Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China

Abstract

2-18. We compared epidemiological characteristics across periods and different demographic groups. We developed a susceptible-exposed-infectious-recovered model to study the epidemic and evaluate the impact of interventions. RESULTSThe median age of the cases was 57 years and 50.3% were women. The attack rate peaked in the third period and substantially declined afterwards across geographic regions, sex and age groups, except for children (age <20) whose attack rate continued to increase. Healthcare workers and elderly people had higher attack rates and severity risk increased with age. The effective reproductive number dropped from 3.86 (95% credible interval 3.74 to 3.97) before interventions to 0.32 (0.28 to 0.37) post interventions. The interventions were estimated to prevent 94.5% (93.7 to 95.2%) infections till February 18. We found that at least 59% of infected cases were unascertained in Wuhan, potentially including asymptomatic and mild-symptomatic . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.is the (which was not peer-reviewed) The copyright holder for this preprint .

How lethal is the novel coronavirus, and how many undetected cases there are? The importance of being tested

“…This result is consistent with recent reports from China [8,9], and with the high rates of asymptomatic but positive cases observed in Vo Euganeo (Italy) [10], in the cruise ship Diamond Princess [11] and among children [12](see also [13]). The only countries that were able to keep the fraction of undetected infections low appear to be South Korea and Germany, which explains their better ability to control the epidemics in the initial stages.…”

The effects of containment measures in the Italian outbreak of COVID-19

“…We find that the recent growth of ICU patients and deaths is consistent with a linear growth, rather than an exponential growth, suggesting that the lockdown measures have effectively reduced the spread of the infection ( Fig. 6), as it has been for Hubei region (1). Specifically, for the number of ICU patients, we obtain a good fit using an exponential curve (ICU patients(t) ∝ exp[r t], t = days) up to five days after the lockdown (equal to the median incubation time of COVID-19 (9)), and a line (ICU patients(t) ∝ b t, t = days) for later datapoints, up to fifteen days after the lockdown.…”

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

“…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.…”

“…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).…”