{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T14:04:48Z","timestamp":1791813888017,"version":"4.3.4"},"reference-count":66,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research (NU) and the National Science, Research and Innovation Fund (NSRF)","award":["R2567B018"],"award-info":[{"award-number":["R2567B018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["TropicalMed"],"abstract":"<jats:p>Hand, Foot, and Mouth Disease (HFMD) predominantly affects children under the age of five and remains a significant public health concern in the Asia-Pacific region. HFMD outbreaks are closely linked to seasonal changes and meteorological factors, particularly in tropical and subtropical areas. In Thailand, a total of 657,570 HFMD cases were reported between 2011 and 2022 (12 years). This study aimed to identify the high- and low-risk HFMD outbreak areas using machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Our findings showed that the XGBoost model outperformed the other models in predicting unseen data and defining the best model. The best model can be used to detect high-risk outbreak areas and to explore the relationship between meteorological factors and HFMD outbreaks. The results highlighted the seasonal distribution of high-risk HFMD outbreak months across different provinces in Thailand, with average maximum temperature, average rainfall, and average vapor pressure identified as the most influential factors. Furthermore, the best model was used to analyze HFMD outbreaks during the COVID-19 pandemic, showing a notable reduction in high-risk outbreak months and areas, likely due to the control measures implemented during this period. Overall, our model shows great potential as a tool for warnings, providing useful insights to help public health officials reduce the impact of HFMD outbreaks.<\/jats:p>","DOI":"10.3390\/tropicalmed10020048","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T08:53:41Z","timestamp":1738832021000},"page":"48","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8585-1780","authenticated-orcid":false,"given":"Pakorn","family":"Lonlab","sequence":"first","affiliation":[{"name":"Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suparinthon","family":"Anupong","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Mahidol Wittayanusorn School (MWIT), Salaya, Nakhon Pathom 73170, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9376-3298","authenticated-orcid":false,"given":"Chalita","family":"Jainonthee","sequence":"additional","affiliation":[{"name":"Research Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1368-2437","authenticated-orcid":false,"given":"Sudarat","family":"Chadsuthi","sequence":"additional","affiliation":[{"name":"Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/rmv.1827","article-title":"Comparative Epidemiology and Virology of Fatal and Nonfatal Cases of Hand, Foot and Mouth Disease in Mainland China from 2008 to 2014","volume":"25","author":"Liu","year":"2015","journal-title":"Rev. Med. Virol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.pedneo.2013.07.007","article-title":"Epidemiology of Enterovirus 71 Infections in Taiwan","volume":"55","author":"Chia","year":"2014","journal-title":"Pediatr. Neonatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s12250-011-3195-8","article-title":"Hand Foot and Mouth Disease Due to Enterovirus 71 in Malaysia","volume":"26","author":"Chua","year":"2011","journal-title":"Virol. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.3201\/eid1311.070632","article-title":"Epidemiologic and Virologic Investigation of Hand, Foot, and Mouth Disease, Southern Vietnam, 2005","volume":"13","author":"Thao","year":"2007","journal-title":"Emerg. Infect Dis."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Min, N., Ong, Y.H.B., Han, A.X., Ho, S.X., Yen, E.W.P., Ban, K.H.K., Maurer-Stroh, S., Chong, C.Y., and Chu, J.J.H. (2021). An Epidemiological Surveillance of Hand Foot and Mouth Disease in Paediatric Patients and in Community: A Singapore Retrospective Cohort Study, 2013\u20132018. PLoS Negl. Trop. Dis., 15.","DOI":"10.1371\/journal.pntd.0008885"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.ijid.2018.12.020","article-title":"Longitudinal Study on Enterovirus A71 and Coxsackievirus A16 Genotype\/Subgenotype Replacements in Hand, Foot and Mouth Disease Patients in Thailand, 2000\u20132017","volume":"80","author":"Noisumdaeng","year":"2019","journal-title":"Int. J. Infect. Dis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e285","DOI":"10.1097\/INF.0000000000001242","article-title":"The Epidemiology of Hand, Foot and Mouth Disease in Asia: A Systematic Review and Analysis","volume":"35","author":"Koh","year":"2016","journal-title":"Pediatr. Infect. Dis. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e50","DOI":"10.1017\/S0950268818003035","article-title":"Meteorological Factors and Its Association with Hand, Foot and Mouth Disease in Southeast and East Asia Areas: A Meta-Analysis","volume":"147","author":"Duan","year":"2019","journal-title":"Epidemiol. Infect."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wei, J., Hansen, A., Liu, Q., Sun, Y., Weinstein, P., and Bi, P. (2015). The Effect of Meteorological Variables on the Transmission of Hand, Foot and Mouth Disease in Four Major Cities of Shanxi Province, China: A Time Series Data Analysis (2009\u20132013). PLoS Negl. Trop. Dis., 9.","DOI":"10.1371\/journal.pntd.0003572"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hao, J., Yang, Z., Yang, W., Huang, S., Tian, L., Zhu, Z., Lu, Y., Xiang, H., and Liu, S. (2020). Impact of Ambient Temperature and Relative Humidity on the Incidence of Hand-Foot-Mouth Disease in Wuhan, China. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17020428"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, Z., Hu, W., Jiao, K., Ren, C., Jiang, B., and Ma, W. (2019). The Effect of Temperature on Childhood Hand, Foot and Mouth Disease in Guangdong Province, China, 2010\u20132013: A Multicity Study. BMC Infect Dis., 19.","DOI":"10.1186\/s12879-019-4594-y"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"18","DOI":"10.5365\/wpsar.2017.8.1.003","article-title":"Meteorological Factors Associated with Hand, Foot and Mouth Disease in a Central Highlands Province in Viet Nam: An Ecological Study","volume":"10","author":"Phan","year":"2019","journal-title":"West. Pac. Surveill. Response J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"91","DOI":"10.5539\/gjhs.v12n12p91","article-title":"Comparison of Climatic Factors Contributing to Hand-Foot-and-Mouth Disease Outbreak in the Northern and the Central Regions of Thailand","volume":"12","author":"Phromsin","year":"2020","journal-title":"Glob. J. Health Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"312","DOI":"10.3390\/ijerph110100312","article-title":"Spatio-Temporal Distribution and Hotspots of Hand, Foot and Mouth Disease (HFMD) in Northern Thailand","volume":"11","author":"Samphutthanon","year":"2014","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1093\/infdis\/jiy569","article-title":"The Norovirus Epidemiologic Triad: Predictors of Severe Outcomes in US Norovirus Outbreaks, 2009\u20132016","volume":"219","author":"Burke","year":"2019","journal-title":"J. Infect. Dis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e07371","DOI":"10.1016\/j.heliyon.2021.e07371","article-title":"The Roles of Machine Learning Methods in Limiting the Spread of Deadly Diseases: A Systematic Review","volume":"7","author":"Alfred","year":"2021","journal-title":"Heliyon"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Salim, N.A.M., Wah, Y.B., Reeves, C., Smith, M., Yaacob, W.F.W., Mudin, R.N., Dapari, R., Sapri, N.N.F.F., and Haque, U. (2021). Prediction of Dengue Outbreak in Selangor Malaysia Using Machine Learning Techniques. Sci. Rep., 11.","DOI":"10.1038\/s41598-020-79193-2"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.37268\/mjphm\/vol.19\/no.2\/art.176","article-title":"Artificial Intelligence Model as Predictor for Dengue Outbreaks","volume":"19","author":"Raja","year":"2019","journal-title":"Malays. J. Public Health Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100358","DOI":"10.1016\/j.onehlt.2021.100358","article-title":"Mapping the Spatial Distribution of the Dengue Vector Aedes Aegypti and Predicting Its Abundance in Northeastern Thailand Using Machine-Learning Approach","volume":"13","author":"Rahman","year":"2021","journal-title":"One Health"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.actatropica.2018.06.021","article-title":"Mapping the Transmission Risk of Zika Virus Using Machine Learning Models","volume":"185","author":"Jiang","year":"2018","journal-title":"Acta Trop."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Farooq, Z., Rockl\u00f6v, J., Wallin, J., Abiri, N., Sewe, M.O., Sj\u00f6din, H., and Semenza, J.C. (2022). Artificial Intelligence to Predict West Nile Virus Outbreaks with Eco-Climatic Drivers. Lancet Reg. Health-Eur., 17.","DOI":"10.1016\/j.lanepe.2022.100370"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jia, W., Li, X., Tan, K., and Xie, G. (2019, January 10\u201313). Predicting the Outbreak of the Hand-Foot-Mouth Diseases in China Using Recurrent Neural Network. Proceedings of the 2019 IEEE International Conference on Healthcare Informatics (ICHI), Xi\u2019an, China.","DOI":"10.1109\/ICHI.2019.8904736"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Laor, P., Apidechkul, T., Khunthason, S., Keawdounglek, V., Sudsandee, S., Fakkaew, K., and Siriratruengsuk, W. (2020). Association of Environmental Factors and High HFMD Occurrence in Northern Thailand. BMC Public Health, 20.","DOI":"10.1186\/s12889-020-09905-w"},{"key":"ref_24","unstructured":"Bureau of Epidemiology (2023, April 01). Thailand National Disease Surveillance Report 506 Hand Foot Mouth. Available online: http:\/\/doe.moph.go.th\/surdata\/disease.php?ds=71."},{"key":"ref_25","unstructured":"(2023, January 31). Official Statistics Registration Systems Monthly Thai Population by Province. Available online: https:\/\/stat.bora.dopa.go.th\/stat\/statnew\/statMONTH\/statmonth\/#\/mainpage."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"170191","DOI":"10.1038\/sdata.2017.191","article-title":"TerraClimate, a High-Resolution Global Dataset of Monthly Climate and Climatic Water Balance from 1958\u20132015","volume":"5","author":"Abatzoglou","year":"2018","journal-title":"Sci. Data"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zaki, M.J., and Meira, J.W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9780511810114"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"McCullagh, P. (2019). Generalized Linear Models, Routledge.","DOI":"10.1201\/9780203753736"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_34","unstructured":"(2023, May 14). R Core Team R: A Language and Environment for Statistical Computing. Available online: https:\/\/www.R-project.org."},{"key":"ref_35","first-page":"1","article-title":"Caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_36","unstructured":"Therneau, T., Atkinson, B., Ripley, B., and Ripley, M.B. (2023, April 22). Package \u2018Rpart\u2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/rpart\/index.html."},{"key":"ref_37","unstructured":"Greenwell, B., Boehmke, B., Cunningham, J., Developers, G.B.M., and Greenwell, M.B. (2024, June 28). Package \u2018gbm.\u2019 R Package Version 2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/gbm\/index.html."},{"key":"ref_38","unstructured":"Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., M\u00fcller, M., Siegert, S., Doering, M., and Billings, Z. (2023, April 11). Package \u2018pROC.\u2019 2021. Available online: https:\/\/cran.r-project.org\/web\/packages\/pROC\/index.html."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.21105\/joss.01686","article-title":"Welcome to the Tidyverse","volume":"4","author":"Hadley","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_40","unstructured":"Alboukadel, K. (2024, April 23). Ggpubr: \u201cggplot2\u201d Based Publication Ready Plots. Available online: https:\/\/CRAN.R-project.org\/package=ggpubr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1007\/s11749-018-0599-x","article-title":"Comparing Implementations of Global and Local Indicators of Spatial Association","volume":"27","author":"Bivand","year":"2018","journal-title":"Test"},{"key":"ref_42","first-page":"e24","article-title":"Package \u2018Corrplot\u2019","volume":"56","author":"Wei","year":"2017","journal-title":"Statistician"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"439","DOI":"10.32614\/RJ-2018-009","article-title":"Simple Features for R: Standardized Support for Spatial Vector Data","volume":"10","author":"Pebesma","year":"2018","journal-title":"R J."},{"key":"ref_44","first-page":"1177","article-title":"Modeling Vaccination Strategies with Limited Early COVID-19 Vaccine Access in Low- and Middle-Income Countries: A Case Study of Thailand","volume":"8","author":"Anupong","year":"2023","journal-title":"Infect. Dis. Model."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e006178","DOI":"10.1136\/bmjgh-2021-006178","article-title":"Responding to the COVID-19 Second Wave in Thailand by Diversifying and Adapting Lessons from the First Wave","volume":"6","author":"Rajatanavin","year":"2021","journal-title":"BMJ. Glob. Health"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1007\/s00477-018-1547-8","article-title":"Using a Bayesian Belief Network Model for Early Warning of Death and Severe Risk of HFMD in Hunan Province, China","volume":"32","author":"Liao","year":"2018","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ong, S.Q., Isawasan, P., Ngesom, A.M.M., Shahar, H., Lasim, A.M., and Nair, G. (2023). Predicting Dengue Transmission Rates by Comparing Different Machine Learning Models with Vector Indices and Meteorological Data. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-46342-2"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mulwa, D., Kazuzuru, B., Misinzo, G., and Bett, B. (2024). An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors. Big Data Cogn. Comput., 8.","DOI":"10.20944\/preprints202407.0187.v1"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104648","DOI":"10.1016\/j.psj.2024.104648","article-title":"Data-Driven Insights into Pre-Slaughter Mortality: Machine Learning for Predicting High Dead on Arrival in Meat-Type Ducks","volume":"104","author":"Jainonthee","year":"2025","journal-title":"Poult. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e22569","DOI":"10.1016\/j.heliyon.2023.e22569","article-title":"A Spatiotemporal XGBoost Model for PM2.5 Concentration Prediction and Its Application in Shanghai","volume":"9","author":"Wang","year":"2023","journal-title":"Heliyon"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yoo, D.S., Chun, B.C., Hong, K., and Kim, J. (2022). Risk Prediction of Three Different Subtypes of Highly Pathogenic Avian Influenza Outbreaks in Poultry Farms: Based on Spatial Characteristics of Infected Premises in South Korea. Front. Vet. Sci., 9.","DOI":"10.3389\/fvets.2022.897763"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1099\/vir.0.80784-0","article-title":"Temperature-Sensitive Mutants of Enterovirus 71 Show Attenuation in Cynomolgus Monkeys","volume":"86","author":"Arita","year":"2005","journal-title":"J. Gen. Virol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1017\/S0950268810002256","article-title":"Is Hand, Foot and Mouth Disease Associated with Meteorological Parameters?","volume":"138","author":"Ma","year":"2010","journal-title":"Epidemiol. Infect."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"138548","DOI":"10.1016\/j.scitotenv.2020.138548","article-title":"Quantifying the Risk of Hand, Foot, and Mouth Disease (HFMD) Attributable to Meteorological Factors in East China: A Time Series Modelling Study","volume":"728","author":"Qi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.scitotenv.2011.09.055","article-title":"The Influence of Temperature and Humidity on the Incidence of Hand, Foot, and Mouth Disease in Japan","volume":"410\u2013411","author":"Onozuka","year":"2011","journal-title":"Sci. Total Environ."},{"key":"ref_56","first-page":"199","article-title":"Enteroviruses in Water Environment\u2014A Potential Threat to Public Health","volume":"15","author":"Rajtar","year":"2008","journal-title":"Ann. Agric. Environ. Med."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, P., Goggins, W.B., and Chan, E.Y.Y. (2016). Hand, Foot and Mouth Disease in Hong Kong: A Time-Series Analysis on Its Relationship with Weather. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0161006"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.annepidem.2008.12.008","article-title":"Influence of Weather Conditions and Season on Physical Activity in Adolescents","volume":"19","author":"Paradis","year":"2009","journal-title":"Ann. Epidemiol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yang, F., Ma, Y., Liu, F., Zhao, X., Fan, C., Hu, Y., Hu, K., Chang, Z., and Xiao, X. (2020). Short-Term Effects of Rainfall on Childhood Hand, Foot and Mouth Disease and Related Spatial Heterogeneity: Evidence from 143 Cities in Mainland China. BMC Public Health, 20.","DOI":"10.1186\/s12889-020-09633-1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"20369","DOI":"10.1007\/s11356-022-23643-z","article-title":"Exploring the Influence of COVID-19 on the Spread of Hand, Foot, and Mouth Disease with an Automatic Machine Learning Prediction Model","volume":"30","author":"Yang","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_61","first-page":"48","article-title":"Seasonal Models of Herpangina and Hand-Foot-Mouth Disease to Simulate Annual Fluctuations in Urban Warming in Tokyo","volume":"56","author":"Urashima","year":"2003","journal-title":"Jpn. J. Infect. Dis."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"eadq3003","DOI":"10.1126\/science.adq3003","article-title":"COVID-19 Pandemic Interventions Reshaped the Global Dispersal of Seasonal Influenza Viruses","volume":"386","author":"Chen","year":"2024","journal-title":"Science"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Saita, S., Maeakhian, S., and Silawan, T. (2022). Temporal Variations and Spatial Clusters of Dengue in Thailand: Longitudinal Study before and during the Coronavirus Disease (COVID-19) Pandemic. Trop. Med. Infect. Dis., 7.","DOI":"10.3390\/tropicalmed7080171"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/S1473-3099(22)00025-1","article-title":"Measuring the Effects of COVID-19-Related Disruption on Dengue Transmission in Southeast Asia and Latin America: A Statistical Modelling Study","volume":"22","author":"Chen","year":"2022","journal-title":"Lancet Infect. Dis."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s41235-023-00528-4","article-title":"The COVID-19 Pandemic and Changes in Social Behavior: Protective Face Masks Reduce Deliberate Social Distancing Preferences While Leaving Automatic Avoidance Behavior Unaffected","volume":"9","author":"Diekhof","year":"2024","journal-title":"Cogn. Res. Princ. Implic."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yan, S., Wei, L., Duan, Y., Li, H., Liao, Y., Lv, Q., Zhu, F., Wang, Z., Lu, W., and Yin, P. (2019). Short-Term Effects of Meteorological Factors and Air Pollutants on Hand, Foot and Mouth Disease among Children in Shenzhen, China, 2009\u20132017. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16193639"}],"container-title":["Tropical Medicine and Infectious Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2414-6366\/10\/2\/48\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:28:09Z","timestamp":1760027289000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2414-6366\/10\/2\/48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,6]]},"references-count":66,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["tropicalmed10020048"],"URL":"https:\/\/doi.org\/10.3390\/tropicalmed10020048","relation":{},"ISSN":["2414-6366"],"issn-type":[{"value":"2414-6366","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,6]]}}}