Flattening the curve and the effect of atypical events on mitigation measures in Mexico: a modeling perspective
Santana-Cibrian, M.; Acuna-Zegarra, M. A.; Velasco-Hernandez, J. X.
Show abstract
On 23 and 30 March 2020 the Mexican Federal government implemented social distancing measures to mitigate the COVID-19 epidemic. We use a mathematical model to explore atypical transmission events within the confinement period, triggered by the timing and strength of short time perturbations of social distancing. We show that social distancing measures were successful in achieving a significant reduction of the effective contact rate in the early weeks of the intervention. However, "flattening the curve" had an undesirable effect, since the epidemic peak was delayed too far, almost to the government preset day for lifting restrictions (01 June 2020). If the peak indeed occurs in late May or early June, then the events of childrens day and mothers day may either generate a later peak (worst case scenario), a long plateau with relatively constant but high incidence (middle case scenario) or the same peak date as in the original baseline epidemic curve, but with a post-peak interval of slower decay.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Downsizing of contact tracing during COVID-19 vaccine roll-out 96%
- Modeling the effect of vaccination strategies in an Excel spreadsheet: The rate of vaccination, and not only the vaccination coverage, is a determinant for containing COVID-19 in urban areas 96%
- Estimating the impact of interventions against COVID-19: from lockdown to vaccination 96%
Similar papers in this journal
Similar papers in this journal
- Projection of Healthcare Demand in Germany and Switzerland Urged by Omicron Wave (January-March 2022) 96%
- Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories 95%
- Game theory of vaccination and depopulation for managing avian influenza on poultry farms 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.