Predicting the impact of asymptomatic transmission, non-pharmaceutical intervention and testing on the spread of COVID19

Abstract

We introduce a novel mathematical model to analyze the effect of removing non-pharmaceutical interventions on the spread of COVID19 as a function of disease testing rate. We find that relaxing interventions has a strong impact on the size of the epidemic peak as a function of intervention removal time. We show that it is essential for predictive models to explicitly capture transmission from asymptomatic carriers and important to obtain precise information on asymptomatic transmission by testing. The asymptomatic reservoir, reported to account for as much as 85% of transmission, will contribute to resurgence of the epidemic if public health interventions are removed too soon.Use of more basic models that fail to capture asymptomatic transmission can result in large errors in predicted clinical caseload or in fitted epidemiological parameters and, therefore, may be unreliable in estimating the risk of a second wave based on the timing of terminated interventions.

Optimal periodic closure for minimizing risk in emerging disease outbreaks

“…For instance, recent estimates point to asymptomatic infection accounting for around 20–30% of the total, with a similar percentage for pre-symptomatic infections [ 8 ]– together producing a majority. These findings have been supported by other experimental studies [ 9 ] and analysis of the existing data [ 10 , 11 ].…”

“…Such asymptomatic transmission is thought to be a significant driver for the worldwide distribution of the disease [ 23 , 24 ], since symptomatic individuals can be easily identified for quarantining while asymptomatics cannot (without widespread testing). Many models have been proposed to incorporate the broad spectrum of COVID-19 symptoms, as well as control strategies such as testing-plus-quarantining [ 11 , 20 ]. A common feature of such models is the assumption that exposed individuals enter into one of several possible infectious states according to a prescribed probability distribution (e.g., asymptomatic, mild, severe, tested-and-infectious, etc.)…”

“…Examples are shown in Fig 4 , where each panel shows results for an M = 2 model in which asymptomatics are significantly more (a) and less (b) infectious than symptomatics [ 11 ]. Symptomatic infectives are denoted with the subscript 1 and asymptomatics with the subscript 2.…”

“…A natural question is, how does T min depend on model parameters? Our approach in the following is to develop theory for T min in the SEIR-model, and then show how such a theory can be easily adapted to predict T min in more complete models, e.g., in COVID-19 models that include heterogeneous infectivity and asymptomatic spread [ 11 , 20 ].…”

Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails

“…For example, from time of infection to the time the person can transmit disease has a time distribution, that, for enough people in the compartment, will tend to center on an average by the central limit theorem for large enough samples drawn from any given distribution. There is evidence that COVID-19 presents symptomatic cases and asymptomatic cases, with asymptomatic cases [10][11][12] less likely to be identified and isolated [13][14][15][16][17] . There is an incubation period after infection that lasts until the incubating individuals become infectious.…”