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Brazil, Russia, India, China, and South Africa – also referred to as the BRICS – have in common the fact that they are major emerging market economies. But apart from that they exhibit large differences in terms of development level, economic structure and socio-political context. Does this diversity carry through to pandemic outcomes? In what ways do the BRICS differ? And why are several of them remarkably similar?
The reported data on confirmed cases and deaths suggest enormous differences in country experiences across the BRICS. The chart below shows cumulative COVID-19 cases per capita since the start of the pandemic. Until early 2023, Brazil and China occupy the extremes of the spectrum, with India, Russia and South Africa in the middle. Following its reopening, China saw a rapid surge that propelled it to levels on par with South Africa.
We see not only large differences in levels but also in the shape of the curve, where the curviness reflects the effect of successive waves. The Brazilian and Russian curves are particularly wavy. India’s huge wave during the major outbreak in May 2021 also comes through clearly. All countries saw a major steepening of the curve related to the recent Omicron/Delta outbreak, with China following suit eventually.
In terms of reported COVID-19 mortality, the picture is equally diverse. Brazil and China at opposite extremes and India, South Africa and Russia in the middle. The shape of the mortality curve however looks quite different from the cases curve. Most notable is the fact that the recent Omicron/Delta wave has not led to a major spike in mortality despite the very considerable increase in cases. This pattern is especially noticeable in China. The exception here is Russia, where reported mortality has continued to climb quite considerably.
Excess mortality captures the gap between the total number of deaths that occur for any reason and the amount that would be expected under normal circumstances. Given the massive undercounting of the mortality toll both directly and indirectly attributed to COVID-19, excess mortality provides a useful way to get a glimpse of the true mortality toll.
Below we show the cumulative estimated excess mortality rate, where the solid lines are actual data supplemented with estimates based on the excess death model produced by The Economist. The shaded areas represent the range of alternative possibilities within the 95% confidence interval (for the part of the curve based on estimates as opposed to actual data). The estimates taken into account over 120 variables that include seroprevalence, demography and urbanization, access and quality of healthcare, political regime and media freedom, connectivity and mobility and government responses to COVID-19. The model is discussed here.
Three BRICS countries – Brazil, India and South Africa – have remarkably similar excess mortality rates. China and Russia remain worlds apart, with China at the bottom of the spectrum and Russia registering rates that are well above the Brazil-India-South Africa cluster. Even when taking into account the uncertainty, the 95% confidence intervals suggest that China and Russia are indeed remarkably different. As to the Brazil-India-South Africa cluster, we have high confidence in Brazil and South Africa having similar rates. But it may well be the case that India’s rates are significantly larger or smaller.
As far as the mid-point estimates of excess mortality go, mortality outcomes are surprisingly similar for Brazil, India and South Africa, even though they clearly are not for China and Russia. What could explain this convergence in outcomes even if structurally these countries are very different?
In terms of infection control, the strictness of containment measures has been a crucial factor. The two graphs below illustrate how measures of strictness have varied significantly both over time and across nations. Despite these variations, some consistent differences emerge.
In the first chart, it is evident that throughout the majority of the pandemic, China’s containment measure indicator surpasses Russia’s. This observation aligns with China’s adherence to a zero-COVID policy, which has been unique among most countries worldwide. Interestingly, even after China’s reopening, its indicator remains significantly higher than Russia’s.
The second chart shows that government response stringency has shown considerable variation among Brazil, India, and South Africa. Generally, India’s indicator surpasses South Africa’s, with Brazil often falling in between. However, drawing direct comparisons is challenging due to factors such as the seasonality of viral outbreaks, the comparison across hemispheres, and unique elements that lead to desynchronized outbreak timings among these countries.
Urbanization is another factor that varies significantly among BRICS countries, as seen in the contrast between Brazil and India. Generally, higher urbanization may contribute to increased infection rates, assuming other factors remain equal. However, it is crucial to consider the context of each country.
For instance, Brazil’s high urbanization level, combined with dense populations in urban areas, may have facilitated the spread of the virus. This is seemingly consistent with Brazil’s high cumulative case rate, even though case numbers are not a perfect indicator of underlying infections.
On the other hand, India has a lower urbanization rate, with most of its population residing in rural areas, which could potentially slow the spread of the virus. However, it is essential to note that India’s rural population density is quite high, which may limit the advantageous effects of lower urbanization. Interestingly, this contrasts with Sub-Saharan Africa, which has a similar urbanization rate but much lower rural density.
The elevated excess mortality rate in Russia could be partially attributed to its population’s age structure. Among the BRICS countries, Russia has the highest proportion of individuals aged 60+ and 70+. Although an older population does not necessarily result in high mortality, as demonstrated by China’s zero-COVID approach and Japan’s low excess mortality rate despite being the world’s second-oldest country after Monaco, it can contribute to increased mortality when combined with other factors that promote the spread of the virus. In Russia’s case, this combination offers a plausible explanation for its high excess mortality rate.
Conversely, India and South Africa have significantly lower proportions of elderly individuals, which offers their demographic structure a degree of protection against the severe consequences of COVID-19. However, considering that excess mortality rates are estimated to be relatively high and at similar levels, other factors must offset this demographic advantage. These factors could include a higher infection prevalence (noting that reported cumulative case rates are lower than Brazil and Russia but higher than China) and, most likely, higher age-adjusted infection fatality rates.
The age-adjusted infection fatality rates may be influenced by patterns of co-morbidities, access to healthcare, and healthcare quality, among numerous other host-specific and environmental factors. These factors are likely to correlate with life expectancy at birth, which helps explain the seemingly paradoxical situation of high excess mortality rates in countries with lower elderly age shares.
The BRICS are highly heterogenous. Not just in terms of the way the countries differ in terms of institutions and structures, but also in terms of pandemic outcomes. That comes through very clearly when we look at the reported data on COVID-19 cases and fatalities.
When it comes to excess mortality outcomes, the various factors that seem to differentiate the BRICS are working together to produce a more homogenous outcome, at least for Brazil, India and South Africa, which all have experienced considerable excess mortality, even on a per capita basis. The exceptions are China and Russia, which continue to stand out at the opposite extremes within this sample of countries.
Disclaimer: Posts by the Center for Global Development reflect the views of the authors, drawing on prior research and experience in their areas of expertise. CGD is a nonpartisan, independent organization and does not take institutional positions. Likewise, views expressed do not necessarily reflect those of the United Nations, the United Nations Development Programme, its programmes/projects or governments. The designations employed do not imply the expression of any opinion whatsoever concerning the legal status of any country, territory or area, or its frontiers or boundaries.
