Modelling malaria in southernmost provinces of Thailand: a two-step process for analysis of highly right-skewed data with a large proportion of zeros

Ammatawiyanon, Lumpoo; Tongkumchum, Phattrawan; Lim, Apiradee; McNeil, Don

doi:10.1186/s12936-022-04363-8

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Regions with higher malaria endemicity had higher CC incidence rates and a higher proportion of high-grade cancer cases [35]. The EV lncRNAs related to the malaria pathway found in patients with stage III CC indicate that these individuals might have co-infection with malaria, as malaria is a serious health concern in the southern border provinces of Thailand, including Songkhla [36], where serum samples were collected. However, malaria pathway-related lncRNA targets, including THBS3, HBA1, THBS1, HBB, TNF, and THBF4, have also been implicated in the tumor microenvironment and immune response of cancer.…”

Section: Discussionmentioning

confidence: 99%

Exploration of Extracellular Vesicle Long Non-Coding RNAs in Serum of Patients with Cervical Cancer

Molika¹,

Bissanum²,

Surachat³

et al. 2023

Oncology

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Cervical cancer (CC) is the fourth most common cancer type and a leading cause of cancer-related deaths in women worldwide. Its underlying molecular mechanisms are unclear. Cancer cell-derived extracellular vesicles (EVs) are involved in cancer development and progression by delivering regulatory factors, including microRNAs and long non-coding RNAs (lncRNAs). Here, we identified the EV lncRNA expression profiles associated with different developmental stages of CC using next-generation sequencing. EVs from the serum of patients with stages I–III CC and healthy donors were characterized using EV marker immunoblotting and transmission electron microscopy. The EV concentration increases with progression of the disease. Most particles had a 100–250 nm diameter, and their sizes were similar in all groups. We identified many lncRNAs that were uniquely and differentially expressed (DE) in patients with different stages of CC. The pathway analysis results indicated that the upregulated DE EV lncRNAs abundant in stages I and II were associated with cell proliferation and inflammatory and cancer progression pathways, respectively. LINC00941, LINC01910, LINC02454, and DSG2-AS1 were highly expressed, suggesting poor overall survival of CC patients. Interestingly, DSG2-AS1 was associated with human papillomavirus infection pathway through AKT3, DLG1, and COL6A2 genes. This is the first study that reports the levels of EVs and their lncRNA contents change during cancer development, demonstrating the existence of a unique vesicle-mediated cell-to-cell communication network underlying cancer progression.

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Section: Discussionmentioning

confidence: 99%

Exploration of Extracellular Vesicle Long Non-Coding RNAs in Serum of Patients with Cervical Cancer

Molika¹,

Bissanum²,

Surachat³

et al. 2023

Oncology

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“…A logistic model for occurrence and a log-linear regression model for CHKIF incidence rates were separately fitted, and the results were merged. This two-stage analysis has previously been described 30 . This method can be applied to rare diseases with data that contains a large proportion of zeros.…”

Section: Discussionmentioning

confidence: 99%

“…The CHIKF occurrence is a binary variable, whereas the incidence is a continuous variable. A two-step approach comprised of logistic model for occurrence and linear model for incidence rate was used 30 . These two models included sub-district, year, gender–age group as determinants.…”

Section: Methodsmentioning

confidence: 99%

Statistical modeling for identifying chikungunya high-risk areas of two large-scale outbreaks in Thailand's southernmost provinces

Ammatawiyanon,

Tongkumchum,

McNeil

et al. 2023

Sci Rep

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Chikungunya fever (CHIKF) has re-emerged in the southernmost Thailand and presents a significant threat to public health. The problem areas can be identified using appropriate statistical models. This study aimed to determine the geographic epidemic patterns and high-risk locations. Data on CHIKF’s case characteristics, including age, gender, and residence sub-district, were obtained from the Office of Disease Prevention and Control of Thailand from 2008 to 2020. A logistic model was applied to detect illness occurrences. After removing records with no cases, a log-linear regression model was used to determine the incidence rate. The results revealed that two large-scale infections occurred in the southernmost provinces of Thailand between 2008 and 2010, and again between 2018 and 2020, indicating a 10-year epidemic cycle. The CHIKF occurrence in the first and second outbreaks was 28.4% and 15.5%, respectively. In both outbreaks of occurrence CHIKF, adolescents and working-age groups were the most infected groups but the high incidence rate of CHIKF was elderly groups. The first outbreak had a high occurrence and incidence rate in 39 sub-districts, the majority of which were in Narathiwat province, whilst the second outbreak was identified in 15 sub-districts, the majority of which were in Pattani province. In conclusion, the CHIKF outbreak areas can be identified and addressed by combining logistic and log-linear models in a two-step process. The findings of this study can serve as a guide for developing a surveillance strategy or an earlier plan to manage or prevent the CHIKF outbreak.

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“…6 of the 7 outbreaks were reported at a provincial level while 1 (2017 Kanchanaburi) was reported at a subdistrict level. Reported outbreaks are generally clustered along provinces bordering Laos, Cambodia, and Myanmar and could have resulted from factors like migrant movement, limited access to malaria prevention and diagnostics, inadequate monitoring measures, dense forest regions, and political and social unrest [ 30 ]. The summary of outbreak dates are shown in Table 2 (see Additional file 12 for detailed descriptions).…”

Section: Methodsmentioning

confidence: 99%

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

Srimokla,

Pan-Ngum,

Khamsiriwatchara

et al. 2024

Malar J

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Background Malaria continues to pose a significant health threat. Rapid identification of malaria infections and the deployment of active surveillance tools are crucial for achieving malaria elimination in regions where malaria is endemic, such as certain areas of Thailand. In this study, an anomaly detection system is introduced as an early warning mechanism for potential malaria outbreaks in countries like Thailand. Methods Unsupervised clustering-based, and time series-based anomaly detection algorithms are developed and compared to identify abnormal malaria activity in Thailand. Additionally, a user interface tailored for anomaly detection is designed, enabling the Thai malaria surveillance team to utilize these algorithms and visualize regions exhibiting unusual malaria patterns. Results Nine distinct anomaly detection algorithms we developed. Their efficacy in pinpointing verified outbreaks was assessed using malaria case data from Thailand spanning 2012 to 2022. The historical average threshold-based anomaly detection method triggered three times fewer alerts, while correctly identifying the same number of verified outbreaks when compared to the current method used in Thailand. A limitation of this analysis is the small number of verified outbreaks; further consultation with the Division of Vector Borne Disease could help identify more verified outbreaks. The developed dashboard, designed specifically for anomaly detection, allows disease surveillance professionals to easily identify and visualize unusual malaria activity at a provincial level across Thailand. Conclusion An enhanced early warning system is proposed to bolster malaria elimination efforts for countries with a similar malaria profile to Thailand. The developed anomaly detection algorithms, after thorough comparison, have been optimized for integration with the current malaria surveillance infrastructure. An anomaly detection dashboard for Thailand is built and supports early detection of abnormal malaria activity. In summary, the proposed early warning system enhances the identification process for provinces at risk of outbreaks and offers easy integration with Thailand’s established malaria surveillance framework.

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Modelling malaria in southernmost provinces of Thailand: a two-step process for analysis of highly right-skewed data with a large proportion of zeros

Cited by 5 publications

References 23 publications

Exploration of Extracellular Vesicle Long Non-Coding RNAs in Serum of Patients with Cervical Cancer

Exploration of Extracellular Vesicle Long Non-Coding RNAs in Serum of Patients with Cervical Cancer

Statistical modeling for identifying chikungunya high-risk areas of two large-scale outbreaks in Thailand's southernmost provinces

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach

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