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In this paper, a Chikungunya dynamical model with virus mutation and transovarial transmission is developed, which incorporates the effect of seasonal temperature changes on disease transmission through time-dependent parameters. Firstly, the threshold parameter R 0 m that determines the persistence and extinction of mosquito populations is given, and then the disease reproduction number R 0 is defined. Secondly, it is proved that if R 0 m > 1 and R 0 < 1, the disease disappears; if R 0 m > 1 and R 0 > 1, then Chikungunya with mutants and non-mutants will persist simultaneously. Finally, a case study is carried out with the data in Kerala, India, where the virus mutation causes the outbreak of Chikungunya. Data on newly confirmed human cases in the state between 2007 and 2010 is fitted and the theoretical results obtained in the previous section are validated. In addition, the effects of seasonal temperature change, virus mutation and transovarial transmission on the prevalence of the disease are studied by numerical simulations from different aspects. 2020 MSC: 34K13; 37N25; 92D30. • A Chikungunya model with virus mutation and transovarial transmission is developed. • The effect of seasonal temperature changes is incorporated. • If there is no seasonality, R 0 will be overestimated. • If there is no virus mutation, the infected population will be underestimated. • When R 0 is less than and close to 1, increasing of transovarial transmission rate will lead to disease outbreak. [ABSTRACT FROM AUTHOR]

The Severe Acute Respiratory Syndrome Coronavirus 2 (SAR-CoV-2) is the strain of coronavirus that causes coronavirus disease (COVID-19), the respiratory illness that resulted in COVID-19 pandemic in early December 2019. Due to lack of knowledge of the epidemiological cycle and absence of any type of vaccine or medications, the Government issued various non-pharmacological measures to end the COVID-19 pandemic. Several researchers applied the Susceptible-Infected-Recovered-Deceased (SIRD) compartmental epidemiology process model to identifying the effect of different governments intervention methods enforced to mollify the spread of COVID-19 epidemic. In this paper, we aim to provide a modified SIRD model for COVID-19 spread prediction. We have analyzed the data of the Northern and Southern states of India from January 30, 2020, to October 24, 2020 using the proposed SIRD model and existing SIRD model. We have made the predictions with reasonable assumptions based on real data, considering that the precise course of an epidemic is highly dependent on how and when quarantine, isolation, and precautionary measures were imposed. The proposed method gives better approximation values of new cases, R0 (Reproductive Number), daily deaths, daily infectious, transmission rate, and recovered individuals.Through the analysis of the reported results, the proposed SIRD model can be an effective method for investigating the effect of government interventions on COVID-19 associated transmission and mortality rate at the time of epidemic. [ABSTRACT FROM AUTHOR]