Mathematical Modeling of SARS-CoV-2 Transmission between Minks and Humans Considering New Variants and Mink Culling

Trop Med Infect Dis. 2023 Aug 3;8(8):398. doi: 10.3390/tropicalmed8080398.

Abstract

We formulated and studied mathematical models to investigate control strategies for the outbreak of the disease caused by SARS-CoV-2, considering the transmission between humans and minks. Two novel models, namely SEIR and SVEIR, are proposed to incorporate human-to-human, human-to-mink, and mink-to-human transmission. We derive formulas for the reproduction number R0 for both models using the next-generation matrix technique. We fitted our model to the daily number of COVID-19-infected cases among humans in Denmark as an example, and using the best-fit parameters, we calculated the values of R0 to be 1.58432 and 1.71852 for the two-strain and single-strain models, respectively. Numerical simulations are conducted to investigate the impact of control measures, such as mink culling or vaccination strategies, on the number of infected cases in both humans and minks. Additionally, we investigated the possibility of the mutated virus in minks being transmitted to humans. Our results indicate that to control the disease and spread of SARS-CoV-2 mutant strains among humans and minks, we must minimize the transmission and contact rates between mink farmers and other humans by quarantining such individuals. In order to reduce the virus mutation rate in minks, culling or vaccination strategies for infected mink farms must also be implemented. These measures are essential in managing the spread of SARS-CoV-2 and its variants, protecting public health, and mitigating the potential risks associated with human-to-mink transmission.

Keywords: COVID-19; SARS-CoV-2; SEIR and SVEIR compartmental models; culling and vaccination strategies; human-to-mink transmission; mink-to-human transmission; reproduction number; virus mutation.

Grants and funding

This research was supported by the ÚNKP-21-3-New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development, and Innovation Fund and by project no. TKP2021-NVA-09. M.A.I. was supported by a fellowship from the government of the Arab Republic of Egypt. A.D. was supported by the Hungarian National Research, Development, and Innovation Office grant nos. NKFIH PD 128363 and NKFIH FK 12401. This research was supported by project no. TKP2021-NVA-09, implemented with the support of the Ministry of Innovation and Technology of Hungary from the National Research, Development, and Innovation Fund, financed under the TKP2021-NVA funding scheme.