Trade, uneven development and people in motion: Used territories and the initial spread of COVID-19 in Mesoamerica and the Caribbean

Abstract

Mesoamerica and the Caribbean form a region comprised by middle- and low-income countries affected by the COVID-19 pandemic differently. Here, we ask whether the spread of COVID-19, measured using early epidemic growth rates ( r ), reproduction numbers ( R t ), accumulated cases, and deaths, is influenced by how the ‘used territories’ across the regions have been differently shaped by uneven development, human movement and trade differences. Using an econometric approach, we found that trade openness increased cases and deaths, while the number of international cities connected at main airports increased r , cases and deaths. Similarly, increases in concentration of imports, a sign of uneven development, coincided with increases in early epidemic growth and deaths. These results suggest that countries whose used territory was defined by a less uneven development were less likely to show exacerbated COVID-19 patterns of transmission. Health outcomes were worst in more trade-dependent countries, even after controlling for the impact of transmission prevention and mitigation policies, highlighting how structural effects of economic integration in used territories were associated with the initial COVID-19 spread in Mesoamerica and the Caribbean.

Conditioning factors in the spreading of Covid-19 – Does geography matter?

“…This is probably due to the fact that high GDP implies more intense international economic exchanges and consequently higher levels of global interconnection among individuals increasing the possibilities of virus transmission [ 135 ]. This is supported also by some studies which conclude that trade intensity showed a positive correlation with COVID-19 infections and deaths [ 25 , 136 ].…”

“…The upturned arrow represents a positive association, the downturned stands for negative association and the horizontal bar is when no or non-statistically significant association was found. Citation Elev Long Lat Urb other Country/Region of study Territorial variable detail COVID-19 outcomes Amdaoud et al [ 21 ] X Europe Urban regions Deaths ↑ Armillei et al [ 22 ] X Italy Peripheral areas Deaths ↑ Ascani et al [ 23 ] X Italy Presence of an airport in the province Infections ↑ Boterman [ 24 ] X Netherlands Distance from train/motorway Infections ― Chaves et al [ 25 ] X Central America and Caribbean Number of international territories/cities connected through the main airport Death ↑ Chen et al [ 26 ] X Hubei (China) Distance by road from Wuhan Infections ↓ Coker et al [ 27 ] X Northern Italy Distance in meters to the closest airport Death ↑ ...…”

Dominant Modes of Agricultural Production Helped Structure Initial COVID-19 Spread in the U.S. Midwest

“…The latter networks connect what may be, in other senses, 'distant' counties into regional livestock outbreaks or-for pathogens that have already spilled over into human populations-urbanized pandemics [32,[44][45][46]. Chaves et al [47] showed that moderating certain sorts of international interconnection (whether by strategic 'delinking' [48] or unwanted peripheralization [49,50]) reduced the burden of COVID-19 in countries of Mesoamerica and the Caribbean.…”

Governance is key to controlling SARS-CoV-2’s vaccine resistance

“…The context of COVID-19 outcome can be expanded from public health responses to dominant modes of economic production. Across Mesoamerica and the Caribbean, clusters of countries for epidemiological variables such as the exponential growth rate, and COVID cases and deaths 100 days after detection of first case, were matched with clusters based on uneven development variables, including HDI, the WHO Universal Health Care index, and several trade indicators ( 10 ). In Mesoamerica and the Caribbean, trade openness was associated with increases in COVID cases and deaths.…”

“…Public health interventions and traditional SEIR modeling from which such campaigns often take direction must reach beyond cataloguing the emergent properties of populations of interacting individuals ( 11 ). The broader social landscapes in which pathogens evolve drive disease outbreaks ( 10, 11 ). The causes for the evolution of diseases are found as much in the field of the social determinants of health as in the object of the pathogen or patient population.…”