# Solution for the Mathematical Modeling and Future Prediction of the COVID-19 Pandemic Dynamics
## 2 Data Processing for Model Identification
Other studies determine the temperature range for this effect. For instance, the virus transmission is hindered by a specific humidity above 6 g/kg and an average air temperature above 11 \({}^{\circ}\)C.  
In some studies, the temperature is expressed as the average daily temperature or the average temperature over a time period. Others use average temperatures (an average between the minimum and the maximum recorded temperatures) [35], underlying that those countries, from a total of 204 considered, having high temperatures, showed fewer infections. Recently, there have been several attempts to correlate temperature and humidity with the seasonality of the COVID-19 infection rate [36]. However, temperature and humidity are not the only factors that affect the seasonal virus infection rate. Additionally, climate data are recorded outdoors, whereas most virus transmission is considered to occur indoors, which can have different conditions than the outside environment due to: heating, ventilation and air-conditioning (HVAC). The indoor conditions are affected by the socioeconomic status of the sample population and the geometric location of the country as well [37]. The main points in preventing the spread in society are hand hygiene, social distancing and quarantine. With increased testing capacity, detecting more COVID-19-positive patients in the community also enables the secondary case number reduction with stricter quarantine rules. Social distancing is advised, particularly in locations that have community transmission. Romanian authorities have installed quarantine and social/physical distancing as measures to prevent the further spread of the virus. As an essential aspect of this research, the measures taken by the authorities with the purpose of lowering the COVID-19 pandemic spread were quantified based on the impact of the legislation existing on each date from the considered time interval over the population's freedom of movement and socializing. The mentioned quantified values are enclosed between 0% and 100%. The number of tests is also essential for pandemic dynamics. However, the processing of this signal is difficult due to the testing decrease during the weekends. Using the daily gathered data, oscillations can appear on the data representation graphic due to the consistent differences between the number of tests taken during the working days and the number of tests taken during the weekend. The mentioned oscillations, in almost all cases, represent an impediment in the process identification of the mathematical models, an aspect which implies a deficit in the accuracy of the determined \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **No. of New** & **No. of New** & **Total No. of** & **Total No. of** \\  & **Cases** & **Deaths** & **Cases** & **Deaths** \\ \hline
28 February 2020 & 2 & 0 & 3 & 0 \\ \hline
28 March 2020 & 160 & 5 & 1452 & 29 \\ \hline
28 April 2020 & 277 & 19 & 11,616 & 650 \\ \hline
28 May 2020 & 197 & 10 & 18,791 & 1229 \\ \hline
28 June 2020 & 291 & 23 & 26,313 & 1612 \\ \hline
28 July 2020 & 1151 & 33 & 47,053 & 2239 \\ \hline
28 August 2020 & 1318 & 48 & 84,468 & 3507 \\ \hline
28 September 2020 & 1271 & 30 & 123,944 & 4748 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of output data. mathematical model. To address this issue, the effect of the oscillations is reduced by applying the 3-day average. This procedure will be useful in our future research based on Artificial Intelligence (AI) techniques. In order not to distort the original dynamics of the experimental responses, the averaging procedure was not extended for more than 3 days.