory linkage model can express the real conditions of the patient relationship. Furthermore, our spreader centrality can capture the real superspreaders more effectively than can the existing centrality measure in social epidemic networks.

Multivariate syndromic surveillance for cattle diseases: Epidemic simulation and algorithm performance evaluation

Multivariate Syndromic Surveillance (SyS) systems that simultaneously assess and combine information from different data sources are especially useful for strengthening surveillance systems for early detection of infectious disease epidemics. Despite the strong motivation for implementing multivariate SyS and there being numerous methods reported, the number of operational multivariate SyS systems in veterinary medicine is still very small. One possible reason is that assessing the performance of such surveillance systems remains challenging because field epidemic data are often unavailable. The objective of this study is to demonstrate a practical multivariate event detection method (directionally sensitive multivariate control charts) that can be easily applied in livestock disease SyS, using syndrome time series data from the Swiss cattle population as an example. We present a standardized method for simulating multivariate epidemics of different diseases using four diseases as examples: Bovine Virus Diarrhea (BVD), Infectious Bovine Rhinotracheitis (IBR), Bluetongue virus (BTV) and Schmallenberg virus (SV). Two directional multivariate control chart algorithms, Multivariate Exponentially Weighted Moving Average (MEWMA) and Multivariate Cumulative Sum (MCUSUM) were compared. The two algorithms were evaluated using 12 syndrome time series extracted from two Swiss national databases. The two algorithms were able to detect all simulated epidemics around 4.5 months after the start of the epidemic, with a specificity of 95%. However, the results varied depending on the algorithm and the disease. The MEWMA algorithm always detected epidemics earlier than the MCUSUM, and epidemics of IBR and SV were detected earlier than epidemics of BVD and BTV. Our results show that the two directional multivariate control charts are promising methods for combining information from multiple time series for early detection of subtle changes in time series from a population without producing an unreasonable amount of false alarms. The approach that we used for simulating multivariate epidemics is relatively easy to implement and could be used in other situations where real epidemic data are unavailable. We believe that our study results can support the implementation and assessment of multivariate SyS systems in animal health.

Effects of human mobility, temperature and mosquito control on the spatiotemporal transmission of dengue

Dengue transmission exhibits evident geographic variations and seasonal differences. Such heterogeneity is caused by various impact factors, in which temperature and host/vector behaviors could drive its spatiotemporal transmission, but mosquito control could stop its progression. These factors together contribute to the observed distributions of dengue incidence from surveillance systems. To effectively and efficiently monitor and response to dengue outbreak, it would be necessary to systematically model these factors and their impacts on dengue transmission. This paper introduces a new modeling framework with consideration of multi-scale factors and surveillance data to clarify the hidden dynamics accounting for dengue spatiotemporal transmission. The model is based on compartmental system which takes into account the biting-based interactions among humans, viruses and mosquitoes, as well as the essential impacts of human mobility, temperature and mosquito control. This framework was validated with real epidemic data by applying retrospectively to the 2014 dengue epidemic in the Pearl River Delta (PRD) in southern China. The results indicated that suitable condition of temperature could be responsible for the explosive dengue outbreak in the PRD, and human mobility could be the causal factor leading to its spatial transmission across different cities. It was further found that mosquito intervention has significantly reduced dengue incidence, where a total of 52,770 (95% confidence interval [CI]: 29,231–76,308) dengue cases were prevented in the PRD in 2014. The findings can offer new insights for improving the predictability and risk assessment of dengue epidemics. The model also can be readily extended to investigate the transmission dynamics of other mosquito-borne diseases.

Assessing inference of the basic reproduction number in an SIR model incorporating a growth‐scaling parameter

The standard mass action, which assumes that infectious disease transmission occurs in well-mixed populations, is popular for formulating compartmental epidemic models. Compartmental epidemic models often follow standard mass action for simplicity and to gain insight into transmission dynamics as it often performs well at reproducing disease dynamics in large populations. In this work, we formulate discrete time stochastic susceptible-infected-removed models with linear (standard) and nonlinear mass action structures to mimic varying mixing levels. Using simulations and real epidemic data, we demonstrate the sensitivity of the basic reproduction number to these mathematical structures of the force of infection. Our results suggest the need to consider nonlinear mass action in order to generate more accurate estimates of the basic reproduction number although its uncertainty increases due to the addition of one growth scaling parameter.

A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks

Efficient vaccination strategy is crucial for controlling recurrent epidemic spreading on networks. In this paper, based on the analysis of real epidemic data and simulations, it’s found that the risk indicator of recurrent epidemic outbreaks could be determined by the ratio of the epidemic infection rate of the year to the average infected density of the former year. According to the risk indicator, the dynamic vaccination probability of each year can be designed to suppress the epidemic outbreaks. Our simulation results show that the dynamic vaccination strategy could effectively decrease the maximal and average infected density, and meanwhile increase the time intervals of epidemic outbreaks and individuals attacked by epidemic. In addition, our results indicate that to depress the influenza outbreaks, it is not necessary to keep the vaccination probability high every year; and adjusting the vaccination probability at right time could decrease the outbreak risks with lower costs. Our findings may present a theoretical guidance for the government and the public to control the recurrent epidemic outbreaks.

A metapopulation model for chikungunya including populations mobility on a large-scale network

In this paper we study the influence of populations mobility on the spread of a vector-borne disease. We focus on the chikungunya epidemic event that occurred in 2005–2006 on the Réunion Island, Indian Ocean, France, and validate our models with real epidemic data from the event. We propose a metapopulation model to represent both a high-resolution patch model of the island with realistic population densities and also mobility models for humans (based on real-motion data) and mosquitoes. In this metapopulation network, two models are coupled: one for the dynamics of the mosquito population and one for the transmission of the disease. A high-resolution numerical model is created from real geographical, demographical and mobility data. The Island is modeled with an 18,000-nodes metapopulation network. Numerical results show the impact of the geographical environment and populations’ mobility on the spread of the disease. The model is finally validated against real epidemic data from the Réunion event.

A social model for the evolution of sexually transmitted diseases

We have introduced recently a model for the spread of sexually transmitted diseases, in which the social behavior is incorporated as a key factor for the further propagation of the infection. The system may be regarded as a society of agents where in principle anyone can sexually interact with any other one in the population. The social behavior is taking into account by means of two parameters: the fraction of singles ρ s and the promiscuity p. The promiscuity parameter defines the per individual daily probability of going out to look for a sexual partner, abandoning its eventual mate. In this contribution we show that the interaction between this two parameters give rise to a non-trivial epidemic threshold condition, when going from the homogeneous case ( ρ s =1) to heterogeneous cases ( ρ s <1). These results can have profound implication in the interpretation of real epidemic data.

A social model for the evolution of sexually transmitted diseases

We have introduced recently a model for the spread of sexually transmitted diseases, in which the social behavior is incorporated as a key factor for the further propagation of the infection. The system may be regarded as a society of agents where in principle anyone can sexually interact with any other one in the population. The social behavior is taking into account by means of two parameters: the fraction of singles ρs and the promiscuity p. The promiscuity parameter defines the per individual daily probability of going out to look for a sexual partner, abandoning its eventual mate. In this contribution we show that the interaction between this two parameters give rise to a non-trivial epidemic threshold condition, when going from the homogeneous case (ρs=1) to heterogeneous cases (ρs<1). These results can have profound implication in the interpretation of real epidemic data.

Estimating likelihoods for spatio-temporal models using importance sampling

This paper describes how importance sampling can be applied to estimate likelihoods for spatio-temporal stochastic models of epidemics in plant populations, where observations consist of the set of diseased individuals at two or more distinct times. Likelihood computation is problematic because of the inherent lack of independence of the status of individuals in the population whenever disease transmission is distance-dependent. The methods of this paper overcome this by partitioning the population into a number of sectors and then attempting to take account of this dependence within each sector, while neglecting that between-sectors. Application to both simulated and real epidemic data sets show that the techniques perform well in comparison with existing approaches. Moreover, the results confirm the validity of likelihood estimates obtained elsewhere using Markov chain Monte Carlo methods.