Why some countries but not others? Urbanisation, GDP and endemic disease predict global SARS-CoV-2 excess mortality patterns. (preprint)

The global impact of the SARS-CoV-2 pandemic has been uneven, with some regions experiencing significant excess mortality while others have been relatively unaffected. Yet factors which predict this variation remain enigmatic, particularly at large spatial scales. We used spatially explicit Bayesian models that integrate socio-demographic and endemic disease data at the country level to provide robust global estimates of excess SARS-CoV-2 mortality (P scores) for the years 2020 and 2021. We find that gross domestic product (GDP), spatial patterns and urbanization are strong predictors of excess mortality, with countries characterized by low GDP but high urbanization experiencing the highest levels of excess mortality. Intriguingly, we also observed that the prevalence of malaria and human immunodeficiency virus (HIV) are associated with country-level SARS-CoV-2 excess mortality in Africa and the Western Pacific, whereby countries with low HIV prevalence but high malaria prevalence tend to have lower levels of excess mortality. While these associations are correlative in nature at the macro-scale, they emphasize that patterns of endemic disease and socio-demographic factors are needed to understand the global dynamics of SARS-CoV-2.

Patterns of SARS-CoV-2 exposure and mortality suggest endemic infections, in addition to space and population factors, shape dynamics across countries (preprint)

Some countries have been crippled by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic while others have emerged with few infections and fatalities; the factors underscoring this macro-epidemiological variation is one of the mysteries of this global catastrophe. Variation in immune responses influence SARS-CoV-2 transmission and mortality, and factors shaping this variation at the country level, in addition to other socio-ecological drivers, may be important. Here, we construct spatially explicit Bayesian models that combine data on prevalence of endemic diseases and other socio-ecological characteristics to quantify patterns of confirmed deaths and cases across the globe before mass vaccination. We find that the prevalence of parasitic worms, human immunodeficiency virus and malaria play a surprisingly important role in predicting country-level SARS-CoV-2 patterns. When combined with factors such as population density, our models predict 63% (56-67) and 76% (69-81) of confirmed cases and deaths among countries, respectively. While our findings at this macro-scale are necessarily associative, they highlight a need for studies to consider factors, such as infection by other pathogens, on global SARS-CoV-2 dynamics. These relationships are vital for developing countries that already have the highest burden of endemic disease and are becoming the most affected by the SARS-CoV-2 pandemic.

Emerging phylogenetic structure of the SARS-CoV-2 pandemic (preprint)

Since spilling over into humans, SARS-CoV-2 has rapidly spread across the globe, accumulating significant genetic diversity. The structure of this genetic diversity, and whether it reveals epidemiological insights, are fundamental questions for understanding the evolutionary trajectory of this virus. Here we use a recently developed phylodynamic approach to uncover phylogenetic structures underlying the SARS-CoV-2 pandemic. We find support for three SARS-CoV-2 lineages co-circulating, each with significantly different demographic dynamics concordant with known epidemiological factors. For example, Lineage C emerged in Europe with a high growth rate in late February, just prior to the exponential increase in cases in several European countries. Mutations that characterize Lineage C in particular are non-synonymous and occur in functionally important gene regions responsible for viral replication and cell entry. Even though Lineages A and B had distinct demographic patterns, they were much more difficult to distinguish. Continuous application of phylogenetic approaches to track the evolutionary epidemiology of SARS-CoV-2 lineages will be increasingly important to validate the efficacy of control efforts and monitor significant evolutionary events in the future.