Evolutionary game theory has been successful in describing phenomena from bacterial population dynamics to the evolution of social behaviour. However, it has typically focused on a single game describing the interactions between individuals. Organisms are simultaneously involved in many intraspecies and interspecies interactions. Therefore, there is a need to move from single games to multiple games. However, these interactions in nature involve many players. Shifting from 2-player games to multiple multiplayer games yield richer dynamics closer to natural settings. Such a complete picture of multiple game dynamics (MGD), where multiple players are involved, was lacking. For multiple multiplayer games—where each game could have an arbitrary finite number of players and strategies, we provide a replicator equation for MGD having many players and strategies. We show that if the individual games involved have more than two strategies, then the combined dynamics cannot be understood by looking only at individual games. Expected dynamics from single games is no longer valid, and trajectories can possess different limiting behaviour. In the case of finite populations, we formulate and calculate an essential and useful stochastic property, fixation probability. Our results highlight that studying a set of interactions defined by a single game can be misleading if we do not take the broader setting of the interactions into account. Through our results and analysis, we thus discuss and advocate the development of evolutionary game(s) theory, which will help us disentangle the complexity of multiple interactions.

The novel Coronavirus pathogen Covid-19 is a cause of concern across the world as the human-to-human infection caused by it is spreading at a fast pace. The virus that first manifested in Wuhan, China has travelled across continents. The increase in number of deaths in Italy, Iran, USA, and other countries has alarmed both the developed and developing countries. Scientists are working hard to develop a vaccine against the virus, but until now no breakthrough has been achieved. India, the second most populated country in the world, is working hard in all dimensions to stop the spread of community infection.Health care facilities are being updated; medical and paramedical staffs are getting trained, and many agencies are raising awareness on the issues related to this virus and its transmission. The administration is leaving no stone unturned to prepare the country to mitigate the adverse effects. However, as the number of infected patients, and those getting cured is changing differently in different states everyday it is difficult to predict the spread of the virus and its fate in Indian context. Different states have adopted measures to stop the community spread. Considering the vast size of the country, the population size and other socio-economic conditions of the states, a single uniform policy may not work to contain the disease. In this paper, we discuss a predictive mathematical model that can give us some idea of the fate of the virus, an indicative data and future projections to understand the further course this pandemic can take. The data can be used by the health care agencies, the Government Organizations and the Planning Commission to make suitable arrangements to

Males and females follow distinct life‐history strategies that have co‐evolved with several sex‐specific traits. Higher investment into parental investment (PI) demands an increased lifespan. Thus, resource allocation toward an efficient immune system is mandatory. In contrast, resources allocated toward secondary sexual signals (ornamentation) may negatively correlate with investment into immunity and ultimately result in a shorter lifespan. Previous studies have addressed how resource allocation toward single sex‐specific traits impacts lifetime reproductive success (LRS). However, the trade‐offs between diverse sex‐specific characteristics and their impact on LRS remain largely unassessed impeding our understanding of life‐history evolution. We have designed a theoretical framework (informed by experimental data and evolutionary genetics) that explores the effects of multiple sex‐specific traits and assessed how they influence LRS. From the individual sex‐specific traits, we inferred the consequences at the population level by evaluating adult sex ratios (ASR). Our theory implies that sex‐specific resource allocation toward the assessed traits resulted in a biased ASR. Our model focuses on the impact of PI, ornamentation, and immunity as causal to biased ASR. The framework developed herein can be employed to understand the combined impact of diverse sex‐specific traits on the LRS and the eventual population dynamics of particular model systems.

Males and females evolved distinct life-history strategies, reflected in diverse life-12 history traits, summarized as sexual dimorphism. Life-history traits are highly interlinked. 13 The sex that allocates more resources towards offspring is expected to increase its life 14 span, and this might require an efficient immune system. However, the other sex might 15 allocate its resources towards ornamentation, and this might have immunosuppressive 16 effects. Activity of immune response may not be specific to the sex that produces the 17 eggs but could correlate with the amount of parental investment given. Informed by 18 experimental data, we designed a theoretical framework that combines multiple life-19 history traits. We disentangled sex-biased life-history strategies from a particular sex to 20 include species with reversed sex-roles, and male parental investment. We computed the 21 lifetime reproductive success from the fitness components arising from diverse sex-biased 22 life-history traits, and observed a strong bias in adult sex ratio depending on sex-specific 23 resource allocation towards life-history traits. Overall, our work provides a generalized 24 method to combine various life-history traits with sex-specific differences to calculate the 25 lifetime reproductive success. This was used to explain certain empirical observations as 26 a consequence of sexual dimorphism in life-history traits. 27 28 lifetime reproductive success, adult sex ratio 29 life-history traits. Theoretical models assessing the interaction of multiple life-history traits are 33 thus crucial to understand organisms' overall life-history and how they impact fitness. Theoretical 34 and experimental studies have shown how multiple life-history traits define an individual's lifetime 35 reproductive success (MooreIn this study, we present a model that addresses the interaction of essential sex-specific life-history 39 traits aiming to obtain the lifetime reproductive success of both sexes. This sheds light on how these 40 traits are contributing to an individual's life-history. We further present the consequences of various 41 sex-specific strategies affecting an evolving population. 42Most life-history traits have sex-specific differences. Sex-specific life histories have evolved in 43 the animal kingdom as a consequence of difference in gamete size known as anisogamy (Bell, 44 1978); females contribute large costly eggs to reproduction and males small cheap sperm. The 45 distinct resource allocation into the offspring asks for sex-specific life-history strategies (Trivers, 46 we focus on the sex-specific differences in three life history traits namely 1. Parental investment 2. 48Ornamentation and 3. Immunocompetence 49In many species, parental investment is not restricted to sperm and egg production. Parental 50 investment (PI) is any behavioural and physiological investment by a parent provided to the off-51 spring (Trivers, 1972(Trivers, , 2002. The sex that needs to allocate more resources towards the offspring 52 strives...