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ISDS 2016 Conference Abstracts

Integrated surveillance: Joint modeling of rodent and 
human tularemia cases in Finland
Chawarat Rotejanaprasert*
National Center for Genetic Engineering and Biotechnology, Bangkok, Thailand

Objective
We seek to integrate multiple streams of geo-coded information with 

the aim to improve public health surveillance accuracy and efficiency. 
Specifically for vector-borne diseases, knowledge of spatial and 
temporal patterns of vector distribution can help early prediction of 
human incidence. To this end, we develop joint modeling approaches 
to evaluate the contribution of vector or reservoir information on early 
prediction of human cases. A case study of spatiotemporal modeling 
of tularemia human incidence and rodent population data from Finnish 
health care districts during the period 1995-2013 is provided. Results 
suggest that spatial and temporal information of rodent abundance is 
useful in predicting human cases.

Introduction
An increasing number of geo-coded information streams are 

available with possible use in disease surveillance applications.  
In this setting, multivariate modeling of health and non-health data 
allows assessment of concurrent patterns among data streams and 
conditioning on one another. Therefore it is appropriate to consider 
the analysis of their spatial distributions together. Specifically for 
vector-borne diseases, knowledge of spatial and temporal patterns 
of vector distribution could inform incidence in humans. Tularemia 
is an infectious disease endemic in North America and parts of 
Europe. In Finland tularemia is typically mosquito-transmitted with 
rodents serving as a host; however a country-wide understanding of 
the relationship between rodents and the disease in humans is still 
lacking. We propose a methodology to help understand the association 
between human tularemia incidence and rodent population levels.

Methods
Data on rodent population levels are collected around the country 

by the Finnish Natural Resources Institute. Human Tularaemia cases 
are recorded as laboratory-confirmed and reported to the National 
Infectious Disease Register (NIDR). Human cases and rodent data 
were aggregated to match the 20 Finnish health districts over the period 
1995-2013 [1]. We develop our methodology in a Bayesian setting. 
The counts of human cases for each health district in a given year 
are assumed to follow a Poisson distribution and the rodent data are 
assumed to have a categorical likelihood. The linear predictors linked 
to the human and rodent likelihood functions are then decomposed 
additively into spatial, temporal, and space-time interaction random 
effects. We then link the two likelihoods via the interaction term by 
assuming that the human spatiotemporal variation is dependent on the 
rodent activity with one-year lag. In the case of the rodent data, we 
also included two additional spatial and non-spatial contextual terms 
to better model ecological effects associated with rodent population 
levels as described before [2]. We then finally develop indicators, on 
the scale 0 to 1, to quantify the association between human incidence 
and a rodent vector.

Results
Results suggest that spatial and temporal information of rodent 

abundance is useful in predicting human cases.

Conclusions
Future modeling directions are recommended to include 

environmental and epidemiological factors. To the best of our 
knowledge, this is the first time that rodent data, captured for non-
health related purposes, is used to better inform the human risk of 
tularemia in Finland.

Keywords
Tularemia; surveillance; multivariate; joint modeling

References
[1] Rossow, H., Ollgren, J., Hytönen, J., Rissanen, H., Huitu, O., 

Henttonen, H. & Vapalahti, O. (2015). Incidence and seroprevalence 
of tularaemia in Finland, 1995 to 2013: regional epidemics with 
cyclic pattern. Euro surveillance: bulletin Européen sur les maladies 
transmissibles= European communicable disease bulletin, 20(33).

[2] Sane, J., Ollgren, J., Makary, P., Vapalahti, O., Kuusi, M. and 
Lyytikainen, O. (2016) ‘Regional differences in long-term 
cycles and seasonality of Puumala virus infections, Finland, 
1995–2014’, Epidemiology and Infection, pp. 1–6. doi: 10.1017/
S0950268816000765.

*Chawarat Rotejanaprasert
E-mail: baantoto@gmail.com

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 9(1):e109, 2017