# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
## 1 Introduction
#### Related Works
The motivation for using stacking ensemble learning for this work is that machine learning models have historically been constructed with the presumption that a model will operate effectively if the training and test data are derived from the same feature space and distribution. However, if there is any alteration in the feature space or the distribution of data changes, then there will be a need to create a new model. It is costly to build a new model from scratch each time, as well as gather fresh training data each time. Stacking ensemble learning allows easy retrieval of the enormous volumes of training data with less time and effort. The motivation behind using \"stacked\" ensemble learning is that the \begin{table}
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**Author and Year** & **Technique** & **Contributions** & **Research Gap** \\ \hline [PERSON] et al. [78]. & Define the clinical characteristics of MPX/VZV. & MYX/VZV on the investigated using clinical, epidemiological, and analytical outcomes. & Lack of spatial or temporal connections between coinfections. Risk of cross contamination between different samples from the same case. \\ \hline [PERSON] et al. [79]. & Georeferencing attempt on modeling monkeypox case data distribution and propagation risk. & Discovered macrogeographic variances in environmental niche predictions. & Study is limited to Congo Basin. Additionally, the performance of the proposed technique is poor because of high error rate. \\ \hline [PERSON] et al. [80]. & Human-to-human transmission during a monkeypox using laboratory analysis. & In 48\% of the cases, PCR verified the presence of the MPXV virus. & Lack of specimen collection in the current monitoring program. Inability to determine the incubation time for many sufferers since they failed to properly identify a specific cause of the illness or a date of infection. \\ \hline [PERSON] et al. [81]. & Deep learning for diagnosis of skin disorders. & DLS distinguishes between 26 of the most common skin disorders, which account for around 80\% of all skin issues seen in healthcare system. & Proposed system was not evaluated against existing deep learning systems. Additionally, monkeypox is not one of the skin diseases that the proposed system is designed to identify \\ \hline [PERSON] and [PERSON] [82] & Neuro-fussy based model for diagnosis of monkeypox virus. & Was able to differentiate Monkeypox from other pork families Authors took into account 18 symptoms linked to it. & The system only use 3 out of the 18 monkeypx symptoms as inputs. Additionally, the dataset used was kept a secret. The system’s performance is also mediocre. \\ \hline [PERSON] et al. [83]. & Analysis of the peer-reviewed and unpublished literature on the evolution of the monkeypox epidemiology & Proposed increased surveillance and case detection are crucial for comprehending the epidemiology of this resurgent disease, which is changing continuously. & Inability to conduct a thorough investigation of the percentage of instances that were transmitted from person to person. Additionally, data quantity and quality differed by jurisdiction, No information on the number of reported, suspected, and/or possible cases. \\ \hline \hline \end{tabular}
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Table 1: Summary of Related Works. algorithm has the capacity to intelligently use knowledge acquired earlier for a different task or area to tackle new problems more quickly or effectively.