Investigating the spatiotemporal patterns and clustering of attendances for mental health services to inform policy and resource allocation in Thailand.

BACKGROUND: Mental illness poses a substantial global public health challenge, including in Thailand, where exploration of access to mental health services is limited. The spatial and temporal dimensions of mental illness in the country are not extensively studied, despite the recognized association between poor mental health and socioeconomic inequalities. Gaining insights into these dimensions is crucial for effective public health interventions and resource allocation. METHODS: This retrospective study analyzed mental health service utilization data in Thailand from 2015 to 2023. Temporal trends in annual numbers of individuals visiting mental health services by diagnosis were examined, while spatial pattern analysis employed Moran's I statistics to assess autocorrelation, identify small-area clustering, and hotspots. The implications of our findings for mental health resource allocation and policy were discussed. RESULTS: Between 2015 and 2023, mental health facilities documented a total of 13,793,884 visits. The study found anxiety, schizophrenia, and depression emerged as the top three illnesses for mental health visits, with an increase in patient attendance following the onset of the COVID-19 outbreak. Spatial analysis identified areas of significance for various disorders across different regions of Thailand. Positive correlations between certain disorder pairs were found in specific regions, suggesting shared risk factors or comorbidities. CONCLUSIONS: This study highlights spatial and temporal variations in individuals visiting services for different mental disorders in Thailand, shedding light on service gaps and socioeconomic issues. Addressing these disparities requires increased attention to mental health, the development of appropriate interventions, and overcoming barriers to accessibility. The findings provide a baseline for policymakers and stakeholders to allocate resources and implement culturally responsive interventions to improve mental health outcomes.

Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand.

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.

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand.

BACKGROUND: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs. METHODS: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility. RESULTS: In the simulation study, Getis Ord [Formula: see text] and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord [Formula: see text] and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies. CONCLUSIONS: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.

Evaluation of Kato-Katz and multiplex quantitative polymerase chain reaction performance for clinical helminth infections in Thailand using a latent class analysis.

Using an appropriate diagnostic tool is essential to soil-transmitted helminth control and elimination efforts. Kato-Katz (KK) is the most commonly used diagnostic, but recently other tools, such as real-time quantitative polymerase chain reaction (multiplex qPCR), are starting to be employed more. Here, we evaluated the performance of these two diagnostic tools for five helminth species in Thailand. In the absence of a gold standard, diagnostic performance can be evaluated using latent class analysis. Our results suggest that in moderate to high prevalence settings above 2% multiplex qPCR could be more sensitive than KK, this was particularly apparent for Opisthorchis viverrini in the northeastern provinces. However, for low prevalence, both diagnostics suffered from low sensitivity. Specificity of both diagnostics was estimated to be high (above 70%) across all settings. For some specific helminth infection such as O. viverrini, multiplex qPCR is still a preferable choice of diagnostic test. KK performed equally well in detecting Ascaris lumbricoides and Taenia solium when the prevalence is moderate to high (above 2%). Neither test performed well when the prevalence of infection is low (below 2%), and certainly in the case for hookworm and Trichuris trichiura. Combination of two or more diagnostic tests can improve the performance although the cost would be high. Development of new methods for helminth surveillance at the pre-elimination phase is therefore very important. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction.

Dengue is a major public health problem in Myanmar. The country aims to reduce morbidity by 50% and mortality by 90% by 2025 based on 2015 data. To support efforts to reach these goals it is important to have a detailed picture of the epidemiology of dengue, its relationship to meteorological factors and ideally to predict ahead of time numbers of cases to plan resource allocations and control efforts. Health facility-level data on numbers of dengue cases from 2012 to 2017 were obtained from the Vector Borne Disease Control Unit, Department of Public Health, Myanmar. A detailed analysis of routine dengue and dengue hemorrhagic fever (DHF) incidence was conducted to examine the spatial and temporal epidemiology. Incidence was compared to climate data over the same period. Dengue was found to be widespread across the country with an increase in spatial extent over time. The temporal pattern of dengue cases and fatalities was episodic with annual outbreaks and no clear longitudinal trend. There were 127,912 reported cases and 632 deaths from 2012 and 2017 with peaks in 2013, 2015 and 2017. The case fatality rate was around 0.5% throughout. The peak season of dengue cases was from May to August in the wet season but in 2014 peak dengue season continued until November. The strength of correlation of dengue incidence with different climate factors (total rainfall, maximum, mean and minimum temperature and absolute humidity) varied between different States and Regions. Monthly incidence was forecasted 1 month ahead using the Auto Regressive Integrated Moving Average (ARIMA) method at country and subnational levels. With further development and validation, this may be a simple way to quickly generate short-term predictions at subnational scales with sufficient certainty to use for intervention planning.

Projecting malaria elimination in Thailand using Bayesian hierarchical spatiotemporal models.

Thailand has set a goal of eliminating malaria by 2024 in its national strategic plan. In this study, we used the Thailand malaria surveillance database to develop hierarchical spatiotemporal models to analyze retrospective patterns and predict Plasmodium falciparum and Plasmodium vivax malaria incidences at the provincial level. We first describe the available data, explain the hierarchical spatiotemporal framework underlying the analysis, and then display the results of fitting various space-time formulations to the malaria data with the different model selection metrics. The Bayesian model selection process assessed the sensitivity of different specifications to obtain the optimal models. To assess whether malaria could be eliminated by 2024 per Thailand's National Malaria Elimination Strategy, 2017-2026, we used the best-fitted model to project the estimated cases for 2022-2028. The study results based on the models revealed different predicted estimates between both species. The model for P. falciparum suggested that zero P. falciparum cases might be possible by 2024, in contrast to the model for P. vivax, wherein zero P. vivax cases might not be reached. Innovative approaches in the P. vivax-specific control and elimination plans must be implemented to reach zero P. vivax and consequently declare Thailand as a malaria-free country.

Spatiotemporal reproduction number with Bayesian model selection for evaluation of emerging infectious disease transmissibility: an application to COVID-19 national surveillance data.

BACKGROUND: To control emerging diseases, governments often have to make decisions based on limited evidence. The effective or temporal reproductive number is used to estimate the expected number of new cases caused by an infectious person in a partially susceptible population. While the temporal dynamic is captured in the temporal reproduction number, the dominant approach is currently based on modeling that implicitly treats people within a population as geographically well mixed. METHODS: In this study we aimed to develop a generic and robust methodology for estimating spatiotemporal dynamic measures that can be instantaneously computed for each location and time within a Bayesian model selection and averaging framework. A simulation study was conducted to demonstrate robustness of the method. A case study was provided of a real-world application to COVID-19 national surveillance data in Thailand. RESULTS: Overall, the proposed method allowed for estimation of different scenarios of reproduction numbers in the simulation study. The model selection chose the true serial interval when included in our study whereas model averaging yielded the weighted outcome which could be less accurate than model selection. In the case study of COVID-19 in Thailand, the best model based on model selection and averaging criteria had a similar trend to real data and was consistent with previously published findings in the country. CONCLUSIONS: The method yielded robust estimation in several simulated scenarios of force of transmission with computing flexibility and practical benefits. Thus, this development can be suitable and practically useful for surveillance applications especially for newly emerging diseases. As new outbreak waves continue to develop and the risk changes on both local and global scales, our work can facilitate policymaking for timely disease control.

Bayesian Spatio-Temporal Prediction and Counterfactual Generation: An Application in Non-Pharmaceutical Interventions in COVID-19.

The spatio-temporal course of an epidemic (such as COVID-19) can be significantly affected by non-pharmaceutical interventions (NPIs) such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious diseases (STIFs) (such as COVID-19). In causal inference, it is classically of interest to investigate the counterfactuals. In the context of STIF, it is possible to use nowcasting to assess the possible counterfactual realization of disease in an incidence that would have been evidenced with no NPI. Classic lagged dependency spatio-temporal IF models are discussed, and the importance of the ST component in nowcasting is assessed. Real examples of lockdowns for COVID-19 in two US states during 2020 and 2021 are provided. The degeneracy in prediction over longer time periods is highlighted, and the wide confidence intervals characterize the forecasts. For SC, the early and short lockdown contrasted with the longer NJ intervention. The approach here demonstrated marked differences in spatio-temporal disparities across counties with respect to an adherence to counterfactual predictions.

Hydroxyapatite-binding Silver/Titanium Dioxide as a Potential Control Compound Against Mosquito Vectors, Aedes aegypti (Diptera: Culicidae) and Anopheles dirus (Diptera: Culicidae).

Controlling mosquitoes is vital for counteracting the rising number of mosquito-borne illnesses. Vector control requires the implementation of various measures; however, current methods lack complete effectiveness, and new control agents or substances are urgently needed. Therefore, this study developed a nonwoven fabric sheet coated with hydroxyapatite-binding silver/titanium dioxide compound (hydroxyapatite-binding silver/titanium dioxide sheet [HATS])and evaluated its effectiveness on all stages of laboratory Aedes aegypti (Linnaeus); Diptera: Culicidae and Anopheles dirus (Peyton & Harrison); Diptera: Culicidae. We reared larvae with HATS and control sheets and assessed their mortality, emergence, and hatching rates. The submersion rates of engorged female mosquitoes in submerged HATS and control sheets were also compared. The HATS strongly affected mosquito development, resulting in high mortality rates (mean ± SE) of 99.66 ± 0.58% (L1-L2) and 91.11 ± 9.20% (L3-L4) for Ae. aegypti and 100% of both stages for An. dirus. In contrast, mosquitoes raised in the control sheet showed relatively high survival rates of 92.33 ± 3.21% (L1-L2) and 95.67 ± 0.58% (L3-L4) for Ae. aegypti and 86.07 ± 3.53% (L1-L2) and 92.01 ± 8.67% (L3-L4) for An. dirus. Submersion of engorged females was found in the HATS oviposition cup, leading to a decreased number of eggs and a low hatching rate compared to that of the control. Overall, HATS may be a useful new control method for Ae. aegypti and An. dirus.

Drug resistance of Plasmodium falciparum and Plasmodium vivax isolates in Indonesia.

This review article aims to investigate the genotypic profiles of Plasmodium falciparum and Plasmodium vivax isolates collected across a wide geographic region and their association with resistance to anti-malarial drugs used in Indonesia. A systematic review was conducted between 1991 and date. Search engines, such as PubMed, Science Direct, and Google Scholar, were used for articles published in English and Indonesian to search the literature. Of the 471 initially identified studies, 61 were selected for 4316 P. falciparum and 1950 P. vivax individual infections. The studies included 23 molecular studies and 38 therapeutic efficacy studies. K76T was the most common pfcrt mutation. K76N (2.1%) was associated with the haplotype CVMNN. By following dihydroartemisinin-piperaquine (DHA-PPQ) therapy, the mutant pfmdr1 alleles 86Y and 1034C were selected. Low prevalence of haplotype N86Y/Y184/D1246Y pfmdr1 reduces susceptibility to AS-AQ. SNP mutation pvmdr1 Y976F reached 96.1% in Papua and East Nusa Tenggara. Polymorphism analysis in the pfdhfr gene revealed 94/111 (84.7%) double mutants S108N/C59R or S108T/A16V in Central Java. The predominant pfdhfr haplotypes (based on alleles 16, 51, 59,108, 164) found in Indonesia were ANCNI, ANCSI, ANRNI, and ANRNL. Some isolates carried A437G (35.3%) or A437G/K540E SNPs (26.5%) in pfdhps. Two novel pfdhps mutant alleles, I588F/G and K540T, were associated with six pfdhps haplotypes. The highest prevalence of pvdhfr quadruple mutation (F57L/S58R/T61M/S117T) (61.8%) was detected in Papua. In pvdhps, the only polymorphism before and after 2008 was 383G mutation with 19% prevalence. There were no mutations in the pfk13 gene reported with validated and candidate or associated k13 mutation. An increased copy number of pfpm2, associated with piperaquine resistance, was found only in cases of reinfection. Meanwhile, mutation of pvk12 and pvpm4 I165V is unlikely associated with ART and PPQ drug resistance. DHA-PPQ is still effective in treating uncomplicated falciparum and vivax malaria. Serious consideration should be given to interrupt local malaria transmission and dynamic patterns of resistance to anti-malarial drugs to modify chemotherapeutic policy treatment strategies. The presence of several changes in pfk13 in the parasite population is of concern and highlights the importance of further evaluation of parasitic ART susceptibility in Indonesia.

Effectiveness of Herbal Essential Oils as Single and Combined Repellents against Aedes aegypti, Anopheles dirus and Culex quinquefasciatus (Diptera: Culicidae).

Mosquito repellents reduce human-vector contact of vector-borne diseases. We compared the repellent activity of 10 undiluted essential oils (anise, basil, bergamot, coriander, patchouli, peppermint, petitgrain, rosemary, sage and vetiver) against A. aegypti, A. dirus and C. quinquefasciatus using the arm-in-cage method. Petitgrain oil was the most effective against A. aegypti (270 min). Peppermint oil was the most effective against A. dirus (180 min). Interestingly, all single oils had attributes of repellency against C. quinquefasciatus (ranged, 120−360 min). Moreover, we integrated their binary combinations of highly effective essential oils against A. aegypti and A. dirus to potentially increase the protection time. A 1:1 combination of petitgrain/basil, petitgrain/coriander, basil/coriander and basil/sage reduced the median complete-protection time of 150 min for A. aegypti; a combination of sage and patchouli oils prolonged the median complete-protection time of 270 min for A. dirus. Combining essential oils effect protection time from these two mosquito species.

Spatiotemporal Epidemiology of Tuberculosis in Thailand from 2011 to 2020.

Tuberculosis is a leading cause of infectious disease globally, especially in developing countries. Better knowledge of spatial and temporal patterns of tuberculosis burden is important for effective control programs as well as informing resource and budget allocation. Studies have demonstrated that TB exhibits highly complex dynamics in both spatial and temporal dimensions at different levels. In Thailand, TB research has been primarily focused on surveys and clinical aspects of the disease burden with little attention on spatiotemporal heterogeneity. This study aimed to describe temporal trends and spatial patterns of TB incidence and mortality in Thailand from 2011 to 2020. Monthly TB case and death notification data were aggregated at the provincial level. Age-standardized incidence and mortality were calculated; time series and global and local clustering analyses were performed for the whole country. There was an overall decreasing trend with seasonal peaks in the winter. There was spatial heterogeneity with disease clusters in many regions, especially along international borders, suggesting that population movement and socioeconomic variables might affect the spatiotemporal distribution in Thailand. Understanding the space-time distribution of TB is useful for planning targeted disease control program activities. This is particularly important in low- and middle-income countries including Thailand to help prioritize allocation of limited resources.

Forest malaria and prospects for anti-malarial chemoprophylaxis among forest goers: findings from a qualitative study in Thailand.

BACKGROUND: Across the Greater Mekong Subregion, malaria remains a dangerous infectious disease, particularly for people who visit forested areas where residual transmission continues. Because vector control measures offer incomplete protection to forest goers, chemoprophylaxis has been suggested as a potential supplementary measure for malaria prevention and control. To implement prophylaxis effectively, additional information is needed to understand forest goers' activities and their willingness to use malaria prevention measures, including prophylaxis, and how it could be delivered in communities. Drawing on in-depth interviews with forest goers and stakeholders, this article examines the potential acceptability and implementation challenges of malaria prophylaxis for forest goers in northeast Thailand. METHODS: In-depth interviews were conducted with forest goers (n = 11) and stakeholders (n = 16) including healthcare workers, community leaders, and policymakers. Interviews were audio-recorded, transcribed and coded using NVivo, employing an inductive and deductive approach, for thematic analysis. RESULTS: Forest goers were well aware of their (elevated) malaria risk and reported seeking care for malaria from local health care providers. Forest goers and community members have a close relationship with the forest but are not a homogenous group: their place and time-at-risk varied according to their activities and length of stay in the forest. Among stakeholders, the choice and cost of anti-malarial prophylactic regimen-its efficacy, length and complexity, number of tablets, potential side effects, and long-term impact on users-were key considerations for its feasibility. They also expressed concern about adherence to the preventive therapy and potential difficulty treating malaria patients with the same regimen. Prophylaxis was considered a low priority in areas with perceived accessible health system and approaching malaria elimination. CONCLUSIONS: In the context of multi-drug resistance, there are several considerations for implementing malaria prophylaxis: the need to target forest goers who are at-risk with a clear period of exposure, to ensure continued use of vector control measures and adherence to prophylactic anti-malarials, and to adopt an evidence-based approach to determine an appropriate regimen. Beyond addressing current intervention challenges and managing malaria incidence in low-transmission setting, it is crucial to keep malaria services available and accessible at the village level especially in areas home to highly mobile populations.

Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data.

BACKGROUND: In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases. METHODS: In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand. RESULTS: From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence. CONCLUSIONS: A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.

Helminth Infections in Cattle and Goats in Kanchanaburi, Thailand, with Focus on Strongyle Nematode Infections.

Gastrointestinal helminths are major enteric parasites affecting the health of important livestock ruminants, such as cattle and goats. It is important to routinely survey these animals for helminth infections to allow effective management and control programs to be implemented. A cross-sectional helminth survey carried out in Kanchanaburi Province, Thailand, revealed the infection rate of gastrointestinal helminths in cattle (n = 157) and goats (n = 117) to be 35.7% and 88%, respectively, by microscopic fecal examination, and a 100% herd prevalence was observed in goats. Eggs of strongyle nematodes, Strongyloides spp., Trichuris spp., Capillaria spp., Paramphistomum spp., and Moniezia spp. were detected, with a relatively high rate of strongyle nematode infection in both cattle (28.7%) and goats (86.3%). Mixed infections were observed in 14.3% and 35.9% of egg-positive samples from cattle and goats, respectively. Risk factor analysis showed that dairy cattle were 5.1 times more likely to be infected with strongyles than meat cattle. In contrast, meat goats were 9.3 times more likely to be infected with strongyles than dairy goats. The inverse findings in cattle and goats are discussed. Female gender was associated with a higher risk of strongyle infection in goats. DNA sequencing and in-house semi-nested PCR with primers specific to a region in the internal transcribed spacer 2 (ITS2) were successfully used to identify strongyle genera in randomly selected egg-positive cattle (n = 24) and goat (n = 24) samples. Four strongyle genera, i.e., Cooperia spp., Haemonchus spp., Oesophagostomum spp., and Trichostrongylus spp. were identified by DNA sequencing. By semi-nested PCR, Cooperia spp. were detected as a major parasite of cattle (70.8%), whereas Haemonchus spp. were abundant in goats (100%). The majority of samples from cattle (58.3%) and goats (95.8%) were found to coinfect with at least two strongyle genera, suggesting that coinfection with multiple strongyle genera was more common than single infection in these animals.

Spatiotemporal distributed lag modelling of multiple Plasmodium species in a malaria elimination setting.

In much of the Greater Mekong Sub-region, malaria is now confined to patches and small foci of transmission. Malaria transmission is seasonal with the spatiotemporal patterns being associated with variation in environmental and climatic factors. However, the possible effect at different lag periods between meteorological variables and clinical malaria has not been well studied in the region. Thus, in this study we developed distributed lagged modelling accounting for spatiotemporal excessive zero cases in a malaria elimination setting. A multivariate framework was also extended to incorporate multiple data streams and investigate the spatiotemporal patterns from multiple parasite species via their lagged association with climatic variables. A simulation study was conducted to examine robustness of the methodology and a case study is provided of weekly data of clinical malaria cases at sub-district level in Thailand.

Preliminary estimation of temporal and spatiotemporal dynamic measures of COVID-19 transmission in Thailand.

BACKGROUND: As a new emerging infectious disease pandemic, there is an urgent need to understand the dynamics of COVID-19 in each country to inform planning of emergency measures to contain its spread. It is essential that appropriate disease control activities are planned and implemented in a timely manner. Thailand was one of the first countries outside China to be affected with subsequent importation and domestic spread in most provinces in the country. METHOD: A key ingredient to guide planning and implementation of public health measures is a metric of transmissibility which represents the infectiousness of a disease. Ongoing policies can utilize this information to plan appropriately with updated estimates of disease transmissibility. Therefore we present descriptive analyses and preliminary statistical estimation of reproduction numbers over time and space to facilitate disease control activities in Thailand. RESULTS: The estimated basic reproduction number for COVID-19 during the study ranged from 2.23-5.90, with a mean of 3.75. We also tracked disease dynamics over time using temporal and spatiotemporal reproduction numbers. The results suggest that the outbreak was under control since the middle of April. After the boxing stadium and entertainment venues, the numbers of new cases had increased and spread across the country. DISCUSSION: Although various scenarios about assumptions were explored in this study, the real situation was difficult to determine given the limited data. More thorough mathematical modelling would be helpful to improve the estimation of transmissibility metrics for emergency preparedness as more epidemiological and clinical information about this new infection becomes available. However, the results can be used to guide interventions directly and to help parameterize models to predict the impact of these interventions.

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand.

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.

Spatiotemporal multi-disease transmission dynamic measure for emerging diseases: an application to dengue and zika integrated surveillance in Thailand.

BACKGROUND: New emerging diseases are public health concerns in which policy makers have to make decisions in the presence of enormous uncertainty. This is an important challenge in terms of emergency preparation requiring the operation of effective surveillance systems. A key concept to investigate the dynamic of infectious diseases is the basic reproduction number. However it is difficult to be applicable in real situations due to the underlying theoretical assumptions. METHODS: In this paper we propose a robust and flexible methodology for estimating disease strength varying in space and time using an alternative measure of disease transmission within the hierarchical modeling framework. The proposed measure is also extended to allow for incorporating knowledge from related diseases to enhance performance of surveillance system. RESULTS: A simulation was conducted to examine robustness of the proposed methodology and the simulation results demonstrate that the proposed method allows robust estimation of the disease strength across simulation scenarios. A real data example is provided of an integrative application of Dengue and Zika surveillance in Thailand. The real data example also shows that combining both diseases in an integrated analysis essentially decreases variability of model fitting. CONCLUSIONS: The proposed methodology is robust in several simulated scenarios of spatiotemporal transmission force with computing flexibility and practical benefits. This development has potential for broad applicability as an alternative tool for integrated surveillance of emerging diseases such as Zika.