In March of 2018, about 500,000 desktop computers were infected with cryptocurrency mining malware in less than 24 hours. In addition to attacking desktop computers, malware also attacks laptops, tablets, mobile phones. That is, any device connected via the Internet, or a network is at risk of being attacked. In recent years, mobile phones have become extremely popular that places them as a big target of malware infections. In this study, the effectiveness of treatment for infected mobile devices is examined using compartmental modeling. Many studies have considered malware infections which also include treatment effectiveness. However, in this study we examine the treatment effectiveness of mobile devices based on the type of malware infections accrued (hostile or malicious malware). This model considers six classes of mobile devices based on their epidemiological status: susceptible, exposed, infected by hostile malware, infected by malicious malware, quarantined, and recovered. The malware reproduction number, RM , was identified to discover the threshold values for the dynamics of malware infections to become both prevalent or Lanz et al.; JAMCS, 33(4): 1-10, 2019; Article no.JAMCS.49867 absent among mobile devices. Numerical simulations of the model give insights of various strategies that can be implemented to control malware epidemic in a mobile network.

In the United States, prostitution is considered illegal in all but one state; Nevada allows some legal activities in exchange for substantial guidelines. In 2010, approximately 43,600 females were arrested for prostitution. Numerous intervention programs were established in order to obstruct the lifestyle of a prostitute (PRP, Project ROSE, etc.). There are many documentations and programs that share their forethought on prostitution; however, few target prostitution directly. To determine the dynamics of prostitution, this paper constructs a four-class compartmental model that focuses on the effectiveness of government intervention and rehabilitation of prostitutes mathematically. The basic reproductive number, [Formula: see text], helps to discover the threshold values for the dynamics of prostitution to become both prevalent or absent in society. This paper predominately observes government intervention to curtail a prostitution prevalent society. Various parameters and variables help to define and indicate the dynamics of prostitution to construct viable simulations. Successful prostitution interaction prevention deemed essential in prostitution prevention; however, government intervention corresponding with successful rehabilitation competitively challenges prostitution interaction prevention in reducing basic reproductive values.