Predicting the Time to Detect Moderately Virulent African Swine Fever Virus in Finisher Swine Herds Using a Stochastic Disease Transmission Model

Background: African swine fever (ASF) is a highly contagious and devastating pig disease that has caused extensive global economic losses. Understanding ASF virus (ASFV) transmission dynamics within a herd is necessary in order to prepare for and respond to an outbreak in the United States. Although the transmission parameters for the highly virulent ASF strains have been estimated in several articles, there are relatively few studies focused on moderately virulent strains. Using an approximate Bayesian computation algorithm in conjunction with Monte Carlo simulation, we have estimated the adequate contact rate for moderately virulent ASFV strains and determined the statistical distributions for the durations of mild and severe clinical signs using individual, pig-level data. A discrete individual based disease transmission model was then used to estimate the time to detect ASF infection based on increased mild clinical signs, severe clinical signs, or daily mortality. Results: Our results indicate that it may take two weeks or longer to detect ASF in a finisher swine herd via mild clinical signs or increased mortality beyond levels expected in routine production. A key factor contributing to the extended time to detect ASF in a herd is the fairly long latently infected period for an individual pig (mean 4.5, 95% P.I., 2.4 - 7.2 days). Conclusion: These transmission model parameter estimates and estimated time to detection via clinical signs provide valuable information that can be used not only to support emergency preparedness but also to inform other simulation models of evaluating regional disease spread.

A disease transmission model based on individual cognition

Recently, the study about the disease transmission has received widespread attention. In the dynamics process of infectious disease, individual’s cognition about disease-related knowledge is an important factor that controls disease transmission. The disease-related information includes the cause, symptoms, transmission route and so on. Disease-related knowledge would influence the individual’s attitude toward disease, and influence the transmission rate and scale of the infectious disease. In order to study the impact of individual cognition on the transmission of disease, the disease transmission model based on individual cognition is proposed in this paper. Based on this model, we numerically simulate the transmission of disease in the small-world network and the BA scale-free network, respectively, and analyze the transmission dynamics behavior of the infectious disease. The simulation experiment verifies the validity of the theoretical result, which shows that this model is closer to the reality than traditional models.

The Use of System Dynamics Methodology in Building a COVID-19 Confirmed Case Model

Researchers used a hybrid model (a combination of health resource demand model and disease transmission model), Bayesian model, and susceptible-exposed-infectious-removed (SEIR) model to predict health service utilization and deaths and mixed-effect nonlinear regression. Further, they used the mixture model to predict the number of confirmed cases and deaths or to predict when the curve would flatten. In this article, we show, through scenarios developed using system dynamics methodology, besides close to real-world results, the detrimental effects of ignoring social distancing guidelines (in terms of the number of people infected, which decreased as the percentage of noncompliance decreased).

Über die Wahrscheinlichkeit des Aussterbens einer Population in einer langsamen periodischen Umgebung

International audience Wir studieren die Wahrscheinlichkeit des Aussterbens eines linearen Geburts- und Todesprozesses mit mehreren Typen in einer periodischen Umgebung, wenn die Periode groß ist. Diese Wahrscheinlichkeit hängt von der Jahreszeit ab und zeigt eine Diskontinuität im Zusammenhang mit einem "Canard" in einem langsam-schnellen dynamischen System. Der Diskontinuitätspunkt wird in einem Beispiel mit zwei Typen genau bestimmt. Dieses Beispiel kommt von einem Modell für eine Krankheit, die durch Vektoren übertragen wird. We study the probability of extinction of a population modelled by a linear birth-and-death process with several types in a periodic environment when the period is large compared to other time scales. This probability depends on the season and may present a sharp jump in relation to a "canard" in a slow-fast dynamical system. The point of discontinuity is determined precisely in an example with two types of individuals related to a vector-borne disease transmission model. On s'intéresse à la probabilité d'extinction d'un processus linéaire de naissance et de mort avec plusieurs types dans un environnement périodique dans la limite d'une période très grande. Cette probabilité dépend de la saison et peut présenter à la limite une discontinuité en lien avec un canard dans un système dynamique lent-rapide. On détermine précisément le point de discontinuité dans un exemple avec deux types d'individus provenant d'un modèle de transmission d'une maladie à vecteurs.