Strategy for Locating People to Reduce the Transmission of COVID-19 Using Different Interference Measures

Brenda Valenzuela-Fonseca, Rodrigo Linfati and John Willmer Escobar
Additional contact information
Brenda Valenzuela-Fonseca: School of Industrial Engineering, Universidad del Bío-Bío, Concepción 4030000, Chile
Rodrigo Linfati: Department of Industrial Engineering, Universidad del Bío-Bío, Concepción 4030000, Chile
John Willmer Escobar: Department of Accounting and Finance, Universidad del Valle, Cali 760001, Colombia

Sustainability, 2022, vol. 14, issue 1, 1-19

Abstract: COVID-19 is generally transmitted from person to person through small droplets of saliva emitted when talking, sneezing, coughing, or breathing. For this reason, social distancing and ventilation have been widely emphasized to control the pandemic. The spread of the virus has brought with it many challenges in locating people under distance constraints. The effects of wakes between turbines have been studied extensively in the literature on wind energy, and there are well-established interference models. Does this apply to the propagation functions of the virus? In this work, a parallel relationship between the two problems is proposed. A mixed-integer linear programming (MIP) model and a mixed-integer quadratic programming model (MIQP) are formulated to locate people to avoid the spread of COVID-19. Both models were constructed according to the distance constraints proposed by the World Health Organization and the interference functions representing the effects of wake between turbines. Extensive computational tests show that people should not be less than two meters apart, in agreement with the adapted Wells–Riley model, which indicates that 1.6 to 3.0 m (5.2 to 9.8 ft) is the safe social distance when considering the aerosol transmission of large droplets exhaled when speaking, while the distance can be up to 8.2 m (26 ft) if all the droplets in a calm air environment are taken into account.

Keywords: social distancing; interference functions; mathematical models; virus propagation; optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/1/529/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/1/529/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:1:p:529-:d:717497

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().