Impact assessment of non-pharmaceutical interventions against COVID-19 and influenza in Hong Kong: an observational study

Cowling, Benjamin J.; Ali, Sheikh Taslim; Ng, Tuen Wai; Tsang, Tim K.; Li, Julian C. M.; Fong, Min Whui; Liao, Qiuyan; Kwan, Mike Yat Wah; Lee, So Lun; Chiu, Susan S.; Wu, Joseph T.; Wu, Peng

doi:10.1101/2020.03.12.20034660

Cited by 73 publications

(90 citation statements)

References 19 publications

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“…Recent ILI data for the United States show a markedly lower epidemic peak for the 2019-2020 influenza season compared to what would be expected based on the pre-holiday peak [8]. A similar phenomenon has been observed in other locations including Hong Kong and France [9,10]. Understanding this uncharacteristic ILI behavior is important for understanding influenza epidemiology and optimizing influenza mitigation e↵orts.…”

Section: Introductionmentioning

confidence: 81%

Double trouble? When a pandemic and seasonal virus collide

Zipfel

Bansal

2020

Preprint

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The 2019-2020 influenza sentinel surveillance data exhibits unexpected trends. Typical influenza seasons have a small herald wave, followed by a decrease due to school closure during holidays, and then a main post-holiday peak that is significantly larger than the pre-holiday wave. During the 2019-2020 influenza season, influenza-like illness data in the United States appears to have a markedly lower main epidemic peak compared to what would be expected based on the pre-holiday peak. We hypothesize that the 2019-2020 influenza season does have a lower than expected burden and that this deflation is due to a behavioral or ecological interaction with COVID-19. We apply an intervention analysis to assess if this influenza season deviates from expectations, then we compare multiple hypothesized drivers of the decrease in influenza in a spatiotemporal regression model. Lastly, we develop a mechanistic metapopulation model, incorporating transmission reduction that scales with COVID-19 risk perception. We find that the 2019-2020 ILI season is smaller and decreases earlier than expected based on prior influenza seasons, and that the increase in COVID-19 risk perception is associated with this decrease. Additionally, we find that a 5% average reduction in transmission is su cient to reproduce the observed flu dynamics. We propose that precautionary behaviors driven by COVID-19 risk perception or increased isolation driven by undetected COVID-19 spread dampened the influenza season. We suggest that when surveillance for a novel pathogen is limited, surveillance streams of co-circulating infections may provide a signal.

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Section: Introductionmentioning

confidence: 81%

Double trouble? When a pandemic and seasonal virus collide

Zipfel

Bansal

2020

Preprint

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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%

“…As such, population-level interventions, with their attendant economic costs, have been used to prevent health systems from collapsing (24). While events in China, Singapore, Hong Kong and elsewhere have demonstrated that COVID-19 epidemics can be contained (24)(25)(26)(27), the seeding of epidemics in countries around the globe, many with weak health systems (28), means that reintroduction of COVID-19 will continue to occur for some time. As successful containment efforts maintain a large number of susceptible individuals in populations, vulnerability to repeated epidemics is likely to persist until a COVID-19 vaccine is developed and manufactured at scale; or until large fractions of the population are infected and either die or develop immunity (29).…”

Section: Dynamic Interventionsmentioning

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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“…. DM tDR S DS) 1 S) p = ( − p * p ÷ ( − p Knowing the limitations of previous modelling attempts (Cowling et al;Ganyani et al, 2020;Zhang et al, 2020;Chen et al, 2020;Wu, Leung & Leung, 2020;Lin et al, 2020;Kucharski et al, 2020) , we decided to test a radically different COVID-19 epidemiologic paradigm, i.e. a significantly lower tDR .…”

Section: Model Parametrizationmentioning

confidence: 99%

From a single host to global spread: The global mobility based modelling of the COVID-19 pandemic implies higher infection rate and lower detection ratio than current estimates

Siwiak

Szczesny

Siwiak

2020

Preprint

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Background.Since the outbreak of the COVID-19 pandemic, multiple efforts of modelling of the geo-temporal transmissibility of the virus have been undertaken, but none succeeded in describing the pandemic at the global level. We propose a set of parameters for the first COVID-19 Global Epidemic and Mobility Model (GLEaM). The simulation starting with just a single pre-symptomatic, yet infectious, case in Wuhan, China, results in an accurate prediction of the number of diagnosed cases after 135 days in multiple countries across four continents. refinement of the model and screening tests is crucial for developing an effective strategy for the global epidemiological crisis.

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Impact assessment of non-pharmaceutical interventions against COVID-19 and influenza in Hong Kong: an observational study

Cited by 73 publications

References 19 publications

Double trouble? When a pandemic and seasonal virus collide

Double trouble? When a pandemic and seasonal virus collide

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

From a single host to global spread: The global mobility based modelling of the COVID-19 pandemic implies higher infection rate and lower detection ratio than current estimates

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