Infectious Disease Modelling最新文献

At the end of the year 2019, a virus named SARS-CoV-2 induced the coronavirus disease, which is very contagious and quickly spread around the world. This new infectious disease is called COVID-19. Numerous areas, such as the economy, social services, education, and healthcare system, have suffered grave consequences from the invasion of this deadly virus. Thus, a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious disaster. In this research, the daily reported COVID-19 cases in 92 sub-districts in Johor state, Malaysia, as well as the population size associated to each sub-district, are used to study the propagation of COVID-19 disease across space and time in Johor. The time frame of this research is about 190 days, which started from August 5, 2021, until February 10, 2022. The clustering technique known as spatio-temporal clustering, which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district level. The results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre (Bandar Johor Bahru), and during the festive season. These findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of holidays. On the other hand, the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.

This paper presents a comprehensive agent-based model for the spread of an infection in a network of cities. Directional mobility is defined between each two cities and can take different values. The work examines the role that such mobility levels play in containing the infection with various vaccination coverage and age distributions. The results indicate that mobility reduction is sufficient to control the disease under all circumstances and full lockdowns are not a necessity. It has to be reduced to different ratios depending on the vaccination level and age distribution. A key finding is that increasing vaccination coverage above a certain level does not affect the mobility suppression level required to control the infection anymore for the cases of young population and heterogeneous age distributions. By investigating several migration and commuting patterns, it is found that shutting mobility in a few local places is favored against reducing mobility over the entire country network. In addition, commuting -and not migration-influences the spread level of the infection. The work offers an exclusive combined network-based and agent-based model that makes use of randomly generated mobility matrices.

In this paper, we analyze the global asymptotic behaviors of a mathematical susceptible-infected(SI) age-infection-structured human immunodeficiency virus(HIV) model with heterogeneous transmission. Mathematical analysis shows that the local and global dynamics are completely determined by the basic reproductive number $R_0$. If $R_0<1$, disease-free equilibrium is globally asymptotically stable. If $R_0>1$, it shows that disease-free equilibrium is unstable and the unique endemic equilibrium is globally asymptotically stable. The proofs of global stability utilize Lyapunov functions. Besides, the numerical simulations are illustrated to support these theoretical results and sensitivity analysis of each parameter for $R_0$ is performed by the method of partial rank correlation coefficient(PRCC).

Caused by four serotypes, dengue fever is a major public health concern worldwide. Current modeling efforts have mostly focused on primary and heterologous secondary infections, assuming that lifelong immunity prevents reinfections by the same serotype. However, recent findings challenge this assumption, prompting a reevaluation of dengue immunity dynamics.

In this study, we develop a within-host modeling framework to explore different scenarios of dengue infections. Unlike previous studies, we go beyond a deterministic framework, considering individual immunological variability. Both deterministic and stochastic models are calibrated using empirical data on viral load and antibody (IgM and IgG) concentrations for all dengue serotypes, incorporating confidence intervals derived from stochastic realizations.

With good agreement between the mean of the stochastic realizations and the mean field solution for each model, our approach not only successfully captures primary and heterologous secondary infection dynamics facilitated by antibody-dependent enhancement (ADE) but also provides, for the first time, insights into homotypic reinfection dynamics. Our study discusses the relevance of homotypic reinfections in dengue transmission at the population level, highlighting potential implications for disease prevention and control strategies.

We develop a mathematical model to investigate the effect of contact tracing on containing epidemic outbreaks and slowing down the spread of transmissible diseases. We propose a discrete-time epidemic model structured by disease-age which includes general features of contact tracing. The model is fitted to data reported for the early spread of COVID-19 in South Korea, Brazil, and Venezuela. The calibrated values for the contact tracing parameters reflect the order pattern observed in its performance intensity within the three countries. Using the fitted values, we estimate the effective reproduction number $R_e$ and investigate its responses to varied control scenarios of contact tracing. Alongside the positivity of solutions, and a stability analysis of the disease-free equilibrium are provided.

Key epidemiological parameters, including the effective reproduction number, $R\left(t\right)$, and the instantaneous growth rate, $r\left(t\right)$, generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the “emergency” to “endemic” phase of the pandemic.

The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the “ONS-based” $R\left(t\right)$ and $r\left(t\right)$ across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters.

Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates.

Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.

Since the COVID-19 pandemic was first reported in 2019, it has rapidly spread around the world. Many countries implemented several measures to try to control the virus spreading. The healthcare system and consequently the general quality of life population in the cities have all been significantly impacted by the Coronavirus pandemic. The different waves of contagious were responsible for the increase in the number of cases that, unfortunately, many times lead to death. In this paper, we aim to characterize the dynamics of the six waves of cases and deaths caused by COVID-19 in Rio de Janeiro city using techniques such as the Poincaré plot, approximate entropy, second-order difference plot, and central tendency measures. Our results reveal that by examining the structure and patterns of the time series, using a set of non-linear techniques we can gain a better understanding of the role of multiple waves of COVID-19, also, we can identify underlying dynamics of disease spreading and extract meaningful information about the dynamical behavior of epidemiological time series. Such findings can help to closely approximate the dynamics of virus spread and obtain a correlation between the different stages of the disease, allowing us to identify and categorize the stages due to different virus variants that are reflected in the time series.

Introduction

In sub-Saharan Africa, accurate estimates of the HIV epidemic in female sex workers are crucial for effective prevention and care strategies. These estimates are typically derived from mathematical models that assume certain demographic and behavioural characteristics like age and duration of sex work to remain constant over time. We reviewed this assumption for female sex workers in South Africa.

Methods

We reviewed studies that reported estimates on either the age or the duration of sex work among female sex workers in South Africa. We used Bayesian hierarchical models to synthesize reported estimates and to study time trends. In a simulation exercise, we also investigated the potential impact of the "constant age and sex work duration"-assumption on estimates of HIV incidence.

Results

We included 24 different studies, conducted between 1996 and 2019, contributing 42 estimates on female sex worker age and 27 estimates on sex work duration. There was evidence suggesting an increase in both the duration of sex work and the age of female sex workers over time. According to the fitted models, over each decade the expected duration of sex work increased by 55.6% (95%-credible interval [CrI]: 23.5%–93.9%) and the expected age of female sex workers increased by 14.3% (95%-CrI: 9.1%–19.1%). Over the 23-year period, the predicted mean duration of sex work increased from 2.7 years in 1996 to 7.4 years in 2019, while the predicted mean age increased from 26.4 years to 32.3 years. Allowing for these time trends in the simulation exercise resulted in a notable decline in estimated HIV incidence rate among sex workers over time. This decline was significantly more pronounced than when assuming a constant age and duration of sex work.

Conclusions

In South Africa, age and duration of sex work in female sex workers increased over time. While this trend might be influenced by factors like expanding community mobilization and improved rights advocacy, the ongoing criminalisation, stigmatisation of sex work and lack of alternative employment opportunities could also be contributing. It is important to account for these changes when estimating HIV indicators in female sex workers.