Unprecedented cooperation meant that scientists quickly built a picture of the novel coronavirus. Yet the evidence they accumulated should not be taken at face value. Linda Hantrais (LSE) and Susanne MacGregor (LSHTM) introduce a series about the challenges of learning from statistical datasets and other scientific evidence

Hantrais, Linda

MacGregor, Susanne

English

LSE Research Online

Containing	COVID,	part	1	|	First	things	first:	thedifficulty	of	building	an	evidence	baseUnprecedented	cooperation	meant	that	scientists	quickly	built	a	picture	of	the	novel	coronavirus.	Yet	the	evidencethey	accumulated	should	not	be	taken	at	face	value.	Linda	Hantrais	(LSE)	and	Susanne	MacGregor	(LSHTM)introduce	a	series	about	the	challenges	of	learning	from	statistical	datasets	and	other	scientific	evidence.In	the	months	after	the	pandemic	began,	teams	of	biostatisticians,	virologists,	epidemiologists,	clinicians	andstatistical	modellers,	collaborating	across	national	boundaries,	built	on	the	knowledge	acquired	from	previouspandemics	to	develop	the	scientific	evidence	base	for	COVID-19.	This	collaboration	resulted	in	thousands	ofscientific	papers,	often	posted	pre-publication	and	subject	to	validation,	reporting	early	evidence	about	the	virus.The	impact	of	the	COVID	pandemic	was	found	to	differ	from	previous	epidemics	and	global	crises	in	severalways	—	not	least	due	to	the	over-arching	characteristics	of	modernity,	such	as	ageing	populations	and	the	growthof	science	and	medicine,	as	well	as	overall	Western	economic	prosperity	and	global	inequality.	Initial	evidenceindicated	that	COVID	was	a	novel	and	virulent	strain	of	coronavirus	against	which	populations	appeared	to	have	nonatural	immunity,	and	for	which	no	known	cure	or	proven	therapeutic	strategies	existed.	Unlike	earlier	pandemicsand	global	crises,	older	people	with	co-morbidities	were	found	to	be	more	likely	to	die	from	the	disease.Comparisons	with	death	rates	in	the	Spanish	flu	pandemic	in	1918	and	both	world	wars	show	how	the	impact	ofCOVID	deaths	fell	on	very	different	age	groups.Published	statistics	should	not	be	taken	at	face	valueUsing	the	best	data	available	while	acknowledging	uncertainty	and	gaps	in	the	evidence	base,	statisticiansdeveloped	interactive	online	tools	to	track	the	spread	of	the	pandemic	in	real-time	(nowcasts),	to	identify	cumulativetrends	in	the	numbers	of	infections	and	deaths,	and	to	predict	(forecast)	how	it	might	progress.	Nowcasters	andforecasters	invited	challenges	to	their	interpretations	of	the	data	and	were	exposed	to	harsh	criticisms	by	scientificcolleagues,	politicians	who	claimed	to	have	based	life-changing	decisions	on	the	‘best	scientific	evidence	available’,and	by	an	unforgiving	public	when	events	proved	predictions	to	be	wrong.Based	on	global	datasets	showing	the	total	number	of	reported	deaths	since	the	beginning	of	the	pandemic,	by	lateApril	2021	the	US,	Brazil,	Mexico,	India,	the	UK,	Italy,	Russia	and	France	were	all	recording	over	100,000	COVID-19-related	deaths.	When	analysed	in	relation	to	population	size,	the	cumulative	figures	tell	a	different	story:	41countries	reported	over	1,000	deaths	per	million	inhabitants,	rising	to	over	2,000	per	million	in	11	countries,	withHungary	and	Czechia	in	1st	and	3rd	positions	in	the	rank	order.	According	to	this	indicator,	the	US	was	in	16thposition,	Brazil	in	14th,	Mexico	21st,	India	119th,	the	UK	13th,	Italy	12th,	Russia	58th	and	France	23rd.	Vietnam,Taiwan,	Tanzania	and	New	Zealand	remained	near	to	the	bottom	of	the	list	for	both	indicators.	A	year	earlier	whenEurope	was	at	the	epicentre	of	the	first	wave	of	the	pandemic,	Hungary	and	Czechia	and	three	other	Central	andEastern	European	member	states,	which	had	moved	into	the	2,000	per	million	bracket	in	April	2021,	had	beenamong	the	countries	displaying	the	lowest	death	rates	per	million	inhabitants	in	the	EU27,	suggesting	that	they	hadnot	drawn	lessons	from	their	earlier	experience.	The	low	ranking	per	million	inhabitants	for	India	could	be	attributedboth	to	the	relatively	small	number	of	COVID	deaths	during	the	first	wave	and	to	under-reporting	on	a	massivescale	in	the	second	wave.One	of	the	first	lessons	to	be	drawn	from	COVID	datasets	over	time	and	space,	as	presented	in	internationalcomparative	tables,	was	that	published	statistics	should	not	be	taken	at	face	value.	With	hindsight,	it	was	clear	thatall	but	the	most	circumscribed	lessons	drawn	from	data	about	causal	links	between	death	rates	and	national	policyresponses	should	have	been	treated	with	extreme	caution.	Before	any	meaningful	lessons	could	be	learnt,	the	rawfigures	needed	to	be	contextualised	not	only	in	relation	to	population	size	but	also	with	reference	to	country-specificdata	collection	methods	and	resources.	Even	then,	assessing,	interpreting	and	understanding	the	wider	implicationsof	publicly	available	data	would	never	be	a	straightforward	process.	It	requires	detailed	scrutiny	of	a	multitude	ofpotential	contributing	factors	encompassing	epidemiology	and	epistemology.	In	combination	with	social	andbiological	forces,	these	factors	create	what	has	been	aptly	described	as	a	‘causal	patchwork’.LSE Covid 19 Blog: Containing COVID, part 1 | First things first: the difficulty of building an evidence base Page 1 of 2		Date originally posted: 2021-04-29Permalink: https://blogs.lse.ac.uk/covid19/2021/04/29/containing-covid-part-1-first-things-first-the-difficulty-of-building-an-evidence-base/Blog homepage: https://blogs.lse.ac.uk/covid19/For	scientists	seeking	reliable	evidence	from	their	analysis	of	the	first	year	of	the	pandemic	to	support	more	widelyapplicable	lessons,	COVID	continued	to	pose	unprecedented	challenges.	In	subsequent	posts,	we	assembleinternational	evidence	from	a	range	of	disciplines	to	consider	what	lessons,	if	any,	may	be	gleaned	by	unpicking	thedifferent	threads	in	the	patchwork	and	by	adding	new	pieces.	Scientists,	politicians	and	the	wider	public	faced	asteep	learning	curve.	We	explore	both	‘known	unknowns’	and	‘unknown	unknowns’	in	searching	for	causalexplanations	and	in	recognising	the	limits	of	scientific	knowledge,	as	well	as	the	opportunities	that	ignoranceprovides	for	further	investigation.This	post	represents	the	views	of	the	authors	and	not	those	of	the	COVID-19	blog,	nor	LSE.LSE Covid 19 Blog: Containing COVID, part 1 | First things first: the difficulty of building an evidence base Page 2 of 2		Date originally posted: 2021-04-29Permalink: https://blogs.lse.ac.uk/covid19/2021/04/29/containing-covid-part-1-first-things-first-the-difficulty-of-building-an-evidence-base/Blog homepage: https://blogs.lse.ac.uk/covid19/

Containing COVID, part 1. First things first: the difficulty of building an evidence base

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Containing COVID, part 1. First things first: the difficulty of building an evidence base

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