# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
## 1 Introduction
The majority of cases present with lacerations in the region of the sexual organ, implying that spread occurred most likely through direct physical contact during sexual intercourse. Cases have been recorded in a number of countries that appear to be linked to occurrences in Spain (Madrid and the Canary Islands) and Belgium (Antwerp). Several countries, on the other hand, record instances with little or no established pathogenic connection to travel, contact with other patients or animals, or participation in particular activities. There have been 557 confirmed cases worldwide (including the EU and EEA) [74]. The symptoms of monkeypox infection are less severe than those of smallpox, although they are identical. The fact that monkeypox causes enlarged lymph nodes (lymphedenopathy) but smallpox does not is a massive distinction. Inflammation can occur in many different places on the body, particularly in the neck and armpits, and it can be restricted. Monkeypox has a 7-14-day gestation period (from infection to development of symptoms), although it can be as short as 521 days. Fever, headache, muscle pain, tiredness, stiffness, and shivering are all early indications and symptoms, in addition to swollen lymph nodes [75]. Within 1 to 3 days (occasionally more) following the onset of fever, a rash appears on the body, probably beginning on the face and expanding to other areas of the body. Prior to coming off, the infections advance through the phases of red bumps, enlarged pores, blisters, abscesses, and eventually open sores. Biomedical predictions are commonly inaccurate, and their inaccuracy is frequently overlooked [76]. Because the number of incidents to be analyzed cannot be approximated by a single person, forecasting the future of outbreaks and epidemics is difficult [77]. Irrespective of the flaws involved in medical forecasting, it is nonetheless vital to give a summary of the situation to the public. This will enable extensive preparation for future problems to be undertaken. Monkeypox research has been published in recent years, but it lacked exact measurements because most of the studies were country-specific rather than performance comparisons using machine learning techniques in some locations. When it comes to monkeyova data, they also lacked the time variable. Much of the research is based on linear knowledge, whereas such problems are not linear in nature [77]. Observational study models, which can only be initiated based on hypotheses, have been examined in some investigations. The bulk of monkeyova research does not take advantage of artificial intelligence or machine learning techniques. This is despite their benefits in diagnosing and preventing the disease. This paper seeks to apply different machine learning algorithms to forecast the transmission rate of the monkeyova endemic. Machine learning has proven over time to be a very effective and robust algorithm that can handle large volumes of data effectively. It can therefore be employed to prudently forecast the spread of the monkeyova pandemic. In specific terms, the main contributions of this work include:  
1. A survey of machine learning algorithms that can be used for the prediction and control of monkeyova was presented. 2. The study determined the best predictive model for confirmed monkey pox cases in various continents around the world that have become monkey pox hotspots. 3. A forecasting model for monkeyova outbreaks in countries around the world, focusing on Africa, Europe, and the Americas, was developed using different machine learning models. The models include adaptive boosting regression, gradient boosting regression, random forest regression, the least absolute shrinkage selection operator, ridge regression, ordinary least squares regression, and proposed stacking ensemble learning methods for predicting monkeyova transmission rate. 4. An evaluation of the performance of the proposed stacking ensemble machine learning technique for monkeyova transmission time series forecasting was done.