Summary Online social networks such as Facebook and Twitter have become part of our daily lives. Their influence on business, politics, and society is considerable. Sensitive or unreliable information can adversely affect individuals, organizations, and governments. Due to the effects of the Covid‐19 epidemic, online news is more plentiful and accessible, which raises concerns about its reliability, quality, and authenticity. This article proposes the use of population dynamics model to study information dissemination on Facebook and a Susceptible‐Infected‐Recovered (SIR) model to examine information propagation as an outbreak of disease. We investigated 27 datasets with more than 270,000 messages, and the experiments showed that the population dynamics model is suitable for modeling the spread of information. The results revealed that information propagation could occur rapidly; after only 1–2 days. Additionally, we discovered that it is very crucial to find immediate solutions for preventing fake information as soon as it appears. This work enables us to understand the mechanism of information dissemination on social networks. This can help control and prevent the spread of misleading information, avoiding unintended consequences.

Summary The spread of brown plant hoppers (BPHs) can be affected by pesticides, migration/immigration, and natural enemies; in particular, using pesticides is the common method of farmers in fact. The article introduces a new approach to simulate the spread of BPH using cellular automata. The proposed model uses a geographic information system map as a simulation space to make results more visual and realistic. This model can give the accurate predictions about the spread of BPHs, thereby helping farmers make the appropriate decisions to prevent BPHs.

Finding an optimal design for the environmental surveillance network is a realistic need for any ecosystem manager. There are two main factors related to the optimization of a surveillance network: number of sampling points (deciding the sampling density) and locations of such sampling points. This paper aims at proposing an agent-based model to add k measuring devices into a current surveillance network. The simulation is used to verify multiple possibilities of a heterogeneous environment. A Correlation & Disk graph-based Surveillance Network (CDSN) is also implemented and then optimized. Gaussian process is used to model the measuring data and different covariance functions (e.g. variogram in geostatistics). The experimental results of the model are performed for the insect monitoring in Mekong Delta region, Vietnam.

In this paper, a new agent-based model is proposed to model and simulate visually growth and invasion of brown plant hoppers in different localities and at different times under effects of weather conditions such as temperature, humidity, rainfall and wind factors. The results from simulation are validated with light traps data collected in communes in the Mekong Delta region and show that brown plant hoppers life is affected by weather.