The Red meat tax

I am just going to add this for discussion. When things "appear " in the news, usually is the agenda being pushed.
 In this case its veganism.  Now I am not a Vegan nor do I hold any opinions. I am struggling with the dichotomy of Vegan v Vegetarian v Non vegetarian. but the agenda towards a plant based and even insect based diet is being pushed by the scumbaggery.  Why? Is the trend towards global cooling putting stress on the supply or is asimple case of greed. Those that control the food. anyway here is a paper pushing a Tax on red meat.

Health-motivated taxes on red and

processed meat: A modelling

study on optimal tax levels and

associated health impacts

Open Access
Peer-reviewed
Research Article
Marco Springmann,
Daniel Mason-D’Croz,
Sherman Robinson,
Keith Wiebe,
H. Charles J. Godfray,
Mike Rayner,
Peter Scarborough

Published: November 6, 2018


Abstract
Background
The consumption of red and processed meat has been
associated with increased mortality from chronic diseases,
and as a result, it has been classified by the World Health
Organization as carcinogenic (processed meat) and
probably carcinogenic (red meat) to humans. One policy
response is to regulate red and processed meat
consumption similar to other carcinogens and foods of
public health concerns. Here we describe a market-based
approach of taxing red and processed meat according to its
health impacts.
Methods
We calculated economically optimal tax levels for 149 world
regions that would account for (internalize) the health costs
associated with ill-health from red and processed meat
consumption, and we used a coupled modelling framework
to estimate the impacts of optimal taxation on consumption,
health costs, and non-communicable disease mortality.
Health impacts were estimated using a global comparative
risk assessment framework, and economic responses were
estimated using international data on health costs, prices,
and price elasticities.
Findings
The health-related costs to society attributable to red and
processed meat consumption in 2020 amounted to USD
285 billion (sensitivity intervals based on epidemiological
uncertainty (SI), 93–431), three quarters of which were due
to processed meat consumption. Under optimal taxation,
prices for processed meat increased by 25% on average,
ranging from 1% in low-income countries to over 100% in
high-income countries, and prices for red meat increased by
4%, ranging from 0.2% to over 20%. Consumption of
processed meat decreased by 16% on average, ranging
from 1% to 25%, whilst red meat consumption remained
stable as substitution for processed meat compensated
price-related reductions. The number of deaths attributable
to red and processed meat consumption decreased by 9%
(222,000; SI, 38,000–357,000), and attributable health costs
decreased by 14% (USD 41 billion; SI, 10–57) globally, in
each case with greatest reductions in high and middle-
income countries.
Interpretation
Including the social health cost of red and processed meat
consumption in the price of red and processed meat could
lead to significant health and environmental benefits, in
particular in high and middle-income countries. The optimal
tax levels estimated in this study are context-specific and
can complement the simple rules of thumb currently used
for setting health-motivated tax levels.

Competing interests: The authors have declared that no
competing interests exist.
Introduction
The consumption of red and processed meat exceeds
recommended levels in most high and middle-income
countries and has been associated with a range of negative
health and environmental impacts [1,2]. In 2015, the cancer
agency of the World Health Organization, the International
Agency for Research on Cancer (IARC), classified the
consumption of red meat, which includes beef, lamb, and
pork, as carcinogenic to humans if eaten in processed form,
and as probably carcinogenic if eaten unprocessed [3]. In
addition to being linked with cancer, the consumption of red
and processed meat has also been associated with
increased rates of coronary heart disease[4], stroke [5], type
2 diabetes mellitus [6], and overall mortality [7,8]. Those
impacts and the IARC’s classification raise the question
whether the consumption of red and processed meat should
be regulated similar to other carcinogens or to other foods of
public health concern, such as sugary drinks [9].
Market-based approaches to regulation have gained
popularity in public health research and the public debate. In
particular health-motivated taxes have been widely
discussed [10–12], and implemented in some countries, e.g.
for sugar-sweetened beverages [9,13], and saturated fats
[14]. The tax levels discussed or implemented have mostly
been based on practical considerations on their likely
impact. However, from an economic perspective, health-
motivated taxes are so-called Pigouvian taxes whose
purpose it is to correct for the unintended and previously
unaccounted consequences to society of an economic
activity (in this case, the negative health impacts associated
with red and processed meat consumption) by incorporating
the cost of those consequences into the price of the activity
or good [9,15,16]. Thus, the economically optimal tax level
of a health-motivated Pigouvian tax is determined such that
market prices include the marginal health costs of
consumption, i.e. the cost of treating the health conditions
that are associated with one additional serving of the good
in question.
Here we provide estimates of the health costs to society and
optimal tax levels for red and processed meat for all major
world regions, and we estimate the impacts that health-
motivated taxation of red and processed meat could have
on food consumption, and mortality from diet-related, non-
communicable diseases. In our analysis, we treated red
meat and processed meat as separate risk factors, and
estimated their health burden and health-motivated taxes
individually and when combined. We assumed the risk
associations between red and processed meat and diet-
related diseases as causal based on the existence of
plausible pathways, mechanistic evidence, and dose-
response relationships (see section A1 in S1 File)
[3,6,17–19]. We accounted for changes between red meat
consumption and processed meat consumption as a result
of differentiated taxation, but also for impacts on other food
groups that are considered substitutes, such as poultry, or
complements, such as vegetable oils. We focus on the year
2020 as a possible future year for implementation, and we
considered other implementation dates (2010 and 2050) in
sensitivity analyses.
Methods
We used a coupled modelling framework to calculate
optimal tax levels for red and processed meat and the
associated health and climate change impacts in the year
2020 for 149 world regions (Fig 1). Our calculation included
several steps. First, we estimated the health impacts
associated with the current and projected consumption
levels of red and processed meat. Second, we estimated
the health costs associated with those health impacts. Third,
we repeated that calculation for a scenario in which we
increased red and processed meat consumption by a
marginal increase which we take to be one additional
serving per day in each region. (Note that we are interested
in the change in mortality and health costs per marginal
increase in consumption. Because the dose-response
functions we use are linear and we divide over the marginal
increase when levying the damage costs on baseline prices,
it does not matter what we define as marginal.) Fourth, we
calculated the marginal health costs of red and processed
meat consumption by subtracting the cost estimates of the
two scenarios. Fifth, we levied the marginal health costs per
marginal change in consumption onto the initial market
prices of red and processed meat in each region, and
calculated the impacts of those price changes on
consumption levels, health impacts, and health costs.
For calculating the health impacts associated with red and
processed meat consumption, we used a global
comparative risk assessment framework [20]. We estimated
the mortality burden attributable to changes in the
consumption of red and processed meat by calculating
population attributable fractions (PAFs) which represent the
proportions of disease cases that are attributable to the risk
exposure and that would be avoided due to changes in risk
exposure, respectively [21–23]. The disease states included
coronary heart disease (CHD), stroke, colorectal cancer,
and type 2 diabetes mellitus (T2DM). There are indications
that red and processed meat consumption increases the risk
for other cancers and cardiovascular diseases [24–26]. In a
sensitivity analysis, we therefore adopted broader risk
associations of red and processed meat consumption with
total cancer and cardiovascular diseases in general Cause-specific mortality
rates and population numbers were adopted from data
reported by the Global Burden of Disease project and
projected forward using data from the United Nations
Population Division. We treated red and processed meat
consumption as two separate risk factors, and adopted the
relevant relative risk parameters describing the association
between red and processed meat consumption and
mortality from meta-analyses of prospective cohort studies
(Table A1 in S1 File) [19,4,6,5]. For calculating the joint risk
of red and processed meat consumption, we combined each
PAF mutiplicatively [21–23]. Given that the diseases
included in the modelling framework predominantly affect
adults, we focused on the health implications for individuals
aged 20 and older. In a sensitivity analysis, we estimated
the impacts that tax-related changes in food consumption
could have on weight distributions and weight-related
mortality by using derived relationships between body mass
index and food availability (Table A2 in S1 File) [20].
For estimating the health costs associated with changes in
mortality, we adopted cost-of-illness (CoI) estimates and
used a cost transfer method to estimate the costs of illness
in different parts of the world and in different years (section
A2 in S1 File) [1]. We based our cost-of-illness estimates for
CHD, stroke, and cancer on a comparative assessment of
the economic burden of CVD [27,28] and cancer [29] across
the European Union which included direct costs (healthcare
expenditure, health service utilization, expenditure on
medication) and indirect costs (opportunity costs of informal
care, productivity costs due to mortality and morbidity). We
calculated costs per death based on mortality statistics [28],
and estimated the costs per death by disease in other
regions and years by scaling the EU base values by the
ratio of health expenditure per capita for direct costs, and by
the ratio of GDP per capita (adjusted for purchasing power
parity) for indirect costs. Productivity losses due to morbidity
and mortality, which are a part of the indirect costs, were
only included for deaths occurring among those of working
age which we took to be below 65 years in all regions, in
line with other assessments [29]. For the CoI analysis
related to diabetes, we adopted country-specific cost
estimates [30], and to avoid double-counting of
complications related to cardiovascular diseases, adjusted
those for the incremental cost component specifically
attributable to diabetes [31,32]. No data was available to
estimate indirect costs for T2DM. Where possible, we
included both direct and indirect costs in our analysis in
order to account for the full health costs of red meat
consumption to society, and we explored the relative
contributions of direct and indirect costs to the final
estimates in a sensitivity analysis. On average, indirect
costs represented half to two thirds of the total cost of illness
for CHD, stroke, and cancer (Table A3 in S1 File).
For estimating the consumption feedbacks of levying taxes
on red and processed meat, we used a global agriculture-
economic model, the International Model for Policy Analysis
of Agricultural Commodities and Trade (IMPACT) [33], and
adjusted it to account for differences between red and
processed meat. The IMPACT model is based on a global
partial equilibrium multi-market model of agricultural
production, demand, trade, and prices (section A3 in S1
File). For our analysis, we adopted IMPACT data on current
and future food availability, consumer prices, and on own
and cross-price elasticities that determine how the demand
of a commodity and related commodities, such as other
types of meat, changes when its price changes [34]. To
obtain a better proxy for food consumption, we adjusted
food availability data for waste at the consumption level
using regional estimates from the Food and Agriculture
Organization of the United Nations (FAO) (section A4 in S1
File) [35], and we disaggregated total red meat consumption
into processed and unprocessed components using
compositional data from the Global Dietary Database [2].
Processed meat is generally defined as any meat preserved
by salting, curing, smoking, or by adding chemical
preservatives, including bacon, sausages, salami, hot dogs
and processed deli meats. It can also include processed
white meat, but because we disaggregate processed meat
from total red meat, we only include processed red meat
from beef, lamb and pork in our analysis. We treated red
meat and processed meat as substitutes, and used the
same cross-price elasticities that describe the substitution of
different types of meat (e.g. between beef and poultry).
Processed meats are generally cheaper than non-
processed meat, because of the quality of the parts of
meats used. In our main scenario, we used a price wedge
between processed and unprocessed meats of 15%, which
is in line with the average price difference over the last five
years in the UK [36], and we tested price wedges of zero
and 30% in a sensitivity analysis (Tables A27-A28 in S1
File). All monetary data were converted to the value of the
US dollar in 2010 by using changes in the consumer price
index by region based on data from the International
Monetary Fund.
In our uncertainty analysis, we accounted for
epidemiological and economic uncertainties. In our analysis,
the main source of epidemiological uncertainty is related to
the relative risk estimates used for calculating health
impacts, and the main source of economic uncertainty is
related to the projections of health care-related costs for
each region. In each case, we recalculated the endpoints of
our analysis (tax levels, consumption changes, health
impacts) by using the low and high values of the 95%
confidence interval of relative risk estimates, and the
standard deviation of health-cost estimates. In the main text,
we focus on the epidemiological uncertainty. Using the high
and low values of the health-cost estimates resulted in
smaller uncertainty intervals than using the high and low
values of the epidemiological uncertainty range (Tables A29-
A30 in S1 File). We also explored the impacts that changes
in price elasticities (which determine consumer responses)
have on our estimates. Varying own-price elasticities by
10%, which is in line with estimated confidence intervals
[37], also resulted in estimates within the epidemiological
uncertainty range (Tables A25-A26 in S1 File).
Results
Impacts of optimal taxation
According to our model projections (Table 1), the
consumption of red meat was associated with 860,000 (95%
confidence interval related to epidemiological uncertainty
(SI) 220–1,410,000) deaths globally in the year 2020, and
that of processed meat with 1,530,000 (SI, 430–2,470,000)
deaths. When assessed together, those represented 4.4%
of all projected deaths in the analysis in that year. About two
thirds of attributable deaths were due to stroke (for red
meat), and coronary heart disease (for processed meat),
followed by type-2 diabetes mellitus (14–17%) and
colorectal cancer (4–11%). About two thirds of attributable
deaths (64%) occurred in middle-income countries, one third
(32%) in high-income countries, and a small portion (4%) in
low-income countries. The associated costs related to
health care amounted to USD 285 billion (SI, 93–431),
which represented 0.3% of expected world GDP in that year.
More than two thirds of the health costs (69%) fell on high-
income countries (due to higher healthcare-related
expenditure), a third (30%) on middle-income countries, and
a small fraction (0.4%) on low-income countries. Country-
level results are listed in Tables A13-A14 in S1 File.
Under optimal taxation, the price for one serving of red and
processed meat reflects the health costs associated with
one additional serving of red and processed meat (Tables
A8-A9 in S1 File). Integrating the health costs associated
with one serving of red and processed meat into the prices
of one serving of red and processed meat increased the
price of red meat by 4% (SI, 1–6) on average, ranging from
less than 1% in low-income countries to 21% in high-income
countries, and the price of processed meat by 25% (SI,
10–32), ranging from 1% in low-income countries to 111% in
high-income countries (Table 1). Country-level impacts on
prices showed a greater range with price changes of up to
34% for red meat and 185% for processed meat Change in the price of red meat (a) and processed meat (b)
under cost-compensating taxation in relation to attributable
health costs (%), change in deaths attributable to red and
processed meat consumption (%) (c). We produced the
figure by mapping our data using ArcGIS (version 10.3.1,
Esri Inc.) and its layer for world countries.
https://doi.org/10.1371/journal.pone.0204139.g002
Associated with the change in prices were changes in
consumption. The greater changes in the price of processed
meat compared to red meat resulted in greater changes in
consumption for processed meat and also lead to
substitution effects, including a shift to poultry and
unprocessed red meat (despite a higher price of
unprocessed red meat in absolute), and smaller changes in
the consumption of milk and eggs, and a small decrease in
vegetable oils which is often consumed alongside meat
products (Fig 3). The consumption of processed meat
decreased by 16% (SI, 9–17; 3 g/d) on average, ranging
from 1% (0.1 g/d) in low-income countries to 25% (12 g/d) in
high-income countries (Table 1), and up to 37% (28 g/d) for
individual countries (Table A11 in S1 File). The consumption
of red meat remained similar to a situation without taxation
as a result of substitution effects, ranging from a reduction
of 0.8% to an increase of 0.7%. Other changes in
consumption were a 5% (2 g/d) increase in poultry
consumption (0.2–9% across income groups), and smaller
increases of 0.4% for milk and eggs (0–0.9% across income
groups), and a small decrease of 0.4% for vegetable oils
(0–0.9% across income groups) (Fig 3; Table A12 in S1
File).
Fig 3. Tax-related changes in food consumption by food
commodity and region.
Food commodities include processed (prcd) and
unprocessed (unprcd) red meats. Changes in food
consumption are shown in g/d, with the exception of Δkcal/d
which denotes changes in overall energy intake in terms of
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kcal/d.
https://doi.org/10.1371/journal.pone.0204139.g003
As a result of the tax-related changes in consumption, the
number of deaths attributable to red and processed meat
consumption decreased by 222,000 (SI, 38,000–357,000;
9%), from 2,400,000 (SI, 650,000–3,880,000) to 2,118,000
(SI, 609,000–3,379,000). The reductions in the number of
attributable deaths were composed of 235,000 (SI,
40,000–380,000) less deaths attributable to processed meat
consumption, and 3,200 (SI, -2,400–1,200) more deaths
attributable to red meat consumption (Table 1) (note that the
combined effect of changes in red and processed meat
consumption is generally lower than the sum of the
individual effects, because individuals can be affected by
both risks simultaneously without two types of the same
disease). The changes in attributable deaths corresponded
to a reduction in the burden attributable to red and
processed meat consumption of 9% on average, ranging
from 1% in low-income countries to 17% in high-income
countries, and up to 26% for individual countries (Fig 2C;
Table A13 in S1 File; https://doi.org/10.5287
/bodleian:j0n1Jd5rb).
Following the reduction in health burden, the healthcare-
related costs associated with red and processed meat
consumption were reduced by USD 41 billion (SI, 10–57),
from USD 285 billion (SI, 93–431) to USD 244 billion (SI,
83–374), which represented a cost reduction of 14% on
average, ranging from 1% in low-income countries to 17% in
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high-income countries (Table 1), and up to 26% for
individual countries (Table A14 in S1 File; https://doi.org
/10.5287/bodleian:j0n1Jd5rb). In comparison, tax revenues
amounted to USD 172 billion (SI, 72–215), two thirds (64%)
of which came from high-income countries, a sixth to a fifth
(16–20%) from middle-income countries, and less than 1%
from low-income countries (Table 1; Table A15 in S1 File).
Thus, healthcare-related costs under taxation exceeded tax
revenues by 42% on average, ranging from 22% in lower
middle-income countries to 50% in high-income countries.
Additional analyses
In a sensitivity analysis, we analysed a cost-compensating
taxing scheme in which we increased the prices of red and
processed meat until the tax revenues were equal to (i.e.
could pay for) the healthcare-related costs associated with
their consumption whilst taking into account the feedbacks
on consumption and health (section A5 in S1 File). Under
cost-compensating taxation, the price increases for red and
processed meat approximately doubled compared to
marginal-cost pricing, and the reductions in consumption,
attributable deaths, and healthcare-related costs of red and
processed meat increased by about a third (Table A16 in S1
File).
In addition to changes in diet-related risk factors,
consumption changes can influence weight levels and
weight-related risks associated with underweight,
overweight, and obesity [23,38]. In a sensitivity analysis, we
analysed those changes and found that the health impacts
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from tax-related changes in weight levels associated with
changes in calorie intake were small and mostly positive as
modest reductions in calorie intake reduced the number of
overweight and obese people which in most regions exceed
the number of underweight people. The weight impacts led
to an additional 3,800 (SI, 3,600–4,100) avoided deaths
globally, ranging from 9 additional deaths in low-income
countries (which compare to 860 avoided deaths due to
reduced red and processed meat consumption) to 2,900
avoided deaths in high-income countries (Tables A17-A18 in
S1 File).
Livestock-related emissions are responsible for the majority
of food-related greenhouse-gas (GHG) emissions, and for
about 14.5% of GHG emissions overall, a similar proportion
as from transport [39,40]. Consumption changes towards
lower red and processed meat consumption could therefore
have major implications for climate change. In a sensitivity
analysis, we analysed the potential changes in food-related
emissions using emissions intensities of foods obtained
from meta-analyses of life-cycle analyses (section A6 in S1
File). We note that the emissions intensities do not account
for changes in production methods and technologies that
might be associated with changes in consumption. In this
static framework, we found that optimal taxation could
reduce food-related GHG emissions by 109 MtCO2-eq (CI,
50–139), most of which due to reduced beef consumption
(Table A18 in S1 File). The change in emissions represents
a reduction of 1.2% globally, ranging from less than one
percent (0.6 MtCO2-eq) in low-income countries to 3% (62
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MtCO2-eq) in high-income countries, and up to 7% in
individual countries (Tables A19-A20 in S1 File).
Red and processed meat consumption is expected to
increase in the future, in particular in low and middle-income
countries [41,42]. Increases in red and processed meat
consumption have implications for optimal tax levels when
associated with changes in disease-specific mortality rates
and healthcare-related costs. In a final sensitivity analysis,
we projected optimal taxes on red and processed meat for
the year 2050. As a consequence of socio-economic
changes and changes in healthcare-related costs, we found
that optimal tax rates more than doubled, ranging from two-
fold increases in high-income countries to five-fold increases
in low-income countries (Table A21 in S1 File).
Discussion
The consumption of red and processed meat has been
associated with increased mortality from chronic diseases,
and red and processed meat have been declared by the
World Health Organization to be carcinogenic (processed
meat) and probably carcinogenic (red meat) to humans
[3–6,17,19,24,25]. One possible policy response to these
impacts is market-based regulation in the form of taxes.
Here we estimated optimal tax levels for red and processed
meat that are based on the (marginal) health cost
associated with red and processed meat consumption. By
design, the level of health-motivated taxes is context-
specific and accounts for disease-specific health costs and
mortality in a given location. Consequently, we find that
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health-motivated taxation of red and processed meat would
be low in low-income countries which currently experience a
low health and economic burden from red and processed
meat consumption, and taxation would be high in high and
middle-income countries which currently experience a
greater health and economic burden. As income is projected
to increase in future years, in particular in low and middle-
income countries, it can be expected that optimal tax levels
would increase in line with dietary and socio-economic
changes.
In our analysis, we estimated a health burden associated
with red and processed meat consumption of 2.4 (SI,
0.7–3.9) million attributable deaths in 2020, which
represented 4.4% of all projected deaths in the analysis in
that year. For the year 2010, the estimates of the number of
deaths attributable to red and processed meat consumption
are 2.0 (SI, 0.5–3.2) million (Table A22 in S1 File). Our
estimates are more comprehensive than the Global Burden
of Disease estimate of 0.9 (95% confidence interval (CI),
0.2–1.5) million deaths attributable to red and processed
meat in 2010 [22], and 0.7 (CI, 0.6–1.0) million deaths in
2013 [23]. Compared to the GBD estimates, we considered
a greater number of disease associations of red and
processed meat consumption (CHD, stroke, colorectal
cancer, and T2DM compared to CHD, colorectal cancer and
T2DM), and we considered minimal exposure levels of zero
instead of 11·4–17·1 g/d for red meat and 0–14·3 g/d for
processed meat assumed for the GBD estimate for 2013
[22,23]. Both choices are supported by epidemiological
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evidence (see section A1 in S1 File for a more
comprehensive discussion) [43,7,25,24]. Another difference
is that we used consumption data that is not standardised to
an energy intake of 2000 kcal/d, something that accounts for
over and underconsumption. Our analysis might therefore
reflect more accurately absolute consumption levels than
one based on the energy-standardised data of food
composition used by the GBD. Harmonising risk factors,
minimum exposures, and energy intake reduced the
difference between the GBD estimate and ours from 170%
to 78% (risk factors), 72% (risk factors and minimum
exposure), 47% (risk factors and energy intake), and 41%
(risk factors, minimum exposure, and energy intake),
respectively, with overlapping confidence intervals (Table
A23 in S1 File).
We estimated an economic burden associated with red and
processed meat consumption of USD 285 billion (SI,
93–431) in 2020, which represented 0.3% of the total health
expenditure estimated for that year. Our estimate included
both direct costs (healthcare expenditure, health service
utilization, expenditure on medication) and indirect costs
(opportunity costs of informal care, productivity costs due to
mortality and morbidity) to provide an estimate of the full
health costs of red and processed meat consumption to
society. On average, indirect costs represented half to two
thirds of the total cost of illness for CHD, stroke, and cancer,
but no estimates of indirect costs were available for T2DM.
Our estimate of the economic burden attributable to red and
processed meat consumption can therefore be considered
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an underestimate of all costs. Focusing only on the direct
cost component would roughly half our estimate (Table A24
in S1 File), and using a more general valuation approach
based on a measure for the willingness to pay for a
reduction in mortality risk, the so-called value-of-statistical-
life approach, would increase our estimate by about a factor
of ten [1]. Using disease associations for total cancer
(instead of colorectal cancer only) and cardiovascular
disease (instead of CHD and stroke only) would roughly
double the health and economic burden (Table A23 in S1
File).
Our analysis highlights significant differences between the
tax-related impacts on the prices and consumption of red
and processed meat. For example, in order to account for
the health costs attributable to red and processed meat by
adjusting prices, red meat prices would have to increase by
more than 20% in high-income countries, and processed
meat prices would have to more than double for those
countries. Price changes in upper middle-income countries
would amount to 7% and 47% for red meat and processed
meat, respectively. As a result, processed meat
consumption would decrease by about one serving per
week (12 g/d) in high-income countries and less than a third
of a serving per week (4 g/d) in upper middle-income
countries. As consumers are projected to partially switch
from processed meat to unprocessed meat and other
substitutes such as poultry, red meat consumption would
remain largely unchanged in those regions despite its
increase price. The total reduction in red and processed
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meat consumption is therefore lower than one would expect
based on the associated changes in prices. Although the
changes in red and processed meat consumption are still
substantial on a population level, absolute levels of red and
processed meat consumption would remain higher in each
region (130 g/d in high-income countries and 88 g/d in upper
middle-income countries) than recommended by bodies
such as the World Cancer Research Institute, which advises
consumption of less than 300 g of (uncooked) red meat per
week (about 40 g/d), little if any in processed form [26].
Market-based approaches, such as health-motivated
taxation, can therefore best be considered as one of a range
of measures that would be needed to move diets towards
more healthy and sustainable consumption patterns [44].
With respect to the environmental co-benefits of health
motivated taxation of red and processed meat, we estimated
an emissions reduction potential of about 110 MtCO2-eq
globally in 2020, in absence of changes in production
methods and technologies that might be associated with
changes in consumption. The change in emissions
represented a reduction in food-related GHG emissions of
1.2%. The reduction potential is similar to that of technical
greenhouse-gas mitigation options, such as rice, livestock,
and manure management, which have been estimated to be
below 250 MtCO2-eq each [45,46]. Thus, health-motivated
taxation of red and processed meat, alongside other
measures, could make meaningful contributions to food-
related emissions-reduction targets [47]. In another study,
we estimated the mitigation potential of environmentally
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motivated taxation of foods in general to be up to 1 GtCO2-
eq in 2020 [48]. However, in environmentally motivated
taxation schemes, we found that care has to be taken to
compensate for potential reductions in food security, e.g. by
using tax revenues for health promotion measures, whereas
in the health-motivated approach analysed here, health
concerns are built into by design, but all red meat (beef,
lamb, pork) is treated equally despite differing emissions
intensities. How to optimally combine health and
environmentally motivated schemes remains an important
question for future research.
Several caveats apply. We assumed that the risk
associations between red and processed meat and diet-
related diseases are causal based on mechanistic evidence
from analyses of the digestive tract for colorectal cancer [3],
there are several pathways that plausibly explain the
increase in risk for other disease [6,17–19], and the disease
associations show a dose-response relationship in cohort
studies [17–19]. Whilst the cohort studies controlled for
major confounding factors, such as body mass and
smoking, we cannot rule out a residual effect of other
confounding risk factors. We did not track changes in the
nutritional quality of diets, such as levels of micronutrients
that could be of concern especially in low-income countries.
However, our analysis suggests that cost-compensating tax
levels would be zero or close to zero in such environments,
and the magnitude of estimated changes is unlikely to have
any detrimental impacts in high and middle-income
environments where most micronutrient levels are adequate
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and can be easily obtained from other sources [49]. Due to
our focus on consumption, we did not analyse the
implications for agricultural production, livelihoods, market
adjustments between countries and across time, or how
health systems might change under different funding
schemes. We hope our comparative regional analysis
provides a good starting point for such research.
In our analysis of consumption changes, we used a set of
regionally comparable own and cross-price elasticities that
describe the substitution between different animal-based
foods, and between animal-based foods and some
complementary foods, such as vegetable oils. Such
substitution is in line with recent reviews of country-level
data [50]. However, we cannot rule out substitution effects
not captured by the data, such as replacement of processed
meat with fish, legumes or grains, especially when changes
in caloric intake would be substantial. Both our health
estimates and our emissions estimates would change
depending on the food groups that would compensate for
the reductions in processed meat consumption. For
example, greater consumption of sugar and refined
carbohydrates, something that is associated with negative
health impacts [51], could compensate some of the health
benefits associated with lower consumption of processed
meat. Similarly, a switch from beef to fish caught by trawling
could offset a portion of the emissions reductions associated
with reduced processed meat consumption [52]. On the
other hand, replacement of red and processed meat with
legumes, fruits and vegetables, or whole grains could lead
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to additional health benefits without significantly affecting
the emissions reductions identified here [23,51,52].

The Corona virus scam

I'm going to preface this by stating , I am not knowledgeable about this and this is the best I could find out and there is every chance I have mis-interpolated something. However the main source has been Wikipedia  though.........

In occult circles there may be a truth or maxim that kinda says if you cross your fingers behind your back then the lie you are telling doesnt count ,  As kids we did this all the time .."pax" i would shout" meaning you cant touch me
In the Jesuit faith there is a similar concept and probably used in other societies  that is called "The revelation of the Method "
This idea of Tacit agreement runs though our society like that red pen you left in your shirt pocket and now has given all your whites a lovely pink hue ,,,

Survivors is a British post-apocalyptic fiction drama television series created by Terry Nation and produced by Terence Dudley at the BBC, that broadcast from 1975 to 1977. It concerns the plight of a group of people who have survived an apocalyptic plague pandemic, which was accidentally released by a Chinese scientist and quickly spread across the world via air travel. Referred to as "The Death", the plague kills approximately 4,999 out of every 5,000 human beings on the planet within a matter of weeks of being released.    In episode 1 people are in the hospital lining up for a influenza jab! The doctor replies to his girlfriend , " oh it doesnt do any good , it just make them feel better " 

 https://youtu.be/zAyjkaFYnzE

https://youtu.be/3GSMQmbTGOg     ( the Japanese press release )

mere coincidence ?  most likely, but other movies have had similar fortune telling abilities..

 The official narrative for the corona virus is as follow Snip; ! 

 

Nations around the world are preparing for a possible major outbreak of a new deadly virus. The coronavirus, which started in the Chinese city of Wuhan, has already killed 17 people. It has spread to the USA, Japan, Korea and Thailand. More than 540 people have caught the virus and are in hospital.

The World Health Organization (WHO) is meeting to decide whether the outbreak is a global health emergency. China is urging people not to panic ahead of the Chinese New Year next week. Millions of Chinese will be traveling across the country to spend the holiday season with their families. Meanwhile, the city of Wuhan has suspended its public transport systems to help stop the spread of the virus.

The new corona virus is suspected to have come from illegally traded animals in a Wuhan market. The virus mutated and spread from an animal to a human. There are fears it could mutate and spread further.

Scientists say the virus is contagious and can be passed from person to person through the air. Dr Linfa Wang, a virologist at the Duke-National University of Singapore, said the new corona virus is in the same family as SARS, but it's different from SARS. He said people needed to look for pneumonia-like symptoms, such as fever, cough and difficulty breathing. Fu Ning, a 36-year-old woman from Beijing, said: "I feel fearful because there's no cure for the virus."

 See the fear and predictive programming being set up , " contagious " and "no cure "  hells bells whats a fella to do !

have faith in the powers that be ?  like Bill and Melinda gates and the John Hopkins university ?

Event 201

The Johns Hopkins Center for Health Security in partnership with the World Economic Forum and the Bill and Melinda Gates Foundation hosted Event 201, a high-level pandemic exercise on October 18, 2019, in New York, NY. The exercise illustrated areas where public/private partnerships will be necessary during the response to a severe pandemic in order to diminish large-scale economic and societal consequences.
no probably not , In my humble opinion any time you see the names "Bill and Melinda Gates " Satan is lurking somewhere in the shadows  
But the Security at the Chinese Bio security plant(s) must be of a very high level, after all the deal in quite dangeous viruses on a daily basis:

  "Wuhan Virus Laboratory is accredited to conduct research on three types of viruses: Ebola, Congo-Crimea hemorrhagic fever and Henipavirus"

OH SH#T.

Dont worry, while this might well be a "training exercise for a horseman;  for "I looked, and beheld a pale horse: and his name that sat on him was Death, and Hell followed with him. And power was given unto them over the fourth part of the earth, to kill with sword, and with hunger, and with death, and with the beasts of the earth."

but can the virus spread.  Yes it can and quickly. But there is a "but" !  In epidemiology, the basic reproduction number (sometimes called basic reproductive ratio, or incorrectly basic reproductive rate, and denoted R₀, pronounced R nought or R zero) of an infection can be thought of as the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection.  The definition describes the state where no other individuals are infected or immunized (naturally or through vaccination).

, pronounced R-nought or R-zero) of the virus has been estimated to be between 1.4 and 3.9.   This means that, when unchecked, the virus typically results in 1.4 to 3.9 new cases per established infection. It has been established that the virus is
 able to transmit along a chain of at least four people.   
        Human-to-human transmission of the virus has been confirmed.    Corona viruses are primarily spread through close contact, in particular through respiratory droplets from coughs and sneezes within a range of about 6 feet (1.8 m). Viral RNA has also been found in stool samples from infected patients.

so, Say that an infectious individual makes β contacts per unit time producing new infections with a mean infectious period of 1/γ. Therefore, the basic reproduction number is

Accordingto the japanese Media , 13 people were diagnosed with the virus between the 13th and he 16th of January ,, this would give 13/3 or Ro of 4.33 which would seem about right, 

The infection rate is as follows                                                       During an epidemic, typically the number of diagnosed infections ${\displaystyle N(t)}$ over time ${\displaystyle t}$ is known. In the early stages of an epidemic, growth is exponential, with a logarithmic growth rate!

However,  the most important uses of R₀ are determining if an emerging infectious disease can spread in a population and determining what proportion of the population should be immunized through vaccination to eradicate a disease. In commonly used infection models, when R₀ > 1 the infection will be able to start spreading in a population, but not if R₀ < 1. Generally, the larger the value of R₀, the harder it is to control the epidemic. For simple models and a 100% effective vaccine, the proportion of the population that needs to be vaccinated to prevent sustained spread of the infection is given by 1 − 1/R₀. ( 1-1/4.339 = 0.77 )

So, While I am in no way an knowledgeable ion this subject. I does see to me that a contagious muva-faker of a common cold that has the ability to cause through respiratory pneumonia has been released. weather through design or accident. The conspiracy theorist in me say by design , Thanks Bill and Melinda !  However it will spread but doesn't have the ball to become an all out epidemic. 

Once again I emplore you to take your TV outside and shoot it.

The latest I could find

Clinical Characteristics of Coronavirus Disease...
about:reader?url=https://www.nejm.org/doi/full/...
nejm.org
Clinical Characteristics of
Coronavirus Disease 2019 in China
Nan-shan Zhong
36-46 minutes
24 References
1 Citing Article
Abstract
Background
Since December 2019, when coronavirus disease 2019
(Covid-19) emerged in Wuhan city and rapidly spread
throughout China, data have been needed on the clinical
characteristics of the affected patients.
Methods
We extracted data regarding 1099 patients with laboratory-
confirmed Covid-19 from 552 hospitals in 30 provinces,
autonomous regions, and municipalities in China through
January 29, 2020. The primary composite end point was
admission to an intensive care unit (ICU), the use of
mechanical ventilation, or death.
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Results
The median age of the patients was 47 years; 41.9% of the
patients were female. The primary composite end point
occurred in 67 patients (6.1%), including 5.0% who were
admitted to the ICU, 2.3% who underwent invasive
mechanical ventilation, and 1.4% who died. Only 1.9% of
the patients had a history of direct contact with wildlife.
Among nonresidents of Wuhan, 72.3% had contact with
residents of Wuhan, including 31.3% who had visited the
city. The most common symptoms were fever (43.8% on
admission and 88.7% during hospitalization) and cough
(67.8%). Diarrhea was uncommon (3.8%). The median
incubation period was 4 days (interquartile range, 2 to 7).
On admission, ground-glass opacity was the most common
radiologic finding on chest computed tomography (CT)
(56.4%). No radiographic or CT abnormality was found in
157 of 877 patients (17.9%) with nonsevere disease and in
5 of 173 patients (2.9%) with severe disease.
Lymphocytopenia was present in 83.2% of the patients on
admission.
Conclusions
During the first 2 months of the current outbreak, Covid-19
spread rapidly throughout China and caused varying
degrees of illness. Patients often presented without fever,
and many did not have abnormal radiologic findings.
(Funded by the National Health Commission of China and
others.)
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Introduction
In early December 2019, the first pneumonia cases of
unknown origin were identified in Wuhan, the capital city of
Hubei province. 1 The pathogen has been identified as a
novel enveloped RNA betacoronavirus 2 that has currently
been named severe acute respiratory syndrome coronavirus
2 (SARS-CoV-2), which has a phylogenetic similarity to
SARS-CoV. 3 Patients with the infection have been
documented both in hospitals and in family settings. 4-8
The World Health Organization (WHO) has recently
declared coronavirus disease 2019 (Covid-19) a public
health emergency of international concern. 9 As of February
25, 2020, a total of 81,109 laboratory-confirmed cases had
been documented globally. 5,6,9-11 In recent studies, the
severity of some cases of Covid-19 mimicked that of SARS-
CoV. 1,12,13 Given the rapid spread of Covid-19, we
determined that an updated analysis of cases throughout
China might help identify the defining clinical characteristics
and severity of the disease. Here, we describe the results of
our analysis of the clinical characteristics of Covid-19 in a
selected cohort of patients throughout China.
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Methods
Study Oversight
The study was supported by National Health Commission of
China and designed by the investigators. The study was
approved by the institutional review board of the National
Health Commission. Written informed consent was waived
in light of the urgent need to collect data. Data were
analyzed and interpreted by the authors. All the authors
reviewed the manuscript and vouch for the accuracy and
completeness of the data and for the adherence of the study
to the protocol, available with the full text of this article at
NEJM.org.
Data Sources
We obtained the medical records and compiled data for
hospitalized patients and outpatients with laboratory-
confirmed Covid-19, as reported to the National Health
Commission between December 11, 2019, and January 29,
2020; the data cutoff for the study was January 31, 2020.
Covid-19 was diagnosed on the basis of the WHO interim
guidance. 14 A confirmed case of Covid-19 was defined as a
positive result on high-throughput sequencing or real-time
reverse-transcriptase–polymerase-chain-reaction (RT-PCR)
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assay of nasal and pharyngeal swab specimens. 1 Only
laboratory-confirmed cases were included in the analysis.
We obtained data regarding cases outside Hubei province
from the National Health Commission. Because of the high
workload of clinicians, three outside experts from
Guangzhou performed raw data extraction at Wuhan
Jinyintan Hospital, where many of the patients with Covid-19
in Wuhan were being treated.
We extracted the recent exposure history, clinical symptoms
or signs, and laboratory findings on admission from
electronic medical records. Radiologic assessments
included chest radiography or computed tomography (CT),
and all laboratory testing was performed according to the
clinical care needs of the patient. We determined the
presence of a radiologic abnormality on the basis of the
documentation or description in medical charts; if imaging
scans were available, they were reviewed by attending
physicians in respiratory medicine who extracted the data.
Major disagreement between two reviewers was resolved by
consultation with a third reviewer. Laboratory assessments
consisted of a complete blood count, blood chemical
analysis, coagulation testing, assessment of liver and renal
function, and measures of electrolytes, C-reactive protein,
procalcitonin, lactate dehydrogenase, and creatine kinase.
We defined the degree of severity of Covid-19 (severe vs.
nonsevere) at the time of admission using the American
Thoracic Society guidelines for community-acquired
pneumonia. 15
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All medical records were copied and sent to the data-
processing center in Guangzhou, under the coordination of
the National Health Commission. A team of experienced
respiratory clinicians reviewed and abstracted the data.
Data were entered into a computerized database and cross-
checked. If the core data were missing, requests for
clarification were sent to the coordinators, who subsequently
contacted the attending clinicians.
Study Outcomes
The primary composite end point was admission to an
intensive care unit (ICU), the use of mechanical ventilation,
or death. These outcomes were used in a previous study to
assess the severity of other serious infectious diseases,
such as H7N9 infection. 16 Secondary end points were the
rate of death and the time from symptom onset until the
composite end point and until each component of the
composite end point.
Study Definitions
The incubation period was defined as the interval between
the potential earliest date of contact of the transmission
source (wildlife or person with suspected or confirmed case)
and the potential earliest date of symptom onset (i.e.,
cough, fever, fatigue, or myalgia). We excluded incubation
periods of less than 1 day because some patients had
continuous exposure to contamination sources; in these
cases, the latest date of exposure was recorded. The
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summary statistics of incubation periods were calculated on
the basis of 291 patients who had clear information
regarding the specific date of exposure.
Fever was defined as an axillary temperature of 37.5°C or
higher. Lymphocytopenia was defined as a lymphocyte
count of less than 1500 cells per cubic millimeter.
Thrombocytopenia was defined as a platelet count of less
than 150,000 per cubic millimeter. Additional definitions —
including exposure to wildlife, acute respiratory distress
syndrome (ARDS), pneumonia, acute kidney failure, acute
heart failure, and rhabdomyolysis — are provided in the
Supplementary Appendix, available at NEJM.org.
Laboratory Confirmation
Laboratory confirmation of SARS-CoV-2 was performed at
the Chinese Center for Disease Prevention and Control
before January 23, 2020, and subsequently in certified
tertiary care hospitals. RT-PCR assays were performed in
accordance with the protocol established by the WHO. 17
Details regarding laboratory confirmation processes are
provided in the Supplementary Appendix.
Statistical Analysis
Continuous variables were expressed as medians and
interquartile ranges or simple ranges, as appropriate.
Categorical variables were summarized as counts and
percentages. No imputation was made for missing data.
Because the cohort of patients in our study was not derived
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from random selection, all statistics are deemed to be
descriptive only. We used ArcGIS, version 10.2.2, to plot the
numbers of patients with reportedly confirmed cases on a
map. All the analyses were performed with the use of R
software, version 3.6.2 (R Foundation for Statistical
Computing).
Results
Demographic and Clinical Characteristics
Figure 1.
Figure 1. Distribution of
Patients with Covid-19 across China.
Shown are the official statistics of all documented,
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laboratory-confirmed cases of coronavirus disease 2019
(Covid-19) throughout China, according to the National
Health Commission as of February 4, 2020. The numerator
denotes the number of patients who were included in the
study cohort and the denominator denotes the number of
laboratory-confirmed cases for each province, autonomous
region, or provincial municipality, as reported by the National
Health Commission.
Of the 7736 patients with Covid-19 who had been
hospitalized at 552 sites as of January 29, 2020, we
obtained data regarding clinical symptoms and outcomes for
1099 patients (14.2%). The largest number of patients (132)
had been admitted to Wuhan Jinyintan Hospital. The
hospitals that were included in this study accounted for
29.7% of the 1856 designated hospitals where patients with
Covid-19 could be admitted in 30 provinces, autonomous
regions, or municipalities across China (Figure 1).
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Table 1.
Table 1. Clinical
Characteristics of the Study Patients, According to Disease
Severity and the Presence or Absence of the Primary
Composite End Point.
The demographic and clinical characteristics of the patients
are shown in Table 1. A total of 3.5% were health care
workers, and a history of contact with wildlife was
documented in 1.9%; 483 patients (43.9%) were residents
of Wuhan. Among the patients who lived outside Wuhan,
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72.3% had contact with residents of Wuhan, including
31.3% who had visited the city; 25.9% of nonresidents had
neither visited the city nor had contact with Wuhan
residents.
The median incubation period was 4 days (interquartile
range, 2 to 7). The median age of the patients was 47 years
(interquartile range, 35 to 58); 0.9% of the patients were
younger than 15 years of age. A total of 41.9% were female.
Fever was present in 43.8% of the patients on admission
but developed in 88.7% during hospitalization. The second
most common symptom was cough (67.8%); nausea or
vomiting (5.0%) and diarrhea (3.8%) were uncommon.
Among the overall population, 23.7% had at least one
coexisting illness (e.g., hypertension and chronic obstructive
pulmonary disease).
On admission, the degree of severity of Covid-19 was
categorized as nonsevere in 926 patients and severe in 173
patients. Patients with severe disease were older than those
with nonsevere disease by a median of 7 years. Moreover,
the presence of any coexisting illness was more common
among patients with severe disease than among those with
nonsevere disease (38.7% vs. 21.0%). However, the
exposure history between the two groups of disease
severity was similar.
Radiologic and Laboratory Findings
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Table 2.
Table 2. Radiographic and
Laboratory Findings.
Table 2 shows the radiologic and laboratory findings on
admission. Of 975 CT scans that were performed at the time
of admission, 86.2% revealed abnormal results. The most
common patterns on chest CT were ground-glass opacity
(56.4%) and bilateral patchy shadowing (51.8%).
Representative radiologic findings in two patients with
nonsevere Covid-19 and in another two patients with severe
Covid-19 are provided in Figure S1 in the Supplementary
Appendix. No radiographic or CT abnormality was found in
157 of 877 patients (17.9%) with nonsevere disease and in
5 of 173 patients (2.9%) with severe disease.
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On admission, lymphocytopenia was present in 83.2% of
the patients, thrombocytopenia in 36.2%, and leukopenia in
33.7%. Most of the patients had elevated levels of
C-reactive protein; less common were elevated levels of
alanine aminotransferase, aspartate aminotransferase,
creatine kinase, and d-dimer. Patients with severe disease
had more prominent laboratory abnormalities (including
lymphocytopenia and leukopenia) than those with
nonsevere disease.
Clinical Outcomes
Table 3.
Table 3. Complications,
Treatments, and Clinical Outcomes.
None of the 1099 patients were lost to follow-up during the
study. A primary composite end-point event occurred in 67
patients (6.1%), including 5.0% who were admitted to the
ICU, 2.3% who underwent invasive mechanical ventilation,
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and 1.4% who died (Table 3). Among the 173 patients with
severe disease, a primary composite end-point event
occurred in 43 patients (24.9%). Among all the patients, the
cumulative risk of the composite end point was 3.6%;
among those with severe disease, the cumulative risk was
20.6%.
Treatment and Complications
A majority of the patients (58.0%) received intravenous
antibiotic therapy, and 35.8% received oseltamivir therapy;
oxygen therapy was administered in 41.3% and mechanical
ventilation in 6.1%; higher percentages of patients with
severe disease received these therapies (Table 3).
Mechanical ventilation was initiated in more patients with
severe disease than in those with nonsevere disease
(noninvasive ventilation, 32.4% vs. 0%; invasive ventilation,
14.5% vs. 0%). Systemic glucocorticoids were given to 204
patients (18.6%), with a higher percentage among those
with severe disease than nonsevere disease (44.5% vs.
13.7%). Of these 204 patients, 33 (16.2%) were admitted to
the ICU, 17 (8.3%) underwent invasive ventilation, and 5
(2.5%) died. Extracorporeal membrane oxygenation was
performed in 5 patients (0.5%) with severe disease.
The median duration of hospitalization was 12.0 days
(mean, 12.8). During hospital admission, most of the
patients received a diagnosis of pneumonia from a
physician (91.1%), followed by ARDS (3.4%) and shock
(1.1%). Patients with severe disease had a higher incidence
of physician-diagnosed pneumonia than those with
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nonsevere disease (99.4% vs. 89.5%).
Discussion
During the initial phase of the Covid-19 outbreak, the
diagnosis of the disease was complicated by the diversity in
symptoms and imaging findings and in the severity of
disease at the time of presentation. Fever was identified in
43.8% of the patients on presentation but developed in
88.7% after hospitalization. Severe illness occurred in
15.7% of the patients after admission to a hospital. No
radiologic abnormalities were noted on initial presentation in
2.9% of the patients with severe disease and in 17.9% of
those with nonsevere disease. Despite the number of
deaths associated with Covid-19, SARS-CoV-2 appears to
have a lower case fatality rate than either SARS-CoV or
Middle East respiratory syndrome–related coronavirus
(MERS-CoV). Compromised respiratory status on admission
(the primary driver of disease severity) was associated with
worse outcomes.
Approximately 2% of the patients had a history of direct
contact with wildlife, whereas more than three quarters were
either residents of Wuhan, had visited the city, or had
contact with city residents. These findings echo the latest
reports, including the outbreak of a family cluster, 4
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transmission from an asymptomatic patient, 6 and the three-
phase outbreak patterns. 8 Our study cannot preclude the
presence of patients who have been termed “super-
spreaders.”
Conventional routes of transmission of SARS-CoV, MERS-
CoV, and highly pathogenic influenza consist of respiratory
droplets and direct contact, 18-20 mechanisms that probably
occur with SARS-CoV-2 as well. Because SARS-CoV-2 can
be detected in the gastrointestinal tract, saliva, and urine,
these routes of potential transmission need to be
investigated 21 (Tables S1 and S2).
The term Covid-19 has been applied to patients who have
laboratory-confirmed symptomatic cases without apparent
radiologic manifestations. A better understanding of the
spectrum of the disease is needed, since in 8.9% of the
patients, SARS-CoV-2 infection was detected before the
development of viral pneumonia or viral pneumonia did not
develop.
In concert with recent studies, 1,8,12 we found that the
clinical characteristics of Covid-19 mimic those of SARS-
CoV. Fever and cough were the dominant symptoms and
gastrointestinal symptoms were uncommon, which suggests
a difference in viral tropism as compared with SARS-CoV,
MERS-CoV, and seasonal influenza. 22,23 The absence of
fever in Covid-19 is more frequent than in SARS-CoV (1%)
and MERS-CoV infection (2%), 20 so afebrile patients may
be missed if the surveillance case definition focuses on
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fever detection. 14 Lymphocytopenia was common and, in
some cases, severe, a finding that was consistent with the
results of two recent reports. 1,12 We found a lower case
fatality rate (1.4%) than the rate that was recently
reportedly, 1,12 probably because of the difference in sample
sizes and case inclusion criteria. Our findings were more
similar to the national official statistics, which showed a rate
of death of 3.2% among 51,857 cases of Covid-19 as of
February 16, 2020. 11,24 Since patients who were mildly ill
and who did not seek medical attention were not included in
our study, the case fatality rate in a real-world scenario
might be even lower. Early isolation, early diagnosis, and
early management might have collectively contributed to the
reduction in mortality in Guangdong.
Despite the phylogenetic homogeneity between SARS-
CoV-2 and SARS-CoV, there are some clinical
characteristics that differentiate Covid-19 from SARS-CoV,
MERS-CoV, and seasonal influenza infections. (For
example, seasonal influenza has been more common in
respiratory outpatient clinics and wards.) Some additional
characteristics that are unique to Covid-19 are detailed in
Table S3.
Our study has some notable limitations. First, some cases
had incomplete documentation of the exposure history and
laboratory testing, given the variation in the structure of
electronic databases among different participating sites and
the urgent timeline for data extraction. Some cases were
diagnosed in outpatient settings where medical information
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was briefly documented and incomplete laboratory testing
was performed, along with a shortage of infrastructure and
training of medical staff in nonspecialty hospitals. Second,
we could estimate the incubation period in only 291 of the
study patients who had documented information. The
uncertainty of the exact dates (recall bias) might have
inevitably affected our assessment. Third, because many
patients remained in the hospital and the outcomes were
unknown at the time of data cutoff, we censored the data
regarding their clinical outcomes as of the time of our
analysis. Fourth, we no doubt missed patients who were
asymptomatic or had mild cases and who were treated at
home, so our study cohort may represent the more severe
end of Covid-19. Fifth, many patients did not undergo
sputum bacteriologic or fungal assessment on admission
because, in some hospitals, medical resources were
overwhelmed. Sixth, data generation was clinically driven
and not systematic.
Covid-19 has spread rapidly since it was first identified in
Wuhan and has been shown to have a wide spectrum of
severity. Some patients with Covid-19 do not have fever or
radiologic abnormalities on initial presentation, which has
complicated the diagnosis.
Funding and Disclosures
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Supported by the National Health Commission of China, the
National Natural Science Foundation, and the Department
of Science and Technology of Guangdong Province.
Disclosure forms provided by the authors are available with
the full text of this article at NEJM.org.
Drs. Guan, Ni, Yu Hu, W. Liang, Ou, He, L. Liu, Shan, Lei,
Hui, Du, L. Li, Zeng, and Yuen contributed equally to this
article.
This article was published on February 28, 2020, at
NEJM.org.
We thank all the hospital staff members (see Supplementary
Appendix for a full list of the staff) for their efforts in
collecting the information that was used in this study; Zong-
jiu Zhang, Ya-hui Jiao, Xin-qiang Gao, and Tao Wei
(National Health Commission), Yu-fei Duan and Zhi-ling
Zhao (Health Commission of Guangdong Province), and Yi-
min Li, Nuo-fu Zhang, Qing-hui Huang, Wen-xi Huang, and
Ming Li (Guangzhou Institute of Respiratory Health) for
facilitating the collection of patients’ data; the statistical team
members Zheng Chen, Dong Han, Li Li, Zhi-ying Zhan, Jin-
jian Chen, Li-jun Xu, and Xiao-han Xu (State Key Laboratory
of Organ Failure Research, Department of Biostatistics,
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Guangdong Provincial Key Laboratory of Tropical Disease
Research, School of Public Health, and Southern Medical
University, respectively); Li-qiang Wang, Wei-peng Cai, Zi-
sheng Chen (the Sixth Affiliated Hospital of Guangzhou
Medical University) and Chang-xing Ou, Xiao-min Peng, Si-
ni Cui, Yuan Wang, Mou Zeng, Xin Hao, Qi-hua He, Jing-pei
Li, Xu-kai Li, Wei Wang, Li-min Ou, Ya-lei Zhang, Jing-wei
Liu, Xin-guo Xiong, Wei-juna Shi, San-mei Yu, Run-dong
Qin, Si-yang Yao, Bo-meng Zhang, Xiao-hong Xie, Zhan-
hong Xie, Wan-di Wang, Xiao-xian Zhang, Hui-yin Xu, Zi-
qing Zhou, Ying Jiang, Ni Liu, Jing-jing Yuan, Zheng Zhu,
Jie-xia Zhang, Hong-hao Li, Wei-hua Huang, Lu-lin Wang,
Jie-ying Li, Li-fen Gao, Cai-chen Li, Xue-wei Chen, Jia-bo
Gao, Ming-shan Xue, Shou-xie Huang, Jia-man Tang, and
Wei-li Gu (Guangzhou Institute of Respiratory Health) for
their dedication to data entry and verification; Tencent
(Internet-services company) for providing the number of
hospitals certified to admit patients with Covid-19 throughout
China; and all the patients who consented to donate their
data for analysis and the medical staff members who are on
the front line of caring for patients.
Author Affiliations
From the State Key Laboratory of Respiratory Disease,
National Clinical Research Center for Respiratory Disease,
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Guangzhou Institute of Respiratory Health, First Affiliated
Hospital of Guangzhou Medical University (W.G., W.L., J.H.,
R.C., C.T., T.W., S.L., Jin-lin Wang, N.Z., J.H., W.L.), the
Departments of Thoracic Oncology (W.L.), Thoracic Surgery
and Oncology (J.H.), and Emergency Medicine (Z.L.), First
Affiliated Hospital of Guangzhou Medical University, and
Guangzhou Eighth People’s Hospital, Guangzhou Medical
University (C.L.), and the State Key Laboratory of Organ
Failure Research, Department of Biostatistics, Guangdong
Provincial Key Laboratory of Tropical Disease Research,
School of Public Health, Southern Medical University (C.O.,
P.C.), Guangzhou, Wuhan Jinyintan Hospital (Z.N., J.X.),
Union Hospital, Tongji Medical College, Huazhong
University of Science and Technology (Yu Hu), the Central
Hospital of Wuhan (Y.P.), Wuhan No. 1 Hospital, Wuhan
Hospital of Traditional Chinese and Western Medicine
(L.W.), Wuhan Pulmonary Hospital (P.P.), Tianyou Hospital
Affiliated to Wuhan University of Science and Technology
(Jian-ming Wang), and the People’s Hospital of Huangpi
District (S.Z.), Wuhan, Shenzhen Third People’s Hospital
and the Second Affiliated Hospital of Southern University of
Science and Technology, National Clinical Research Center
for Infectious Diseases (L. Liu), and the Department of
Clinical Microbiology and Infection Control, University of
Hong Kong–Shenzhen Hospital (K.-Y.Y.), Shenzhen, the
Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai
(H.S.), the Department of Medicine and Therapeutics,
Chinese University of Hong Kong, Shatin (D.S.C.H.), and
the Department of Microbiology and the Carol Yu Center for
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Infection, Li Ka Shing Faculty of Medicine, University of
Hong Kong, Pok Fu Lam (K.-Y.Y.), Hong Kong, Medical ICU,
Peking Union Medical College Hospital, Peking Union
Medical College and Chinese Academy of Medical Sciences
(B.D.), and the Chinese Center for Disease Control and
Prevention (G.Z.), Beijing, the State Key Laboratory for
Diagnosis and Treatment of Infectious Diseases, National
Clinical Research Center for Infectious Diseases, First
Affiliated Hospital, College of Medicine, Zhejiang University,
Hangzhou (L. Li), Chengdu Public Health Clinical Medical
Center, Chengdu (Y.L.), Huangshi Central Hospital of Edong
Healthcare Group, Affiliated Hospital of Hubei Polytechnic
University, Huangshi (Ya-hua Hu), the First Hospital of
Changsha, Changsha (J. Liu), the Third People’s Hospital of
Hainan Province, Sanya (Z.C.), Huanggang Central
Hospital, Huanggang (G.L.), Wenling First People’s
Hospital, Wenling (Z.Z.), the Third People’s Hospital of
Yichang, Yichang (S.Q.), Affiliated Taihe Hospital of Hubei
University of Medicine, Shiyan (J. Luo), and Xiantao First
People’s Hospital, Xiantao (C.Y.) — all in China.
Address reprint requests to Dr. Zhong at the State Key
Laboratory of Respiratory Disease, National Clinical
Research Center for Respiratory Disease, Guangzhou
Institute of Respiratory Health, First Affiliated Hospital of
Guangzhou Medical University, 151 Yanjiang Rd.,
Guangzhou, Guangdong, China, or at
nanshan@vip.163.com.
A list of investigators in the China Medical Treatment Expert
Group for Covid-19 study is provided in the Supplementary
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Appendix, available at NEJM.org.
Supplementary Material
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Citing Article (1)
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