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

By Tiia-Maria Pasanen,Jouni Helske, Harri Högmander and Tarmo Ketola in Bayesian Inference

October 10, 2023



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 such that any new infection increased mortality in all three diseases, indicating possibly general immunosuppressive effects at population level.

Posted on:
October 10, 2023
1 minute read, 158 words
Bayesian Inference
Markov Chain Monte Carlo Time Series Spatio-temporal Data
See Also:
Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models
dynamite: An R Package for Dynamic Multivariate Panel Models
A Bayesian spatio-temporal analysis of markets during the Finnish 1860s famine