Modeling the Onset of Symptoms of COVID-19 Effects of SARS-CoV-2 Variant
Joseph R. Larsen, Margaret R. Martin, John D. Martin, James B. Hicks, Peter Kuhn
Abstract
Identifying the order in which symptoms of infectious diseases appear can be valuable in early detection and differentiation of symptomatic infections. This information can aid in implementing non-pharmaceutical interventions and reducing the spread of the disease. Previously, we developed a mathematical model based on data from the initial outbreak of SARS-CoV-2 in China, which predicted the order of symptoms at the time of diagnosis. We discovered that the order of COVID-19 symptoms differed from that of other infectious diseases, including influenza. However, it remains unclear if this order of COVID-19 symptoms holds true in the United States under changing conditions.
Introduction Coronavirus Disease 2019 (COVID-19) has emerged as a global pandemic, with over two hundred million confirmed cases worldwide as of October 12, 2021 [1]. As the number of cases continues to rise globally [2], our understanding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its transmission has grown.
To gain insights into symptomatic patients during the early stages of the pandemic, we previously investigated whether there is a consistent order in which symptoms manifest in respiratory diseases, including COVID-19. Identifying any variations in symptom onset could be beneficial for the public, enabling early recognition and appropriate management of symptomatic infections.
Materials and Methods
Data Collection for Viral Variant Study We gathered multiple reports on symptomatic and asymptomatic cases of COVID-19 from various regions worldwide, including the USA, China, and Japan. These reports provided data on the frequency of symptoms among patients, which we used to simulate individual patient symptom data. In particular, we utilized a comprehensive dataset of 55,924 laboratory-confirmed COVID-19 cases in China, where real-time polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 was performed.
Data Collection for Comorbidity Study In our analysis of symptom order and comorbidities, we relied on a study conducted at the Henry Ford Health System in Detroit, Michigan, USA, between March 9 and 27, 2020. This study provided valuable insights into the order of symptoms and their relationship to pre-existing medical conditions.
Implementation of the Stochastic Progression Model
The Stochastic Progression Model is described in detail in our previous publication, in which we used this model to find likely order of discernible symptoms in patients experiencing COVID-19 early on in the pandemic.
Acknowledgments:
We thank Dr. John C. Martin for helpful discussions and critical reading of the manuscript. We dedicate this work to his memory.
Discussion
Our study highlights the complexity of how viral variant, weather, age, and host factors affect symptoms of infectious diseases. Here, we mathematically modeled datasets that include clinical characteristics in China, the USA, Hong Kong, Brazil, and Japan to predict symptom order, as we had done previously with data from China.
Citation: Larsen JR, Martin MR, Martin JD, Hicks JB, Kuhn P (2021) Modeling the onset of symptoms of COVID-19: Effects of SARS-CoV-2 variant. PLoS Comput Biol 17(12): e1009629. https://doi.org/10.1371/journal.pcbi.1009629
Editor: Bard Ermentrout, University of Pittsburgh, UNITED STATES
Received: February 11, 2021; Accepted: November 10, 2021; Published: December 16, 2021.
Copyright: © 2021 Larsen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files. Publicly available datasets were used for this study. These can be found at: https://www.who.int/publications/i/item/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19) https://www.cdc.gov/mmwr/volumes/69/wr/mm6924e2.htm https://www.gastrojournal.org/article/S0016-5085(20)30448-0/fulltext https://www.nature.com/articles/s41562-020-0928-4 https://www.mdpi.com/2077-0383/9/9/2925 https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa1470/5912544 https://www.journalofinfection.com/article/S0163-4453(20)30119-5/fulltext https://www.nejm.org/doi/full/10.1056/NEJMc2010419 https://www.cdc.gov/mmwr/volumes/69/wr/mm6925e1.htm https://erj.ersjournals.com/content/55/5/2000547 https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2767216 https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045%2820%2930310-7/fulltext https://www.nature.com/articles/s41591-020-0979-0 https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(20)30259-5/fulltext https://academic.oup.com/jid/advance-article/doi/10.1093/infdis/jiaa380/5864898 Code for this study can be found at: https://github.com/j-larsen/Stochastic_Progression_of_COVID-19_Symptoms
Funding: We acknowledge funding support by the Dr. Peter N. Schlegel, M.D., Family Endowed Fellowship Fund awarded to JRL; Hsieh Family Foundation and Kathy & Richard Leventhal Research Fund awarded to PK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: JDM is employed by Materia Therapeutics. The remaining authors have declared that no competing interests exist.
