Joseph R. Larsen, Margaret R. Martin, John D. Martin, James B. Hicks, Peter Kuhn
Abstract
Identifying order of symptom onset of infectious diseases might aid in differentiating symptomatic infections earlier in a population thereby enabling non-pharmaceutical interventions and reducing disease spread. Previously, we developed a mathematical model predicting the order of symptoms based on data from the initial outbreak of SARS-CoV-2 in China using symptom occurrence at diagnosis and found that the order of COVID-19 symptoms differed from that of other infectious diseases including influenza. Whether this order of COVID-19 symptoms holds in the USA under changing conditions is unclear.
Introduction
The Coronavirus Disease 2019 (COVID-19) is a global pandemic that has reached over two hundred million confirmed cases worldwide as of October 12, 2021 [1]. As cases continue to rise globally [2], understanding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and how it transmits has increased.
To understand symptomatic patients early on in the pandemic, we previously investigated whether there is a likely order of symptom onset in respiratory diseases, including COVID-19, to discern any differences that could be beneficial for the public towards early recognition and course of a symptomatic infection.
Materials and methods
Data collection to study viral variant
We obtained multiple reports of COVID-19 symptomatic and asymptomatic cases around the world, such as the USA, China, and Japan, that contained symptom frequency data of patients. The reports cited here were used to simulate symptom data of individual patients.
A large dataset consisting of 55,924 laboratory confirmed cases, which includes real-time polymerase chain reaction (RT-PCR) testing SARS-CoV-2, of COVID-19 in China was used.
Data collection to study comorbidities
A study that took place at the Henry Ford Health System in Detroit, Michigan in USA from March 9 to 27, 2020 was used in the analysis of symptom order and comorbidities.
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.
