In an open letter ^[1] to Prime Minister Scott Morrison on 19 April, more than 200 economists rejected commentators’ calls for a rapid return to work and labelled the notion of a ‘trade-off’ between public health and the economy a ‘false distinction’. They said that while the measures adopted to contain the spread of Covid-19 had caused economic damage, those negative effects were far outweighed by the lives saved.

Their charge that this is a false distinction is correct, but it needs further examination. To someone from a poor country, people on moral high horses can appear to be so far from the ground that they can no longer see reality. There are different policy pathways to saving lives while allowing them to be lived in dignity.

In India, a 12-year-old girl, Jamlo Madkam ^[2], was working the fields far from home when, with characteristic lack of advance preparation ^[3] to manage and ameliorate the harsh consequences, Prime Minister Narendra Modi imposed a stringent lockdown at just four hours’ notice ^[4]. For three days Jamlo walked through thick forests to reach her village 100 kilometres away, but muscle fatigue, hunger and dehydration claimed her just 11 kilometres short of her home. Might a more flexible official response have saved that child?

As of 4 May, India’s Covid-19 death toll was 1,566 ^[5] and confirmed cases were at 46,437 (though those figures may be understated.) UN estimates ^[6] show that more than 800,000 Indian infants died in 2019, a mortality rate of 3%. Nearly 1 million children under the age of 5 years died, an even higher mortality rate of 3.7%. Those figures are 10 times higher than the infant and child mortality rates in Australasia. Most of these Indian children died from preventable causes—nutritional deficiencies, lack of sanitation and lack of access to healthcare.

Oxfam ^[7] has warned that the Covid-19 pandemic could push an extra half billion people into poverty, and the UN estimated ^[8] in mid-April that the global economic downturn could cause hundreds of thousands of additional child deaths in 2020.

Jayant Menon, a visiting fellow at the ISEAS–Yusof Ishak Institute in Singapore, has raised the need to flatten the ‘misery curve ^[9]’ in poor countries. The inflection point for the virus curve is when the health system is overwhelmed. Menon says that the analogous point on the misery curve is when the harm caused by the curtailment measures starts impinging on health or survival.

Both curves must be kept below their tipping points—but in poor countries, misery has outpaced data. Because both curves are highly sensitive to rapid changes in a highly dynamic environment, policy measures must be adjusted as circumstances evolve.

The signatories on the letter to Australia’s prime minister are concerned experts engaging in a vital public debate about saving lives even at some cost to the economy.

Some political heavyweights in the UK are also talking the language of trade-offs. Scotland’s First Minister Nicola Sturgeon has called for a ‘better balance ^[10]’ between tackling the disease and protecting the economy. Former Chancellor George Osborne agrees, urging an open discussion about the hard trade-offs ^[10] that may be needed in a nation that will be living with the virus for the foreseeable future.

Multiple reports have documented the immediate and likely long-term economic harm caused by lockdowns. The World Trade Organization ^[11] warns of dramatic decelerations and contractions in GDP and trade, with a resulting ballooning of job losses and fall in incomes. The IMF estimates ^[12] that the global economy will shed around $9 trillion in 2020. World output will contract by 3%, hitting both advanced and emerging economies and sparking steep rises in unemployment, debt and bankruptcies.

For this pain to be justified, lockdowns must be effective in saving lives in big numbers. Conversely, if health gains are negligible but economic, social and even healthcare costs are high, the economists are right, but for the opposite reason to what they meant.

The onus is on the proponents of lockdowns to prove the case for the extensive cessation of economic activities, suspension of individual freedoms and mass house arrest of entire populations.

As Sweden’s chief epidemiologist Anders Tegnell has observed ^[13], shutting down and locking down have no ‘historical scientific basis’; the sole basis for such tough love is epidemiological modelling.

Tegnell told the BBC ^[14] on 24 April that Sweden was better placed than most European countries to face a second wave of the outbreak. The strategic goal was to slow the progression of the disease so the country’s healthcare system didn’t become overwhelmed. Between 15% and 20% of the population is estimated to have become immune, which is enough to slow and control the spread of the disease further. Regarding Sweden’s high death toll relative to its Nordic neighbours, he noted that ‘as many as 50% of deaths had come in care homes for the elderly, which have banned visitors’, so it’s ‘hard to know how a lockdown would have stopped that’.

John Lee ^[15], a retired professor of pathology and consultant pathologist with the UK National Health Service, is sceptical about the effectiveness of lockdowns ^[15] in limiting the spread of the virus and says ‘there isn’t any direct evidence that what we are doing is actually affecting the peak’.

John Ioannidis, a professor at Stanford University’s School of Medicine, holds multiple appointments in statistics, biomedical data, health research and policy and is ranked among the world’s 100 most-cited scientists on Google Scholar. He calls the existing data on coronavirus infections ‘utterly unreliable ^[16]’ and dismisses much of the early Covid-19 epidemiological modelling, which forecast a death toll of 500,000 in the UK, over 2 million in the US and 40 million globally, as ‘speculation and science fiction ^[17]’ feeding a ‘mob mentality’.

Viruses may be highly infectious or highly lethal, but rarely are they both. In large-scale community tests, a Stanford study ^[18] of 3,200 people in California showed the number of infections to be 50 to 85 times higher than the number of confirmed cases, and a study by Gangelt ^[19] in Germany showed an infection fatality rate of 0.37% rather than the modelled 0.9%.

These studies indicate that Covid-19 spread earlier and much further than was initially imagined, which suggests that it is correspondingly less deadly.

There’s a growing body of evidence that lockdowns are ineffective. Carl Heneghan, professor of evidence-based medicine at Oxford, argues ^[20] that UK infections peaked a week before the lockdown was imposed on 23 March.

Lyman Stone ^[21] concludes that measures that work include closing schools, restricting travel, prohibiting gatherings of more than 50 or 100 people, quarantining people who test positive, and wearing face-masks in public—but not lockdowns. He compared daily deaths in selected European locked down countries from all causes for the same February–April period in 2019 and this year. In all cases, death spikes had plateaued before lockdowns would have had time to show results.

Examining data from all the US states, Wilfred Reilly ^[22] concludes that lockdowns do not result in lower coronavirus mortality than social-distancing measures done well. T.J. Rodgers’ study of the US ^[23] found fewer deaths where shutdowns were delayed, but not to a statistically significant degree.

Scott W. Atlas ^[24], a former chief of neuroradiology at Stanford Medical Center, looked at death rates in New York, the epicentre of US infections and deaths. He found death rates of 0% for those under 18 and 0.01% for those 18 to 45 years old without underlying conditions. Of the 6,570 killed by the virus to that date, 99.2% had underlying conditions. So even older people without pre-existing conditions may not need to self-isolate.

Localised shutdowns, targeted quarantine and sheltering in place can complement more generalised border closures and social distancing—a conclusion reinforced by a new study ^[25] from the National Bureau of Economic Research in the US: ‘we find that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter lockdown policies on the oldest group’.