Modeling the transmission dynamics and the impact of the control interventions for the COVID-19 epidemic outbreak

Abstract

<abstract> <p>In this paper we develop a compartmental epidemic model to study the transmission dynamics of the COVID-19 epidemic outbreak, with Mexico as a practical example. In particular, we evaluate the theoretical impact of plausible control interventions such as home quarantine, social distancing, cautious behavior and other self-imposed measures. We also investigate the impact of environmental cleaning and disinfection, and government-imposed isolation of infected individuals. We use a Bayesian approach and officially published data to estimate some of the model parameters, including the basic reproduction number. Our findings suggest that social distancing and quarantine are the winning strategies to reduce the impact of the outbreak. Environmental cleaning can also be relevant, but its cost and effort required to bring the maximum of the outbreak under control indicate that its cost-efficacy is low.</p> </abstract>

Possible fates of the spread of SARS-CoV-2 in the Mexican context

“…The result of these early-stage exposures to the virus is the appearance of individuals without knowledge of their own condition. It is well established that an essential aspect of the spread of SARS-CoV-2 in the population is the high proportion of pre-symptomatic and asymptomatic [16,19,20] individuals that may infect vulnerable sectors without being aware of the situation. Most of these individuals are unreported cases that play a determinant role in the spread of the virus and, together with the unreported symptomatic cases, should be considered in the dynamics of a separated compartment U.…”

“…Open Sci. 7: 200886 parameters using several techniques [15][16][17][18][19][20][21]. Here, we used those values as starting ones but improved them through the fits of the data.…”

“…Mathematically, it is equivalent to avoid these parameters and only change the rate constants appropriately during the fit of the data. However, their introduction helps maintain the stoichiometry of the compartment I. Additionally, they keep unaltered the dependence of the rates on the intrinsic interaction of single individuals with the virus [15][16][17][18][19][20][21]. The parameter γ −1 measures the characteristic time after the which the symptoms from a seriously ill individual disappear (between three to six weeks), and δ −1 is the characteristic time endured from the beginning of symptoms until death (which ranges between 17 and 21 days) [16,17,20,22,28,[31][32][33].…”

“…Given the global character of SARS-CoV-2 spread, innovative epidemiological analyses have been recently proposed, including complex network analysis and conceptual maps, as well as compartmental models specifically created for the COVID-19 evolution, e.g. [13][14][15][16][17][18][19][20][21], among many others. Compartmental models are valuable since they can address important aspects of the spread of the disease by analysing the effects that social distancing and domestic confinement or pharmaceutical interventions may have on the evolution of the pandemics [14,17,[19][20][21].…”

“…Several compartmental models about the spread of the SARS-CoV-2 pandemic have been proposed in the past few months [15][16][17][18][19][20][21]. Their contribution to the establishment of the correlations between clinical observations and the transmission rates is valuable and represents a solid basis for further development.…”