Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19

Abstract

Understanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model computes, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study quantifies the sensitivity of the outbreak dynamics of COVID-19 to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups. For nine distinct locations, our model estimates the fraction of the population that has been infected and recovered by Jun 15, 2020 to 24.15% (95% CI: 20.48%-28.14%) for Heinsberg (NRW, Germany), 2.40% (95% CI: 2.09%-2.76%) for Ada County (ID, USA), 46.19% (95% CI: 45.81%-46.60%) for New York City (NY, USA), 11.26% (95% CI: 7.21%-16.03%) for Santa Clara County (CA, USA), 3.09% (95% CI: 2.27%-4.03%) for Denmark, 12.35% (95% CI: 10.03%-15.18%) for Geneva Canton (Switzerland), 5.24% (95% CI: 4.84%-5.70%) for the Netherlands, 1.53% (95% CI: 0.76%-2.62%) for Rio Grande do Sul (Brazil), and 5.32% (95% CI: 4.77%-5.93%) for Belgium. Our method traces the initial outbreak date in Santa Clara County back to January 20, 2020 (95% CI: December 29, 2019–February 13, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant.

Assessing mandatory stay‐at‐home and business closure effects on the spread of COVID‐19

“…It is important to note that because the true number of infections is not visible in any country, it is impossible to assess the impact of national policies on transmission or new infections 28 . Instead, we follow other studies evaluating the effects of NPIs that use case numbers, implicitly assuming that their observed dynamics may represent a consistent shadow of the underlying infection dynamics 18 …”

Assessing the impact of data-driven limitations on tracing and forecasting the outbreak dynamics of COVID-19

“…Of course, more detailed models can surely be cast, but the very simple SIR model is sufficient to explain the infection dynamics when the quality of the real data is fairly good to provide a well model fitting, but not high enough to justify additional parameters, which can barely be inferred by the available data. Certainly, the usage of more complex models is mandatory when interested in understanding how the infection dynamics depends on specific variables, like the deploying different non-pharmaceutical interventions by governments around the world [ 51 ], the enforcing/relaxing ‘stay-at-home’ restrictions [ 33 , 34 ], the level of casual contacts [ 34 ], the number of patients taken to hospitals or to intensive care units [ 34 ], the percentage of patients with a weak immunity system [ 35 ] and so forth [ 32 ].…”

“…Of course, the deterministic nature of these SIR-type models disregards the diffusion of the uncertainty in the considered variables and therefore does not allow to get an estimate of the fluctuations in the number of hospitalized patients or to capture changes in disease dynamics. To overcome this limitation, more complex models have been proposed [ [32] , [33] , [34] , [35] ]. They mainly focused on exploring possible future epidemics scenarios of the long-term behaviour of the COVID-19 epidemic in order to assess the probability of further waves of infection [ [33] , [34] , [35] ].…”

Memory-Dependent Forecasting of COVID-19: The Flexibility of Extrapolated Kernel Least Mean Square Algorithm

“…where in (10), e(n) = s n+h − (Φ(y n ), w(n − 1) H is the apriori error.The update equation for w is then…”

“…Epidemiological models are driven by prior assumptions which are translated into a set of assumed parameterized mathematical equations. However, it has been reported that COVID-19 can behave in unexpected ways; for example, asymptomatic cases that can be infectious agents for several weeks [9], [10], there are also reports of different mutations of the virus in certain countries [11]- [13]. These challenges lead to the possibility of unknown dynamics and can limit the ability of conventional models in capturing certain intrinsic trends of the spread.…”