<p>The Human Metapneumovirus (HMPV) has become a major public health concern, especially in areas with high population density. It causes mild to severe respiratory infections, which frequently result in crowded in hospitals and higher death rates among vulnerable groups including the elderly and children. Designing and implementing plans to stop the spread and lessen its effects on healthcare systems requires quick decision making. To solve this complex problem, we develop an emergency control system based on fuzzy credibility neural network to evaluate and select the best HMPV strategy. For this, we collect the expert’s information about emergency control system for the HMPV strategies. Subsequently, compute the expert decision matrices’ criterion weights using Hausdorff’s distance measure. Next, calculate the hidden and the output layer information about the HMPV strategies by using the Fuzzy credibility Yager weighted aggregation operator. After that, we use the fuzzy credibility number score function to determine the score values of the output information and rank the output information of the proposed model to select the best HMPV strategy. Lastly, we are used the three MCDM methodologies to verify our proposed method.</p>