A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies

Abstract

We present a stochastic compartmental network model of SARS-CoV-2 and COVID-19 exploring the e↵ects of policy choices in three domains: social distancing, hospital triaging, and testing. We distinguished between high-risk and low-risk members of the population, and modeled di↵erences in social interactions due to context, risk level, infection status, and testing status. The model incorporates many of the currently important characteristics of the disease, including overcapacity in the healthcare system and uncertainties surrounding the proportion and transmission potential of asymptomatic cases. We compared current policy guidelines from public health agencies with alternative options, and investigated the e↵ects of policy decisions on the overall proportion of COVID-19-related deaths. Our results support current policies to contain the outbreak but also suggest possible refinements, including emphasizing the need to reduce public, random contacts more than private contacts, and testing low-risk symptomatic individuals before high-risk symptomatic individuals. Our model furthermore points to interactions among the three policy domains; the e cacy of a particular policy choice depends on other implemented policies. Finally, our results provide an explanation for why societies like Germany, with lower average rates of social contact, are more successful at containing the outbreak than highly social societies such as Italy, despite the implementation of similar policy measures.

Recent Insight into SARS-CoV2 Immunopathology and Rationale for Potential Treatment and Preventive Strategies in COVID-19

“…Recent estimates based on aggregate data from China adjusted for demography and under-ascertainment bias suggest that the overall case fatality rate of Covid-19 could be close to 1.38% [ 4 ]. However, taking into account the ratio of asymptomatic cases support infection fatality ratios hovering 0.4–0.7% [ 5 ]. These figures are lower than that of Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which had case fatality rates around 10% and 36% respectively [ 6 ], yet they seem to exceed those of most severe influenza strains, which have case fatality rates averaging 0.1%.…”

Effect of clustering and correlation of belief systems on infectious disease outbreaks

“…An important characteristic of COVID-19 is the increased risk of severe disease and death for older adults and people with comorbidities [20]. A recent COVID model captures this differential risk by distinguishing between low-risk (2/3 of all individuals in the United States) and high-risk individuals [17]. Here, we adapted this model to investigate the effects of both increased contact between individuals of the same risk group, as well as differential vaccination coverage and social distancing levels between risk groups.…”

“…or not. Then, in a more complex model developed specifically for COVID-19 [17], we add a third binary attribute distinguishing between high- and low-risk individuals.…”

COVID‐19: Current knowledge in clinical features, immunological responses, and vaccine development

“…33 At the present time, while the true efficacy of these mitigation measures in controlling the current COVID-19 pandemic remain to be fully established, mathematical modeling provides insight into what is expected to occur or what may have transpired. [34][35][36] Mathematical modeling of COVID-19 transmission in Wuhan showed that the implementation of quarantine measures resulted in the R0 value decreasing from 2.65 to 1.98. This study also predicted that implementing lockdown 7 days earlier would have resulted in a 72% decrease of infected individuals.…”