Temporal and Spatial Monitoring and Prediction of Epidemic Outbreaks

Abstract

This paper introduces a nonlinear dynamic model to study spatial and temporal dynamics of epidemics of susceptible-infected-removed type. It involves modeling the respective collections of epidemic states and syndromic observations as random finite sets. Each epidemic state consists of the number of infected individuals in an isolated population system and the corresponding partially known parameters of the epidemic model. The infectious disease could spread between population systems with known probabilities based on prior knowledge of ecological and biological features of the environment. The problem is then formulated in the context of Bayesian framework and estimated via a probability hypothesis density filter. Each population system under surveillance is assumed to be homogenous and fixed, with daily reports on the number of infected people available for monitoring and prediction. When model parameters are partially known, results of numerical studies indicate that the proposed approach can help early prediction of the epidemic in terms of peak and duration.

Epidemics control model with consideration of seven-segment population model

“…Similarly, MDP for breast and ovarian cancer has been proposed in [10]. There has been some work on the stochastic prediction of the epidemic curve [24] as well as on the analysis of epidemics behavior under stochastic perturbations [6], delay [5], and geographical data [25]. A dynamic Bayesian networkbased approach is proposed in [26] but this approach is specific to the prognosis of coronary heart disease.…”

Artificial Intelligence Transforms the Future of Health Care

“…Early prediction of the epidemic (such as peak and duration of infection) is possible if model parameters are partially known. 50 Potential outbreak areas for filoviruses were predicted in West, Southwest and Central parts of Uganda which is related to bat distribution and previous outbreaks areas. 51 In another study, Kesorn K. et al 52 predicted the morbidity rate of dengue hemorrhagic fever in central Thailand by estimating the infection rate in the female Aedes aegypti larvae mosquitoes and achieved a prediction accuracy of >95% and 88% in the training and test set, respectively.…”

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

“…In the SIR model, the population is divided into three classes, namely, N sus : susceptible, C inf : infected, and N rem : removed (by recovery and death) [62]. The time-varying number of cases in each class is governed by the infectivity rate σ i and the removal rate σ r [64]. Thus, when we know the initial N sus , C inf , N rem , and the constant values of σ i and σ r , we can predict the number of infectors in the future, based on which the number of daily new cases C new are calculated.…”