About: Optimizing testing policies for detecting COVID-19 outbreaks

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About: Optimizing testing policies for detecting COVID-19 outbreaks Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Optimizing testing policies for detecting COVID-19 outbreaks
Creator	Barak, Boaz Nitzan, Mor Tannenbaum, Neta Yuval, Janni
Source	ArXiv
abstract	The COVID-19 pandemic poses challenges for continuing economic activity while reducing health risk to individuals and preventing uncontrolled outbreaks. These challenges can be mitigated by extensive testing. We study testing policies that optimize a fixed budget of tests within a single institution (e.g., business, school, nursing home, etc.) by varying the number of batches that the tests are split into. We prove that, in an exponential spread model and for reasonable parameter values, the expected size of an outbreak at initial detection is smaller when random subgroups of the population are tested frequently, as opposed to periodic testing of the entire population. We also simulate the effect of different policies in a network SEIR model taking into account factors such as variable connectivity between individuals, incubation period, and inaccurate testing results. We show that under a broad set of early-outbreak scenarios, given a certain budget of tests, increasing testing frequency of random samples of the population will reduce the societal risk, defined as the number of infection opportunities until first detection. For example, testing a quarter of the institution members every week is generally better than testing the entire institution every month. In fact, in many settings, sufficiently frequent testing (combined with mitigation once an outbreak is detected) can decrease the risk even compared to the baseline when the institution is closed and testing is not conducted. The bottom-line is a simple policy prescription for institutions: distribute the total tests over several batches instead of using them all at once.
has issue date	2020-07-09(xsd:dateTime)
has license	arxiv
sha1sum (hex)	be28b16b75c86af14398fdb5e20ba67cb6d8c849
resource representing a document's title	Optimizing testing policies for detecting COVID-19 outbreaks
resource representing a document's body	covid:be28b16b75c86af14398fdb5e20ba67cb6d8c849#body_text
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