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

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.

Authors

Institutions

Publication Details

Journal
PeerJ
Published
2024-09-26
DOI
https://doi.org/10.7717/peerj.18155
Citations
2
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.79

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Tarmo Ketola, Jouni Helske, Tiia-Maria Pasanen, Harri Högmander
2 citations
PeerJ
COVID-19 epidemiological studies
0.79
article

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

Tarmo Ketola, Jouni Helske, Tiia-Maria Pasanen, Harri Högmander
article en
2 citations

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.

PeerJVol. 12
University of Helsinki (FI), University of Turku (FI), University of Jyväskylä (FI)
China Scholarship Council
Zero hunger
Openalex Percentile: Top 30%
COVID-19 epidemiological studies
0.79
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Spatio-temporal modeling of co-dynamics of smallpox, measles, and pertussis in pre-healthcare Finland — Tarmo Ketola, Jouni Helske, et al. · PeerJ (2024) | TGRS Research Map | TGRS