Quantifying collective intelligence and behaviours of SARS-CoV-2 via environmental resources from virus' perspectives

Collective intelligence of viruses is witnessed in many research articles. Most of the researches focus on the qualitative properties or observations. In this research, we model the behaviours and collective intelligence of SARS-CoV-2 by minimal spanning trees (MSTs), which specify the underlying mechanisms of resource allocation in the viral colony. The vertices of the trees are 50 states, DC and NYC in the USA. The weights of the edges are assigned by the reciprocal of the sum of cases or deaths of COVID-19. The types of trees are decided by the chosen 18 factors. We sample 304 time-series data and compute their MST-based auto-correlations for stability analysis. Then we perform correlated analysis and comparative analysis on these stable factors. Our results show MST approach fits the collective intelligence modelling very well; the total cases and total deaths over areas are highly correlated in terms of MSTs; and these stable factors have little to do with the geographical distance. The results also indicate the colonisation of SARS-CoV-2 is pretty mature and organised. Based on the results, for environmental or health policies, we should also turn our attention to the transmission routes that are independent of or far away from human population or densities. The viruses' colonies might already exist in the wild in a large scale, not only in the populated or polluted cities. We shall build or conduct a monitoring system of their colonisation and survival techniques, in order to terminate, contain or live with their communities.