**1. Introduction**

The unfolding COVID-19 has turned the world upside down [1], and this unprecedented trend is set to be the worst pandemic of a generation in terms of the increasing number of infected people. In its report on April 7, 2020, the US Centers for Disease Control and Prevention (CDC) [2] indicated that the COVID-19 poses a severe threat to public health. In its report, the CDC indicated that the "complete clinical picture with regard to COVID-19 is not fully known." To deal with this blurred picture, the World Health Organization (WHO) has compiled an overwhelmingly pertinent database [3]. The CDC provides a daily report that includes new data reported to the CDC by 55 USA jurisdictions [4]. Many other organizations have also provided similar resources and statistics including the Chinese

Medical Association Publishing House [5] and the European Centre for Disease Prevention and Control [6]. All the available approaches suggest that the number of new COVID-19 cases plays a key role in mapping its trajectory [7] worldwide.

COVID-19 is an evolving epidemic, and its up-and-down spread (trend or pattern commonly referred to as "curve") is a sign of its elusiveness. As of today (July 25, 2020), the COVID-19 is striking back with record-setting blows. In general, the COVID-19 issue relates to various facets such as public health and social as well as culture characteristics, and the world seems lacking sound methodologies on how to address this problem. Using predictive tracking or forecasting quantitative measures can assist the authorities, officials, organizations, and users to be proactive rather than reactive, and thus better prepared to mitigate potential adversaries.

#### **2. The model**

The literature seems to suggest that using the number of new cases and the level of social distancing are the key variables to analyze the COVID-19 in various ways. In what follows, we provide a background information about the four main COVID-19 modeling techniques: system dynamics, agent-based modeling, discrete event simulation, and hybrid simulation [8]. System dynamics uses differential equations to model resources, knowledge, people, and money, and the flows between these parameters explains the simulation behavior. The agent-based techniques are stochastic, enabling the variability of human behavior to be incorporated to help understand the likely effectiveness of proposed protective measures. The discrete event technique is also stochastic and models operations over time where entities flow through a number of activities. The hybrid simulation combines two or more techniques and is used for complex behavior. These techniques focus mainly on the unfolding phases of disease transmission such as quarantine, lock down, testing, and health care services. Some of these approaches have been rooted in the literature since 1777, and are complex, and cumbersome to implement. Without adequate specialists in advanced and complex mathematical theories and/ or computers, the logical question is thus: how could the proper personnel ascertain the COVID-19 spread in order to make proactive intervention decisions; e.g. to prepare hospitals and intensive care units, to mitigate the adverse impacts of what may happen in the near future? In search for accurate answer and based on the popular utilization of COVID-19 relationship between the number of cases [9] and population per land area, the idea of a new index was conceptualized in this study. It represents the number of reported confirmed new cases per population in the specific region the data was recorded. This new concept harnesses the number of cases and the regional crowdedness of people, which varies in the US from single digit to multi-thousand [2]. The index increases with more cases and with more dense populations (assumed shorter social distancing).

In this study, a combined linear regression analysis and data-fitting model is used. To deal with data fluctuation, this model adopted the hypothesis that was successfully used in other published studies of a short time span of one month maximum for forecasting, [10–13]. That hypothesis is logical and rational because the world knows that the virus spread in unpredictable; thus, longer time spans may encompass inaccurate data. The data is obtained from the New York Times Journal database [14]. The journal publishes the daily cases of COVID-19 by state and county in the US. The data from eleven states was used: New York State (NYS), Florida (FL), California (CA), Colorado (CO), Illinois (IL), Texas (Tx), Louisiana (LA), Washington (WA), Georgia (GA), New Jersey (NJ), and Michigan (MI).

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**Figure 1.**

*Dashboard of the CORVITT presented in this chapter.*

*How Can We Be Ahead of COVID-19 Curve? A Hybrid Knowledge-Based and Modified…*

We first constructed the slope for the confirmed cases and population in each of the states from March 27 to May 11, 2020, and then used polynomials fitting and linear regression analysis for forecasting. Linear regression is a direct way to deal with the

To accurately and proactively capture the big picture of COVID-19 spread, this study transfers the expertise of problem-solving from humans into a KB toolkit that takes in the same data, and yields the same conclusion but faster. This new KB-statistic hybrid approach effectively assists humans in dealing with COVID-19 massive daily data in addition to save time which is an essential requirement in dealing with the virus illusiveness. The study introduces for the first time in this field, to our knowledge, a novel KB toolkit to visualize the data and make it easier to understand and use without either mathematical or computer expertise. The CORVITT is a promising incubator for COVID-19 future forecasting platforms. Its VBA-based architecture blueprint emerges from an open-end modular adaptable structure encompassing a graphical-interface client allowing the users to easily operate it. This KB technology has been proven in other applications and thus applied in this study for COVID-19 [15–17]. To the author's knowledge, the concept of CORVITT has not been attempted to date for COVID-19. **Figure 1** shows the dashboard of CORVITT. The user could simply click the button that represents the state/province of interest, and the dashboard will display the microdata or the relative comparison of all states. **Figure 2** shows the data used in **Figure 1**. Although the amount of collected data is massive, the use of the dashboard is intuitive and user

*DOI: http://dx.doi.org/10.5772/intechopen.93867*

connections between variables.

**3. New knowledge-based toolkit**

We first constructed the slope for the confirmed cases and population in each of the states from March 27 to May 11, 2020, and then used polynomials fitting and linear regression analysis for forecasting. Linear regression is a direct way to deal with the connections between variables.
