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.].

Case-Control Study of Use of Personal Protective Measures and Risk for SARS-CoV 2 Infection, Thailand.

We evaluated effectiveness of personal protective measures against severe acute respiratory disease coronavirus 2 (SARS-CoV-2) infection. Our case-control study included 211 cases of coronavirus disease (COVID-19) and 839 controls in Thailand. Cases were defined as asymptomatic contacts of COVID-19 patients who later tested positive for SARS-CoV-2; controls were asymptomatic contacts who never tested positive. Wearing masks all the time during contact was independently associated with lower risk for SARS-CoV-2 infection compared with not wearing masks; wearing a mask sometimes during contact did not lower infection risk. We found the type of mask worn was not independently associated with infection and that contacts who always wore masks were more likely to practice social distancing. Maintaining >1 m distance from a person with COVID-19, having close contact for <15 minutes, and frequent handwashing were independently associated with lower risk for infection. Our findings support consistent wearing of masks, handwashing, and social distancing to protect against COVID-19.

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.

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.