Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

Maier, Benjamin F.; Brockmann, Dirk

doi:10.1126/science.abb4557

Cited by 857 publications

(982 citation statements)

References 20 publications

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“…On the other hand, the removal rate of symptomatic individuals is much higher, leading to a strongly decreased effective reproduction number, and a moderately high quarantine probability. These SIR-X model parameters are somewhat similar to values estimated for the Beijing region of China [4]. When extrapolating the number of deaths using the SIR-X model, the predicted death toll due to COVID-19 will exceed 10 3 by April 3rd.…”

Section: Resultssupporting

confidence: 61%

“…Moreover, it allows for the formulation of an effective reproduction number R 0,eff = α/(β + κ + κ 0 ), which is always smaller than the basic reproduction number in free unconstrained growth R 0,free = α/β. Parameters α and β represent intrinsic properties of infectiousness and are not varied, but fixed at α = 0.775 and β = 0.125, corresponding to a recovery time of 8 days, and a free reproduction number of R 0,free = 6.2, as was assumed by the original authors [4]. The free parameters during the fitting procedure are κ, κ 0 and I 0 /X 0 , the initial fraction of infectious individuals.…”

Section: Modelmentioning

confidence: 99%

“…(1-3) are integrated using the Dormand-Prince method, which uses a fourth-order Runge-Kutta method. The implementation of the fitting routine for the SIR-X model was kindly provided by the original authors [4]. The fitting of power law models was performed in double logarithmic space, and discarded the first five data points (March 12-16), which account for the exponential behavior.…”

Section: Fitting Routinementioning

confidence: 99%

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Scaling analysis of COVID-19 spreading based on Belgian hospitalization data

Smeets

Watté

Ramón

2020

Preprint

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We analyze the temporal evolution of accumulated hospitalization cases due to COVID-19 in Belgium. The increase of hospitalization cases is consistent with an initial exponential phase, and a subsequent power law growth. For the latter, we estimate a power law exponent of ≈ 2.2, which is consistent with growth kinetics of COVID-19 in China and indicative of the underlying small world network structure of the epidemic. Finally, we fit an SIR-X model to the experimental data and estimate the effect of containment policies in comparison to their effect in China. This model suggests that the base reproduction rate has been significantly reduced, but that the number of susceptible individuals that is isolated from infection is very small. Based on the SIR-X model fit, we analyze the COVID-19 mortality and the number of patients requiring ICU treatment over time.

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Section: Resultssupporting

confidence: 61%

Section: Modelmentioning

confidence: 99%

Section: Fitting Routinementioning

confidence: 99%

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Scaling analysis of COVID-19 spreading based on Belgian hospitalization data

Smeets

Watté

Ramón

2020

Preprint

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“…NPIs have been linked to the spread of SARS-CoV-2 primarily within or from a single country [8][9][10][11][12][13][14][15][16][17][18][19] , mostly focusing on the novel coronavirus outbreak in China. The majority of findings are based on transmission models where epidemiological parameters are informed by previous studies or corroborated via simulation.…”

Section: Introductionmentioning

confidence: 99%

Impact of non-pharmaceutical interventions on documented cases of COVID-19

Banholzer

Kratzwald

et al. 2020

Preprint

131

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Background: The novel coronavirus (SARS-CoV-2) has rapidly evolved into a global epidemic. To control its spread, countries have implemented non-pharmaceutical interventions (NPIs), such as school or border closures, while others have even enforced a complete lockdown. Here we study the effectiveness of NPIs in reducing documented cases of COVID-19. Methods: We empirically estimate the impact of NPIs on documented COVID-19 cases in a cross-country analysis. A Bayesian hierarchical model with a time-delayed effect for each NPI allows us to quantify the relative reduction in the number of new cases attributed to each NPI. Based on this model, a cross-country analysis is performed using documented cases through April 15, 2020 from n = 20 countries (i.e., the United States, Canada, Australia, the EU-15 countries, Norway, and Switzerland). Documented case numbers are selected because they are essential for decision-makers in the area of health-policy when monitoring and evaluating current control mechanisms. Findings: Based on our model, we compare the effectiveness of NPIs up to now, i.e., in the early stages of the outbreak. Venue closures are associated with a reduction in the number of new cases by 36 % (95% credible interval [CrI] 20-48 %), closely * Corresponding author. † These authors contributed equally. : medRxiv preprint followed by gathering bans (34 %; 95% CrI 21-45 %), border closures (31 %; 95% CrI 19-42 %), and work bans on non-essential business activities (31 %; 95% CrI 16-44 %). Event bans lead to a slightly less pronounced reduction (23 %; 95% CrI 8-35 %). School closures (8 %; 95% CrI 0-23 %) and lockdowns (5 %; 95% CrI 0-14 %) appear to be the least effective among the NPIs considered in this analysis.Interpretation: This cross-country analysis provides early estimates regarding the effectiveness of different NPIs for controlling the COVID-19 epidemic. These findings are relevant for evaluating current health-policies and will be refined as more data becomes available.

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“…7 China's extensive interventions appear to have been successful at limiting the outbreak. 8,9 These include quarantines both for those diagnosed 2 and those undiagnosed but who had been in Hubei province during the outbreak, 10 and restrictions on travel to and from affected areas. 11 In contrast, school closures across East Asia were estimated to be much less effective.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Stay-at-Home Orders on COVID-19 Cases and Fatalities in the United States

Fowler

Obradovich

et al. 2020

Preprint

119

105

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Background In March and April 2020, public health authorities in the United States acted to mitigate transmission of and hospitalizations from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 . These actions were not coordinated at the national level, which raises the question of what might have happened if they were. It also creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy. MethodsWe combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases at the county level in the United States ( N = 124,027). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. This enables us to estimate the effect of stay-at-home orders while accounting for local variation in factors like health systems and demographics, and temporal variation in national mitigation actions, access to tests, or exposure to media reports that could influence the course of the disease.Findings Mean county-level daily growth in COVID-19 infections peaked at 17.2% just before stay-at-home orders were issued. Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95% CI 1.2 to 6.6) reduction in the growth rate after one week and a 6.9 percentage point (2.4 to 11.5) reduction after two weeks. By day 27 the reduction (22.6 percentage points, 14.8 to 30.5) had surpassed the growth at the peak, indicating that growth had turned negative and the number of new daily infections was beginning to decline. A hypothetical national stay-at-home order issued on March 13, 2020 when a national emergency was declared might have reduced cumulative infections by 63.3%, and might have helped to reverse exponential growth in the disease by April 10.Interpretation Although stay-at-home orders impose great costs to society, delayed responses and piecemeal application of these orders generate similar costs without obtaining the full potential benefits suggested by this analysis. The results here suggest that a coordinated 1 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) : medRxiv preprint nationwide stay-at-home order might have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19. Future efforts in the United States and elsewhere to control pandemics should coordinate stay-at-home orders at the national level, especially for diseases for which local spread has already occurred and testing availability is delayed. Since stay-at-home orders reduce infection growth rates, early implementation when infection counts are still low would be most beneficial.Funding None.

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Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

Cited by 857 publications

References 20 publications

Scaling analysis of COVID-19 spreading based on Belgian hospitalization data

Scaling analysis of COVID-19 spreading based on Belgian hospitalization data

Impact of non-pharmaceutical interventions on documented cases of COVID-19

The Effect of Stay-at-Home Orders on COVID-19 Cases and Fatalities in the United States

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