Assessing the impact of Sars-CoV-2 on water consumption in São Paulo State, Brazil
Description
The pandemic caused by Sars-CoV-2 in 2020 led to a significant change in human behaviors, mainly because of the quarantine to avoid the spread of the virus. Measures affected both economic activities and citizens´ behaviors as they developed more intense hygiene habits to avoid contamination and switched to home office. These exceptional behaviors also affect the way that water is consumed and need to be fully understood to manage supply systems. Therefore, this study aims to investigate changes in residential and commercial water consumption in 31 municipalities in the State of São Paulo during the Sars-CoV-2. To do this, the expected consumption for the first half of 2020 was forecasted using the Holt-Winters Multiplicative method and compared to the data observed for the same period. The data and algorithm presented in this work allow the reproducibility of the results showed in the article mentioned above. For use the algorithm, it is necessary a Matlab software. In our work, we used the Matlab version R2015a. All instructions are presented in the file "README - INSTRUCTIONS". Adriano Anadinho Ferreira (Federal University of Goiás) downloaded all the materials, installed, ran the models using the data and functions in "analise_consumo_covid_matlab.m" and "boxplot_theils_mape.m" and reproduce the results in Tables 2, 3 and 4 and in Figures 4-6, 8 and 9.
Files
Steps to reproduce
------------------------------ 3. I N S T R U C T I O N S ------------------------------ Matlab version used: 8.5.0.197613 (R2015a) * Runing the algorithm in "analise_consumo_covid_matlab.m": 1. Open the file in Matlab; 2. Run the algorithm; 3. Choose the type of consumption to be analysed: enter "1" for "residential consumption" or "2" for "commercial consumption"; 4. Wait until the end of the Genetic Algorithm optimization process; 5. The process will yield: (i) the changes in water consumption between Jan-20 to Jun-20 for the type of consumption chosen in step 3 (Figure 4 of the article), as figure and Matlab spreadsheet "DIF"; (ii) the absolute variation per capita in residential water consumption, when "residential" is chosen in step 3 (Figure 6 of the article), as figure and Matlab spreadsheet "MEANDIF"; (iii) the overall forecast and real consumption for water, according to the type of consumption chosen in step 3, in Matlab spreadsheet "forecast"; (iv) the correlation between Sars-CoV-2 and rate of change for the type of consumption chosen in step 3 (Figure 8 of the article); (v) the Table 2 based on Matlab spreadsheet "meanmonth" and "stdmonth"; (vi) the Table 4 based on Matlab spreadsheet "Imeanmonth" and "Istdmonth"; (vii) the MAPE and Theil's U data, as "MAPE" and "TU" Matlab spreadsheet, respectively; (viii) extra files that contain the computed information. 6. To generate the plot of the forecast and real consumption for each municipality separetly, please, change the variable "YN" in the line 261 to "1", such as "YN=1;". Otherwise, keep the variable "YN" as 2, such as "YN=2;". NOTE: Some extra files will be generated in this process, as a basis for others analysis. The Table 3 was prepared separately in Excel based on the Matlab spreadsheet "forecast". The Figure 5 and Figure 8 were also prepared separetly in Excel based on the Matlab spreadsheet "forecast" and "DIF". * Run the algorithm in "boxplot_theils_mape.m": 1. Before runing this algorithm, it is necessary to run the file "analise_consumo_covid_matlab.m" first (both for the analysis of residential and commercial consumption), as it yield some necessary files in the process; 2. Open the file and run the algorithm in Matlab; 3. The process will yield: (i) a boxplot figure for MAPE and another for Theil's U; (ii) extra files that contain the computed information.