a Laboratory of Multiscale Studies in Building Physics, Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
b Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zurich, Switzerland
c Department of Mathematics, Swiss Federal Institute of Technology, Zurich, Switzerland
d Institute of Microbiology, D-BIOL, Swiss Federal Institute of Technology, Zurich, Switzerland
e Department of Mechanical and Process Engineering, Swiss Federal Institute of Technology, IFD, Zurich, Switzerland
Relevant pandemic-spread scenario simulations can provide guiding principles for containment and mitigation policies. We devised a compartmental model to predict the effectiveness of different mitigation strategies with a main focus on mass testing. The model consists of a set of simple differential equations considering the population size, reported and unreported infections, reported and unreported recoveries, and the number of COVID-19-inflicted deaths. We assumed that COVID-19 survivors are immune (e.g., mutations are not considered) and that the virus is primarily passed on by asymptomatic and pre-symptomatic individuals. Moreover, the current version of the model does not account for age-dependent differences in the death rates, but considers higher mortality rates due to temporary shortage of intensive care units. The model parameters have been chosen in a plausible range based on information found in the literature, but it is easily adaptable, i.e., these values can be replaced by updated information any time. We compared infection rates, the total number of people getting infected and the number of deaths in different scenarios. Social distancing or mass testing can contain or drastically reduce the infections and the predicted number of deaths when compared with a situation without mitigation. We found that mass testing alone and subsequent isolation of detected cases can be an effective mitigation strategy, alone and in combination with social distancing. It is of high practical relevance that a relationship between testing frequency and the effective reproduction number of the virus can be provided. However, unless one assumes that the virus can be globally defeated by reducing the number of infected persons to zero, testing must be upheld, albeit at reduced intensity, to prevent subsequent waves of infection. The model suggests that testing strategies can be equally effective as social distancing, though at much lower economic costs. We discuss how our mathematical model may help to devise an optimal mix of mitigation strategies against the COVID-19 pandemic. Moreover, we quantify the theoretical limit of contact tracing and by how much the effect of testing is enhanced, if applied to sub-populations with increased exposure risk or prevalence.
The recent outbreak of COVID-19 in Wuhan, China has led to a pandemic with significant impacts on public health and economies across the globe. As the number of infected people increases in a community, public health policies move away from containment of the outbreak to mitigation strategies such as social distancing and isolation, with considerable detrimental effects on public life and the economy. Although less restrictive mitigation strategies would be desirable, alternative choices are limited owing to a lack of resources and technologies. To better understand the potential effects of a particular mitigation strategy, we must assess the underlying factors that impact the spread of the outbreak, and for this mathematical models integrating the relevant underlying mechanisms are a good tool.
Various biomathematical approaches have been proposed and pursued for epidemic-spread modelling. At the highest level, one can categorise them into agent based , network [2, 3] and compartmental models . The chosen model can then be closed using empirical/machine-learning, statistical or deterministic approaches . Agent/network based models may provide highly refined scenario analysis tools, but their black-box nature may not be appropriate for large-scale mitigation scenario assessments. Compartmental models, on the other hand, can provide us with insightful and explicit solutions more relevant for drawing fundamental conclusions. These type of models may employ deterministic or stochastic methodologies to tackle the evolution of the epidemic within a susceptible population. The former category belongs to deterministic descriptions, which include susceptible-infectious-removed (SIR), susceptible-infectious-susceptible (SIS) and susceptible-exposed-infectious-removed (SEIR) models . A more complicated class of models incorporates the stochastic nature of the epidemic spread via the framework of, for example, Ito- or Levy-type processes [7–9]. Both deterministic and stochastic descriptions, at their fundamental level, rely on reaction mechanisms that characterise infections, recoveries and deaths within different sub-groups of the population.
Although recently there has been a massive effort in pandemic-spread investigations of COVID-19, for example using SEIR models [10–13], network models  and agent-based simulations [15, 16], no studies that investigated the effect of mass testing are found in the literature. As recent efforts in, for example, China are channelled towards mass testing in relatively large cities , it is necessary to also provide theoretical foundations for mass testing-based mitigation strategies. To achieve this, we separated the category of detected cases from infected ones who remain undetected (e.g., by the sheer lack of test kits) and predicted their coupled dynamics. Note that detected here refers to persons being isolated, which comprises not only those who tested positive, but also those who have strong symptoms and thus stay in self-quarantine. It is also important to note that in the case of this specific virus, the asymptomatic and pre-symptomatic infected people contribute significantly to the spread of the pandemic . Therefore, early detection and containment of infected but asymptomatic individuals can be extremely relevant for the dynamic behaviour. Hence, we devised a set of reaction equations focusing on both detected and undetected categories. Moreover, the impact of the shortage of intensive care units during peaks of the pandemic were integrated in the outcome of the scenarios. The model coefficients were calibrated on the basis of existing data, and the model was employed to investigate two main mitigation approaches, one relying on social distancing and one on more frequent infection testing. Also, a combination of the two is studied, and we quantified by how much the testing efficiency is enhanced, if available resources are focused on subpopulations with increased prevalence. Finally, theoretical limitations of contact tracing, which relies on isolating contacts of symptomatic individuals, was studied. We argue that as contact tracing provides little means to prevent virus transmission from asymptomatic cases, it cannot lead to the pandemic containment as a stand-alone approach.
Materials and methods
This section is available in the PDF version of this manuscript.
This section is available in the PDF version of this manuscript.
As more and more affected populations are focusing on risk mitigation plans for COVID-19, it is important to identify strategies to mitigate and eventually suppress the pandemic. For this reason, it is crucial to understand the mechanisms underlying this pandemic-spread. Outcomes of social distancing and mass testing are investigated in this paper. It was found that the latter can significantly reduce the percentage of people getting infected and the death toll. It is important to emphasise here that repetitive testing of individuals without symptoms (if sufficiently large numbers of tests are available and can be applied) has a much stronger effect on the reproduction number than testing people with symptoms, since in most cases the latter are contained. As testing capacities improve, our approach may help to decide by how much social distancing measures can be relaxed. Whereas social distancing is currently essential, it is of utmost importance that testing capabilities are upgraded such that they cover large portions of affected populations in the near future.
From our analysis we conclude that testing every individual without symptoms every few days (with our assumptions roughly every week) would reduce the reproduction number of COVID-19 to 1 and thereby stabilise the pandemic, which is very promising. After a while, fewer and fewer infected people (who spread the virus) will be detected. In this way, continued large scale testing can verify the success of the mitigation strategy. Mass testing should be continued beyond this point, though at a reduced frequency. This would allow determination of whether the fraction of infected persons tends to increase. If this were the case, testing frequencies should again be ramped up. In any case, unless the virus can be defeated completely and globally by reducing the number of infected individuals to zero, there is a risk of COVID-19 re-emergence after such mitigation measures are abandoned.
In this context, the estimates for mass testing that are provided by our analysis should be regarded as estimates for the upper boundary of the tests needed. This upper boundary test number can be used as a guideline for development of mass testing technology and logistics. For further improvements of the predictions a more reliable data base for parameter tuning would be necessary. The model itself can be refined by accounting for different age groups and latency, which would involve additional parameters. In the future it would be of utmost interest to further investigate combined strategies such as social distancing for old and endangered persons and mass testing for the remaining population, including the work force. Ideally, contact tracing and repetitive testing should be combined with some sort of social distancing to successfully suppress the virus spread and to keep the death toll low.
The authors are very thankful to Alexandre Duc for his contribution to the former version of the manuscript with a bluetooth app-based implementation of targeted testing, Dario Ackermann for the website corona-lab.ch, containing the simulation tool based on the model presented in this paper, and Emma Slack, Erik Bakkeren and Noemi Santamaria for helpful comments on the manuscript. Hossein Gorji acknowledges the funding provided by Swiss National Science Foundation under the grant number 174060.
No potential conflict of interest relevant to this article was reported.
Dr Hossein Gorji, Empa Materials Science and Technology, Ueberlandstrasse 129, CH-8600 Dübendorf, mohammadhossein.gorji[at]empa.ch
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