Risk Mapping

What is the probability of encountering someone with SARS-CoV-2 given a gathering size and the rolling seven day average case rate for a each county?

Assessing Risk

This application allows you to understand the risk of encountering someone with SARS-CoV-2 given a gathering size, or more simply, given a number of different contacts per day.

Simulation Tool

Probability of Encountering a Person with Covid-19

Given a Gathering Size of:

Data: NC DHHS, Analysis: Cone Health Data Science

By taking the seven day average of new SARS-CoV-2 cases by county, adding a correction factor for under-testing and asymptomatic transmission, and an average infectious period of 12 days1. Based on this information we can estimate given the number of contacts/ gathering size and the number of likely persons who are infected given the county population what is the probability that you will contact someone who is infected with SARS-CoV-2. For more details on the mathematics, see this simulation example.

Note that this does not estimate the probability that you will become infected by SARS-CoV-2, only the probability that you will contact someone with SARS-CoV-2. Many different components influence the probability of infection including the environment (e.g. indoors vs outdoors), the use of masks, symptoms (e.g. someone coughing and sneezing vs not) and behavior (e.g. social distancing, hygiene). Regardless, by making more contacts or going to places with more people increases the probability that you will encounter someone with SARS-CoV-2 and thus have more opportunities to become infected2.

Ashcroft, Peter, Jana S. Huisman, Sonja Lehtinen, Judith A. Bouman, Christian L. Althaus, Roland R. Regoes, and Sebastian Bonhoeffer. 2020. COVID-19 Infectivity Profile Correction.” arXiv:2007.06602 [q-Bio, Stat], July. http://arxiv.org/abs/2007.06602.
Boyce, Ross M., Raquel Reyes, Michael Matte, Moses Ntaro, Edgar Mulogo, Feng-Chang Lin, and Mark J. Siedner. 2016. “Practical Implications of the Non-Linear Relationship Between the Test Positivity Rate and Malaria Incidence.” PLoS ONE 11 (3). https://doi.org/10.1371/journal.pone.0152410.
Hartman, Melissa, and JH Bloomberg School of Public Health. n.d. COVID-19 Testing: Understanding the Percent Positive.” Johns Hopkins Bloomberg School of Public Health. Accessed October 21, 2020. https://www.jhsph.edu/covid-19/articles/covid-19-testing-understanding-the-percent-positive.html.
He, Xi, Eric H. Y. Lau, Peng Wu, Xilong Deng, Jian Wang, Xinxin Hao, Yiu Chung Lau, et al. 2020. “Temporal Dynamics in Viral Shedding and Transmissibility of COVID-19.” Nature Medicine 26 (5): 672–75. https://doi.org/10.1038/s41591-020-0869-5.
Jarvis, Christopher I, Kevin Van Zandvoort, Amy Gimma, Kiesha Prem, CMMID COVID-19 working group, Petra Klepac, G James Rubin, and W John Edmunds. 2020. “Quantifying the Impact of Physical Distance Measures on the Transmission of COVID-19 in the UK.” Preprint. Epidemiology. https://doi.org/10.1101/2020.03.31.20049023.
Lauer, Stephen A, Kyra H Grantz, Qifang Bi, Forrest K Jones, Qulu Zheng, Hannah Meredith, Andrew S Azman, Nicholas G Reich, and Justin Lessler. n.d. “The Incubation Period of 2019-nCoV from Publicly Reported Confirmed Cases: Estimation and Application,” 13.
Linton, Natalie M., Tetsuro Kobayashi, Yichi Yang, Katsuma Hayashi, Andrei R. Akhmetzhanov, Sung-Mok Jung, Baoyin Yuan, Ryo Kinoshita, and Hiroshi Nishiura. 2020. “Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data.” Journal of Clinical Medicine 9 (2). https://doi.org/10.3390/jcm9020538.
Siddarth, Divya. 2020. “Evidence Roundup: Why Positive Test Rates Need to Fall Below 3%.” Harvard Global Health Institute. https://globalhealth.harvard.edu/evidence-roundup-why-positive-test-rates-need-to-fall-below-3/.
Wu, Sean L., Andrew N. Mertens, Yoshika S. Crider, Anna Nguyen, Nolan N. Pokpongkiat, Stephanie Djajadi, Anmol Seth, et al. 2020. “Substantial Underestimation of SARS-CoV-2 Infection in the United States.” Nature Communications 11 (1): 4507. https://doi.org/10.1038/s41467-020-18272-4.

  1. This is based on an estimated three days of pre-symptomatic transmission and 9 days of post-symptomatic transmission see Linton et al. (2020); Lauer et al. (n.d.); He et al. (2020); Ashcroft et al. (2020). Additionally, the impact of testing positivity on estimated undercount proposed by Boyce et al. (2016) is included in the estimated number of “true” infections in the community. These values are also supported by a study by Wu et al. (2020). Higher testing positivity rates indicate a likely undercount in reported cases–something that should be corrected when considering the probability of an infectious encounter. See Hartman and Health (n.d.) and Siddarth (2020) for detailed explanations of the importance of understanding the positivity rate.↩︎

  2. See Jarvis et al. (2020) for a deeper discussion of the impact of social distancing on the spread of SARS-CoV-2↩︎



Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".