Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

doi:10.21203/rs.3.rs-17453/v1

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Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

https://doi.org/10.21203/rs.3.rs-17453/v1

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As of February 13, 2020, there have been 59,863 laboratory-confirmed cases of COVID-19 infections in mainland China, including 1,367 deaths. A key public health priority during the emergence of a novel pathogen is estimating clinical severity. Here we estimated the symptomatic case-fatality risk (sCFR; the probability of dying from the infection after developing symptoms) of COVID-19 in Wuhan using public and published information. We estimated that sCFR was 0.5% (0.1%-1.3%), 0.5% (0.2%-1.1%) and 2.7% (1.5%-4.7%) for those aged 15-44, 45-64 and >64 years. The overall sCFR among those aged ≥15 years was 1.4% (0.8%-2.0%).

Authors Joseph T Wu and Kathy Leung contributed equally to this work

Epidemiology

COVID-19

estimating clinical severity

symptomatic case-fatality risk (sCFR)

Infection fatality risk (IFR): IFR defines a case as a person who has shown evidence of infection, either by clinical detection of the pathogen or by seroconversion or other immune response. Such cases may or may not be symptomatic, though asymptomatic ones may go undetected.

Symptomatic case fatality risk (sCFR): sCFR defines a case as someone who is infected and shows certain symptoms.

Hospitalization fatality risk (HFR): HFR defines a case as someone who is infected and needs to be hospitalized.

On 9 January 2020, a novel coronavirus, COVID–19, was officially identified as the cause of an outbreak of viral pneumonia in Wuhan, China. As of Feb 13, 2020 (1000 h Hong Kong time), there have been 59,863 laboratory-confirmed cases of COVID–19 infections in mainland China, including 1,367 deaths.

One of the most critical clinical and public health questions during the emergence of a completely novel pathogen, especially one that could cause a global pandemic, pertains to the spectrum of illness presentation or severity profile. For the patient and clinician, it affects triage and diagnostic decision-making, especially in settings without ready access to laboratory testing or when surge capacity has been exceeded. It also influences therapeutic choice and prognostic expectations. For managers of health services, it is important for rapid forward planning in terms of procurement of supplies, readiness of human resources to staff beds at different intensities of care and generally ensuring sustainability of the health system through the peak and duration of the epidemic.

At the population level, determining the shape and size of the “clinical iceberg”, both above and below the observable threshold, is key to understanding transmission dynamics and interpreting epidemic trajectories. Both inform the development and evaluation of public health strategies that need to be traded off against economic, social and personal freedom costs.

For the general public, the overriding concern about uncertainty can breed fear, even panic. There is arguably no greater cause of such anxiety than the relative probability of death and disability caused by infection. For a completely novel pathogen, especially one with a high (say >2) basic reproductive number, assuming homogeneous mixing and mass action dynamics, the majority of the population will be infected eventually unless drastic public health interventions are applied over prolonged periods and/or vaccines become available sufficiently quickly. Even under more realistic assumptions about mixing, at least a quarter to a half of the population will very likely become infected absent drastic control measures or a vaccine. Therefore, the number of severe outcomes or deaths in the population is most strongly dependent on how ill an infected person is likely to become, and this question should be the focus of attention.

Therefore, we have extended our previously published transmission dynamics model ¹, updated with real time input data and enriched with additional new data sources, to infer a preliminary set of clinical severity estimates that could guide clinical and public health decision making as the epidemic continues to spread and evolve globally.

Estimation of true case numbers – necessary to determine the severity per case – is challenging in the setting of an overwhelmed health care system that cannot ascertain cases effectively. Therefore as in our prior work ¹,our approach has been to use a range of data sources to build a picture of the full number of cases and deaths by age group. Because the health care structure has been overwhelmed in Wuhan, and milder cases were unlikely to have been tested, we used the prevalence of infection in travelers (both on the commercial flights prior to 19 January and on the charter flights from 29 January to 4 February) to estimate the true prevalence of infection in Wuhan, and used the Wuhan case numbers from the first 425 cases solely to estimate the growth rate of the epidemic (assuming these were a constant proportion of true cases). See Figure 1 for illustration.

We relied on publicly available and recently published information. Specifically, we inferred the epidemiologic parameters listed in Table 1 by fitting an age-structured transmission model (an extension of the model from our previous study) to the following data:

The epidemic curve of confirmed cases of COVID-19 in Wuhan with no epidemiologic links to Huanan Seafood Wholesale Market (which was presumed to be the index zoonotic source of the COVID-19 epidemic) between 1 December 2019 and 3 January 2020 (Figure 1; Table S1) ².
The number of cases exported from Wuhan to cities outside mainland China via air travel on each day between 25 December 2019 and 19 January 2020 (Figure 1; Table S2) ¹.
The number of expatriates who returned to their countries from Wuhan on charter flights between 29 January and 4 February 2020 and the proportion of passengers on each flight who had laboratory confirmed infection with COVID-19 upon arrival (Figure 1 and Table S3).
The age distribution of 425 confirmed cases of COVID-19 from the early stages of the epidemic in Wuhan ² (Table S4).
The age distribution of 41 fatal cases of COVID-19 infection during the early stages of the epidemic in Wuhan ^3-5 (Table S5).
The cumulative number of deaths among laboratory-confirmed cases of COVID-19 infection in Wuhan up to 11 February 2020 ⁶ (Table S6).
The time between onset and death or the time between admission and death for 41 death cases of COVID-19 in Wuhan ^3-5 (Table S7).
The time between the onset dates (i.e. serial intervals) of 40 infector-infectee pairs (Table S8).

We made the following assumptions in the model:

The incidence rate of infection, onset and death for age group i at time t were calculated as follows:

where

were the probability density function of the incubation period and the time between onset and death for those who died from the infection. We inferred the parameters listed in Table 1 assuming that the remaining parameters were fixed at the values shown in Table 2.

In the baseline scenario

we estimated that sCFR was 0.5% (0.1%-1.3%), 0.5% (0.2%-1.1%) and 2.7% (1.5%-4.7%) for those aged 15-44, 45-64 and >64 years. The overall sCFR among those aged ≥15 years was 1.4% (0.8%-2.0%). Compared to those aged 15-44 years, those 45-65 and >64 years were 3.1 (2.4-4.0) and 6.4 (4.8-8.4) times more susceptible to infection.

Figure 2 summarizes our estimates for the key epidemiologic parameters of COVID-19 in Wuhan. The basic reproductive number was 2.42 (2.11-2.88) and the mean serial interval was 7.7 (6.1-9.2) days. The mean time from onset to death was 19 (16-24) days with standard deviation 10 (7-14) days. The mean latent and infectious period was 4.5 (3.0-6.4) and 3.2 (1.9-5.2) days, respectively.

We estimated that the epidemic doubling time (the time it takes for daily incidence to double) was 5.5 (4.4-6.9) days before Wuhan was quarantined and public health interventions implemented within Wuhan reduced transmissibility by 53% (21%-82%). We estimated that only 0.3% (0.2%-0.6%) of symptomatic cases that occurred between 1 December 2019 and 3 January 2020 were laboratory confirmed.

There is clear and considerable age dependency in infection and outcome risks, by several fold in each case. For simplicity, we only considered the adult population given empirical reports of only very rare observations of cases below age 15 years. Given that we have parameterized the model using death rates inferred from projected case numbers (from traveler data) and observed death numbers in Wuhan, the precise fatality risk estimates may not generalize to those outside of that original epicenter especially during subsequent phases of the epidemic. The experience gained from managing those initial patients and the increasing availability of newer, and potentially better, treatment modalities to more patients would presumably lead to fewer deaths, all else being equal. Public health control measures widely imposed since the Wuhan alert have also kept case numbers down elsewhere such that their health systems are not nearly as overwhelmed beyond surge capacity, thus again perhaps leading to better outcomes. Indeed, to date, the death-to-case ratio in Wuhan has been consistently much higher than that among all the other mainland Chinese cities (Figure S1). Therefore, as and when data from outside Wuhan become available over the next few weeks, our model should be reparameterised accordingly to give a fuller account of the clinical iceberg.

Considering the risk estimates in context, Table 3 compares infection/case/hospitalization fatality risks for pandemic influenza in 1918 and 2009, SARS and MERS. SARS causes moderate to severe disease requiring hospitalization, thus the infection fatality risk and case fatality risk are essentially the same as the hospitalisation fatality risk. The hospitalization fatality risk for MERS is well documented although the shape and depth of the clinical iceberg remains less well defined. In contrast, the majority of COVID–19 infections do not cause severe disease requiring hospitalization, and the symptomatic case fatality risk would be substantially lower than the hospitalisation fatality risk. However, despite a lower symptomatic case fatality risk, COVID–19 is likely to infect many more people and ultimately cause many more deaths than SARS and MERS. Compared with the 1918 and 2009 influenza pandemics, our estimates are intermediate but substantially higher than 2009, which was generally regarded as a low severity pandemic. Like 2009 we find symptomatic case-fatality risk is highest in the oldest age group; unlike any previously reported pandemic or seasonal influenza, we find that infection risk is also increasing with age group, though this may be in part due to preferential ascertainment of older and thus more severe cases. One largely unknown factor at present is the number of asymptomatic, undiagnosed infections. These do not enter our estimates of the symptomatic CFR, but if such asymptomatic or clinically very mild cases existed and were not detected, the infection fatality risk would be lower than the sCFR. Further clarifying this requires new data sources not yet available, including serologic studies.

Our inferences were based on a variety of sources, and have a number of caveats highlighted below, but considering the totality of the findings nevertheless indicate that COVID–19 transmission is difficult to control. With a reproductive number in the range of 2–2.5, we might expect at least half of the population to be infected even with aggressive use of community mitigation measures. Perhaps the most important target of mitigation measures would be to “flatten out” the epidemic curve, reducing the peak demand on healthcare services and buying time for better treatment pathways to be developed. In due course, vaccines may also be available to protect against infection or severe disease. While our estimates of symptomatic case fatality risk are concerning, these could be reduced if effective antivirals were identified and widely adopted for treatment of severe cases. Timely data from clinical trials of remdesivir, lopinavir/ritonavir, and other potential chemotherapies, as well as supportive care modalities, would be extremely informative.

Several important caveats bear mention, as follows. First and most importantly, our modelled estimates have necessarily relied on numerous strong assumptions, given the paucity of definitive data elements such as serosurveys, serial viral shedding studies, robust ascertainment of sufficient transmission chains, incomplete testing of travelers and returnees from Wuhan, all of which needed to be underpinned by systematic unbiased sampling of the underlying population and by important age- and other sub-groups.

Our estimates of symptomatic case-fatality risk are inevitably affected by underascertainment of cases and deaths of COVID–19. On one hand, overstretched and overwhelmed health care surge capacity in Wuhan could result in symptomatic case-fatality risks that are higher than they would be in a less stressed health care setting. We have accounted for limited sensitivity of detection of cases among travelers, and our sensitivity analyses show that lower detection among travelers implies larger populations infected in Wuhan and correspondingly lower severity. On the other hand, the numerator of the number of deaths could also have been undercounted although much less likely so by comparison to enumerating the denominator, for the same surge capacity reason or due to imperfect test sensitivity especially during the first month of the outbreak ⁸. If deaths in Wuhan were under-ascertained, this would bias our severity estimates downward.

Another caveat concerns one of our key inputs of the infection prevalence among expatriates airlifted out of Wuhan. Their point prevalence might well be lower than that among local residents, because of a generally more advantaged socioeconomic background, thus it would be a lower bound of the cross-sectional disease prevalence. If this were the case, then we would have overestimated the reduction in transmissibility conferred by public health interventions in Wuhan and overestimated severity.

Based on only publicly available data, there is necessarily substantial uncertainty in our estimates of the effectiveness of intra-Wuhan public health interventions in reducing transmissibility. Calculating the instantaneous reproductive number from a set of line lists that are updated daily would be the most reliable method for detecting changes in transmissibility associated with interventions.

There has been refinement of case definitions at both the national and provincial levels, such as excluding RT-PCR test-positive asymptomatics (or perhaps in fact very mildly symptomatics) from being labelled an officially “confirmed” case ⁹ or including test-naïve clinically diagnosed cases with clear epidemiologic links as “confirmed” ¹⁰. While these should not affect our estimation given our data sources from the earlier phase of the epidemic, such changes in the reporting criteria may influence the interpretation of future data. Finally, given that Wuhan is no longer the only, albeit the first, location with sustained local spread, it would be important to assess and take into account the experience from elsewhere both domestically in mainland China and overseas. These secondary epicenters, having learnt from the early phase of the Wuhan epidemic, might have had a systematically different epidemiology and response that could impact the parameters estimated here.

Table 3. Severity estimates of SARS (2002-3), MERS (2014-), 1918 influenza pandemic (1918-20) and 2009 influenza pandemic (2009-10)

Parameter	SARS*	MERS†	1918 influenza pandemic	2009 influenza pandemic
Infection fatality risk (IFR) (risk of death among all infections)	Worldwide (WHO) ¹ 9.6% (774/8096) Mainland China ¹¹ 6.4% (343/5327) Hong Kong* ^12,13 Overall: 17.2% (302/1755) <60 yrs: 13.2% (9.8-16.8) ≥60 yrs: 43.3% (35.2-52.4) Taiwan* ¹⁴ Overall: 27.6% (180/664) <60 yrs: 15.3% (72/470) ≥60 yrs: 48.6% (88/181)	--	Worldwide ^15,16 2.5% Copenhagen ¹⁷ 1.7%	Hong Kong ¹⁸ <60 yrs: <0.1% ≥60 yrs: 1.1% (0.2-4.7)
Symptomatic case fatality risk (sCFR) (risk of death among symptomatic infections)		--	--	United Kingdom ¹⁹ Overall: 26 (11-66) per 100,000 5-14 yrs: 11 (3-36) per 100,000 ≥65 yrs: 980 (300-3200) per 100,000
Hospitalization fatality risk (HFR) (risk of death among infections that require hospitalization for medical reasons, not only for case isolation)		Worldwide (WHO)* 34.4% (858/2494) Saudi Arabia* ²⁰ 40.7% (726/1783) South Korea*** ²⁰ 20.4% (38/186)	--	North America ²¹ Overall: 2.6% (1.6, 3.9) ≤19 yrs: 0.8% (0.5, 1.1) 20-64 yrs: 5.4% (3.5, 7.5) ≥65 yrs: 10.7% (5.3, 17.6)

^*IFR=CFR=HFR given virtually every infected person required hospitalization and seroprevalence amongst close contacts and in general community approximated zero

^**Among probable cases

^***Among laboratory-confirmed cases

† There are more infections “undetected” in MERS compared with SARS. The seroprevalence amongst individuals exposed to camels was estimated as 6.2% in Arabian Peninsula ²².

Acknowledgement

We thank Di Liu, Miky Wong and Chi-Kin Lam from School of Public Health, The University of Hong Kong for technical support. This research was supported by a commissioned grant from the Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region.

Funding

This research was supported by a commissioned grant from the Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region and Award Number U54GM088558 from the US National Institute of General Medical Sciences. P.M.D was supported by the Fellowship Foundation Ramon Areces. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health. The funding bodies had no role in study design, data collection and analysis, preparation of the manuscript, or the decision to publish. All authors have seen and approved the manuscript. All authors have contributed significantly to the work. All authors report no conflicts of interest. The manuscript and the data contained within have not been published and are not being considered for publication elsewhere.

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Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

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Case Definition In Clinical Severity Measures

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