La Multi Ani Svetlana
Alana Fekety
Svetlana Boriskina develops new materials and technologies to harvest and manipulate light and other forms of radiation. Her multi-disciplinary research blends nanophotonics, plasmonics, electronics, thermodynamics and mechanics. Svetlana makes smart fabrics that provide thermal comfort indoors and outdoors and stay clean no matter what, new meta-materials that exhibit color without any dyes or pigments and change it in response to external stimuli, and novel solar harvesting platforms that can provide clean energy and fresh water to off-electrical-grid and disaster-stricken communities. She is the author and co-author of more than 130 peer-reviewed papers, several award-winning courses, and multiple patens. Svetlana is a passionate advocate for science education and science public communication, which she supports via leadership in professional science organizations, conferences, and journal editorial boards, mentorship of student groups, and public outreach.
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Meet Svetlana Shmulyian. She's an incredibly talented artist who I connected with recently. Listen to her terrific album Night at the Movies. When I learned that she was also a management consultant, naturally it made sense to feature her on this very newsletter. Svetlana is able to deftly manage multiple careers, and she's indeed an inspiration. Her comments below are both entertaining and enlightening.
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Svetlana Boriskina, PhD, conducts multi-disciplinary research that blends photonics, opto-electronics, polymer physics, thermodynamics, and mechanics. Her Multifunctional Metamaterials (META) Research Lab develops new materials and technologies to harvest and manipulate light, heat, and acoustic waves. The lab makes smart stain-resistant fabrics that provide thermal comfort indoors and outdoors, new meta-materials that bend light in unusual ways and exhibit tunable color without any dyes or pigments, polymer-based solid-state cooling technologies to replace conventional HVACs, and optothermo-mechanical technologies to provide clean energy and fresh water to off-electrical-grid and disaster-stricken communities. Boriskina authored over 130 peer-reviewed papers, taught several award-winning courses, and received multiple patents.
Multi-strain pandemics have emerged as a major concern. We introduce a new model for assessing the connection between multi-strain pandemics and mortality rate, basic reproduction number, and maximum infected individuals. The proposed model provides a general mathematical approach for representing multi-strain pandemics, generalizing for an arbitrary number of strains. We show that the proposed model fits well with epidemiological historical world health data over a long time period. From a theoretical point of view, we show that the increasing number of strains increases logarithmically the maximum number of infected individuals and the mean mortality rate. Moreover, the mean basic reproduction number is statistically identical to the single, most aggressive pandemic strain for multi-strain pandemics.
Copyright: 2022 Lazebnik, Bunimovich-Mendrazitsky. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Moreover, due to multiple socioeconomic processes, there is an increase in the speed at which new infections are spread [9]. To be exact, globalization has facilitated strain spread among countries through the growth of trade and travel [10]. Diseases are usually caused by pathogenic agents, including viruses and bacteria, which can be denoted as multiple variants, generally named strains. The emergence of a multi-strain pathogen imposes a new challenge to control the spread of disease [11]. Since new strains occur as it reproduces in new hosts, the large population of infected individuals offers a fertile ground for new strains to appear [12, 13]. For example, in the case of COVID-19, already in the first year and a half of the pandemic, four (globally common) strains were detected [7].
Cooper et al. [24] used the SIR model on the COVID-19 pandemic while relaxing the assumption that the population is mixing homogeneously and that the total population is constant in time. The authors show that the model has a fair fitting on six countries (China, South Korea, India, Australia, USA, Italy).
Similarly, Bunimovich-Mendrazitsky and Stone [26] proposed a two-age group, extension (adults and children), for the Polio pandemic spread. Using the model in [26], the extraordinary jump in the number of paralytic polio cases that emerged at the beginning of the 20th century can be explained. The model does not take into consideration some strains of Polio [27] which results in an increased divergence from the actual dynamics over time.
Indeed, the occurrence of pandemics with multiple mutations is common. For example, Minayev and Ferguson [35] investigate the interaction between epidemiological and evolutionary dynamics for antigenically variable pathogens. The authors proposed a set of relatively simple deterministic models of the transmission dynamics of multi-strain pathogens which provide increased biological realism. However, these models assume clinical-epidemiological dynamics that hold only for a subset of pathogens with cross-immunity of less than 0.4 [35]. In a similar manner, Dang et al. [36] developed a multi-scale immuno-epidemiological model of influenza viruses including direct and environmental transmission. The authors showed how two time-since-infection structural variables outperform classical SIR models of influenza. During the modelization, they used a within-host model that holds only for the influenza pandemic. In addition, Gordo et al. [12] proposed a SIRS model with reinfection and selection with two strains. The authors used a metapopulation of individuals where each individual is depicted as a vector in the metapopulation. This model has been validated on the influenza pandemic in the State of New York (USA), based on the genetic diversity of influenza gathered between 1993 and 2006, showing superior results compared to other SIR-based models [12]. Nonetheless, the sophistication of the model is both in its strength and shortcoming, from an analytical point of view, due to its stochastic and chaotic nature.
Moreover, the usage of multi-strain models that are used for specific pathogens is not restricted to influenza. Marquioni and de Aguiar [37] proposed a model where a pandemic starts with a single strain and the other strains occur in a stochastic manner as a by-product of the infection. The authors fitted their model onto the COVID-19 pandemic in China showing improved results when strain dynamics are taken into consideration compared to the other case [37]. Likewise, Khayar and Allali [38] proposed a SEIR (E-exposed) model for the COVID-19 pandemic with two strains. The authors analyzed the influence of the delay between exposure and becoming infectious on several epidemiological properties. Furthermore, they proposed an extension to the model (in the Single and two mutations model S1 Appendix) for multi-strain dynamics. In their model, an individual can be infected only once and develop immunity to all strains [38]. In our model, we relax this assumption, allowing individuals to be infected once by each strain. Comparably, Gubar et al. [39] proposed an extended SIR model with two strains with different infection and recovery rates. The authors considered a group of latent individuals who are already infected but do not have any clinical symptoms.
Furthermore, Fudolig et al. [42] proposed a multi-strain SIR based model with selective immunity by vaccination. The authors examined the influence of the introduction of a new strain. In particular, the authors examined the case where a new strain emerges in the population while the preexisting strain is near to extinction or reached a global equilibrium. The emergence of strains during the pandemic rather at the beginning, as suggested by the proposed model, is more realistic. However, it is not in the scope of the proposed model which aims to study the properties of a static number of strains.
In this research, we developed an extension of the SIRD-based model which allows an arbitrary number of strains M that originated from a single strain and is generic for any type of pathogen. The model allows each strain to have its unique epidemiological properties. In addition, we developed a computer simulation that provides an in silico tool for evaluating several epidemiological properties such as the mortality rate, max infections, and average basic reproduction number of a pandemic. The proposed model allows for a more accurate investigation of the epidemiological dynamics while keeping the data required to use the model relatively low. The main contribution of the proposed model compared to other SIR-based multi-strain models is two-fold: the proposed model does not assume any pathogen-specific properties keeping it as generic as possible by the standard SIR model and the order of infection from different strains is taken into consideration.
This paper is organized as follows: In Section 2, we introduce our multi-strain epidemiological model. Based on the model, we present a numerical analysis of three epidemiological properties as a function of the number of strains (M). In Section 3, we present the implementation of the model for the case of two strains (M = 2) and provide an analytical analysis of the stable equilibria states of the model and a basic reproduction number analysis. Afterward, we show the ability of the model to fit historical epidemiological data known to have two strains. In Section 4, we discuss the main advantages and limitations of the model and propose future work.
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