Background: More than 278 million cases and more than 5.4 million deaths due to coronavirus disease  (COVID-19) were reported worldwide by the end of 2021. More than 34 million cases and more than 478,000 deaths have been reported in India. Epidemiologists, physicians and virologists are working on a number of conceptual, theoretical or mathematical modelling techniques in the battle against COVID-19.  Protocol: This systematic review aims to provide a comprehensive review of published mathematical models on COVID-19 in India and the concepts behind the development of mathematical models on COVID-19, including assumptions, modelling techniques, and data inputs. Initially, related keywords and their synonyms will be searched in the Global Literature on Coronavirus Disease database managed by World Health Organisation (WHO). The database includes searches of bibliographic databases (MEDLINE, Scopus, Web of Science, EMBASE etc.,), preprints (MEDRXIV), manual searching, and the addition of other expert-referred scientific articles. This database is updated daily (Monday through Friday). Two independent reviewers will be involved in screening the titles and abstracts at the first stage and full-texts at the second stage, and they will select studies as per the inclusion and exclusion criteria. The studies will be selected for their quality, transparency, and ethical aspects, using the Overview, Design concepts, Details (ODD) protocol and International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making (ISPOR-SMDM) guidelines. Data will be extracted using standardized data extraction tools and will be synthesized for analysis. Disagreements will be resolved through discussion, or with a third reviewer. Conclusions: This systematic review will be performed to critically examine relevant literature of existing mathematical models of COVID-19 in India. The findings will help to understand the concepts behind the development of mathematical models on COVID-19 conducted in India in terms of their assumptions, modelling techniques, and data inputs.

Background: COVID-19 has tormented the global health and economy like no other event in the recent past. Researchers and policymakers have been working strenuously to end the pandemic completely. Methodology/ Principal Findings: Infectious disease dynamics could be well-explained at an individual level with established contact networks and disease models that represent the behaviour of the infection. Hence, an Agent-Based Model, SHIVIR (Susceptible, Infected, Admitted, ICU, Ventilator, Recovered, Immune) that can assess the transmission dynamics of COVID-19 and the effects of Non-Pharmaceutical Interventions (NPI) was developed. Two models were developed using to test the synthetic populations of Rangareddy, a district in Telangana state, and the state itself respectively. NPI such as lockdowns, masks, and social distancing along with the effect of post-recovery immunity were tested across scenarios. The actual and forecast curves were plotted till the unlock phase began in India. The Mean Absolute Percentage Error of scenario MD100I180 was 6.41 percent while those of 3 other scenarios were around 10 percent each. Since the model anticipated lifting of lockdowns that would increase the contact rate proportionately, the forecasts exceeded the actual estimates. Some possible reasons for the difference are discussed. Conclusions: Models like SHIVIR that employ a bottom-up Agent-Based Modelling are more suitable to investigate various aspects of infectious diseases owing to their ability to hold details of each individual in the population. Also, the scalability and reproducibility of the model allow modifications to variables, disease model, agent attributes, etc. to provide localized estimates across different places.