A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines

Submission pattern model parameter estimates reported as rate ratios.

<p>Submission pattern model parameter estimates reported as rate ratios.</p

Model results for four commonly reported cattle diagnoses.

<p>Posterior mean estimates are per week, per field veterinary surgeon, reported as rate ratios. Maximum daily temperature and total precipitation are computed for each district and month. District reports are the number of cases within the district in the previous week.</p

Posterior mean of the state variable.

<p>The model-adjusted posterior mean state for each field veterinarian surgeon by week, in each of the study districts for commonly reported cattle diagnoses. Red indicates state one and white indicates state two, and yellow intermediate values for a) Milk Fever, b) Ephemeral Fever, c) Babesiosis, and d) Mastits.</p

Data Generating Processes.

<p>Conceptual model of data generating processes in the Infectious Disease Surveillance and Analysis System in the context of hidden markov models. The hidden states of interest are the normal or abnormal state of animal health as seen by field veterinary surgeons. Observed data may include weekly submission counts, or counts of specific reported diagnoses.</p

Study Area Map.

<p>Map of Sri Lanka and study districts that were part of the Infectious Disease Surveillance and Analysis System.</p

Monthly total cases for commonly reported diagnoses in each of the four districts.

<p>Anauradhapura (red), Nuwara Eliya (blue), Matara (green), and Ratnapura (grey). Monthly averages for district-wide total precipitation and maximum temperature.</p

Description of prior distributions and hyper-parameters for model parameters.

<p>*Parameterized as mean and precision (1/variance, as in WinBUGS). For disease-level models, a <i>Normal</i>(0,10) prior was used to accommodate very small expected counts.</p

Model results from simulation study for five different outbreak scenarios occurring during a 52 week simulated surveillance system.

<p>Model results from simulation study for five different outbreak scenarios occurring during a 52 week simulated surveillance system.</p