  Latin America is experiencing severe impacts of the SARS-CoV-2 pandemic, but
poverty and weak public health institutions hamper gathering the kind of
refined data needed to inform classical SEIR models of epidemics. We present an
alternative approach that draws on advances in statistical ecology and
conservation biology to enhance the value of sparse data in projecting and
ameliorating epidemics. Our approach, leading to what we call a Stochastic
Epidemic Gompertz model, with few parameters can flexibly incorporate
heterogeneity in transmission within populations and across time. We
demonstrate that poverty has a large impact on the course of the pandemic,
across fourteen Latin American countries, and show how our approach provides
flexible, time-varying projections of disease risk that can be used to refine
public health strategies.
