Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19)

Abstract

BackgroundEstimation of the fraction and contagiousness of undocumented novel coronavirus infections is critical for understanding the overall prevalence and pandemic potential of this disease. Many mild infections are typically not reported and, depending on their contagiousness, may support stealth transmission and the spread of documented infection. MethodsHere we use observations of reported infection and spread within China in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with the emerging coronavirus, including the fraction of undocumented infections and their contagiousness. ResultsWe estimate 86% of all infections were undocumented (95% CI: [82%-90%]) prior to the Wuhan travel shutdown (January 23, 2020). Per person, these undocumented . 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under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not peer-reviewed) The copyright holder for this preprint .

Intervention Serology and Interaction Substitution: Modeling the Role of ‘Shield Immunity’ in Reducing COVID-19 Epidemic Spread

“…Early estimates of R 0 from Wuhan are consistent with a 95% CI of between 2.1 and 4.5 [28], putting our high scenario on the conservative end of estimated ranges. However, the R 0 of the high scenario we examine here is consistent with the range of 2.0 to 2.6 considered by the Imperial College London group [5], and with the median of R ef f = 2.38 (95% CI: 2.04-2.77) as estimated via stochastic model fits to outbreak data in China that accounts for undocumented transmission [26]. Moreover, control measures reduce transmission, and our low scenario is consistent with estimates of R ef f = 1.36 (95% CI: 1.14-1.63) in China from Jan. 24 to Feb. 3 after travel restrictions and other control measures were imposed.…”

“…In a population of size 10,000,000 for the low scenario, the final epi- demic predictions are 50,000 deaths in the baseline case vs. 34,000 deaths given intermediate shielding, and 8,300 deaths given enhanced shielding. The majority of deaths are in those ages 60 and above, despite the lower fraction of individuals in those ranges (see Figure 3), consistent with estimates in related COVID-19 models [5,25,26] and from outbreaks in Italy and China [29]. Note that our simulation results consider impacts based on shielding alone; whereas ongoing restrictions via social distancing and shelter in place orders will reduce interaction rates (a point we revisit later).…”

“…The model assumes that the circulating pool is not interacting with hospitalized patients, which must be incorporated into implementation scenarios with healthcare workers (HCW-s), who represent an intended target for shield immunity [23]). The baseline epidemiological parameters, age stratified risk, and population structure are listed in the SI (adapted from [5,[24][25][26][27]; see github for code and full implementation details).…”

“…The effectiveness of shielding depends on the product of the number of potential shields identified and their effective substitutability, i.e, αR, combining identification of and interaction rate by shields. The population-scale impacts of shielding depends on multiple factors, including demographic distributions, the fraction of asymptomatic transmission ( [24,26]), and the duration of immunity. The SI treats each of these items at length.…”

Partial unlock model for COVID-19 or similar pandemic averts medical and economic disaster

“…The lower numbers are more critical due to the way the model 110 calculates ventilator requirements. Published numbers are typically around 14% [7], so our assumption 111 has some margin of safety. 112 A precise number for how long a case of COVID-19 lasts is of course not obtainable due to the 113 wide variation.…”