COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

Abstract

We consider here an extended model, including several features of the recent COVID-19 outbreak: in particular the infected and recovered individuals can either be detected (+) or undetected (-) and we also integrate an intensive care unit (ICU) capacity. Our model enables a tractable quantitative analysis of the optimal policy for the control of the epidemic dynamics using both lockdown and detection intervention levers. With parametric specification based on literature on COVID-19, we investigate the sensitivities of various quantities on the optimal strategies, taking into account the subtle trade-off between the sanitary and the socio-economic cost of the pandemic, together with the limited capacity level of ICU. We identify the optimal lockdown policy as an intervention structured in 4 successive phases: First a quick and strong lockdown intervention to stop the exponential growth of the contagion; second a short transition phase to reduce the prevalence of the virus; third a long period with full ICU capacity and stable virus prevalence; finally a return to normal social interactions with disappearance of the virus. The optimal scenario hereby avoids the second wave of infection, provided the lockdown is released sufficiently slowly. We also provide optimal intervention measures with increasing ICU capacity, as well as optimization over the effort on detection of infectious and immune individuals. Whenever massive resources are introduced to detect infected individuals, the pressure on social distancing can be released, whereas the impact of detection of immune individuals reveals to be more moderate.

Transport effect of COVID-19 pandemic in France

“…Inspired by [10], the basic SIRD model is extended to a more sophisticated compartmental model which includes several features of the recent COVID-19…”

“…More sophisticated models could be considered, however it is important that the model we consider can be calibrated with the available data for French regions. On the basis of SIDU HR +/− model in [10], this model additionally considers that the infected undetected individuals I − and the infected detected individuals I +…”

“…As one of the most critical epidemiological parameters, the basic reproductive ratio R 0 defines the average number of secondary cases an average primary case produces in a totally susceptible population (see [13]). As for the model in [10], for the considered model in this paper, only the I − individuals transmit the disease to the susceptible individuals during the early phase of outbreak.…”

“…The copyright holder for this preprint this version posted July 29, 2020. ; https://doi.org/10.1101/2020.07.27.20161430 doi: medRxiv preprint reported values (see, e.g., [10] and references therein). Lower and upper bounds for each parameter are reported in Table 1 shows that the prior uncertainty is pretty high, since for example the difference between the 75 % and the 25 % quantiles for the number of people in hospital is more than 50000 at the end of the lockdown period.…”

Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA

“…Among the infected cases, hundreds of thousands of people are hospitalized. The COVID-19 pandemic has exceeded the capacities of healthcare resources, including for example healthcare personnel, testing resources, hospital beds, beds in intensive care units (ICUs) and ventilators [1][2][3]. This increased demand for healthcare resources has caused or exacerbated health disparities in access to healthcare [4].…”