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Abstract Background In Thailand, the Department of Disease Control (DDC) regularly performs visual larval surveys throughout the country to monitor dengue fever outbreaks. Since 2016, the DDC switched from a paper-based to a digital-based larval survey process. The significant amount of larval survey data collected digitally presents a valuable opportunity to precisely identify the villages and breeding habitats that are vulnerable to dengue transmission. Methods The study used digitally collected larval survey data from 2017 to 2019. It employed larval indices to evaluate the risk of dengue transmission in villages based on seasonal, regional, and categorical perspectives. Furthermore, the study comprehensively scrutinized each container category by employing different measures to determine its breeding preference ratio. Results The result showed that villages with a very high-risk of dengue transmission were present year-round in all regions, with the highest proportion during the rainy season. The Southern region had more high-risk villages during the winter season due to rainfall. Slums and residential communities were more vulnerable to dengue than commercial areas. All container categories could potentially serve as breeding habitats for dengue-carrying mosquitoes, with abandoned containers being the most significant breeding sites. Conclusions The risk of dengue transmission was present year-round throughout Thailand. This underscores the importance of community and government initiatives, along with sustained public awareness campaigns and active community engagement, to efficiently and permanently eradicate mosquito breeding habitats. It should be noted that larval indices may not strongly correlate with dengue cases, as indicated by the preliminary analysis. However, they offer valuable insights into potential breeding sites for targeted preventive measures.

Abstract Background Dengue infection ranges from asymptomatic to severe and life-threatening, with no specific treatment available. Vector control is crucial for interrupting its transmission cycle. Accurate estimation of outbreak timing and location is essential for efficient resource allocation. Timely and reliable notification systems are necessary to monitor dengue incidence, including spatial and temporal distributions, to detect outbreaks promptly and implement effective control measures. Methods We proposed an integrated two-step methodology for real-time spatiotemporal cluster detection, accounting for reporting delays. In the first step, we employed space-time nowcasting modeling to compensate for lags in the reporting system. Subsequently, anomaly detection methods were applied to assess adverse risks. To illustrate the effectiveness of these detection methods, we conducted a case study using weekly dengue surveillance data from Thailand. Results The developed methodology demonstrated robust surveillance effectiveness. By combining space-time nowcasting modeling and anomaly detection, we achieved enhanced detection capabilities, accounting for reporting delays and identifying clusters of elevated risk in real-time. The case study in Thailand showcased the practical application of our methodology, enabling timely initiation of disease control activities. Conclusion Our integrated two-step methodology provides a valuable approach for real-time spatiotemporal cluster detection in dengue surveillance. By addressing reporting delays and incorporating anomaly detection, it complements existing surveillance systems and forecasting efforts. Implementing this methodology can facilitate the timely initiation of disease control activities, contributing to more effective prevention and control strategies for dengue in Thailand and potentially other regions facing similar challenges.

BackgroundIn response to the 2015-2016 Zika virus (ZIKV) outbreak and the causal relationship established between maternal ZIKV infection and adverse infant outcomes, we conducted a cohort study to estimate the incidence of ZIKV infection in pregnancy and assess its impacts in women and infants.Methodology/principal findingsFrom May 2018-January 2020, we prospectively followed pregnant women recruited from 134 participating hospitals in two non-adjacent provinces in northeastern Thailand. We collected demographic, clinical, and epidemiologic data and blood and urine at routine antenatal care visits until delivery. ZIKV infections were confirmed by real-time reverse transcriptase polymerase chain reaction (rRT-PCR). Specimens with confirmed ZIKV underwent whole genome sequencing. Among 3,312 women enrolled, 12 (0.36%) had ZIKV infections, of which two (17%) were detected at enrollment. Ten (83%, 3 in 2nd and 7 in 3rd trimester) ZIKV infections were detected during study follow-up, resulting in an infection rate of 0.15 per 1,000 person-weeks (95% CI: 0.07-0.28). The majority (11/12, 91.7%) of infections occurred in one province. Persistent ZIKV viremia (42 days) was found in only one woman. Six women with confirmed ZIKV infections were asymptomatic until delivery. Sequencing of 8 ZIKV isolates revealed all were of Asian lineage. All 12 ZIKV infected women gave birth to live, full-term infants; the only observed adverse birth outcome was low birth weight in one (8%) infant. Pregnancies in 3,300 ZIKV-rRT-PCR-negative women were complicated by 101 (3%) fetal deaths, of which 67 (66%) had miscarriages and 34 (34%) had stillbirths. There were no differences between adverse fetal or birth outcomes of live infants born to ZIKV-rRT-PCR-positive mothers compared to live infants born to ZIKV-rRT-PCR-negative mothers.Conclusions/significanceConfirmed ZIKV infections occurred infrequently in this large pregnancy cohort and observed adverse maternal and birth outcomes did not differ between mothers with and without confirmed infections.

Abstract Thailand’s National Malaria Elimination Strategy 2017–2026 seeks to increase domestic support and financing for malaria elimination. During 2018–2020, through a series of training sessions, public health officials in Thailand utilized foci-level malaria data to engage subdistrict-level government units known as Local Administrative Organizations (LAOs) with the aim of increasing their understanding of their local malaria situation, collaboration with public health networks, and advocacy for financial support of targeted interventions in villages within their jurisdictions. As a result of these efforts, total LAO funding support for malaria nearly doubled from the 2017 baseline to 2020. In 2021, a novel “LAO collaboration” feature was added to Thailand’s national malaria information system that enables tracking and visualization of LAO financial support of malaria in areas with transmission, by year, down to the subdistrict level. This case study describes Thailand’s experience implementing the LAO engagement strategy, quantifying and monitoring the financial support mobilized from LAOs, and results from a qualitative study in five high-performance provinces examining factors and approaches that foster successful local collaboration between LAOs, public health networks, and communities for malaria prevention and response. Results from the study showed that significant malaria endemicity or local outbreaks helped spur collaboration in multiple provinces. Increases in LAO support and involvement were attributable to four approaches employed by public health officials: (a) strengthening malaria literacy and response capacity of LAOs, (b) organizational leadership in response to outbreaks, (c) utilization of structural incentives, and (d) multisectoral involvement in malaria response. In two provinces, capacity building of LAOs in malaria vector control, following a precedent set by Thailand’s dengue programme, enabled LAO personnel to play both funding and implementation roles in local malaria response. Wider replication of the LAO engagement strategy across Thailand may sustain gains and yield efficiencies in the fight against malaria as the vector-borne disease workforce declines. Lessons from Thailand’s experience may be useful for malaria programmes in other geographies to support the goals and sustainability of elimination and prevention of re-establishment by improving financing through local collaboration between the health system and elected officials.

Abstract Background Integrated drug efficacy surveillance (iDES) was formally introduced nationally across Thailand in fiscal year 2018 (FY2018), building on a history of drug efficacy monitoring and interventions. According to the National Malaria Elimination Strategy for Thailand 2017–2026, diagnosis is microscopically confirmed, treatment is prescribed, and patients are followed up four times to ensure cure. Methods Routine patient data were extracted from the malaria information system for FY2018–FY2020. Treatment failure of first-line therapy was defined as confirmed parasite reappearance within 42 days for Plasmodium falciparum and 28 days for Plasmodium vivax. The primary outcome was the crude drug efficacy rate, estimated using Kaplan–Meier methods, at day 42 for P. falciparum treated with dihydroartemisinin–piperaquine plus primaquine, and day 28 for P. vivax treated with chloroquine plus primaquine; day 60 and day 90 efficacy were secondary outcomes for P. vivax. Results The proportion of patients with outcomes recorded at day 42 for P. falciparum malaria and at day 28 for P. vivax malaria has been increasing, with FY2020 follow-up rates of 61.5% and 57.2%, respectively. For P. falciparum malaria, day 42 efficacy in FY2018 was 92.4% (n = 249), in FY2019 93.3% (n = 379), and in FY2020 98.0% (n = 167). Plasmodium falciparum recurrences occurred disproportionally in Sisaket Province, with day 42 efficacy rates of 75.9% in FY2018 (n = 59) and 49.4% in FY2019 (n = 49), leading to an update in first-line therapy to pyronaridine–artesunate at the provincial level, rolled out in FY2020. For P. vivax malaria, day 28 efficacy (chloroquine efficacy) was 98.5% in FY2018 (n = 2048), 99.1% in FY2019 (n = 2206), and 99.9% in FY2020 (n = 2448), and day 90 efficacy (primaquine efficacy) was 94.8%, 96.3%, and 97.1%, respectively. Conclusions In Thailand, iDES provided operationally relevant data on drug efficacy, enabling the rapid amendment of treatment guidelines to improve patient outcomes and reduce the potential for the spread of drug-resistant parasites. A strong case-based surveillance system, integration with other health system processes, supporting biomarker collection and molecular analyses, and cross-border collaboration may maximize the potential of iDES in countries moving towards elimination.

Abstract Thailand’s National Malaria Elimination Strategy 2017–2026 introduced the 1-3-7 strategy as a robust surveillance and response approach for elimination that would prioritize timely, evidence-based action. Under this strategy, cases are reported within 1 day, cases are investigated within 3 days, and foci are investigated and responded to within 7 days, building on Thailand’s long history of conducting case investigation since the 1980s. However, the hallmark of the 1-3-7 strategy is timeliness, with strict deadlines for reporting and response to accelerate elimination. This paper outlines Thailand’s experience adapting and implementing the 1-3-7 strategy, including success factors such as a cross-sectoral Steering Committee, participation in a collaborative regional partnership, and flexible local budgets. The programme continues to evolve to ensure prompt and high-quality case management, capacity maintenance, and adequate supply of lifesaving commodities based on surveillance data. Results from implementation suggest the 1-3-7 strategy has contributed to Thailand’s decline in malaria burden; this experience may be useful for other countries aiming to eliminate malaria.

Abstract Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.

Abstract Background The ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time. Methods Predicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010. Results The overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data. Conclusions A persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.