Leveraging A Multiple-Strain Model with Mutations in Analyzing the Spread of Covid-19

The spread of COVID-19 has been among the most devastating events affecting the health and well-being of humans worldwide since World War II. A key scientific goal concerning COVID-19 is to develop mathematical models that help us to understand and predict its spreading behavior, as well as to provide guidelines on what can be done to limit its spread. In this paper, we discuss how our recent work on a multiple-strain spreading model with mutations can help address some key questions concerning the spread of COVID-19. We highlight the recent reports on a mutation of SARS-CoV-2 that is thought to be more transmissible than the original strain and discuss the importance of incorporating mutation and evolutionary adaptations (together with the network structure) in epidemic models. We also demonstrate how the multiple-strain transmission model can be used to assess the effectiveness of mask-wearing in limiting the spread of COVID-19. Finally, we present simulation results to demonstrate our ideas and the utility of the multiple-strain model in the context of COVID-19.

[1]  N. Cox,et al.  Genetic analysis of human H2N2 and early H3N2 influenza viruses, 1957-1972: evidence for genetic divergence and multiple reassortment events. , 2004, Virology.

[2]  W. Marsden.  I and J , 2012 .

[3]  H. Vincent Poor,et al.  Modeling and Analysis of the Spread of COVID-19 Under a Multiple-Strain Model with Mutations , 2021 .

[4]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[5]  M. Newman.  Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  F. Balloux,et al.  Emergence of genomic diversity and recurrent mutations in SARS-CoV-2 , 2020, Infection, Genetics and Evolution.

[7]  D. Watts,et al.  Multiscale, resurgent epidemics in a hierarchical metapopulation model. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[8]  J. Gamarra,et al.  Metapopulation Ecology , 2007 .

[9]  Charles H. Calisher,et al.  Cross-Species Virus Transmission and the Emergence of New Epidemic Diseases , 2008, Microbiology and Molecular Biology Reviews.

[10]  T. Day,et al.  Risk factors for the evolutionary emergence of pathogens , 2010, Journal of The Royal Society Interface.

[11]  Rustom Antia,et al.  The role of evolution in the emergence of infectious diseases , 2003, Nature.

[12]  Kathleen M. Carley,et al.  The effects of evolutionary adaptations on spreading processes in complex networks , 2018, Proceedings of the National Academy of Sciences.

[13]  M. Woolhouse,et al.  Emerging pathogens: the epidemiology and evolution of species jumps , 2005, Trends in Ecology & Evolution.

[14]  A. Barrat,et al.  Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors , 2013, PloS one.

[15]  Maria A. Kazandjieva,et al.  A high-resolution human contact network for infectious disease transmission , 2010, Proceedings of the National Academy of Sciences.

[16]  Xiang Li,et al.  On the origin and continuing evolution of SARS-CoV-2 , 2020, National science review.

[17]  E. Kostelich,et al.  To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic , 2020, Infectious Disease Modelling.

[18]  Piet Van Mieghem,et al.  Epidemic processes in complex networks , 2014, ArXiv.

[19]  F. Brauer,et al.  Mathematical Models in Population Biology and Epidemiology , 2001 .

[20]  Bruce A. Reed,et al.  A Critical Point for Random Graphs with a Given Degree Sequence , 1995, Random Struct. Algorithms.