# Cooperative Attention-Based Learning between Diverse Data Sources
## 4 Methodology
### Spread-Based Analysis for Cooperative Learning  
In the context of spread-based analysis, \"spread\" refers to the dissemination or propagation of a phenomenon, such as the transmission of infectious diseases within a population. Spread-based analysis focuses on understanding and predicting the patterns of this propagation, considering various factors and data sources that may influence the spread. Here, we introduce a spread-based analysis approach for cooperative learning, designed to evaluate the potential of social data in forecasting the spread of disease with the help of the susceptible, exposed, infectious, and recovered (SEIR) model. The SEIR model is a popular epidemiological model for the spread of diseases. To predict the development of infectious diseases in a community, the SEIR model reflects the progression of individuals through several stages of infection, namely from susceptible to infected to recovered. Individuals are divided into four stages according to the SEIR model [12; 31; 32; 33; 34]. Susceptible (S): People in this stage are at risk of contracting the disease, but have not been exposed to it yet. Exposed (E): People in this stage have been exposed to the infection but are not yet contagious. They may not have symptoms just yet because they are still in the incubation stage. Infected (I): People who are in this stage can spread the illness to others. Recovered (R): Those in this stage have made a full recovery from the illness and have developed immunity. In order to explain how people move between various stages based on variables such as the transmission rate, recovery rate, and incubation duration, the SEIR model uses differential equations. By utilizing these equations, the SEIR model can forecast the progression of the disease over time and assist decision-makers in comprehending the effects of various control methods. In this study, the spread analysis model is defined through reward optimization of a social network with the help of physical network data. The model represents basic stages of susceptible, exposed, infectious, and recovered, in which the probable infections are defined with respect to an event, situation, or state. **Lemma 3**. : _Cooperative learning enables the multi-agent system to adapt to the spread of a disease, minimizing its impact on the network._  
Proof. : Assume agents in the multi-agent system can cooperate and learn from the spread of the disease. Agents utilize spread-based analysis to update their policies based on the current state of the disease and its impact on the network. As the disease spreads, agents adapt their strategies to minimize its impact, leading to a reduction in the rate of infection and an overall improvement in the network health. \(\square\)  
Here, we propose using cooperative strategy learning, which has a low reward with respect to reward accumulation dynamics. However, to create a consensus in reward distribution, CBF is selected for any unmatched micro events in case of any macro event. The micro and macro events refer to different levels of analysis or granularity when examining events within a system, such as a network or a social setting. Hence, the reward spread is defined as the exponential of the learning-based cooperation, cumulative infection rate, and environment. The spread process is then defined as the maximization of reward spread, which is to maximize the spread estimation \(\xi(s)\) by considering the exponential of the transmission rate between individuals \((i,\ j)\), and is formulated as shown below,\[ \begin{split} Spread\ \xi(s)=max_{x^{i}}\ [exp(\frac{\beta^{i^{j}_{d}(s)}_{x^{i}} )}{\text{minimax}(\sigma,\mu^{i^{j}_{d}})})\times L_{t}\ (cumulative\ inf\,\text{fection\ location\ rate})\times\\ \eta_{t}\ (Dynamic\ Environment\ Variable))\end{split}  \tag{11}\]  
This is considered in order to understand the spread and reward processes considering granularity. The social data bank analysis provides a building block to identify susceptible nodes by monitoring the activities of friends and relatives.