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.

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.

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.

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.

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.