Objectives This study aimed to develop prevalence-informed post-release dengue control strategies for settings in which Wolbachia-carrying Aedes aegypti mosquitoes have become established through population replacement and to quantify how optimal supplementary intervention intensity and duration vary with adult Wolbachia prevalence, pW. Methods: A deterministic human–mosquito model was formulated that included Wolbachia-free and Wolbachia-carrying mosquitoes and incorporated cytoplasmic incompatibility, maternal transmission, and reduced vector competence. Two bounded supplementary controls were optimized over a 3-year horizon: reduction of effective human–mosquito contact and conventional adult mosquito suppression, represented by increased adult mortality. A quadratic-cost optimal-control problem was solved numerically using the Pontryagin maximum principle and a forward/backward sweep algorithm. Outcomes were compared across representative pW scenarios and prevalence scans. Results: The optimal policies applied high-intensity supplementary control early in the planning horizon and then relaxed control as infection pressure declined. Higher pW reduced the uncontrolled infection burden, decreased the incremental benefit of additional intervention, and shortened the duration of maximum control. In coarse scans with umax=0.30, the terminal dengue infection near-elimination indicator, defined as IH (T)≤10–4 at T=3 years, changed between pW=0.45 and pW=0.50 for all tested cost-weight combinations. A separate refined continuation example demonstrated non-monotonic mean control use and a sharp decrease in cumulative incidence. Conclusion: Adult Wolbachia prevalence measured through entomological surveillance may represent an actionable index for post-release dengue management. The identified transition band is a model-based planning indicator, not a universal field threshold or evidence of mosquito population elimination.
Objectives This study investigated associations between building structure and managers’ knowledge, attitudes, and practices (KAP) regarding indoor air quality in welfare facilities for persons with disabilities, comparing facilities with and without coronavirus disease 2019 (COVID-19) cluster outbreaks. Methods: A web-based survey was conducted among managers of 242 welfare facilities for persons with disabilities in the Republic of Korea. Facilities were recruited from a national sampling frame of approximately 3,000 facilities, yielding a response rate of 8.1%. The 80-item questionnaire assessed building characteristics and air quality-related KAP scores. Logistic regression was used to identify factors associated with cluster outbreaks. Results: Of the 242 facilities, 50.8% had experienced cluster outbreaks. Facilities in complex/multi-use buildings had higher odds of outbreaks than those in detached buildings did (adjusted odds ratio, 2.21; 95% confidence interval, 1.25–3.91; p=0.006). For managerial factors, facilities with outbreaks tended to have lower attitude scores (p=0.02); however, this association did not remain statistically significant after Bonferroni correction (adjusted α=0.0083). No significant differences were observed in knowledge or practice scores. Conclusion: Building structure, particularly complex/multi-use configuration, was associated with COVID-19 cluster outbreaks. Managerial KAP scores were not definitively associated with outbreaks under the conservative significance threshold, but the observed trends suggest that institutional support, ventilation guidance, and practical training for facility managers may help strengthen future infection-prevention efforts.
Objectives Although it is widely used as a measure for mortality, the case fatality rate (CFR) ofcoronavirus disease 2019 (COVID-19) can vary over time and fluctuate for many reasons otherthan viral characteristics. To compare the CFRs of different countries in equal measure, weestimated comparable CFRs after adjusting for multiple covariates and examined the mainfactors that contributed to variability in the CFRs among 21 countries.Methods: For statistical analysis, time-series cross-sectional data were collected from OurWorld in Data, CoVariants.org, and GISAID. Biweekly CFRs of COVID-19 were estimated bypooled generalized linear squares regression models for the panel data. Covariates includedthe predominant virus variant, reproduction rate, vaccination, national economic status,hospital beds, diabetes prevalence, and population share of individuals older than age 65. Intotal, 21 countries were eligible for analysis.Results: Adjustment for covariates reduced variation in the CFRs of COVID-19 across countriesand over time. Regression results showed that the dominant spread of the Omicron variant,reproduction rate, and vaccination were associated with lower country-level CFRs, whereasage, the extreme poverty rate, and diabetes prevalence were associated with higher countrylevel CFRs.Conclusion: A direct comparison of crude CFRs among countries may be fallacious, especiallyin a cross-sectional analysis. Our study presents an adjusted comparison of CFRs over timefor a more proper comparison. In addition, our findings suggest that comparing CFRs amongdifferent countries without considering their context, such as the epidemic phase, medicalcapacity, surveillance strategy, and socio-demographic traits, should be avoided.
Citations
Citations to this article as recorded by
Socioeconomic determinants of COVID-19 vaccination and mortality: a cross-national path analysis Fulya Ipek-Erdem, Esra Erpek, Volkan Genç, Aybars Bars, Sabri Erdem, Servet Akar BMC Public Health.2026;[Epub] CrossRef
From mono to multi-causality: Towards a comprehensive perspective on understanding death Peter Harteloh Health Policy.2024; 147: 105121. CrossRef
Comments on the article "Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates" Gaetano Perone Osong Public Health and Research Perspectives.2023; 14(2): 146. CrossRef
As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.
Citations
Citations to this article as recorded by
Analytics for Resource Allocation in Pandemic Management Jarmo Haferkamp, Martin Bicher, Peiman Ghasemi, Marlin W. Ulmer, Nikolas Popper, Jan Fabian Ehmke INFORMS Journal on Applied Analytics.2026;[Epub] CrossRef
A Hybrid EBM–ABM Framework for COVID-19 Modeling via Sequential SEIRD Calibration with Particle Swarm Optimization in Mexican Cities Alfredo-Israel Ramírez-Mejía, Joselito Medina-Marín, Norberto Hernández-Romero, Eduardo-Antonio Cendejas-Castro, Grettel Barceló-Alonso Systems.2026; 14(8): 925. CrossRef
A simultaneous simulation of human behavior dynamics and epidemic spread: A multi-country study amidst the COVID-19 pandemic Ann Osi, Navid Ghaffarzadegan Mathematical Biosciences.2025; 380: 109368. CrossRef
Hierarchical Time Series Forecasting of COVID-19 Cases Using County-Level Clustering Data Sonaxy Mohanty, Airi Shimamura, Charles D. Nicholson, Andrés D. González, Talayeh Razzaghi Operations Research Forum.2025;[Epub] CrossRef
Detection of covid- 19 on GGO segmented CT image using stacking-based ensemble deep learning V. L. Nisha, D. Menaka Multimedia Tools and Applications.2025; 84(35): 43693. CrossRef
Characterizing pandemic-related publications: a retrospective study using spatial citation network analysis Zhiyang Xiao, Lingyu Fan, Zidong Yu, Xintao Liu Computational Urban Science.2025;[Epub] CrossRef
An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics Foziye Tahmasbi, Esmaeel Toni, Zohreh Javanmard, Niloofar Kheradbin, Somayeh Nasiri, Farahnaz Sadoughi Archives of Public Health.2025;[Epub] CrossRef
COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors Arezoo Abasi, Seyed Abbas Motevalian, Haleh Ayatollahi BMC Public Health.2025;[Epub] CrossRef
Artificial intelligence-based techniques for predicting outcomes in COVID-19 patients Victoria Moghildea, Cristina Trofimov, Ion Grabovschi, Ruslan Baltaga, Serghei Sandru, Sergiu Cobiletchi, Oleg Arnaut Moldovan Journal of Health Sciences.2025; 12(1): 61. CrossRef
Forecasting Upper Bounds for Daily New COVID-19 Infections Using Tolerance Limits Hsiuying Wang Mathematics.2025; 13(18): 2908. CrossRef
Bridging statistics and deep learning: a causal data-fusion framework for epidemic prediction Vasileios E. Papageorgiou, Georgios Petmezas, Aristeidis Georgakis, Nicos Maglaveras, George Tsaklidis Communications in Statistics - Simulation and Comp.2025; : 1. CrossRef
The Telemedicine Demand Index and its Utility in Managing COVID-19 Case Surges Martin Yong Kwong Lee, Kie Beng Goh, Deanna Xiuting Koh, Si Jack Chong, Raymond Swee Boon Chua Telemedicine and e-Health.2024; 30(2): 545. CrossRef
Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants J. Cuevas-Maraver, P.G. Kevrekidis, Q.Y. Chen, G.A. Kevrekidis, Y. Drossinos Mathematical Biosciences.2024; 367: 109109. CrossRef
The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews Persefoni Talimtzi, Antonios Ntolkeras, Georgios Kostopoulos, Konstantinos I. Bougioukas, Eirini Pagkalidou, Andreas Ouranidis, Athanasia Pataka, Anna-Bettina Haidich Journal of Clinical Epidemiology.2024; 167: 111264. CrossRef
A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care Junyi Gao, Yinghao Zhu, Wenqing Wang, Zixiang Wang, Guiying Dong, Wen Tang, Hao Wang, Yasha Wang, Ewen M. Harrison, Liantao Ma Patterns.2024; 5(4): 100951. CrossRef
A study of learning models for COVID-19 disease prediction Sakshi Jain, Pradeep Kumar Roy Journal of Ambient Intelligence and Humanized Comp.2024; 15(4): 2581. CrossRef
AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods Muhammad Usman Tariq, Shuhaida Binti Ismail Osong Public Health and Research Perspectives.2024; 15(2): 115. CrossRef
Climate change, its impact on emerging infectious diseases and new technologies to combat the challenge Hongyan Liao, Christopher J. Lyon, Binwu Ying, Tony Hu Emerging Microbes & Infections.2024;[Epub] CrossRef
Digital Technology Ecotone to Revolutionize Health Sector Mario Coccia SSRN Electronic Journal.2024;[Epub] CrossRef
Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling Shi Chen, Daniel Janies, Rajib Paul, Jean-Claude Thill Epidemics.2024; 48: 100782. CrossRef
Assessing the Utility of Prediction Scores PAINT, ISARIC4C, CHIS, and COVID-GRAM at Admission and Seven Days after Symptom Onset for COVID-19 Mortality Alina Doina Tanase, Oktrian FNU, Dan-Mihai Cristescu, Paula Irina Barata, Dana David, Emanuela-Lidia Petrescu, Daliana-Emanuela Bojoga, Teodora Hoinoiu, Alexandru Blidisel Journal of Personalized Medicine.2024; 14(9): 966. CrossRef
An effective drift-diffusion model for pandemic propagation and uncertainty prediction Clara Bender, Abhimanyu Ghosh, Hamed Vakili, Preetam Ghosh, Avik W. Ghosh Biophysical Reports.2024; 4(4): 100182. CrossRef
An epidemical model with nonlocal spatial infections Su Yang, Weiqi Chu, Panayotis Kevrekidis Proceedings of the European Academy of Sciences an.2024;[Epub] CrossRef
Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan Berik Koichubekov, Aliya Takuadina, Ilya Korshukov, Anar Turmukhambetova, Marina Sorokina Healthcare.2023; 11(5): 752. CrossRef
Early triage echocardiography to predict outcomes in patients admitted with COVID‐19: a multicenter study Daniel Peck, Andrea Beaton, Maria Carmo Nunes, Nicholas Ollberding, Allison Hays, Pranoti Hiremath, Federico Asch, Nitin Malik, Christopher Fung, Craig Sable, Bruno Nascimento Echocardiography.2023; 40(5): 388. CrossRef
Static Seeding and Clustering of LSTM Embeddings to Learn From Loosely Time-Decoupled Events Christian G. Manasseh, Razvan Veliche, Jared Bennett, Hamilton Scott Clouse IEEE Access.2023; 11: 64219. CrossRef
Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting Muhammad Usman Tariq, Shuhaida Binti Ismail, Muhammad Babar, Ashir Ahmad, Lin Wang PLOS ONE.2023; 18(7): e0287755. CrossRef
Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study Riku Klén, Ivan A Huespe, Felipe Aníbal Gregalio, Antonio Lalueza Lalueza Blanco, Miguel Pedrera Jimenez, Noelia Garcia Barrio, Pascual Ruben Valdez, Matias A Mirofsky, Bruno Boietti, Ricardo Gómez-Huelgas, José Manuel Casas-Rojo, Juan Miguel Antón-Santos eLife.2023;[Epub] CrossRef
Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era Gabriel Berg de Almeida, Lorena Mendes Simon, Ângela Maria Bagattini, Michelle Quarti Machado da Rosa, Marcelo Eduardo Borges, José Alexandre Felizola Diniz Filho, Ricardo de Souza Kuchenbecker, Roberto André Kraenkel, Cláudia Pio Ferreira, Suzi Alves Cam PLOS Global Public Health.2023; 3(12): e0002679. CrossRef
Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic Jose M. Martin-Moreno, Antoni Alegre-Martinez, Victor Martin-Gorgojo, Jose Luis Alfonso-Sanchez, Ferran Torres, Vicente Pallares-Carratala International Journal of Environmental Research an.2022; 19(9): 5546. CrossRef
Artificial intelligence and clinical deterioration James Malycha, Stephen Bacchi, Oliver Redfern Current Opinion in Critical Care.2022; 28(3): 315. CrossRef
<sec>
<title>Objectives</title>
<p>Over the past few decades, the prevalence of cesarean sections (CS) have risen dramatically worldwide, particularly in Iran. The aim of this study was to determine the prevalence of CS in Tehran, and to examine the associated risk factors.</p></sec>
<sec>
<title>Methods</title>
<p>A cross-sectional study of 4,308 pregnant women with singleton live-births in Tehran, Iran, between July 6–21, 2015 was performed. Multilevel logistic regression analysis was performed using demographic and obstetrical variables at the first level, and hospitals as a variable at the second level.</p></sec>
<sec>
<title>Results</title>
<p>The incidence of CS was 72.0%. Multivariate analysis showed a significant relationship between CS and the mother’s age, socioeconomic status, body mass index, parity, type of pregnancy, preeclampsia, infant height, and baby’s head circumference. The intra-class correlation using the second level variable, the hospital was 0.292, indicating approximately 29.2% of the total variation in the response variable accounted for by the hospital.</p></sec>
<sec>
<title>Conclusion</title>
<p>The incidence of CS was substantially higher than other countries. Therefore, educational and psychological interventions are necessary to reduce CS rates amongst pregnant Iranian women.</p></sec>
Citations
Citations to this article as recorded by
Development and clinical application of an intrapartum ultrasound-based predictive model for cesarean section conversion in nulliparous women following unsuccessful vaginal delivery attempts: A single-center retrospective study Zhanpeng Yu, Shunlan Du, Lili Xu, Yun Cheng, Feijun Hu, Qiaoyang Xu Medicine.2026; 105(1): e46879. CrossRef
Effectiveness of Virtual Reality for Pain Relief in Procedures Related to Obstetrics and Gynaecology: A Systematic Review and Meta‐Analysis of Randomised Controlled Trials Jhia Jiat Teh, Filipa Campos, Adam Koczoski, Trisha Valencia, Michael P. Rimmer, Bassel H. Al Wattar BJOG: An International Journal of Obstetrics & Gyn.2026; 133(7): 1358. CrossRef
Prevalence and factors associated with caesarean section among Tanzanian women of reproductive age: evidence from the 2022 Tanzania demographic and health survey data Bonfils Nahayo, Gbenga Olorunfemi, Samuel Ndayishimye, Charles Nsanzabera BMC Public Health.2025;[Epub] CrossRef
What factors shape an individual’s probability to be enrolled in a professionally-managed community-based health insurance? Results from a cross-sectional case-control study in two districts in Mali Hamidou Niangaly, Manuella De Allegri, Laurence Touré, Dansiné Diarra, Valéry Ridde, Ama Pokuaa Fenny, PLOS Global Public Health.2025; 5(7): e0004892. CrossRef
Associations between maternal overweight and obesity with cesarean section delivery in Middle East and North Africa region: a systematic review, meta-analysis, and attributable risk Basem Al-Omari, Zufishan Alam, Joviana Farhat, Salma Abdelrahman Mohamed, Sumyia Mehrin, Linda Östlundh, Mohammed Altigani Abdalla, Rami H. Al-Rifai Systematic Reviews.2025;[Epub] CrossRef
Determinants of cesarean mode of childbirth among Rwandan women of childbearing age: Evidence from the 2019–2020 Rwanda Demographic and Health Survey (RDHS) Nsereko Etienne, Uwase Aline, Mpinganzima Ornella, Usanzineza Henriette, Niyitegeka Jean Pierre, Turabayo Jean Léonard, Mwiseneza Marie Josee, Mugeni Girimpundu Candide, Moreland Patricia Public Health Challenges.2024;[Epub] CrossRef
Factors Associated with Cesarean Section in Preterm Births at a Tertiary Hospital in Rasht, Iran Forozan Milani, Seyedeh Hajar Sharami, Seyedeh Maryam Attari, Zahra Rafiee Sorouri, Sara Farzadi, Soodabeh Kazemi Journal of Kermanshah University of Medical Scienc.2024;[Epub] CrossRef
Virtual Reality, Fear of Pain and Labor Pain Intensity: A Randomized Controlled Trial Halimeh Mohammadi, Javad Rasti, Elham Ebrahimi Anesthesiology and Pain Medicine.2023;[Epub] CrossRef
Prevalence of Cesarean Section at Georgetown Public Hospital Corporation, Guyana: An Institution-Based Cross-Sectional Study Gary Joseph, Cecil Boston, Gbenankpon Mathias Houvessou Open Journal of Clinical Diagnostics.2023; 13(03): 29. CrossRef
The double burden of maternal overweight and short stature and the likelihood of cesarean deliveries in South Asia: An analysis of national datasets from Bangladesh, India, Maldives, Nepal, and Pakistan Mosiur Rahman, Syed Emdadul Haque, Md. Jahirul Islam, Nguyen Huu Chau, Izzeldin Fadl Adam, Md. Nuruzzaman Haque Birth.2022; 49(4): 661. CrossRef
Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services Alireza Mohammadi, Elahe Pishgar, Zahra Salari, Behzad Kiani BMC Pregnancy and Childbirth.2022;[Epub] CrossRef
Factors associated with cesarean delivery in Bangladesh: A multilevel modeling Md. Akhtarul Islam, Mst. Tanmin Nahar, Md. Ashfikur Rahman, Sutapa Dey Barna, S.M. Farhad Ibn Anik Sexual & Reproductive Healthcare.2022; 34: 100792. CrossRef
The Birth Satisfaction Scale-Revised Indicator (BSS-RI): a validation study in Iranian mothers Reza Omani-Samani, Caroline J. Hollins Martin, Colin R. Martin, Saman Maroufizadeh, Azadeh Ghaheri, Behnaz Navid The Journal of Maternal-Fetal & Neonatal Medicine.2021; 34(11): 1827. CrossRef
The effect of familiarization with preoperative care on anxiety and vital signs in the patient’s cesarean section: A randomized controlled trial Mehrnush Mostafayi, Behzad Imani, Shirdel Zandi, Faeze Jongi European Journal of Midwifery.2021; 5(June): 1. CrossRef
Dynamic prediction of liver cirrhosis risk in chronic hepatitis B patients using longitudinal clinical data Ying Wang, Xiang-Yong Li, Li-Li Wu, Xiao-Yan Zheng, Yu Deng, Meng-Jie Li, Xu You, Yu-Tian Chong, Yuan-Tao Hao European Journal of Gastroenterology & Hepatology.2020; 32(1): 120. CrossRef
Factors Contributing to Iranian Pregnant Women’s Tendency to Choice Cesarean Section Soraya Nouraei Motlagh, Zahra Asadi-piri, Razyeh Bajoulvand, Fatemeh Seyed Mohseni, Katayoun Bakhtiar, Mehdi Birjandi, Maryam Mansouri Medical - Surgical Nursing Journal.2020;[Epub] CrossRef
Trends and correlates of cesarean section rates over two decades in Nepal Aliza K. C. Bhandari, Bibha Dhungel, Mahbubur Rahman BMC Pregnancy and Childbirth.2020;[Epub] CrossRef
The potential beneficial effects of education and familiarity with cesarean section procedure and the operating room environment on promotion of anxiety and pain intensity: A randomized controlled clinical trial Jamshid Eslami, Neda Hatami, Azadeh Amiri, Marzieh Akbarzadeh Journal of Education and Health Promotion.2020;[Epub] CrossRef
Symptoms of Discomfort and Problems Associated with Mode of Delivery During the Puerperium: An Observational Study Juan Miguel Martínez-Galiano, Miguel Delgado-Rodríguez, Julián Rodríguez-Almagro, Antonio Hernández-Martínez International Journal of Environmental Research an.2019; 16(22): 4564. CrossRef
Objectives
A hierarchical generalized linear model (HGLM) was applied to estimate the transmission pattern of scrub typhus from 2001 to 2011 in the Republic of Korea, based on spatial and temporal correlation. Methods
Based on the descriptive statistics of scrub typhus incidence from 2001 to 2011 reported to the Korean Centers for Disease Control and Prevention, the spatial and temporal correlations were estimated by HGLM. Incidences according to age, sex, and year were also estimated by the best-fit model out of nine HGLMs. A disease map was drawn to view the annual regional spread of the disease. Results
The total number of scrub typhus cases reported from 2001 to 2011 was 51,136: male, 18,628 (36.4%); female, 32,508 (63.6%). The best-fit model selected was a combination of the spatial model (Markov random-field model) and temporal model (first order autoregressive model) of scrub typhus transmission. The peak incidence was 28.80 per 100,000 persons in early October and the peak incidence was 40.17 per 100,000 persons in those aged 63.3 years old by the best-fit HGLM. The disease map showed the spread of disease from the southern central area to a nationwide area, excepting Gangwon-do (province), Gyeongsangbuk-do (province), and Seoul. Conclusion
In the transmission of scrub typhus in Korea, there was a correlation to the incidence of adjacent areas, as well as that of the previous year. According to the disease map, we are unlikely to see any decrease in the incidence in the near future, unless ongoing aggressive measures to prevent the exposure to the vector, chigger mites, in rural areas, are put into place.
Citations
Citations to this article as recorded by
2024년 털진드기 밀도 감시 결과 현승 이, 정원 주, 희일 이 Public Health Weekly Report.2025; 18(49): 1969. CrossRef
Prevalence of chigger mites and Orientia tsutsugamushi strains in northern regions of Gangwon-do, Korea Soojin Kim, In Yong Lee, Sezim Monoldorova, Jiro Kim, Jang Hoon Seo, Tai-Soon Yong, Bo Young Jeon Parasites, Hosts and Diseases.2023; 61(3): 263. CrossRef
Urine Metabolite of Mice with Orientia tsutsugamushi Infection Sangho Choi, Do-Hwan Ahn, Min-Gyu Yoo, Hye-Ja Lee, Seong Beom Cho, Hee-Bin Park, Sung Soon Kim, Hyuk Chu The American Journal of Tropical Medicine and Hygi.2023; 108(2): 296. CrossRef
Spatiotemporal dynamics and environmental determinants of scrub typhus in Anhui Province, China, 2010–2020 Xianyu Wei, Junyu He, Wenwu Yin, Ricardo J. Soares Magalhaes, Yanding Wang, Yuanyong Xu, Liang Wen, Yehuan Sun, Wenyi Zhang, Hailong Sun Scientific Reports.2023;[Epub] CrossRef
Epidemiological characteristics of cases with scrub typhus and their correlation with chigger mite occurrence (2019–2021): A focus on case occupation and activity locations Se‐Jin Jeong, Jin‐Hwan Jeon, Kyung won Hwang Entomological Research.2023; 53(7): 247. CrossRef
Epidemiological characteristics and spatiotemporal patterns of scrub typhus in Yunnan Province from 2006 to 2017 Pei-Ying Peng, Lei Xu, Gu-Xian Wang, Wen-Yuan He, Ting-Liang Yan, Xian-Guo Guo Scientific Reports.2022;[Epub] CrossRef
Clinical and Laboratory Predictors associated with Complicated Scrub Typhus Mi-Hee Kim, Si-Hyun Kim, Jung-Hyun Choi, Seong-Heon Wie Infection & Chemotherapy.2019; 51(2): 161. CrossRef
Awareness and Work-Related Factors Associated with Scrub Typhus: A Case-Control Study from South Korea Dong-Seob Kim, Dilaram Acharya, Kwan Lee, Seok-Ju Yoo, Ji-Hyuk Park, Hyun-Sul Lim International Journal of Environmental Research an.2018; 15(6): 1143. CrossRef
Estimating the burden of scrub typhus: A systematic review Ana Bonell, Yoel Lubell, Paul N. Newton, John A. Crump, Daniel H. Paris, Janet Foley PLOS Neglected Tropical Diseases.2017; 11(9): e0005838. CrossRef
Spatiotemporal Dynamics of Scrub Typhus Transmission in Mainland China, 2006-2014 Yi-Cheng Wu, Quan Qian, Ricardo J. Soares Magalhaes, Zhi-Hai Han, Wen-Biao Hu, Ubydul Haque, Thomas A. Weppelmann, Yong Wang, Yun-Xi Liu, Xin-Lou Li, Hai-Long Sun, Yan-Song Sun, Archie C. A. Clements, Shen-Long Li, Wen-Yi Zhang, Mathieu Picardeau PLOS Neglected Tropical Diseases.2016; 10(8): e0004875. CrossRef
Larval Chigger Mites Collected from Small Mammals in 3 Provinces, Korea In-Yong Lee, Hyeon-Je Song, Yeon-Joo Choi, Sun-Hye Shin, Min-Kyung Choi, So-Hyun Kwon, E-Hyun Shin, Chan Park, Heung-Chul Kim, Terry A. Klein, Kyung-Hee Park, Won-Jong Jang The Korean Journal of Parasitology.2014; 52(2): 225. CrossRef
Objectives
The spatial and temporal correlations were estimated to determine <i>Plasmodium vivax</i> malarial transmission pattern in Korea from 2001–2011 with the hierarchical generalized linear model. Methods
Malaria cases reported to the Korea Centers for Disease Control and Prevention from 2001 to 2011 were analyzed with descriptive statistics and the incidence was estimated according to age, sex, and year by the hierarchical generalized linear model. Spatial and temporal correlation was estimated and the best model was selected from nine models. Results were presented as diseases map according to age and sex. Results
The incidence according to age was highest in the 20–25-year-old group (244.52 infections/100,000). Mean ages of infected males and females were 31.0 years and 45.3 years with incidences 7.8 infections/100,000 and 7.1 infections/100,000 after estimation. The mean month for infection was mid-July with incidence 10.4 infections/100,000. The best-fit model showed that there was a spatial and temporal correlation in the malarial transmission. Incidence was very low or negligible in areas distant from the demilitarized zone between Republic of Korea and Democratic People’s Republic of Korea (North Korea) if the 20–29-year-old male group was omitted in the diseases map. Conclusion
Malarial transmission in a region in Korea was influenced by the incidence in adjacent regions in recent years. Since malaria in Korea mainly originates from mosquitoes from North Korea, there will be continuous decrease if there is no further outbreak in North Korea.
Citations
Citations to this article as recorded by
Source separation in municipal solid waste management: Practical means to its success in Asian cities Premakumara Jagath Dickella Gamaralalage, Sadhan Kumar Ghosh, Kazunobu Onogawa Waste Management & Research: The Journal for a Sus.2022; 40(3): 360. CrossRef
Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions Sophie A. Lee, Christopher I. Jarvis, W. John Edmunds, Theodoros Economou, Rachel Lowe Journal of the Royal Society Interface.2021;[Epub] CrossRef
Effects of climate change on Plasmodium vivax malaria transmission dynamics: A mathematical modeling approach Jung Eun Kim, Yongin Choi, Chang Hyeong Lee Applied Mathematics and Computation.2019; 347: 616. CrossRef
Spatially Filtered Multilevel Analysis on Spatial Determinants for Malaria Occurrence in Korea Sehyeong Kim, Youngho Kim International Journal of Environmental Research an.2019; 16(7): 1250. CrossRef
Is it necessary to take anthelmintics every year in Korea? Sun Huh Journal of the Korean Medical Association.2018; 61(3): 198. CrossRef
Research on Factors Influencing Municipal Household Solid Waste Separate Collection: Bayesian Belief Networks Zhujie Chu, Wenna Wang, Bairong Wang, Jun Zhuang Sustainability.2016; 8(2): 152. CrossRef
Chemotherapeutic drugs for common parasitic diseases in Korea Sun Huh Journal of the Korean Medical Association.2013; 56(6): 513. CrossRef
Are There Spatial and Temporal Correlations in the Incidence Distribution of Scrub Typhus in Korea? Maengseok Noh, Youngjo Lee, Chaeshin Chu, Jin Gwack, Seung-Ki Youn, Sun Huh Osong Public Health and Research Perspectives.2013; 4(1): 39. CrossRef
Years of Epidemics (2009–2011): Pandemic Influenza and Foot-and-Mouth Disease Epidemic in Korea Hae-Wol Cho, Chaeshin Chu Osong Public Health and Research Perspectives.2013; 4(3): 125. CrossRef
A New Statistical Approach to Analyze Plasmodium vivax Malaria Endemic in Korea Hae-Wol Cho, Chaeshin Chu Osong Public Health and Research Perspectives.2012; 3(4): 191. CrossRef
Objectives
Ebola hemorrhagic fever has killed over 1300 people, mostly in equatorial Africa. There is still uncertainty about the natural reservoir of the virus and about some of the factors involved in disease transmission. Until now, a maximum incubation period of 21 days has been assumed. Methods
We analyzed data collected during the Ebola outbreak (subtype Zaire) in Kikwit, Democratic Republic of the Congo, in 1995 using maximum likelihood inference and assuming a log-normally distributed incubation period. Results
The mean incubation period was estimated to be 12.7 days (standard deviation 4.31 days), indicating that about 4.1% of patients may have incubation periods longer than 21 days. Conclusion
If the risk of new cases is to be reduced to 1% then 25 days should be used when investigating the source of an outbreak, when determining the duration of surveillance for contacts, and when declaring the end of an outbreak.
Citations
Citations to this article as recorded by
Risk behaviour associated with contracting the Ebola virus at funerals: a study of Liberians attending the funerals of individuals deceased from Ebola Moses Tende Stephens, Arunrat Tangmunkongvorakul, Natthapol Kosashunhanan, Mosoka Fallah, Julius S. M. Gilayeneh, Timothy Kie, Kriengkrai Srithanaviboonchai Frontiers in Public Health.2026;[Epub] CrossRef
Physico-chemical properties analysis from the docking of some imidazole derivatives with the Ebola-viral matrix protein (VP40) Magdalena Álvarez-Ramírez, Marcela Rosas-Nexticapa, Enrique Bonilla-Zavaleta, Montserrat Alheli Melgarejo-Gutiérrez, Martha Paola Rascón-Díaz, Abril Ramírez-Higuera Brazilian Journal of Science.2026; 6(3): 32. CrossRef
Estimation of end-of-outbreak probabilities in the presence of delayed and incomplete case reporting M. J. Plank, W. S. Hart, J. Polonsky, M. Keita, S. Ahuka-Mundeke, R. N. Thompson Proceedings of the Royal Society B: Biological Sci.2025;[Epub] CrossRef
Prevention and post-exposure management of occupational exposure to Ebola virus Michael A Moso, Chuan K Lim, Eloise Williams, Caroline Marshall, James McCarthy, Deborah A Williamson The Lancet Infectious Diseases.2024; 24(2): e93. CrossRef
Systems immunology of transcriptional responses to viral infection identifies conserved antiviral pathways across macaques and humans Kalani Ratnasiri, Hong Zheng, Jiaying Toh, Zhiyuan Yao, Veronica Duran, Michele Donato, Mario Roederer, Megha Kamath, John-Paul M. Todd, Matthew Gagne, Kathryn E. Foulds, Joseph R. Francica, Kizzmekia S. Corbett, Daniel C. Douek, Robert A. Seder, Shirit E Cell Reports.2024; 43(2): 113706. CrossRef
A comprehensive analysis of non-pharmaceutical interventions and vaccination on Ebolavirus disease outbreak: Stochastic modeling approach Youngsuk Ko, Jacob Lee, Yubin Seo, Eunok Jung, Aleksandra Inic-Kanada PLOS Neglected Tropical Diseases.2024; 18(6): e0011955. CrossRef
Isothermal nucleic acid amplification technology for rapid detection of virus Zhenting Wei, Xiaowen Wang, Huhu Feng, Fanpu Ji, Dan Bai, Xiaoping Dong, Wei Huang Critical Reviews in Biotechnology.2023; 43(3): 415. CrossRef
Predicting the combined effects of case isolation, safe funeral practices, and contact tracing during Ebola virus disease outbreaks Aliou Bouba, Kristina Barbara Helle, Kristan Alexander Schneider, Jan Rychtář PLOS ONE.2023; 18(1): e0276351. CrossRef
Stability analysis of an SIR epidemic model with homestead-isolation on the susceptible and infectious, immunity, relapse and general incidence rate Amine Bernoussi International Journal of Biomathematics.2023;[Epub] CrossRef
Path analysis method in an epidemic model and stability analysis Yong Zhou, Yiming Ding, Minrui Guo Frontiers in Physics.2023;[Epub] CrossRef
Tradeoff between speed and reproductive number in pathogen evolution Andreas Eilersen, Bjarke Frost Nielsen, Kim Sneppen Physical Review Research.2023;[Epub] CrossRef
A Reinforcement Learning Based Decision Support Tool for Epidemic Control: Validation Study for COVID-19 Mohamed-Amine Chadi, Hajar Mousannif Applied Artificial Intelligence.2022;[Epub] CrossRef
Spatially-heterogeneous embedded stochastic SEIR models for the 2014–2016 Ebola outbreak in West Africa Kaitlyn Martinez, Grant Brown, Stephen Pankavich Spatial and Spatio-temporal Epidemiology.2022; 41: 100505. CrossRef
Staff Scheduling During a Pandemic: The Case of Radiation Therapy Department Hossein Abouee Mehrizi, Arian Aminoleslami, Johnson Darko, Ernest Osei, Houra Mahmoudzadeh SSRN Electronic Journal .2022;[Epub] CrossRef
Review of Ebola virus disease in children – how far have we come? Devika Dixit, Kasereka Masumbuko Claude, Lindsey Kjaldgaard, Michael T. Hawkes Paediatrics and International Child Health.2021; 41(1): 12. CrossRef
Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity Rachid Muleia, Marc Aerts, Christel Faes, Maria Vittoria Barbarossa PLOS ONE.2021; 16(5): e0250765. CrossRef
Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates Shakti Davis, Lauren Milechin, Tejash Patel, Mark Hernandez, Greg Ciccarelli, Siddharth Samsi, Lisa Hensley, Arthur Goff, John Trefry, Sara Johnston, Bret Purcell, Catherine Cabrera, Jack Fleischman, Albert Reuther, Kajal Claypool, Franco Rossi, Anna Honk Frontiers in Physiology.2021;[Epub] CrossRef
Advances and insights in the diagnosis of viral infections Julija Dronina, Urte Samukaite-Bubniene, Arunas Ramanavicius Journal of Nanobiotechnology.2021;[Epub] CrossRef
Treatment of Ebola-related critical illness Peter Kiiza, S. Mullin, K. Teo, N. K. J. Adhikari, R. A. Fowler Intensive Care Medicine.2020; 46(2): 285. CrossRef
AAV Vectored Immunoprophylaxis for Filovirus Infections Amira D. Rghei, Laura P. van Lieshout, Lisa A. Santry, Matthew M. Guilleman, Sylvia P. Thomas, Leonardo Susta, Khalil Karimi, Byram W. Bridle, Sarah K. Wootton Tropical Medicine and Infectious Disease.2020; 5(4): 169. CrossRef
Vaccination strategies to control Ebola epidemics in the context of variable household inaccessibility levels Gerardo Chowell, Amna Tariq, Maria Kiskowski, Benjamin Althouse PLOS Neglected Tropical Diseases.2019; 13(11): e0007814. CrossRef
Application of the CDC EbolaResponse Modeling tool to disease predictions Robert H. Gaffey, Cécile Viboud Epidemics.2018; 22: 22. CrossRef
A mathematical model with isolation for the dynamics of Ebola virus Amira Rachah Journal of Physics: Conference Series.2018; 1132: 012058. CrossRef
Multiscale model for pedestrian and infection dynamics during air travel Sirish Namilae, Pierrot Derjany, Anuj Mubayi, Mathew Scotch, Ashok Srinivasan Physical Review E.2017;[Epub] CrossRef
Modeling ebola virus disease transmissions with reservoir in a complex virus life ecology Tsanou Berge, Samuel Bowong, Jean Lubuma, Martin Luther Mann Manyombe Mathematical Biosciences and Engineering.2017; 15(1): 21. CrossRef
Application of a quantitative entry assessment model to compare the relative risk of incursion of zoonotic bat-borne viruses into European Union Member States Verity Horigan, Paul Gale, Rowena D. Kosmider, Christopher Minnis, Emma L. Snary, Andrew C. Breed, Robin R.L. Simons Microbial Risk Analysis.2017; 7: 8. CrossRef
Multigroup deterministic and stochastic SEIRI epidemic models with nonlinear incidence rates and distributed delays: A stability analysis Hong Zhang, Juan Xia, Paul Georgescu Mathematical Methods in the Applied Sciences.2017; 40(18): 6254. CrossRef
Modeling spatial invasion of Ebola in West Africa Jeremy P. D’Silva, Marisa C. Eisenberg Journal of Theoretical Biology.2017; 428: 65. CrossRef
The potential impact of a prophylactic vaccine for Ebola in Sierra Leone Erin N. Bodine, Connor Cook, Mikayla Shorten Mathematical Biosciences and Engineering.2017; 15(2): 337. CrossRef
Ebola virus – from neglected threat to global emergency state Daniela Alexandra de Meneses Rocha Aguiar Pacheco, Acácio Agostinho Gonçalves Rodrigues, Carmen Maria Lisboa da Silva Revista da Associação Médica Brasileira.2016; 62(5): 458. CrossRef
Neglected filoviruses Robin Burk, Laura Bollinger, Joshua C. Johnson, Jiro Wada, Sheli R. Radoshitzky, Gustavo Palacios, Sina Bavari, Peter B. Jahrling, Jens H. Kuhn, Urs Greber FEMS Microbiology Reviews.2016; 40(4): 494. CrossRef
Treatment–donation-stockpile dynamics in ebola convalescent blood transfusion therapy Xi Huo, Xiaodan Sun, Kunquan Lan, Jianhong Wu Journal of Theoretical Biology.2016; 392: 53. CrossRef
Updates in diagnosis and management of Ebola hemorrhagic fever Salah Mohamed El Sayed, Ali A. Abdelrahman, Hani Adnan Ozbak, Hassan Abdullah Hemeg, Ali Mohammed Kheyami, Nasser Rezk, Mohamed Baioumy El-Ghoul, Manal Mohamed Helmy Nabo, Yasser Mohamed Fathy Journal of Research in Medical Sciences.2016;[Epub] CrossRef
Ebola virus disease in nonendemic countries Samson Sai-Yin Wong, Sally Cheuk-Ying Wong Journal of the Formosan Medical Association.2015; 114(5): 384. CrossRef
Ebola Hemorrhagic Fever Maryam Keshtkar Jahromi, Batool Sharifi Mood International Journal of Infection.2015;[Epub] CrossRef
Tactics and Strategies for Managing Ebola Outbreaks and the Salience of Immunization Wayne M. Getz, Jean-Paul Gonzalez, Richard Salter, James Bangura, Colin Carlson, Moinya Coomber, Eric Dougherty, David Kargbo, Nathan D. Wolfe, Nadia Wauquier Computational and Mathematical Methods in Medicine.2015; 2015: 1. CrossRef
What is Ebola? R. A. Stein International Journal of Clinical Practice.2015; 69(1): 49. CrossRef
Ebola virus disease outbreak in Nigeria: Transmission dynamics and rapid control C.L. Althaus, N. Low, E.O. Musa, F. Shuaib, S. Gsteiger Epidemics.2015; 11: 80. CrossRef
A review of epidemiological parameters from Ebola outbreaks to inform early public health decision-making Maria D. Van Kerkhove, Ana I. Bento, Harriet L. Mills, Neil M. Ferguson, Christl A. Donnelly Scientific Data.2015;[Epub] CrossRef
Modeling Post-death Transmission of Ebola: Challenges for Inference and Opportunities for Control Joshua S. Weitz, Jonathan Dushoff Scientific Reports.2015;[Epub] CrossRef
Transmission Models of Historical Ebola Outbreaks John M. Drake, Iurii Bakach, Matthew R. Just, Suzanne M. O’Regan, Manoj Gambhir, Isaac Chun-Hai Fung Emerging Infectious Diseases.2015; 21(8): 1447. CrossRef
Theoretical perspectives on the infectiousness of Ebola virus disease Hiroshi Nishiura, Gerardo Chowell Theoretical Biology and Medical Modelling.2015;[Epub] CrossRef
Effect of Ebola Progression on Transmission and Control in Liberia Dan Yamin, Shai Gertler, Martial L. Ndeffo-Mbah, Laura A. Skrip, Mosoka Fallah, Tolbert G. Nyenswah, Frederick L. Altice, Alison P. Galvani Annals of Internal Medicine.2015; 162(1): 11. CrossRef
Transmission dynamics and control of Ebola virus disease (EVD): a review Gerardo Chowell, Hiroshi Nishiura BMC Medicine.2014;[Epub] CrossRef
Analysis of Ebolavirus with Decision Tree and Apriori algorithm Eunby Go, Seungmin Lee, Taeseon Yoon International Journal of Machine Learning and Comp.2014; 4(6): 543. CrossRef
Calculation of incubation period and serial interval from multiple outbreaks of Marburg virus disease Boris I Pavlin BMC Research Notes.2014; 7(1): 906. CrossRef
Outbreaks of Ebola virus disease in Africa: the beginnings of a tragic saga Jean-Philippe Chippaux Journal of Venomous Animals and Toxins including T.2014; 20(1): 44. CrossRef
Association between temperature, humidity and ebolavirus disease outbreaks in Africa, 1976 to 2014 S Ng, B J Cowling
Eurosurveillance.2014;[Epub] CrossRef
Early transmission dynamics of Ebola virus disease (EVD), West Africa, March to August 2014 H Nishiura, G Chowell
Eurosurveillance.2014;[Epub] CrossRef
Transmission dynamics and control of Ebola virus disease outbreak in Nigeria, July to September 2014 F O Fasina, A Shittu, D Lazarus, O Tomori, L Simonsen, C Viboud, G Chowell
Eurosurveillance.2014;[Epub] CrossRef
The Road Less Traveled Chaeshin Chu Osong Public Health and Research Perspectives.2011; 2(1): 1. CrossRef
First
Prev
