# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
## 1 Introduction  
Public health, peace, and safety are being threatened by a number of diseases all over the world today. Over the past three (3) years, infectious diseases like COVID-19 (SARS-CoV-2) have spread throughout the world [1]. Different diseases are transmitted in different ways. For instance, SARS-CoV-2 is typically spread by contact with bodily fluids and human touch, among other things [2]. Millions of individuals have died as a result of SARS-CoV-2 around the world [3, 4]. HIV can be passed from one person to another through sexual contact, the use of unsterilized, contaminated needles, and blood transfusions, among other methods [5]. Tuberculosis can spread from person to person when an infected person coughs into the air and an uninfected person breathes in the contaminated air [6, 7, 8]. Ebola Virus Disease (EVD) is an infectious disease that is transmitted or spread at alarming rates through body fluid contact and human body contact, among others [9, 10, 11]. Malariais another disease that can be transmitted by vectors such as _Anopheles_ mosquitoes, by sucking human blood infected with Plasmodium falciparum [12; 13]. Vaccine candidates have been developed to deal with the spread of the COVID-19 virus, and some of the outcomes of the vaccination effort have been impressive [14; 15]. Computational health applications and software have been developed and applied to support the diagnosis and self-management of HIV disease and reduce the transmission of the disease [16; 17]. Docking-based molecular simulation has been applied towards simulating possible drug targets to combat the transmission of Ebola virus disease (EVD) [18]. In another study, informatics tools such as the open data kit and the innovation hub were used to reduce the spread of an Ebola pandemic [19]. Bioinformatics approaches have been used to control Ebola virus disease (EVD) transmission [20]. Biological methods such as polymerase chain reaction (PCR) have been applied toward the diagnosis of malaria. This strategy can be employed as a viable tool for minimizing malaria transmission [21]. Other methods, such as computational informatics approaches, have been implemented for the correct dosage prescription of anti-malaria drugs [22], reducing the transmission of the disease. Recently, vaccine development approaches have been used to reduce malaria transmissions [23]. [PERSON] and colleagues [24] have modeled clinical immunity on malaria infection to reduce the transmission of the disease [24]. Malaria transmission has been controlled using modeling and simulation approaches [25; 26; 27; 28]. Computational frameworks have also been developed to reduce the transmission of different diseases [29; 30; 31]. One of the methods other scientists have utilized for controlling and reducing the transmission of diseases is the development of health-related information systems [32; 33; 34; 35]. Different medical and computational approaches have been applied towards combating hereditary diseases [36; 37]. Biological and bioinformatics methods have been applied to control and reduce the transmission rates of tuberculosis [38; 39; 40; 41; 42]. Mathematical modeling techniques have been utilized and applied to control other types of diseases to reduce their transmissions [43; 44; 45; 46; 47]. Phylogenomics, mathematical modeling, and bioinformatics methods have been applied towards controlling the monkeypva virus [48; 49; 50; 51]. However, recently, the monkeypox (MPX) virus was identified and listed as a disease of global emergency and international concern by the World Health Organization (WHO) [52]. Computational approaches such as machine learning can be used to forecast time-series transmission of the monkeypox virus to improve preparedness and reduce the transmission of the disease. The monkeypox (MPX) virus was discovered in orangutans in a zoo in Indonesia in 1949 by [PERSON] [53]. It was not until about a decade later, in 1958, that it was recognized as a member of the poxvirus family, and [PERSON] et al.