Correction: Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand.

[This corrects the article DOI: 10.1371/journal.pntd.0004761.].

Cost Effectiveness and Budget Impact Analyses of Influenza Vaccination for Prisoners in Thailand: An Application of System Dynamic Modelling.

Influenza outbreaks in Thai prisons were increasing in number every year and to address this, the Thai Ministry of Public Health (MOPH) initiated a policy to promote vaccination for prisoners. The objective of this study was to assess the cost effectiveness and budget impact of the influenza vaccination policy for prisoners in Thailand. The study obtained data from the Division of Epidemiology, Department of Disease Control (DDC), MOPH. Deterministic system dynamic modelling was exercised to estimate the financial implication of the vaccination programme in comparison with routine outbreak control. The incremental cost-effectiveness ratio (ICER) was calculated via a DDC perspective. The reproductive number was estimated at 1.4. A total of 143 prisons across the country (375,763 prisoners) were analysed. In non-vaccination circumstances, the total healthcare cost amounted to 174.8 million Baht (US$ 5.6 million). Should all prisoners be vaccinated, the total healthcare cost would reduce to 90.9 million Baht (US$ 2.9 million), and 46.8 million Baht (US$ 1.5 million) of this is related to the vaccination. The ICER of vaccination (compared with routine outbreak control) varied between 39,738.0 to 61,688.3 Baht per disability-adjusted life year (DALY) averted (US$ 1281.9-1989.9). Should the vaccination cover 30% of the prisoners, the ICER would be equal to 46,866.8 Baht (US$ 1511.8) per DALY averted with the budget burden amounted to Baht (US$ 4.8 million). The vaccination programme would become more cost-effective if the routine outbreak control was intensified. In summary, the vaccination programme was a cost-effective measure to halt influenza outbreak amongst prisoners. Further primary studies that aim to assess the actual impact of the programme are recommended.

Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014.

Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000-2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010-2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.

Challenges 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 such 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 a practical 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 seasonal baseline 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.