An Assessment of Epidemiology Capacity in a One Health Team at the Provincial Level in Thailand

A multi-sectoral core epidemiology capacity assessment was conducted in provinces that implemented One Health services in order to assess the efficacy of a One Health approach in Thailand. In order to conduct the assessment, four provinces were randomly selected as a study group from a total of 19 Thai provinces that are currently using a One Health approach. As a control group, four additional provinces that never implemented a One Health approach were also sampled. The provincial officers were interviewed on the epidemiologic capacity of their respective provinces. The average score of epidemiologic capacity in the provinces implementing the One Health approach was 66.45%, while the provinces that did not implement this approach earned a score of 54.61%. The epidemiologic capacity of surveillance systems in provinces that utilized the One Health approach earned higher scores in comparison to provinces that did not implement the approach (75.00% vs. 53.13%, p-value 0.13). Although none of the capacity evaluations showed significant differences between the two groups, we found evidence that provinces implementing the One Health approach gained higher scores in both surveillance and outbreak investigation capacities. This may be explained by more efficient capacity when using a One Health approach, specifically in preventing, protecting, and responding to threats in local communities

Challenges and best practices in real-time prediction of infectious disease: a case study of dengue in Thailand

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create accurate and actionable real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created an operational and computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing naive seasonal models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making

Real-time Forecasting of the 2014 Dengue Fever Season in Thailand

Real-time surveillance of an infectious disease in a third world country poses many problems that are not conventionally confronted by statistical researchers. As the first ones - to our knowledge - to attempt real-time forecasts of dengue fever in Thailand, we have faced these problems head-on in our quest to build a model that accurately predicts case counts in the presence of erratic reporting, shifting population dynamics, and potential climate change

Real-time Forecasting of the 2014 Dengue Fever Season in Thailand

Real-time surveillance of an infectious disease in a third world country poses many problems that are not conventionally confronted by statistical researchers. As the first ones - to our knowledge - to attempt real-time forecasts of dengue fever in Thailand, we have faced these problems head-on in our quest to build a model that accurately predicts case counts in the presence of erratic reporting, shifting population dynamics, and potential climate change

Mean absolute error (MAE) of our prediction model by province and step forward (in biweeks).

<p>Provinces are sorted by population, with the most populous at the top of the figure.</p

Relative mean absolute error (MAE) comparing our prediction model vs. a model that predicts a seasonal median, by province and step forward (in biweeks).

<p>Results to the left of the dotted line signify more accurate predictions from our models when compared to the seasonal model, and results to the right indicate less accurate predictions.</p

Raw dengue hemorrhagic fever case counts for 77 provinces of Thailand across 47 years (1968–2014).

<p>Provinces are ordered by by population (larger populations on the top). Gray regions indicate periods of time when a province was not in existence.</p

Country-wide real-time predictions for incident dengue hemorrhagic fever.

<p>Red lines show predicted case counts, black bars show cases reported by the end of the 2014 reporting period. The three figures show (top to bottom) one-, two-, and three-biweek ahead predictions. So, for example, every dot on the top graph is a one-biweek ahead real-time prediction made from all available data at the time of analysis.</p