A Covid-19's integrated herd immunity (CIHI) based on classifying people vulnerability

Unfortunately, Covid-19 has infected millions of people very quickly, and it continues to infect people and spreads rapidly. Although there are some common symptoms of Covid-19, its effect varies from one individual to another. Estimating the severity of the infection has become a critical need as it can guide the decision makers to take an accurate and timely response. It will be valuable to provide early warning before infection takes place about susceptibility to the disease, especially since the lack of symptoms is a feature of the Covid-19 pandemic. Asymptomatic patients are considered as silent diffusers of the virus; hence, detecting people who will be asymptomatic before actual infection takes place will certainly safe the society from the uncontrolled and unseen spread of the virus. People can be classified based on their vulnerability to Covid-19 even before they are infected. Accordingly, precautionary measures can be taken individually based on the persons' Covid-19 susceptibility. This paper introduces a Covid-19's Integrated Herd Immunity (CIHI) strategy. The aim of CIHI is to keep the society safe with the minimal losses even with the existence of Covid-19. This can be accomplished by two basic factors; the first is an accurate prediction of the cases who will be asymptomatic if they were infected by the virus, while the second is to take suitable precautions for those who are predicted to be badly affected by the virus even before the actual infection takes place. CIHI is realized through a new classification strategy called Distance Based Classification Strategy (DBCS) which classifies people based on their vulnerability to Covid-19 infection. The proposed DBCS classifies individuals into six different types, then suitable precautionary measures can be taken for every type. DBCS can also identify future symptomatic and asymptomatic cases. In fact, DBCS consists of three sequential phases, which are; (i) Outlier Rejection Phase (ORP) using Hybrid Outlier Rejection (HOR) method, (ii) Feature Selection Phase (FSP) using Hybrid Feature Selection (HFS) method, and (iii) Classification Phase (CP) using Accumulative K-Nearest Neighbors (AKNN). DBCS has been compared with recent Covid-19 diagnosing techniques based on NileDS dataset. Experimental results have proven the efficiency and applicability of the proposed strategy as it provides the best classification accuracy.

Automated summary

This paper introduces a Covid-19's Integrated Herd Immunity (CIHI) strategy, aiming to keep society safe with minimal losses even in the presence of Covid-19. It achieves this by accurately predicting asymptomatic cases who will be infected by the virus and by taking suitable precautions for those predicted to be severely affected before actual infection takes place. The strategy utilizes a Distance Based Classification Strategy (DBCS) to classify individuals into different types based on their vulnerability to Covid-19, providing the best classification accuracy among recent Covid-19 diagnosing techniques.