The Geography of a Pandemic: What Does it Say About COVID-19

Can geography explain India's relatively low rate of COVID infection?

Updated
Fit Connect
6 min read
COVID-19: What explains India's relatively low numbers? What role does geography play?
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In the fifth month of the COVID pandemic, India seems to have escaped the brunt of its ferocity. At this time, with just over a lakh in total cases, a case rate of 90 per million and a death rate of 3 per million, along with a case fatality rate (CFR) of 3.1, India appears to be relatively unscathed. This compares very favourably with case rates of 3000-6000 per million, death rates of 400-600 per million and CFR of 5-15% of US and Western Europe (the developed world).

As a corollary, there has been some jingoistic chest thumping in India about its prescient interventions. But, it begs a question. Is India the only country to do dramatically better than the most advanced countries? The answer is an emphatic, No! The reason for our skepticism becomes apparent, if we look at the broader geographical patterns of spread of the disease across the world, unrestricted by mere political boundaries of nation-states.

Looking at the world map, a broad discernible pattern of the effect of the pandemic becomes apparent. A wide swath of countries in South Asia, which includes us and our neighbours, Pakistan, Bangladesh, Sri Lanka and Nepal, and South-East Asia (Thailand, Malaysia, Indonesia, among others) have very low case rates ranging anywhere between 40 to 230 per million, death rates of anywhere between 0.4 per million in Sri Lanka to 5 per million in Indonesia & Pakistan, with India & Bangladesh at 3 per million. The CFR for this whole region ranges from 0.9% for Sri Lanka to 6.5% for Indonesia. India is at 3.1% lagging a little behind Pakistan at 2.1% and Bangladesh at 1.4%. Interestingly, vast areas of Africa and Central Asia seem to be doing as well or better than South and South-East Asia, but due to the sketchiness of the available data we have refrained from analysing it.

The comparative devastation wrought by the pandemic on Western Europe and US has led Dr Richard Cash (a renowned global public health expert who co-invented the ORS and never took a patent for it) and Vikram Patel to write an opinion piece in the Lancet, saying,

The Geography of a Pandemic: What Does it Say About COVID-19
“For the first time in the post-war history of epidemics, there is a reversal of which countries are most heavily affected by a disease pandemic……..The rest of the world—historically is far more used to being depicted as the reservoir of pestilence and disease that wealthy countries sought to protect themselves from.”

What could be the possible explanation for this? It cannot be healthcare systems. The public and tertiary healthcare systems of the West are way more advanced compared to South Asia or even South-East Asia.

The Geography of a Pandemic: What Does it Say About COVID-19

Demographics

One possible explanation could surely be demographics. Nearly a quarter to a third of the population of Western Europe and US are older than 60 years and COVID seems to kill the elderly disproportionately. More than 85% of the deaths in these regions were in people above the age of 60. More than a third of the deaths in the UK and US took place in old age homes. Compared to this, the percentage of those above 60 years in South & South-East Asia ranges from 8% to 10%. This is true of Africa too, with probably an even younger population.

The Weather

Studies from China and mathematical models from MIT suggest the possibility of decreased transmission of the virus in hot and humid conditions. The jury though is still out on that. But, if we look at South & South-East Asia and Africa, the vast majority of these countries fall in the tropical or equatorial belt and are known to be hot and humid. Most respiratory viruses seem to transmit better in cool, dry climates which makes up most of Europe and North America during winter and spring. This could be a reason for the higher incidence not only in these regions but also the relatively higher incidence in West Asia and the Middle East. The cooler drier regions of Iran, Turkey and Israel seem to have had a higher incidence, (deaths ranging from 30-85 per million) compared to South & South-East Asia, inspite of having an elderly population in the range of only 8 -12%. This could also be a reason for South America being hit by the pandemic harder than Southern Asia. It is winter in the southern hemisphere and many countries like Ecuador, Peru, Colombia though falling within the tropical latitudes are at significantly higher elevations and cooler climes. In India, we should be worrying about what the coming winter has in store for us. Even during the H1N1 pandemic, we got hit in the winter of 2009/10, rather than the spring of 2009 which was when North America and Europe were affected.

Population Density and Urbanisation

Pandemics/epidemics have long been considered “crowd diseases”. They spread more in tighter urban spaces. Could this be the reason behind the significantly lower incidence in Eastern Europe as compared to Western Europe? The weather patterns and age demographics of the two halves of Europe are similar, though not exactly the same. The proportion of elderly is less in East Europe, but not as low as in South Asia. An interesting fact is that there are far fewer cities with a population of over half a million in East Europe. The overall population density is also lower. Only two countries other than Russia - Ukraine and Poland have more than one city with a population over half a million.

Could this, along with a well-established Soviet Era public health system, be a reason for a significantly lower incidence (1,000/million) and deaths (24/million) as compared to their western neighbours? The role of densely populated cities in the spread of the disease is visible in India, Russia and Brazil too. Nearly, 2/3rd of the cases and deaths in India are restricted to seven cities- Mumbai, Delhi, Ahmedabad, Pune, Kolkata, Chennai and Surat. Russia, the only country in East Europe with nearly 32 cities with population of over half a million, has an incidence of infection significantly higher than the rest of Eastern Europe. In Brazil too, the densely populated cities of Sao Paulo and Rio have taken the worst hits.

Socio-Cultural Factors

The Far Eastern countries of South Korea, Taiwan and Japan have similar weather, demographics and population distributions as Western Europe. However, they have much lower rates of infection and death from COVID. What could explain this difference? For that, we may have to look to socio-cultural factors. The SARS epidemic of 2003 has instilled a culture of wearing masks, repeated hand washing/sanitising amongst the people of the Far East. In a way, their prior exposure to SARS has sensitised them to take prompt public health measures as a response to COVID. These measures, along with strategic testing, contact tracing and isolation has paid off for South Korea & Taiwan in this region, and Germany in Western Europe.

Immune Response

Along with the above factors, the immune response of different populations to COVID could play a part in outcomes. A recently published study in Cell (a top research journal) found the possibility of cross immunity for COVID with prior exposure to other coronavirus infections. The commonest being those causing the “common cold”. This, however, is a hotly contested hypothesis.

The intersection of demographics, geography, socio-cultural factors, immunity and strategic public health interventions will determine how this pandemic plays out in India and the rest of the world. In this context, as Dr Richard Cash and Vikram Patel write in the Lancet,

“Context is central to the control of any epidemic….. people continue to die in the millions, of other diseases, and lockdowns have made accessing essential health care much more difficult in some places.”

They go on to say, “A second key principle of global health is social justice and equity: the concerns of the poor who already bear a disproportionate burden of risk factors and disease must be at the center of all decisions. Yet a one-size-fits-all approach to COVID-19 has not only been inequitable in its impact but, is also likely to increase inequalities in the long term. A stark example is the inequitable economic impact of lockdowns on people who barely survive on precarious livelihoods.” We couldn’t agree more.

(Dr Sumit Ray is a Senior Consultant, Critical Care Medicine, in Delhi. Dr Himadri Barthakur is a Senior Consultant, Internal Medicine, in Guwahati.)

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