# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
## 1 Introduction
#### Related Works
First, in 48% of the cases, PCR verified the presence of the MPXV virus; the other cases were discovered through the symptoms of the victims. Because patients were checked after their symptoms had subsided, an accurate diagnosis was not always possible. Confirmation of MPXV infection in many patients was not achievable during the pandemic because of a lack of local resources for obtaining samples. The lack of specimen collection in the current monitoring program has been discovered as a defect. Second, it is difficult to determine the incubation time for many patients. This is because they were unable to identify a specific cause of the illness or the date of infection. [PERSON] et al. [81] applied deep learning as an alternative diagnosis of skin disorders. A deep learning system (DLS) is utilized to provide a diagnostic evaluation of skin problems in clinical situations. The DLS differentiates between 26 of the most prevalent skin disorders, which account for around 80% of all skin issues seen in the healthcare system. The initial 14,021 cases from a tele dermatology practice that served 17 clinical sites were used for creation, and the final 3756 cases were used for validation. These de-identified cases were used for both the development and assessment of the DLS. The findings show that the DLS has the capacity to boost health providers' (GPs') capacity to effectively detect skin problems without additional specialty training by proposing disparity diagnoses that may not have been considered. The work's limitation is that even if the performance was satisfactory, more could have been done. Additionally, the proposed system was not evaluated against any existing deep learning systems. Finally, monkeypox is not one of the skin diseases that the suggested system is designed to identify. [PERSON] and [PERSON] [82] created a neuro-fuzzy-based model for detecting monkeypox virus variants in other pox families. The authors considered 18 symptoms linked to monkeypox, as reported by the medical professionals interviewed, as well as symptoms from the Nigeria Centre for Disease Control and other publications about monkeypox diagnosis. However, only three symptoms were chosen for the simulation when the model was put into practice. The proposed system has the drawback of not being able to employ the 18 inputs that are theoretically analyzed during analysis. Additionally, the study's dataset was kept secret. The system's performance is also mediocre. Moreover, not all the monkeypox symptoms are present in the input set. [PERSON] et al. [83] performed a comprehensive analysis of the peer-reviewed and unpublished literature on the evolution of monkeypox epidemiology, focusing particularly on the quantity of verified, suspected, and/or potential cases, age at presentation, death, and regional distribution. For data extraction, they found 48 peer-reviewed articles and 18 pieces of gray literature. Since the 1970s, there have been more instances of human monkeypox, with the DRC experiencing the largest rise. The analysis reveals an increase in instances of monkeypox, particularly in the DRC, where it is extremely endemic; a spread to other nations; and a rising median age from young children to young adults. These findings could be explained by the rise in human-to-human transmission following the end of smallpox immunization, which offered some cross-protection against monkeypox. The emergence of outbreaks outside of Africa emphasizes the disease's global significance. Increased surveillance and case detection are crucial for comprehending the epidemiology of this resurgent disease, which is changing continuously. The study was limited in that it was not possible to conduct a thorough investigation of the percentage of instances that were transmitted from person to person. Additionally, because data quantity and quality differed by jurisdiction, the writers did not give a clear view of the number of reported, suspected, and/or possible cases.