Modeling the Effect of Lockdown Timing as a COVID-19 Control Measure in Countries with Differing Social Contacts

Abstract

The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model. Applying the R0 formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible R0 for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a “tunneling” effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.

Analysis of intervention effectiveness using early outbreak transmission dynamics to guide future pandemic management and decision-making in Kuwait

“…Previously we showed hypothetical modeling of a lockdown in Kuwait timed 5–10 days before the estimated peak for 90-days in length yielded the optimal reduction in actual incidence and hospitalization. ( Al-Zoughool et al, 2020 ; Oraby et al, 2021 ). Such lengthy lockdowns, while optimal, may not be practical resulting in devastating economic and psychosocial impacts.…”

Assessing regional risk of COVID-19 infection from Wuhan via high-speed rail

“…In recent decades, researchers from different fields have conducted numerous studies on infectious diseases ( Wu et al, 2020a ), such as investigating their whole spectrum and pathophysiology via retrospective studies on infected cases, including clinical spectrum, reproduction interval, incubation period, transmission modes ( Garbino et al, 2006 ; Yin and Wunderink, 2018 ; Zhu et al, 2020 ; Huang et al, 2020 ; Guan et al, 2020 ; Li et al, 2020c ; Weisberg et al, 2021 ; Cortinovis et al, 2021 ), exploring the effect of control measures on reducing their spread ( Kucharski et al, 2015 ; Funk et al, 2017 ; Tian et al, 2020 ; Oraby et al, 2021 ; Anderson et al, 2021 ), and examining their socio-economic impact ( Hai et al, 2004 ; Keogh-Brown and Smith, 2008 ; Dénes and Gumel, 2019 ; Beck et al, 2020 ; Mueller et al, 2021 ).…”

Using a Stochastic Continuous-Time Markov Chain Model to Examine Alternative Timing and Duration of the COVID-19 Lockdown in Kuwait: What Can be Done Now?

“…Previously we developed a CTMC model with eight states to depict the disease transmission and spread of SARS-CoV 2: susceptible (S), exposed (E), infected but asymptomatic (A), mildly infected and symptomatic (M), severely infected, symptomatic and hospitalized (H), detected and quarantined (Q), recovered (R), and dead (D) (SEAMHQRD-V) [11]. A CTMC can capture the initial disease dynamics and accommodate the uncertainties involved in the disease transmission process.…”

“…A 0 formula of the CTMC was determined in Oraby et al (2020) [11] using an approximation of the CTMC by a multi-type branching process to be proportional to the spectral radius ( ) of a simple transformation of the contact matrix ̃. That is,…”