In this paper, we present a systems model for COVID-19 called ‘Multilevel Integrated Model with a Novel Systems Approach’ or MIMANSA for short. This model goes to the individual patient level and mimics small steps in the process of virus spread. Despite many models simulating the growth of COVID-19 cases, it is rare to see a comprehensive model that takes into account the transmissibility of variants, the prevalence of multiple variants, growth of the variants, percentage of vaccinated population, exposure rate, infection rate, silent carrier rate, secondary infection rate, mask usage, and people mobility all in one model.
We begin by categorizing the in-person social interactions of an individual into three areas: household, workplace, and public places. With each interaction, the virus spreads from an infectious
person to a healthy individual. We build the model, one level at a time, covering daily person-to-person interactions. Further, MIMANSA forms a new layer of network for every new day. These layers form a part of the virus proliferation network.
MIMANSA models the virus incubation period using a Weibull distribution. Once the network starts building, the virus growth can be curtailed only through increased vaccinations or through non-pharmaceutical interventions such as mask usage, reduced mobility, and/or quarantine.
MIMANSA takes the mobility data, mask usage data, variant prevalence data, and vaccination data as inputs. Despite the model being intricate, it uses only 5 parameters for training. Once the model is trained, it can be slightly adjusted by the daily environmental variable. This variable corrects for day to events as well as varying environmental conditions in a given location.
We present the results of the training and validation for the USA, California, and the UK. It is seen that during the validation stage, the model accuracy is within 2%. Further, projections are made for about 3 weeks. In the end, we have presented the results of a study on the effect of vaccination on the number of COVID cases in the USA. It shows how MIMANSA can be used for studying the impact of multiple scenarios. Additionally, the model is not only useful for making predictions in the number of cases but also useful as an educational aid for explaining the proliferation network of the virus in real life. Although MIMANSA is originally developed for the SARS-CoV-2, it can be modified to study the spread of any other virus, and in any region.