MalSEIRS: Forecasting Malware Spread Based on Compartmental Models in Epidemiology

Over the last few decades, the Internet has brought about a myriad of benefits to almost every aspect of our daily lives. However, malware attacks have also widely proliferated, mainly aiming at legitimate network users, resulting in millions of dollars in damages if proper protection and response measures are not settled and enforced. In this context, the paper at hand proposes MalSEIRS, a novel dynamic model, to predict malware distribution in a network based on the SEIRS epidemiological model. As a result, the time-dependent rates of infection, recovery, and loss of immunity enable us to capture the complex dynamism of malware spreading behavior, which is influenced by a variety of external circumstances. In addition, we describe both offensive and defensive techniques, based on the proposed MalSEIRS model, through extensive experimentation, as well as disclosing real-life malware campaigns that can be better understood by using the suggested model.

An Adaptive Biosystems Engineering Approach towards Modeling the Soluble-to-Insoluble Phase Transition of Clofazimine

Clofazimine (CFZ) is a weakly basic, small-molecule antibiotic used for the treatment of mycobacterial infections including leprosy and multidrug-resistant tuberculosis. Upon prolonged oral administration, CFZ precipitates and accumulates within macrophages throughout the host. To model the pharmacokinetics of CFZ, the volume of distribution (Vd) was considered as a varying parameter that increases with continuous drug loading. Fitting the time-dependent change in drug mass and concentration data obtained from CFZ-treated mice, we performed a quantitative analysis of the systemic disposition of the drug over a 20-week treatment period. The pharmacokinetics data were fitted using various classical compartmental models sampling serum and spleen concentration data into separate matrices. The models were constructed in NONMEM together with linear and nonlinear sigmoidal expansion functions to the spleen compartment to capture the phase transition in Vd. The different modeling approaches were compared by Akaike information criteria, observed and predicted concentration correlations, and graphically. Using the composite analysis of the modeling predictions, adaptive fractional CFZ sequestration, Vd and half-life were evaluated. When compared to standard compartmental models, an adaptive Vd model yielded a more accurate data fit of the drug concentrations in both the serum and spleen. Including a nonlinear sigmoidal equation into compartmental models captures the phase transition of drugs such as CFZ, greatly improving the prediction of population pharmacokinetics and yielding further insight into the mechanisms of drug disposition.

Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra

Compartmental models are often used to understand and predict the progression of an infectious disease such as COVID-19. The most basic of these models consider the total population of a region to be closed. Many incorporate human mobility into their transmission dynamics, usually based on static and aggregated data. However, mobility can change dramatically during a global pandemic as seen with COVID-19, making static data unsuitable. Recently, large mobility datasets derived from mobile devices have been used, along with COVID-19 infections data, to better understand the relationship between mobility and COVID-19. However, studies to date have relied on data that represent only a fraction of their target populations, and the data from mobile devices have been used for measuring mobility within the study region, without considering changes to the population as people enter and leave the region. This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. We use the telecoms data to both measure mobility within the country and to provide a real-time census of people entering, leaving and remaining in the country. We develop multiple SEIR (compartmental) models parameterized on these metrics and show how dynamic population metrics can improve the models. We find that total daily trips did not have predictive value in the SEIR models while country entrances did. As a secondary contribution of this work, we show how Andorra's serology testing program was likely impacted by people leaving the country. Overall, this case study suggests how using mobile phone data to measure dynamic population changes could improve studies that rely on more commonly used mobility metrics and the overall understanding of a pandemic.

Epidemic Compartmental Models and Their Insurance Applications

Our society’s efforts to fight pandemics rely heavily on our ability to understand, model and predict the transmission dynamics of infectious diseases. Compartmental models are among the most commonly used mathematical tools to explain reported infections and deaths. This chapter offers a brief overview of basic compartmental models as well as several actuarial applications, ranging from product design and reserving of epidemic insurance, to the projection of healthcare demand and the allocation of scarce resources. The intent is to bridge classical epidemiological models with actuarial and financial applications that provide healthcare coverage and utilise limited healthcare resources during pandemics.

The discrete-time Kermack–McKendrick model: A versatile and computationally attractive framework for modeling epidemics

The COVID-19 pandemic has led to numerous mathematical models for the spread of infection, the majority of which are large compartmental models that implicitly constrain the generation-time distribution. On the other hand, the continuous-time Kermack–McKendrick epidemic model of 1927 (KM27) allows an arbitrary generation-time distribution, but it suffers from the drawback that its numerical implementation is rather cumbersome. Here, we introduce a discrete-time version of KM27 that is as general and flexible, and yet is very easy to implement computationally. Thus, it promises to become a very powerful tool for exploring control scenarios for specific infectious diseases such as COVID-19. To demonstrate this potential, we investigate numerically how the incidence-peak size depends on model ingredients. We find that, with the same reproduction number and the same initial growth rate, compartmental models systematically predict lower peak sizes than models in which the latent and the infectious period have fixed duration.

502Modelling Chlamydia trachomatis and Mycoplasma genitalium transmission in men who have sex with men

Background Chlamydia trachomatis (CT) and Mycoplasma genitalium (MG) are two common sexually transmitted infections in men who have sex with men that are assumed to be transmitted only by oral and anal sex. However, this assumption has not been tested in mathematical models before. Methods To test a variety of potential transmission routes against the known proportion of infections at the oropharynx, mouth and anus, we established 20 compartmental models involving different sexual practices. We tested transmission by a) only anal sex and oral sex; b) adding rimming and kissing to anal and oral sex, and c) adding sequential sexual practices (e.g. oral sex followed by oral-anal sex (rimming) or vice versa. Results We could not replicate the proportion of anatomical sites infected with CT using anal or oral sex alone or by adding riming and kissing. However, if we included sequential sexual practices, then we were able to replicate the prevalence of CT at each site including infection at more than one site. In contrast, we were able to replicate infection for MG at the three sites using transmission routes that involved only anal sex and oral sex without the need for adding any other routes of transmission. Conclusions Our model indicates that more complicated transmission routes are required to explain the observed prevalence of infection with CT, but that for MG transmission involving only anal and oral sex is sufficient. Key messages Unlike CT, MG transmission does not require more complicated transmission routes.