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.</p

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 Gi∗, 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 Gi∗ 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 Gi∗ 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

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

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

Effect of Praziquantel on Schistosoma mekongi Proteome and Phosphoproteome

Schistosoma mekongi causes schistosomiasis in southeast Asia, against which praziquantel (PZQ) is the only treatment option. PZQ resistance has been reported, thus increasing the requirement to understand mechanism of PZQ. Herein, this study aimed to assess differences in proteome and phosphoproteome of S. mekongi after PZQ treatment for elucidating its action. Furthermore, key kinases related to PZQ effects were predicted to identify alternative targets for novel drug development. Proteomes of S. mekongi were profiled after PZQ treatment at half maximal inhibitory concentration and compared with untreated worms. A total of 144 proteins were differentially expressed after treatment. In parallel, immunohistochemistry indicated a reduction of phosphorylation, with 43 phosphoproteins showing reduced phosphorylation, as identified by phosphoproteomic approach. Pathway analysis of mass spectrometric data showed that calcium homeostasis, worm antigen, and oxidative stress pathways were influenced by PZQ treatment. Interestingly, two novel mechanisms related to protein folding and proteolysis through endoplasmic reticulum-associated degradation pathways were indicated as a parasiticidal mechanism of PZQ. According to kinase&ndash;substrate predictions with bioinformatic tools, Src kinase was highlighted as the major kinase related to the alteration of phosphorylation by PZQ. Interfering with these pathways or applying Src kinase inhibitors could be alternative approaches for further antischistosomal drug development

Hepatic protein Carbonylation profiles induced by lipid accumulation and oxidative stress for investigating cellular response to non-alcoholic fatty liver disease in vitro

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is caused by excessive accumulation of fat within the liver, leading to further severe conditions such as non-alcoholic steatohepatitis (NASH). Progression of healthy liver to steatosis and NASH is not yet fully understood in terms of process and response. Hepatic oxidative stress is believed to be one of the factors driving steatosis to NASH. Oxidative protein modification is the major cause of protein functional impairment in which alteration of key hepatic enzymes is likely to be a crucial factor for NAFLD biology. In the present study, we aimed to discover carbonylated protein profiles involving in NAFLD biology in vitro. Methods Hepatocyte cell line was used to induce steatosis with fatty acids (FA) in the presence and absence of menadione (oxidative stress inducer). Two-dimensional gel electrophoresis-based proteomics and dinitrophenyl hydrazine derivatization technique were used to identify carbonylated proteins. Sequentially, in order to view changes in protein carbonylation pathway, enrichment using Funrich algorithm was performed. The selected carbonylated proteins were validated with western blot and carbonylated sites were further identified by high-resolution LC-MS/MS. Results Proteomic results and pathway analysis revealed that carbonylated proteins are involved in NASH pathogenesis pathways in which most of them play important roles in energy metabolisms. Particularly, carbonylation level of ATP synthase subunit α (ATP5A), a key protein in cellular respiration, was reduced after FA and FA with oxidative stress treatment, whereas its expression was not altered. Carbonylated sites on this protein were identified and it was revealed that these sites are located in nucleotide binding region. Modification of these sites may, therefore, disturb ATP5A activity. As a consequence, the lower carbonylation level on ATP5A after FA treatment solely or with oxidative stress can increase ATP production. Conclusions The reduction in carbonylated level of ATP5A might occur to generate more energy in response to pathological conditions, in our case, fat accumulation and oxidative stress in hepatocytes. This would imply the association between protein carbonylation and molecular response to development of steatosis and NASH

Additional file 1 of Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand

Additional file 1

Untargeted serum metabolomic profiling for early detection of Schistosoma mekongi infection in mouse model

Mekong schistosomiasis is a parasitic disease caused by blood flukes in the Lao People’s Democratic Republic and in Cambodia. The standard method for diagnosis of schistosomiasis is detection of parasite eggs from patient samples. However, this method is not sufficient to detect asymptomatic patients, low egg numbers, or early infection. Therefore, diagnostic methods with higher sensitivity at the early stage of the disease are needed to fill this gap. The aim of this study was to identify potential biomarkers of early schistosomiasis using an untargeted metabolomics approach. Serum of uninfected and S. mekongi-infected mice was collected at 2, 4, and 8 weeks post-infection. Samples were extracted for metabolites and analyzed with a liquid chromatography-tandem mass spectrometer. Metabolites were annotated with the MS-DIAL platform and analyzed with Metaboanalyst bioinformatic tools. Multivariate analysis distinguished between metabolites from the different experimental conditions. Biomarker screening was performed using three methods: correlation coefficient analysis; feature important detection with a random forest algorithm; and receiver operating characteristic (ROC) curve analysis. Three compounds were identified as potential biomarkers at the early stage of the disease: heptadecanoyl ethanolamide; picrotin; and theophylline. The levels of these three compounds changed significantly during early-stage infection, and therefore these molecules may be promising schistosomiasis markers. These findings may help to improve early diagnosis of schistosomiasis, thus reducing the burden on patients and limiting spread of the disease in endemic areas