A Novel Systems Model to Simulate the Dynamics for the Analytical Study of the Proliferation of COVID-19

doi:10.21203/rs.3.rs-968078/v1

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A Novel Systems Model to Simulate the Dynamics for the Analytical Study of the Proliferation of COVID-19

https://doi.org/10.21203/rs.3.rs-968078/v1

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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.

Mathematical and Theoretical Biology

Infectious Diseases

Computational Biology

Epidemiology

MIMANSA

Systems Modeling

Mathematical modeling

COVID-19

SARS-CoV-2

Complex biological models

Our work is on developing a mathematical model that enables simulation of the SARS-CoV-2 spread by mimicking what happens in nature.

The primary objective of any good COVID-19 model is to be able to evaluate the impact of any possible changes or perturbations in the ecosystem. For a good model, it should be able to consider regional changes in the distribution of variants, the transmissibility of each variant, the vaccinated population percentage, the mask usage, as well as mobility. Additionally, the model should take into account the regional environmental factors and the daily environmental variability factors. In this paper, we have proposed a model called MIMANSA that satisfies all the above-listed requirements.

MIMANSA comprises of representations of major observations, research findings, clinical findings, and every individual’s social interaction into a mathematical form. The multiple representations are tied together logically to show how the SARS-CoV-2 spreads. The virus builds every day as a layer, connects each day by the next day in the form of a new layer. These layers form the virus proliferation network.

To understand how the virus spreads, we divided the in-person social interactions of an individual into three groups, namely home, workplace, and public places. We build a computational block to represent a small unit comprising of the three groups. These interactions are tracked inside the computational block thereby tracking the virus spread.

Multiple sub-models have been created to mimic reality. The major highlights are:

A Weibull distribution is used for representing the incubation period of SARS-CoV-2.
For training, the model uses time-series data of the vaccination, variant growth, mobility, and mask usage.
During training, it needs only 5 parameters. The first 3 are the Regional Environmental Variability in the exposure rate, silent carrier rate, and infection rate. The other two are the vaccination effectivity and quarantining constants.
One needs only the Daily Environmental Variability factor to be adjusted once the model is trained.
The model is trained, validated, and then used for making forecasts. The forecasts depend on how the lockdown values and the mask usage would change. The model predictions come within a 5% margin of error over a 3-to-4-week period provided that there are no immediate government orders on lockdown and/or mask usage.
MIMANSA is a self-driven model. As a starting point, MIMANSA needs to know the presence of silent carriers. The simulator starts with the number of silent carriers as its input. Once started, it will continue to build the virus spread network as more people come in contact with the initial silent carriers.

MIMANSA can simulate SARSCoV-2 spread in any country or region. Although MIMANSA is originally developed for the SARS-CoV-2, it can easily be modified to study the spread of any virus, and thus it has the capability of making a large impact in the field of epidemiology.

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A Novel Systems Model to Simulate the Dynamics for the Analytical Study of the Proliferation of COVID-19

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