Spatio-temporal modeling of co-dynamics of smallpox, measles and pertussis in pre-healthcare Finland

TL;DR

This study introduces a Bayesian spatio-temporal model to analyze the interactions and spread of smallpox, measles, and pertussis in 19th-century rural Finland, revealing positive correlations among these diseases.

Contribution

The paper presents a novel Bayesian model tailored for sparse historical rural data to study infectious disease co-dynamics.

Findings

Diseases show positive correlation in their spread.

Immunosuppressive effects may influence disease interactions.

Rural historical data can be effectively analyzed with the proposed model.

Abstract

Infections are known to interact as previous infections may have an effect on risk of succumbing to a new infection. The co-dynamics can be mediated by immunosuppression or -modulation, shared environmental or climatic drivers, or competition for susceptible hosts. Research and statistical methods in epidemiology often concentrate on large pooled datasets, or high quality data from cities, leaving rural areas underrepresented in literature. Data considering rural populations are typically sparse and scarce, especially in the case of historical data sources, which may introduce considerable methodological challenges. In order to overcome many obstacles due to such data, we present a general Bayesian spatio-temporal model for disease co-dynamics. Applying the proposed model on historical (1820-1850) Finnish parish register data, we study the spread of infectious diseases in pre-healthcare Finland. We observe that measles, pertussis, and smallpox exhibit positively correlated dynamics, which could be attributed to immunosuppressive effects or, for example, the general weakening of the population due to recurring infections or poor nutritional conditions.