Combined effects of prevention and quarantine on a breakout in SIR model

Abstract

Recent breakouts of several epidemics, such as flu pandemics, are serious threats to human health. The measures of protection against these epidemics are urgent issues in epidemiological studies. Prevention and quarantine are two major approaches against disease spreads. We here investigate the combined effects of these two measures of protection using the SIR model. We use site percolation for prevention and bond percolation for quarantine applying on a lattice model. We find a strong synergistic effect of prevention and quarantine under local interactions. A slight increase in protection measures is extremely effective in the initial disease spreads. Combination of the two measures is more effective than a single protection measure. Our results suggest that the protection policy against epidemics should account for both prevention and quarantine measures simultaneously.

Novel coronavirus, poor quarantine, and the risk of pandemic

“…At the time of writing (February 4 th , 2020), cases in 27 countries have been confirmed and the toll is increasing at an alarming rate [2]. In general, control of the spread of infection uses strategies such as increasing awareness, protective clothing, treatment, and perhaps most importantly vaccination [3]. However, in the case of novel coronavirus (2019-nCoV), antiviral treatment and vaccination are not available [4].…”

Suppressing the impact of the COVID-19 pandemic using controlled testing and isolation

“…The importance of these numbers is not only in coping with the first wave of the pandemic, but also monitoring new occurrences of the disease over time since it will significantly reduce the overall quarantine rate and accelerate the containment time in these future waves. This large scale model differs substantially from mean-field differential equations based epidemiological models in that it allows to microscopically track the target individuals, something which cannot be done using generalizations of SIR, SIRS or other common epidemic spread models which are based on large population mean-field techniques (6,9,13,21). It can also easily incorporate new models for infection probability.…”

Skepticism and rumor spreading: The role of spatial correlations

“…Rumor-or opinion-spreading models [5,6] provide simple descriptions of how misinformation and gossips diffuse between networked individuals, in a similar way as contagious diseases propagate through direct contact [7][8][9][10]. In the latter, where the contamination is usually involuntary and unconscious, a deeper understanding of the process has led to a more effective protection of susceptible populations through better designed vaccination [11][12][13][14][15][16][17], isolation [18,19], and quarantine [20][21][22] campaigns. The comprehension of the main underlying mechanisms in the case of rumor propagation, involving an intentional and directed action from the spreader, may help to devise efficient strategies to counteract the effects of anti-and pseudoscience movements.…”