# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
## 1 Introduction
#### Related Works  
Much work has been done in monkeyova detection and diagnosis, as shown in Table 1. [PERSON] et al. [78] carried out research to see if coinfections are common and to define the clinical characteristics of these infections. The MPX/VZV coal infections were investigated using clinical, epidemiological, and analytical outcomes. The findings suggest that diseases with both MPX and VZV may modify the extent of infection. The ability to detect coinfections is enhanced by collecting numerous lesion samples. As the program progresses, it will be critical to have these methods in place to track changes in the particulate matter of situations in which many pathogenic strains simultaneously infect a victim over time. In that same time span, nevertheless, the percentage of coinfection cases has been steady. Many factors may contribute to the lack of spatial or temporal connections between coinfections. The number of reported cases examined may fluctuate because of community engagement operations and climatic conditions like the rainy season. While the monitoring police are gone from their positions, there could be a delay in the reporting of suspected cases, followed by an upsurge in cases after personnel come back for inspections. Any links between case categorization and season or location was not reported in the work. These relatively brief gaps in examinations may lessen as greater monitoring keeps going, leading to a more thorough study. One of the drawbacks of the work is the risk of cross-contamination between different samples from the same case. Another limitation of the study is that artificial intelligence techniques such as machine learning, with all their benefits, are not taken advantage of. [PERSON] et al. [79] explored the implications of georeferencing attempts on modeling monkeypox case data distribution and propagation risk. They evaluated the amount of time and effort put into transforming explanations of sickness prevalence sites into quantitative matrix dimensions (latitude and longitude). The researchers created three datasets from the same original monkeypox ailment occurrence data, each with a different standard of treatment and commitment: the first based on an interactive website; the second enhancing the first by referencing supplementary maps and digital guidebooks; and the third strengthened even more by consulting legacy monitoring records that give excellent extra details of each case. They created environmental habitat models and made predictions of monkeypox propagation risk based on each of the three incidence data sets to demonstrate the ramifications of these apparently minor enhancements in data quality. Subject to the type of georeferencing approach utilized, they discovered macrogeographic variances in environmental niche predictions. In the Africa region and along the periphery of the Congo Basin, less careful georeferencing indicated significantly smaller areas as having potential for monkeypox transmission. These findings have consequences for mapping operations, as each increased degree of georeferencing precision necessitates a significant increase in time investment. The limitation of the study is that it is only limited to the Congo Basin. Additionally, the performance of the proposed technique is poor because of the high error rate. [PERSON] et al. [80] investigated the prolonged human-to-human transmission during a monkeypox epidemic in the Democratic Republic of the Congo. The population attack rate (the percentage of people who live with an infected person and develop MPXV symptoms) was 50%. Nine families within this health zone experienced multiple transmission incidents, totaling more than six transmission episodes. An average of 8 days (between 4 and 14 days) passed during incubation. The increasing incidence and spread seen in this study highlight the need for monkeypox surveillance and early detection. The study has some flaws that need to be pointed out.