COVID-19 Vaccination Strategies Considering Hesitancy Using Particle-Based Epidemic Simulation

Vaccine hesitancy is one of the critical factors in achieving herd immunity and suppressing the COVID-19 epidemic. Many countries face this as an acute public health issue that diminishes the efficacy of their vaccination campaigns. Epidemic modeling and simulation can be used to predict the effects of different vaccination strategies. In this work, we present an open-source particle-based COVID-19 simulator with a vaccination module capable of taking into account the vaccine hesitancy of the population. To demonstrate the efficacy of the simulator, we conducted extensive simulations for the province of Lecco, Italy. The results indicate that the combination of both high vaccination rate and low hesitancy leads to faster epidemic suppression.

Towards a Framework for High-Performance Simulation of Livestock Disease Outbreak: A Case Study of Spread of African Swine Fever in Vietnam

The spread of disease in livestock is an important research topic of veterinary epidemiology because it provides warnings or advice to organizations responsible for the protection of animal health in particular and public health in general. Disease transmission simulation programs are often deployed with different species, disease types, or epidemiological models, and each research team manages its own set of parameters relevant to their target diseases and concerns, resulting in limited cooperation and reuse of research results. Furthermore, these simulation and decision support tools often require a large amount of computational power, especially for models involving tens of thousands of herds with millions of individuals spread over a large geographical area such as a region or a country. It is a matter of fact that epidemic simulation programs are often heterogeneous, but they often share some common workflows including processing of input data and execution of simulation, as well as storage, analysis, and visualization of results. In this article, we propose a novel architectural framework for simultaneously deploying any epidemic simulation program both on premises and on the cloud to improve performance and scalability. We also conduct some experiments to evaluate the proposed architectural framework on some aspects when applying it to simulate the spread of African swine fever in Vietnam.

SEIMR/R-S. General Epidemic Simulation Model Multi-Infected States - Multi Sociodemographic Segments - Multi-Region Mobility. Theory

SEIMR/R-S corresponds to a generalized mathematical model of pandemics that enhances traditional, aggregated simulation models when considering inter-regional impacts in a macro region (conurbed); SEIMR/R-S also considers the impact of modeling the population divided into sociodemographic segments based on age and economic stratum (it is possible to include other dimensions, for example: ethnics, gender, … ). SEIMR/R-S is the core of the SEIMR/R-S/OPT epidemic management optimization model that determines optimal policies (mitigation and confinement) considering the spatial distribution of the population, segmented sociodemographically and multiple type of vaccines. The formulation of SEIMR/R-S/OPT is presented by Velasquez-Bermudez (2021a) that includes the modeling of the vaccination process. SEIMR/R-S can be understood and used by any epidemiologist, and/or physician, working with SIR, SEIR or similar simulation models, and by professionals working on the issue of public policies for epidemic control. Following the theory presented in this document, ITCM (Instituto Tecnologico de Ciudad Madero, México) implemented the SEIMR/R-S epidemic model in a JAVA program (Velasquez-Bermudez et. al, 2021). This program may be used by the organizations that considers the SEIMR/R-S will be useful for management the COVID-19 pandemic, it is presented by VelasquezBermudez et al. (2021).

Model-M: An agent-based epidemic model of a middle-sized municipality

This report presents a technical description of our agent-based epidemic model of a particular middle-sized municipality. We have developed a realistic model with 56 thousand inhabitants and 2.7 million of social contacts. These form a multi-layer social network that serves as a base of our epidemic simulation. The disease is modeled by our extended SEIR model with parameters fitted to real epidemics data for Czech Republic. The model is able to simulate a whole range of non-pharmaceutical interventions on individual level, such as protective measures and physical distancing, testing, contact tracing, isolation and quarantine. The effect of government-issued measures such as contact restrictions in different environments (schools, restaurants, vendors, etc.) can also be simulated. The model is implemented in Python and is available as open source at: www.github.com/epicity-cz/model-m/releases

APPLICATION OF STOCHASTIC MODEL FOR LENGTHY EPIDEMIC FORECASTING

In this paper we consider a stochastic discrete-time epidemic model, with the infectivity depending on the age of infection and existing formula for the maximum likelihood estimation of the parameter responsible for the rate of the infection spread. In order to utilize the real number of infection cases statistics, a detection rate parameter is introduced. A program for automatic parameter estimation using past data with future epidemic simulation is developed. We present the comparison between the simulation of COVID-19 cases in Kyiv and real data using manual and automatic parameter estimation. We consider the possibility of the epidemic partition into several intervals with different parameters in order to simulate lengthy epidemics with significant changes in dynamics. We present the comparison between different numbers of partitions for long-term COVID-19 simulation in Kyiv (Ukraine) and Czech Republic, which have different dynamics of the epidemic development.