Development of a Multivariate Prediction Network Model for epidemic progression in order to study the effects of lockdown time and coverage on a closed community of non-immune individuals.(Datasamples and .m code)

The aim of this study was to develop a realistic network model to predict qualitatively the relationship between lockdown duration and coverage in controlling the progression of the incidence curve of an epidemic with the characteristics of COVID-19 in a closed and non-immune population. Effects of lockdown time and rate on the progression of an epidemic incidence curve in a virtual closed population of 10 thousand subjects. Predictor variables were R0 values established in the most recent literature (2.7 and 5.7), without lockdown and with coverages of 25%, 50%, and 90% for 21, 35, 70, and 140 days in 13 different scenarios for each R0, where individuals remained infected and transmitters for 14 days. We estimated model validity by applying an exponential model on the incidence curve with no lockdown, with growth rate coefficient observed in realistic scenarios. Pairwise comparisons were performed using Wilcoxon test with Bonferroni correction between peak amplitude, peak latency, and total number of cases for each R0 used. For R0=5.7, the flattening of the curve occurs only with long lockdown periods (70 and 140 days) with a 90% coverage. For R0=2.7, coverages of 25 and 50% also result in curve flattening and reduction of total cases, provided they occur for a long period (70 days or more). Short and soft lockdowns had no relevant effect on incidence or casuistry. These data corroborate the importance of lockdown duration regardless of virus transmission.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV