metadata
license: mit
pipeline_tag: question-answering
widget:
- text: What is the delay between illness onset and infection?
context: >-
Epidemiological research priorities for public health control of the
ongoing global novel coronavirus (2019-nCoV) outbreak
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029449/ SHA:
90de2d957e1960b948b8c38c9877f9eca983f9eb Authors: Cowling, Benjamin J;
Leung, Gabriel M Date: 2020-02-13 DOI:
10.2807/1560-7917.es.2020.25.6.2000110 License: cc-by Abstract: Infections
with 2019-nCoV can spread from person to person, and in the earliest phase
of the outbreak the basic reproductive number was estimated to be around
2.2, assuming a mean serial interval of 7.5 days [2]. The serial interval
was not precisely estimated, and a potentially shorter mean serial
interval would have corresponded to a slightly lower basic reproductive
number. Control measures and changes in population behaviour later in
January should have reduced the effective reproductive number. However, it
is too early to estimate whether the effective reproductive number has
been reduced to below the critical threshold of 1 because cases currently
being detected and reported would have mostly been infected in mid- to
late-January. Average delays between infection and illness onset have been
estimated at around 5–6 days, with an upper limit of around 11-14 days
[2,5], and delays from illness onset to laboratory confirmation added a
further 10 days on average [2]. Text: It is now 6 weeks since Chinese
health authorities announced the discovery of a novel coronavirus
(2019-nCoV) [1] causing a cluster of pneumonia cases in Wuhan, the major
transport hub of central China. The earliest human infections had occurred
by early December 2019, and a large wet market in central Wuhan was linked
to most, but not all, of the initial cases [2] . While evidence from the
initial outbreak investigations seemed to suggest that 2019-nCoV could not
easily spread between humans [3] , it is now very clear that infections
have been spreading from person to person [2] . We recently estimated that
more than 75,000 infections may have occurred in Wuhan as at 25 January
2020 [4] , and increasing numbers of infections continue to be detected in
other cities in mainland China and around the world. A number of important
characteristics of 2019-nCoV infection have already been identified, but
in order to calibrate public health responses we need improved information
on transmission dynamics, severity of the disease, immunity, and the
impact of control and mitigation measures that have been applied to date.
Infections with 2019-nCoV can spread from person to person, and in the
earliest phase of the outbreak the basic reproductive number was estimated
to be around 2.2, assuming a mean serial interval of 7.5 days [2] . The
serial interval was not precisely estimated, and a potentially shorter
mean serial interval would have corresponded to a slightly lower basic
reproductive number. Control measures and changes in population behaviour
later in January should have reduced the effective reproductive number.
However, it is too early to estimate whether the effective reproductive
number has been reduced to below the critical threshold of 1 because cases
currently being detected and reported would have mostly been infected in
mid-to late-January. Average delays between infection and illness onset
have been estimated at around 5-6 days, with an upper limit of around
11-14 days [2, 5] , and delays from illness onset to laboratory
confirmation added a further 10 days on average [2] . Chains of
transmission have now been reported in a number of locations outside of
mainland China. Within the coming days or weeks it will become clear
whether sustained local transmission has been occurring in other cities
outside of Hubei province in China, or in other countries. If sustained
transmission does occur in other locations, it would be valuable to
determine whether there is variation in transmissibility by location, for
example because of different behaviours or control measures, or because of
different environmental conditions. To address the latter, virus survival
studies can be done in the laboratory to confirm whether there are
preferred ranges of temperature or humidity for 2019-nCoV transmission to
occur. In an analysis of the first 425 confirmed cases of infection, 73%
of cases with illness onset between 12 and 22 January reported no exposure
to either a wet market or another person with symptoms of a respiratory
illness [2] . The lack of reported exposure to another ill person could be
attributed to lack of awareness or recall bias, but China's health
minister publicly warned that pre-symptomatic transmission could be
occurring [6] . Determining the extent to which asymptomatic or
pre-symptomatic transmission might be occurring is an urgent priority,
because it has direct implications for public health and hospital
infection control. Data on viral shedding dynamics could help in assessing
duration of infectiousness. For severe acute respiratory syndrome-related
coronavirus (SARS-CoV), infectivity peaked at around 10 days after illness
onset [7] , consistent with the peak in viral load at around that time [8]
. This allowed control of the SARS epidemic through prompt detection of
cases and strict isolation. For influenza virus infections, virus shedding
is highest on the day of illness onset and relatively higher from shortly
before symptom onset until a few days after onset [9] . To date,
transmission patterns of 2019-nCoV appear more similar to influenza, with
contagiousness occurring around the time of symptom onset, rather than
SARS. Transmission of respiratory viruses generally happens through large
respiratory droplets, but some respiratory viruses can spread through fine
particle aerosols [10] , and indirect transmission via fomites can also
play a role. Coronaviruses can also infect the human gastrointestinal
tract [11, 12] , and faecal-oral transmission might also play a role in
this instance. The SARS-CoV superspreading event at Amoy Gardens where
more than 300 cases were infected was attributed to faecal-oral, then
airborne, spread through pressure differentials between contaminated
effluent pipes, bathroom floor drains and flushing toilets [13] . The
first large identifiable superspreading event during the present 2019-nCoV
outbreak has apparently taken place on the Diamond Princess cruise liner
quarantined off the coast of Yokohama, Japan, with at least 130 passengers
tested positive for 2019-nCoV as at 10 February 2020 [14] . Identifying
which modes are important for 2019-nCoV transmission would inform the
importance of personal protective measures such as face masks (and
specifically which types) and hand hygiene. The first human infections
were identified through a surveillance system for pneumonia of unknown
aetiology, and all of the earliest infections therefore had Modelling
studies incorporating healthcare capacity and processes pneumonia. It is
well established that some infections can be severe, particularly in older
adults with underlying medical conditions [15, 16] , but based on the
generally mild clinical presentation of 2019-nCoV cases detected outside
China, it appears that there could be many more mild infections than
severe infections. Determining the spectrum of clinical manifestations of
2019-nCoV infections is perhaps the most urgent research priority, because
it determines the strength of public health response required. If the
seriousness of infection is similar to the 1918/19 Spanish influenza, and
therefore at the upper end of severity scales in influenza pandemic plans,
the same responses would be warranted for 2019-nCoV as for the most severe
influenza pandemics. If, however, the seriousness of infection is similar
to seasonal influenza, especially during milder seasons, mitigation
measures could be tuned accordingly. Beyond a robust assessment of overall
severity, it is also important to determine high risk groups. Infections
would likely be more severe in older adults, obese individuals or those
with underlying medical conditions, but there have not yet been reports of
severity of infections in pregnant women, and very few cases have been
reported in children [2] . Those under 18 years are a critical group to
study in order to tease out the relative roles of susceptibility vs
severity as possible underlying causes for the very rare recorded
instances of infection in this age group. Are children protected from
infection or do they not fall ill after infection? If they are naturally
immune, which is unlikely, we should understand why; otherwise, even if
they do not show symptoms, it is important to know if they shed the virus.
Obviously, the question about virus shedding of those being infected but
asymptomatic leads to the crucial question of infectivity. Answers to
these questions are especially pertinent as basis for decisions on school
closure as a social distancing intervention, which can be hugely
disruptive not only for students but also because of its knock-on effect
for child care and parental duties. Very few children have been confirmed
2019-nCoV cases so far but that does not necessarily mean that they are
less susceptible or that they could not be latent carriers. Serosurveys in
affected locations could inform this, in addition to truly assessing the
clinical severity spectrum. Another question on susceptibility is
regarding whether 2019-nCoV infection confers neutralising immunity,
usually but not always, indicated by the presence of neutralising
antibodies in convalescent sera. Some experts already questioned whether
the 2019-nCoV may behave similarly to MERS-CoV in cases exhibiting mild
symptoms without eliciting neutralising antibodies [17] . A separate
question pertains to the possibility of antibody-dependent enhancement of
infection or of disease [18, 19] . If either of these were to be relevant,
the transmission dynamics could become more complex. A wide range of
control measures can be considered to contain or mitigate an emerging
infection such as 2019-nCoV. Internationally, the past week has seen an
increasing number of countries issue travel advisories or outright entry
bans on persons from Hubei province or China as a whole, as well as
substantial cuts in flights to and from affected areas out of commercial
considerations. Evaluation of these mobility restrictions can confirm
their potential effectiveness in delaying local epidemics [20] , and can
also inform when as well as how to lift these restrictions. If and when
local transmission begins in a particular location, a variety of community
mitigation measures can be implemented by health authorities to reduce
transmission and thus reduce the growth rate of an epidemic, reduce the
height of the epidemic peak and the peak demand on healthcare services, as
well as reduce the total number of infected persons [21] . A number of
social distancing measures have already been implemented in Chinese cities
in the past few weeks including school and workplace closures. It should
now be an urgent priority to quantify the effects of these measures and
specifically whether they can reduce the effective reproductive number
below 1, because this will guide the response strategies in other
locations. During the 1918/19 influenza pandemic, cities in the United
States, which implemented the most aggressive and sustained community
measures were the most successful ones in mitigating the impact of that
pandemic [22] . Similarly to international travel interventions, local
social distancing measures should be assessed for their impact and when
they could be safely discontinued, albeit in a coordinated and deliberate
manner across China such that recrudescence in the epidemic curve is
minimised. Mobile telephony global positioning system (GPS) data and
location services data from social media providers such as Baidu and
Tencent in China could become the first occasion when these data inform
outbreak control in real time. At the individual level, surgical face
masks have often been a particularly visible image from affected cities in
China. Face masks are essential components of personal protective
equipment in healthcare settings, and should be recommended for ill
persons in the community or for those who care for ill persons. However,
there is now a shortage of supply of masks in China and elsewhere, and
debates are ongoing about their protective value for uninfected persons in
the general community. The Table summarises research gaps to guide the
public health response identified. In conclusion, there are a number of
urgent research priorities to inform the public health response to the
global spread of 2019-nCoV infections. Establishing robust estimates of
the clinical severity of infections is probably the most pressing, because
flattening out the surge in hospital admissions would be essential if
there is a danger of hospitals becoming overwhelmed with patients who
require inpatient care, not only for those infected with 2019-nCoV but
also for urgent acute care of patients with other conditions including
those scheduled for procedures and operations. In addressing the research
gaps identified here, there is a need for strong collaboration of a
competent corps of epidemiological scientists and public health workers
who have the flexibility to cope with the surge capacity required, as well
as support from laboratories that can deliver on the ever rising demand
for diagnostic tests for 2019-nCoV and related sequelae. The readiness
survey by Reusken et al. in this issue of Eurosurveillance testifies to
the rapid response and capabilities of laboratories across Europe should
the outbreak originating in Wuhan reach this continent [23] . In the
medium term, we look towards the identification of efficacious
pharmaceutical agents to prevent and treat what may likely become an
endemic infection globally. Beyond the first year, one interesting
possibility in the longer term, perhaps borne of wishful hope, is that
after the first few epidemic waves, the subsequent endemic re-infections
could be of milder severity. Particularly if children are being infected
and are developing immunity hereafter, 2019-nCoV could optimistically
become the fifth human coronavirus causing the common cold. None declared.
Model Card for Model longluu/Medical-QA-gatortrons-COVID-QA
The model is an extractive Question Answering algorithm that can find an answer to a question by finding a segment in a text.
Model Details
Model Description
The base pretrained model is GatorTronS which was trained on billions of words in various clinical texts (https://huggingface.co/UFNLP/gatortronS). Then using the COVID-QA dataset (https://huggingface.co/datasets/covid_qa_deepset), I fine-tuned the model for an extractive Question Answering algorithm that can answer a question by finding it within a text.
Model Sources [optional]
The github code associated with the model can be found here: https://github.com/longluu/Medical-QA-extractive.
Training Details
Training Data
This dataset contains 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles regarding COVID-19 and other medical issues. The dataset can be found here: https://github.com/deepset-ai/COVID-QA. The preprocessed data can be found here https://huggingface.co/datasets/covid_qa_deepset.
Training Hyperparameters
The hyperparameters are --per_device_train_batch_size 4
--learning_rate 3e-5
--num_train_epochs 2
--max_seq_length 512
--doc_stride 250
--max_answer_length 200 \
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was trained and validated on train and validation sets.
Metrics
Here we use 2 metrics for QA tasks exact match and F-1.
Results
{'exact_match': 37.12871287128713, 'f1': 64.90491019877854}
Model Card Contact
Feel free to reach out to me at thelong20.4@gmail.com if you have any question or suggestion.