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Coronavirus: Death toll from COVID-19 exceeds 200,000 in US

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Coronavirus: Death toll from COVID-19 exceeds 200,000 in US

Posted | Updated by Insights team:

Publication | Update:

Sep 2020
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The US death toll from the coronavirus topped 200,000 on Tuesday, by far the highest in the world, hitting the once-unimaginable threshold six weeks before an election that is certain to be a referendum in part on President Donald Trump's handling of the crisis.

“It is completely unfathomable that we’ve reached this point,” said Jennifer Nuzzo, a Johns Hopkins University public health researcher, eight months after the scourge first reached the world’s richest nation, with its state-of-the-art laboratories, top-flight scientists and stockpiles of medical supplies.

The number of dead is equivalent to a 9/11 terrorist attack every day for 67 days. It is roughly equal to the population of Salt Lake City, Utah or Huntsville, Alabama.

And it is still climbing. Deaths are running at close to 770 a day on average, and a widely cited model from the University of Washington predicts the US toll will double to 400,000 by the end of the year as schools and colleges reopen and cold weather sets in. A vaccine is unlikely to become widely available until 2021.

“The idea of 200,000 deaths is really very sobering, in some respects stunning,” Dr. Anthony Fauci, the government’s top infectious-disease expert, said on CNN.

"A shame"

The bleak milestone was reported by Johns Hopkins, based on figures supplied by state health authorities. But the real toll is thought to be much higher, in part because many COVID-19 deaths were probably ascribed to other causes, especially early on, before widespread testing.

Trump said it was “a shame” the US reached that number but argued the toll could have been much worse.

“I think if we didn’t do it properly and do it right, you’d have 2.5 million deaths,” Trump told reporters at the White House before leaving for a campaign rally in Pittsburgh. He added that the United States is now “doing well” and “the stock market is up.”

He also gave his often-repeated broadside that China was at fault for the pandemic. In a prerecorded speech to the UN General Assembly, he demanded that Beijing be held accountable for having “unleashed this plague onto the world.” China’s ambassador rejected the accusations as baseless.

On Twitter, Democratic presidential candidate Joe Biden said, "It didn’t have to be this bad."

"It's a staggering number that’s hard to wrap your head around,” he said. "There’s a devastating human toll to this pandemic — and we can’t forget that."

Highest in the world

For five months, America has led the world by far in sheer numbers of confirmed infections — nearly 6.9 million as of Tuesday — and deaths. The US has less than 5 per cent of the globe’s population but more than 20 per cent of the reported deaths.

Brazil is second with about 137,000 deaths, followed by India with approximately 89,000 and Mexico with around 74,000. Only five countries — Peru, Bolivia, Chile, Spain and Brazil — rank higher in COVID-19 deaths per capita.

"All the world’s leaders took the same test, and some have succeeded and some have failed," said Dr Cedric Dark, an emergency physician at Baylor College of Medicine in hard-hit Houston, Texas. "In the case of our country, we failed miserably."

Black and Hispanic people and American Indians have accounted for a disproportionate share of the deaths, underscoring the economic and health care disparities in the US.

Worldwide, the virus has infected more than 31 million people and is closing in fast on 1 million deaths, with nearly 967,000 lives lost, by Johns Hopkins' count, though the real numbers are believed to be higher because of gaps in testing and reporting.

Trump downplayed the threat early on, advanced unfounded notions about the behavior of the virus, promoted unproven or dangerous treatments, complained that too much testing was making the U.S. look bad, and disdained masks, turning face coverings into a political issue.

On April 10, the president predicted the U.S. wouldn't see 100,000 deaths. That milestone was reached May 27.

Lack of leadership

Nowhere was the lack of leadership seen as more crucial than in testing, a key to breaking the chain of contagion.

“We have from the very beginning lacked a national testing strategy,” Nuzzo said. “For reasons I can't truly fathom, we’ve refused to develop one.”

Sandy Brown of Grand Blanc, Michigan, called the death toll “gut-wrenching.” Her husband of 35 years and their 20-year-old son — Freddie Lee Brown Jr. and Freddie Lee Brown III — died of COVID-19 just days apart in March, when there were fewer than 4,000 recorded deaths in the U.S.

“The thing that really gets to me is... if things had been done properly, we could have put a lid on this,” said Brown, who has no other children. “Now it's just unbelievable. It's devastating.”

The real number of dead from the crisis could be significantly higher. As many as 215,000 more people than usual died in the U.S. from all causes during the first seven months of 2020, according to CDC figures. The death toll from COVID-19 during the same period was put at about 150,000 by Johns Hopkins.

Researchers suspect some coronavirus deaths were overlooked, while other deaths may have been caused indirectly by the crisis, by creating such turmoil that people with chronic conditions such as diabetes or heart disease were unable or unwilling to get treatment.

Dark, the emergency physician at Baylor, said that before the crisis, “people used to look to the United States with a degree of reverence. For democracy. For our moral leadership in the world. Supporting science and using technology to travel to the moon.”

“Instead,” he said, "what’s really been exposed is how anti-science we’ve become.”

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

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Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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