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Volume 26, Number 11—November 2020
Online Report

Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19

Matthew Biggerstaff, Benjamin J. Cowling, Zulma M. Cucunubá, Linh Dinh, Neil M. Ferguson, Huizhi Gao, Verity Hill, Natsuko Imai, Michael A. Johansson, Sarah Kada, Oliver Morgan, Ana Pastore y Piontti, Jonathan A. Polonsky, Pragati Venkata Prasad, Talia M. Quandelacy, Andrew Rambaut, Jordan W. Tappero, Katelijn A. VandemaeleComments to Author , Alessandro Vespignani, K. Lane Warmbrod, Jessica Y. Wong, and for the WHO COVID-19 Modelling Parameters Group
Author affiliations: Centers for Disease Control and Prevention, Atlanta, Georgia, USA (M. Biggerstaff, M.A. Johansson, S. Kada, P.V. Prasad, T.M. Quandelacy); University of Hong Kong, Hong Kong, China (B.J. Cowling, H. Gao, J.Y. Wong); Imperial College London, London, UK (Z.M. Cucunubá, N.M. Ferguson, N. Imai); World Health Organization, Geneva, Switzerland (L. Dinh, O. Morgan, J.A. Polonsky, J.W. Tappero, K.A. Vandemaele, K.L. Warmbrod); University of Edinburgh, Edinburgh, Scotland, UK (V. Hill, A. Rambaut); Northeastern University, Boston, Massachusetts, USA (A. Pastore y Piontti, A. Vespignani); ISI Foundation, Turin, Italy (A. Vespignani)

Main Article

Table 3

Summary of studies of NPIs for COVID-19

NPI Summary/results Source/reference
Case detection
(27%–37%) cases detected†
Bhatia et al., unpub. data, https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-international-surveillance-21-02-2020.pdf
Case detection
38% (22%–64%) cases detected
Niehus et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.02.13.20022707v2
Case screening and detection
(36%–65%) cases detected†
Pinotti et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.02.24.20027326v1
Case isolation and contact tracing
Delay of symptom onset to isolation has a high impact on the results, affecting the controllability of the outbreak. Results vary by scenario.
(51)
Travel screening
34% (20%–50%) of travelers identified through both departure and arrival screening using symptoms or risk screening
Gostic et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.01.28.20019224v2
Travel screening
46.5% (35.9%–57.7%) travelers not detected through thermal screening
(52)
Travel screening
Syndromic screening and traveler sensitization in combination could delay outbreaks in yet unaffected countries up to 83 d (75% 36 d, 97.5% 8 d).
Clifford et al., unpub. data, https://cmmid.github.io/topics/covid19/screening-outbreak-delay.html
Travel reduction (transport suspension)
Delay of 2.91 d (95% CI 2.54–3.29) for the arrival of the disease to other cities in China
(53)
Travel reduction (travel quarantine)
130 cities in China had >50% chance of having a COVID-19 case imported from Wuhan in the 3 weeks preceding the quarantine.
(18)
Travel restrictions
Travel restriction imposed on Wuhan delay the epidemic for 3 d.
(15)
Travel reduction (airline suspensions)
Travel restriction imposed on China will delay the disease in other countries, the biggest delay being in Africa (11 d) and South America (9 d).
Adiga et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.02.20.20025882v2
Travel reduction
Travel restriction will delay the epidemic for 2 d.
(54)
Cancellation of mass gathering
37% fewer cases when the interventions started before the first case
(53)
Combination of NPI
66%, 86%, and 95% fewer cases depending on timing of the interventions
Lai et al., upub. data, https://www.medrxiv.org/content/10.1101/2020.03.03.20029843v3.full.pdf
Combination of NPI
50% fewer cases if transmissibility reduced by 25% in all cities in China; delay of epidemic peak for 1 month
(11)
Combination of NPI
Drastic control measures implemented in China have substantially mitigated spread of COVID-19.
(36)
Combination of NPI
Earlier intervention of social distancing could limit the epidemic in mainland China. Number of infections could be reduced up to 98.9%, and number of deaths could be reduced by up to 99.3% as of Feb 23, 2020.
Zhang et al., unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.04.20031187v1
Community behavior modification At least 42% of persons interviewed have modified daily behavior. (55)

*COVID-19, coronavirus disease; NPI, nonpharmaceutical interventions.
†Point estimates

Main Article

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1All authors contributed equally to this article.

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