Identifying and Interrupting Superspreading Events—Implications for Control of Severe Acute Respiratory Syndrome Coronavirus 2

Frieden, Thomas R.; Lee, Christopher T.

doi:10.3201/eid2606.200495

Cited by 271 publications

(291 citation statements)

References 37 publications

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“…23 To capture variability in transmission, specifically the observation that the basic reproduction number for COVID-19 is overdispersed, with some cases transmitting to many others (superspreader events), while many other cases transmit much less, we have added volatility to the transmission term. [24][25][26] This causes each model run to have a different outcome owing to stochasticity (i.e., random variation between model runs). The model was initiated with 750 prevalent cases (based on 150 reported cases in Ontario on Mar.…”

Section: Model Parametersmentioning

confidence: 99%

“…Whereas severe illness, particularly in older individuals, is frequent enough to overwhelm a society's ICU capacity, 27 mild unrecognized illness (particularly in younger individuals) contributes to spread, 28 and outbreaks may be recognized only when superspreader events occur, 25 often in settings like health care facilities. 26 In contrast to severe acute respiratory syndrome (SARS), 29 the high frequency of mild cases means that strategies that focus on case identification and isolation alone are likely to fail to prevent epidemic spread and overburdening of our health care system. 26 As such, population-level interventions, with their attendant economic costs, have been used to prevent health systems from collapsing.…”

Section: Dynamic Interventionsmentioning

confidence: 99%

“…26 In contrast to severe acute respiratory syndrome (SARS), 29 the high frequency of mild cases means that strategies that focus on case identification and isolation alone are likely to fail to prevent epidemic spread and overburdening of our health care system. 26 As such, population-level interventions, with their attendant economic costs, have been used to prevent health systems from collapsing. 30 Although events in China, Singapore, Hong Kong and elsewhere have shown that COVID-19 epidemics can be contained, [30][31][32][33] the seeding of epidemics in countries around the globe, many with weak health systems, 34 means that reintroduction of COVID-19 will continue to occur for some time.…”

Section: Dynamic Interventionsmentioning

confidence: 99%

See 2 more Smart Citations

Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

Tuite

Fisman

Greer

2020

CMAJ

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359

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Section: Model Parametersmentioning

confidence: 99%

Section: Dynamic Interventionsmentioning

confidence: 99%

Section: Dynamic Interventionsmentioning

confidence: 99%

See 1 more Smart Citation

Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

Tuite

Fisman

Greer

2020

CMAJ

394

359

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“…It inevitably follows that such exceptions are vitally important to target if one wishes to eliminate COVID-19. [111][112][113][114] Again, it is worth remembering Typhoid Mary 109 , who resisted repeated efforts to get her out of the kitchen and did nothing to disprove stereotypes about the stubborn Irish. She repeatedly returned to working as a cook because it paid better and frequently changed jobs as people fell ill around her, even changing her name to evade more than 30 years of quarantines imposed on her.…”

Section: The Vital Importance Of Ambition and Rigour To Lockdown Outcmentioning

confidence: 99%

Why lockdown? Simplified arithmetic tools for decision-makers, health professionals, journalists and the general public to explore containment options for the novel coronavirus

Killeen

Kiware

2020

Preprint

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Half the world's population is already under lock-down and the remainder will have to follow if the ongoing novel coronavirus 2019 virus pandemic is to be contained. Faced with such brutally difficult decisions, it is essential that as many people as possible understand (1) why lockdown interventions represent the only realistic way for individual countries to contain their nationallevel epidemics before they turn into public health catastrophes, (2) why these need to be implemented so early, so aggressively and for such extended periods, and (3) why international cooperation to conditionally re-open trade and travel between countries that have successfully eliminated local transmission represents the only way to contain the pandemic at global level. Here we present simplified arithmetic models of COVID-19 transmission, control and elimination in userfriendly Shiny and Excel formats that allow non-specialists to explore, query, critique and understand the containment decisions facing their country and the world at large. Based on parameter values representative of the United Republic of Tanzania, which is still early enough in its epidemic cycle and response to avert a national catastrophe, national containment and elimination with less than 10 deaths is predicted for highly rigorous lock down within 5 weeks of the first confirmed cases and maintained for 15 weeks. However, elimination may only be sustained if case importation from outside the country is comprehensively contained by isolating for three weeks all incoming travellers, except those from countries certified as COVID-free in the future. Any substantive relaxation of these assumptions, specifically shortening the lock-down period, less rigorous lockdown or imperfect importation containment, may facilitate epidemic re-initiation, resulting in over half a million deaths unless rigorously contained a second time. Removing contact tracing and isolation has minimal impact on successful containment trajectories because high incidence of similar mild symptoms caused by other common pathogens attenuates detection success of COVID-19 testing. Nevertheless, contact tracing is recommended as an invaluable epidemiological surveillance platform for monitoring and characterizing the epidemic, and for understanding the influence of interventions on transmission dynamics.

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“…Attack rates were projected to be highest in those aged 5-14 years (77%, 95% CrI: 63-83%) and 15-49 years (63%, 95%CrI: 48-71). Lower attack rates were projected in individuals aged less than 5 years (50%, 95% CrI: 37-58%) and adults aged 50-69 years (47%, 95% CrI: 34-55) and greater than 70 years (30%, 95% CrI: [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]). An example of the outbreak trajectory across model simulations is presented in Figure 2.…”

Section: Base Casementioning

confidence: 99%

Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

Tuite

Fisman

Greer

2020

Preprint

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Background:We evaluated how non-pharmaceutical interventions could be used to control the COVID-19 pandemic and reduce the burden on the healthcare system. Methods:Using an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada, we compared a base case with limited testing, isolation, and quarantine to scenarios with: enhanced case finding; restrictive social distancing measures; or a combination of enhanced case finding and less restrictive social distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected ICU bed occupancy. We present median and credible intervals (CrI) from 100 replicates per scenario using a two-year time horizon. Results:We estimated that 56% (95% CrI: 42-63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107,000 (95% CrI: 60,760-149,000) cases in hospital and 55,500 (95% CrI: 32,700-75,200) cases in ICU. For fixed duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive social distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the two-year period. Dynamic social distancing interventions could reduce the median number of cases in ICU below current estimates of Ontario's ICU capacity.

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Identifying and Interrupting Superspreading Events—Implications for Control of Severe Acute Respiratory Syndrome Coronavirus 2

Cited by 271 publications

References 37 publications

Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

Why lockdown? Simplified arithmetic tools for decision-makers, health professionals, journalists and the general public to explore containment options for the novel coronavirus

Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

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