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Morel N, Giovanetti M, Fonseca V, Burgueño A, Lima M, Castro E, Guimarães NR, Iani FC, Bormida V, Cortinas MN, Ramas V, Coppola L, Bento AI, Rosewell A, Franco L, Mendez Rico J, Lourenço J, Junior Alcantara LC, Chiparelli H. Genomics-based timely detection of dengue virus type I genotypes I and V in Uruguay. Heliyon 2024; 10:e39246. [PMID: 39748983 PMCID: PMC11693888 DOI: 10.1016/j.heliyon.2024.e39246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 10/02/2024] [Accepted: 10/10/2024] [Indexed: 01/04/2025] Open
Abstract
This study details a genomics-based approach for the early detection of mosquito-borne pathogens, marked by Uruguay's first ever complete genomic sequencing of Dengue Virus type I genotypes I and V. This pioneering effort has facilitated the prompt identification of these genotypes within the country, enabling Uruguayan public health authorities to develop timely and effective response strategies. Further integrated into this approach is a climate-driven suitability measure, closely associated with Dengue case reports and indicative of the local climate's role in the virus's transmission in the country within the changing climate context. The detection of multiple DENV-1 genotypes co-circulating in Uruguay underscores the necessity for proactive surveillance, particularly at borders, to prevent the introduction and dissemination of novel viral strains within the country and the region. This approach aids in facilitating prompt public health responses and intervention strategies, which are crucial in mitigating the impact of dengue outbreaks.
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Affiliation(s)
- Noelia Morel
- Laboratorio de Virus Emergentes/reemergentes. Unidad de Virología, Departamento de Laboratorios de Salud Pública, Portugal
- Department of Exact and Earth Sciences, University of the State of Bahia, Salvador, Brazil
- Unidad de Virología. Depto. De Laboratorios de Salud Pública, Uruguay
| | - Marta Giovanetti
- Sciences and Technologies for Sustainable Development and One Health, Universita Campus Bio-Medico di Roma, Italy
- Instituto Rene Rachou, Fundação Oswaldo Cruz, Minas Gerais, Brazil
- Climate Amplified Diseases and Epidemics (CLIMADE), Brazil, Americas
| | - Vagner Fonseca
- Instituto Rene Rachou, Fundação Oswaldo Cruz, Minas Gerais, Brazil
- Climate Amplified Diseases and Epidemics (CLIMADE), Brazil, Americas
- Coordenação de Vigilância, Preparação e Resposta à Emergências e Desastres (PHE), Organização Pan-Americana da Saúde / Organização Mundial da Saúde (OPAS/OMS), Brasília, DF, Brazil
| | - Analía Burgueño
- Laboratorio de Virus Emergentes/reemergentes. Unidad de Virología, Departamento de Laboratorios de Salud Pública, Portugal
| | - Mauricio Lima
- Laboratorio Central de Saúde Pública do Estado de Minas Gerais, Fundação Ezequiel Dias, Brazil
| | - Emerson Castro
- Laboratorio Central de Saúde Pública do Estado de Minas Gerais, Fundação Ezequiel Dias, Brazil
| | - Natália R. Guimarães
- Laboratorio Central de Saúde Pública do Estado de Minas Gerais, Fundação Ezequiel Dias, Brazil
| | - Felipe C.M. Iani
- Laboratorio Central de Saúde Pública do Estado de Minas Gerais, Fundação Ezequiel Dias, Brazil
| | - Victoria Bormida
- Unidad de Genómica. Depto. De Laboratorios de Salud Pública, Uruguay
| | | | - Viviana Ramas
- Laboratorio de Virus Respiratorios, Unidad de Virología. Departamento de Laboratorios de Salud Pública, Uruguay
| | - Leticia Coppola
- Laboratorio de Virus Respiratorios, Unidad de Virología. Departamento de Laboratorios de Salud Pública, Uruguay
| | - Ana I. Bento
- Pandemic Prevention Initiative, The Rockefeller Foundation, Washington DC, USA
| | - Alexander Rosewell
- Coordenação de Vigilância, Preparação e Resposta à Emergências e Desastres (PHE), Organização Pan-Americana da Saúde / Organização Mundial da Saúde (OPAS/OMS), Brasília, DF, Brazil
| | - Leticia Franco
- Infectious Hazards Management, Health Emergencies Department (PHE), Pan American Health Organization / World Health Organization (PAHO/WHO), Washington DC, USA
| | - Jairo Mendez Rico
- Infectious Hazards Management, Health Emergencies Department (PHE), Pan American Health Organization / World Health Organization (PAHO/WHO), Washington DC, USA
| | - José Lourenço
- BioISI (Biosystems and Integrative Sciences Institute), Faculdade de Ciências da Universidade de Lisboa, Portugal
- Católica Biomedical Research Centre, Católica Medical School, Universidade Católica Portuguesa, Portugal
- Climate Amplified Diseases and Epidemics (CLIMADE), Europe, Portugal
| | - Luiz Carlos Junior Alcantara
- Instituto Rene Rachou, Fundação Oswaldo Cruz, Minas Gerais, Brazil
- Climate Amplified Diseases and Epidemics (CLIMADE), Brazil, Americas
| | - Hector Chiparelli
- Laboratorio de Virus Emergentes/reemergentes. Unidad de Virología, Departamento de Laboratorios de Salud Pública, Portugal
- Coordenação de Vigilância, Preparação e Resposta à Emergências e Desastres (PHE), Organização Pan-Americana da Saúde / Organização Mundial da Saúde (OPAS/OMS), Brasília, DF, Brazil
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Chuchuy A, Rodriguero MS, Alonso AC, Stein M, Micieli MV. Wolbachia infection in natural mosquito populations from Argentina. Parasitol Res 2024; 123:343. [PMID: 39382727 DOI: 10.1007/s00436-024-08352-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 09/16/2024] [Indexed: 10/10/2024]
Abstract
The increasing spread of mosquito vectors has made mosquito-borne arboviral diseases a global threat to public health, leading to the urgent need for effective population control methods. Strategies based in the intracellular bacterium Wolbachia Hertig, 1936 are considered environmentally friendly, safe for humans, and potentially cost-effective for controlling arboviral diseases. To minimize undesirable side effects, it is relevant to assess whether Wolbachia is present in the area and understand the diversity associated with native infections before implementing these strategies. With this purpose, we investigated Wolbachia infection status, diversity, and prevalence in populations of Aedes albifasciatus (Macquart, 1838), Aedes fluviatilis (Lutz, 1904), and hybrids of the Culex pipiens (Linnaeus, 1758) complex from Argentina. Aedes albifasciatus and C. pipiens complex samples were collected in the province of Buenos Aires, and A. fluviatilis in the province of Misiones. Aedes albifasciatus was found to be uninfected, while infections with strains wFlu and wPip were detected in A. fluviatilis and hybrids of the C. pipiens complex, respectively. All strains were fixed or close to fixation and clustered within supergroup B. These finding provides valuable information on Wolbachia strains found in natural mosquito populations in Argentina that might be used in heterologous infections in the future or be considered when designing control strategies based on Wolbachia infection.
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Affiliation(s)
- Ailen Chuchuy
- Centro de Estudios Parasitológicos y de Vectores, CONICET (CEPAVE-CCT-La Plata-CONICET-UNLP), Boulevard 120 e/61y 62, 1900, La Plata, Argentina
| | - Marcela S Rodriguero
- Departamento de Ecología, Genética y Evolución, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Intendente Güiraldes 2160, 1428, Autonomous City of Buenos Aires, Argentina.
- Instituto de Ecología, Genética y Evolución (IEGEBA), Universidad de Buenos Aires, CONICET, Intendente Güiraldes 2160, 1428, Autonomous City of Buenos Aires, Argentina.
| | - Ana C Alonso
- Laboratorio de Entomología, Instituto de Medicina Regional, Universidad Nacional del Nordeste, CONICET (CCT Nordeste-CONICET-UNNE), Av. Las Heras 727, 3500, Resistencia, Argentina
- Instituto de Investigaciones en Energía No Convencional, Universidad Nacional de Salta, CONICET (INENCO-CONICET), Salta, Argentina
| | - Marina Stein
- Laboratorio de Entomología, Instituto de Medicina Regional, Universidad Nacional del Nordeste, CONICET (CCT Nordeste-CONICET-UNNE), Av. Las Heras 727, 3500, Resistencia, Argentina
| | - María V Micieli
- Centro de Estudios Parasitológicos y de Vectores, CONICET (CEPAVE-CCT-La Plata-CONICET-UNLP), Boulevard 120 e/61y 62, 1900, La Plata, Argentina
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Talukder H, Muñoz-Zanzi C, Salgado M, Berg S, Yang A. Identifying the Drivers Related to Animal Reservoirs, Environment, and Socio-Demography of Human Leptospirosis in Different Community Types of Southern Chile: An Application of Machine Learning Algorithm in One Health Perspective. Pathogens 2024; 13:687. [PMID: 39204287 PMCID: PMC11357164 DOI: 10.3390/pathogens13080687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Revised: 08/06/2024] [Accepted: 08/08/2024] [Indexed: 09/03/2024] Open
Abstract
Leptospirosis is a zoonosis with global public health impact, particularly in poor socio-economic settings in tropical regions. Transmitted through urine-contaminated water or soil from rodents, dogs, and livestock, leptospirosis causes over a million clinical cases annually. Risk factors include outdoor activities, livestock production, and substandard housing that foster high densities of animal reservoirs. This One Health study in southern Chile examined Leptospira serological evidence of exposure in people from urban slums, semi-rural settings, and farm settings, using the Extreme Gradient Boosting algorithm to identify key influencing factors. In urban slums, age, shrub terrain, distance to Leptospira-positive households, and neighborhood housing density were contributing factors. Human exposure in semi-rural communities was linked to environmental factors (trees, shrubs, and lower vegetation terrain) and animal variables (Leptospira-positive dogs and rodents and proximity to Leptospira-positive households). On farms, dog counts, animal Leptospira prevalence, and proximity to Leptospira-contaminated water samples were significant drivers. The study underscores that disease dynamics vary across landscapes, with distinct drivers in each community setting. This case study demonstrates how the integration of machine learning with comprehensive cross-sectional epidemiological and geospatial data provides valuable insights into leptospirosis eco-epidemiology. These insights are crucial for informing targeted public health strategies and generating hypotheses for future research.
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Affiliation(s)
- Himel Talukder
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019, USA;
| | - Claudia Muñoz-Zanzi
- Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN 55454, USA
| | - Miguel Salgado
- Preventive Veterinary Medicine Department, Faculty of Veterinary Sciences, Universidad Austral de Chile, Valdivia 5090000, Chile;
| | - Sergey Berg
- Department of Computer & Information Science, University of St. Thomas, St. Paul, MN 55105, USA;
| | - Anni Yang
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019, USA;
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Kanga S, Roy P, Singh SK, Meraj G, Kumar P, Debnath J. Delineating dengue risk zones in Jaipur: An interdisciplinary approach to inform public health strategies. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024. [PMID: 38987233 DOI: 10.1111/risa.15102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 06/05/2024] [Accepted: 06/16/2024] [Indexed: 07/12/2024]
Abstract
Dengue fever (DF) is a pervasive public health concern in tropical climates, with densely populated regions, such as India, disproportionately affected. Addressing this issue requires a multifaceted understanding of the environmental and sociocultural factors that contribute to the risk of dengue infection. This study aimed to identify high-risk zones for DF in Jaipur, Rajasthan, India, by integrating physical, demographic, and epidemiological data in a comprehensive risk analysis framework. We investigated environmental variables, such as soil type and plant cover, to characterize the potential habitats of Aedes aegypti, the primary dengue vector. Concurrently, demographic metrics were evaluated to assess the population's susceptibility to dengue outbreaks. High-risk areas were systematically identified through a comparative analysis that integrated population density and incidence rates per ward. The results revealed a significant correlation between high population density and an increased risk of dengue, predominantly facilitated by vertical transmission. Spatially, these high-risk zones are concentrated in the northern and southern sectors of Jaipur, with the northern and southwestern wards exhibiting the most acute risk profiles. This study underscores the importance of targeted public health interventions and vaccination campaigns in vulnerable areas. It further lays the groundwork for future research to evaluate the effectiveness of such interventions, thereby contributing to the development of robust evidence-based strategies for dengue risk mitigation.
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Affiliation(s)
- Shruti Kanga
- Department of Geography, School of Environment and Earth Sciences, Central University of Punjab, Bathinda, Punjab, India
| | - Priyanka Roy
- Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
| | - Suraj Kumar Singh
- Centre for Sustainable Development, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
| | - Gowhar Meraj
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Pankaj Kumar
- Institute for Global Environmental Strategies, Hayama, Japan
| | - Jatan Debnath
- Department of Geography, Gauhati University, Jalukbari, Assam, India
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Buebos-Esteve DE, Dagamac NHA. Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning. Acta Trop 2024; 255:107225. [PMID: 38701871 DOI: 10.1016/j.actatropica.2024.107225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 04/12/2024] [Accepted: 04/19/2024] [Indexed: 05/05/2024]
Abstract
Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collapse amidst 'Big Data', prompt interpretable machine-learning (iML) approaches. Predicting dengue incidence and mortality in the Philippines, a data-limited yet high-burden country, the mlr3 universe of R packages was used to build and optimize ML models based on remotely sensed provincial and dekadal 3 NDVI and 9 rainfall features from 2016 to 2020. Between two tasks, models differ across four random forest-based learners and two clustering strategies. Among 16 candidates, rfsrc-year-case and ranger-year-death significantly perform best for predicting dengue incidence and mortality, respectively. Therefore, temporal clustering yields the best models, reflective of dengue seasonality. The two best models were subjected to tripartite global exploratory model analyses, which encompass model-agnostic post-hoc methods such as Permutation Feature Importance (PFI) and Accumulated Local Effects (ALE). PFI reveals that the models differ in their important explanatory aspect, rainfall for rfsrc-year-case and NDVI for ranger-year-death, among which long-term average (lta) features are most relevant. Trend-wise, ALE reveals that average incidence predictions are positively associated with 'Rain.lta', reflective of dengue cases peaking during the wet season. In contrast, those for mortality are negatively associated with 'NDVI.lta', reflective of urban spaces driving dengue-related deaths. By technologically addressing the challenges of the human-animal-ecosystem interface, this study adheres to the One Digital Health paradigm operationalized under Sustainable Development Goals (SDGs). Leveraging data digitization and predictive modeling for epidemiological research paves SDG 3, which prioritizes holistic health and well-being.
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Affiliation(s)
- Don Enrico Buebos-Esteve
- Initiatives for Conservation, Landscape Ecology, Bioprospecting, and Biomodeling (ICOLABB), Research Center for the Natural and Applied Sciences, University of Santo Tomas, España, Manila 1008, Philippines.
| | - Nikki Heherson A Dagamac
- Initiatives for Conservation, Landscape Ecology, Bioprospecting, and Biomodeling (ICOLABB), Research Center for the Natural and Applied Sciences, University of Santo Tomas, España, Manila 1008, Philippines; Department of Biological Sciences, College of Science, University of Santo Tomas, España, Manila 1008, Philippines; The Graduate School, University of Santo Tomas, España, Manila 1008, Philippines
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Ghazy RM, Gebreal A, Saleeb MRA, Sallam M, El-Deen AESN, Sheriff SD, Tessema EA, Ahurwendeire S, Tsoeu N, Chamambala PC, Cibangu PB, Okeh DU, Traoré AS, Eshun G, Kengo NE, Kubuka AE, Awuah LB, Salah A, Aljohani M, Fadl N. Compulsory Vaccination Coverage in 12 Sub-Saharan African Countries Two Years Following the COVID-19 Pandemic. J Community Health 2024; 49:193-206. [PMID: 37646982 DOI: 10.1007/s10900-023-01261-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 09/01/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is a global threat, challenging health services' provision and utilization. This study aimed to assess compulsory vaccination coverage in 12 Sub-Saharan African countries two years following the COVID-19 pandemic using the Health Belief Model. A cross-sectional survey was conducted from November 1 to December 15, 2022. Multivariate logistic regression was conducted to identify the determinants of vaccination coverage. Among the 5032 respondents, 73.1% reported that their children received compulsory vaccination. The lowest coverage was observed in Ghana (36.5%), while the highest was in Burkina Faso and Congo (92.0%). Factors associated with non-vaccination included older mothers (adjusted odds ratio (AOR) = 1.04, 95%CI: 1.03-1.05), lower mothers' education, older children (AOR = 0.76, 95%CI: 0.60-0.96), children with chronic illnesses (AOR = 0.55, 95%CI: 0.45-0.66), and difficult accessibility to healthcare facilities (AOR = 11.27, 95%CI: 9.48-13.44). Low perceived risk, in which non-vaccinated children were believed to be at no higher risk for infectious diseases and the disease severity would not worsen among non-vaccinated children, increased the likelihood of non-vaccination (AOR = 2.29, 95%CI: 1.75-2.99 and AOR = 2.12, 95%CI: 1.64-2.73, respectively). Perceiving vaccines as unnecessary, and needless for breastfed babies increased the probability of non-vaccination (AOR = 1.38, 95%CI: 1.10-1.73 and AOR = 1.69, 95%CI: 1.31-2.19, respectively). Higher odds of non-vaccination were found when the provision of vaccine information did not motivate parents to vaccinate their children (AOR = 4.29, 95%CI: 3.15-5.85). Conversely, believing that vaccines were safe for children decreased the odds of non-vaccination (AOR = 0.72, 95%CI: 0.58-0.88). Parental perceptions and concerns should be considered in interventions aiming to increase compulsory vaccine acceptance and coverage.
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Affiliation(s)
- Ramy Mohamed Ghazy
- Tropical Health Department, High Institute of Public Health, Alexandria University, Alexandria, Egypt
| | - Assem Gebreal
- Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | | | - Malik Sallam
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, Jordan
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, Jordan
| | - Ahmed El-Sayed Nour El-Deen
- Department of Physiology, Faculty of Medicine, Al-Azhar University, Assiut, Egypt
- Department of Basic Medical and Dental Sciences, Faculty of Dentistry, Zarqa University, PO Box 2000, Zarqa, 13110, Jordan
| | | | | | - Salvias Ahurwendeire
- Department of Epidemiology and Biostatistics, Makerere University School of Public Health, Kampala, Uganda
| | | | | | - Patrick B Cibangu
- Health Officer, Les Ailes du Coeur NGO, Congo, Democratic Republic of Congo
| | - Debra Ukamaka Okeh
- Department of Community Medicine, Federal Medical Centre Umuahia, Abia, Nigeria
| | | | - Gilbert Eshun
- Seventh-Day Adventist Hospital, Agona-Asamang, Ghana
| | - Nathan Ezie Kengo
- Faculty of Medicine and Biomedical Sciences, University of Garoua, Garoua, Cameroun
| | - Amos Elisha Kubuka
- Saint Francis University College of health and allied Sciences, Ifakara, Morogoro, Tanzania
| | - Lydia Baffour Awuah
- Department of Health Promotion and Disability Studies, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Assia Salah
- Faculty of Medicine, Algiers University, Algiers, Algeria
| | - Moath Aljohani
- Department of Family and Community Medicine, Unaizah College of Medicine and Medical Sciences, Qassim University, Unaizah, Saudi Arabia
| | - Noha Fadl
- Family Health Department, High Institute of Public Health, Alexandria University, Alexandria, Egypt.
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Tian N, Zheng JX, Li LH, Xue JB, Xia S, Lv S, Zhou XN. Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data. Trop Med Infect Dis 2024; 9:72. [PMID: 38668533 PMCID: PMC11055163 DOI: 10.3390/tropicalmed9040072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/12/2024] [Accepted: 03/25/2024] [Indexed: 04/29/2024] Open
Abstract
OBJECTIVE This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. METHOD Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) were employed. RESULTS From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and R2 = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. CONCLUSION In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue's complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.
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Affiliation(s)
- Na Tian
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), Shanghai 200025, China; (N.T.); (J.-B.X.); (S.X.); (S.L.)
- School of Public Health, Shandong Second Medical University, Weifang 261000, China;
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
| | - Jin-Xin Zheng
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
| | - Lan-Hua Li
- School of Public Health, Shandong Second Medical University, Weifang 261000, China;
| | - Jing-Bo Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), Shanghai 200025, China; (N.T.); (J.-B.X.); (S.X.); (S.L.)
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), Shanghai 200025, China; (N.T.); (J.-B.X.); (S.X.); (S.L.)
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
| | - Shan Lv
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), Shanghai 200025, China; (N.T.); (J.-B.X.); (S.X.); (S.L.)
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), Shanghai 200025, China; (N.T.); (J.-B.X.); (S.X.); (S.L.)
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
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8
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Pitt SJ, Gunn A. The One Health Concept. Br J Biomed Sci 2024; 81:12366. [PMID: 38434675 PMCID: PMC10902059 DOI: 10.3389/bjbs.2024.12366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/05/2024] [Indexed: 03/05/2024]
Abstract
The concept of One Health has been developed as the appreciation that human health is intricately connected to those of other animals and the environment that they inhabit. In recent years, the COVID-19 pandemic and noticeable effects of climate change have encouraged national and international cooperation to apply One Health strategies to address key issues of health and welfare. The United Nations (UN) Sustainable Development Goals have established targets for health and wellbeing, clean water and sanitation, climate action, as well as sustainability in marine and terrestrial ecosystems. The One Health Quadripartite comprises the World Health Organization (WHO), the World Organization for Animal Health (WOAH-formerly OIE), the United Nations Food and Agriculture Organization (FAO) and the United Nations Environment Programme (UNEP). There are six areas of focus which are Laboratory services, Control of zoonotic diseases, Neglected tropical diseases, Antimicrobial resistance, Food safety and Environmental health. This article discusses the concept of One Health by considering examples of infectious diseases and environmental issues under each of those six headings. Biomedical Scientists, Clinical Scientists and their colleagues working in diagnostic and research laboratories have a key role to play in applying the One Health approach to key areas of healthcare in the 21st Century.
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Affiliation(s)
- Sarah J. Pitt
- School of Applied Sciences, University of Brighton, Brighton, United Kingdom
| | - Alan Gunn
- School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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Salazar Flórez JE, Segura Cardona ÁM, Restrepo Jaramillo BN, Arboleda Naranjo M, Giraldo Cardona LS, Echeverri Rendón ÁP. Immune system gene polymorphisms associated with severe dengue in Latin America: a systematic review. Rev Inst Med Trop Sao Paulo 2023; 65:e58. [PMID: 38055376 DOI: 10.1590/s1678-9946202365058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/25/2023] [Indexed: 12/08/2023] Open
Abstract
One of the main challenges in the clinical management of dengue is the early identification of cases that could progress to severe forms of the disease. A biomarker that may enable this identification is the presence of genetic polymorphisms in genes associated with immune responses. The objective of this study was to perform a systematic review of the Latin American literature on these genes. An electronic literature search was carried out in PubMed, Scopus, Lilacs, and the Virtual Health Library, and reference lists of systematic reviews in the area. Case-control studies conducted in Latin American countries examining at least one form of genetic polymorphism related to immune responses against severe dengue were included. In total, 424 articles were identified and 26 were included in this systematic review. Of the 26 selected articles, 16 reported polymorphisms associated with the risk of developing severe dengue (Risk); Similarly, 16 articles reported polymorphisms associated with a decreased risk of severe dengue (Protective). The final analysis revealed that multiple polymorphisms in immune system genes were early markers of the progression of dengue in Latin Americans and found that polymorphisms of the TNF-alpha gene may have a critical role in dengue pathogenesis.
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Affiliation(s)
- Jorge Emilio Salazar Flórez
- Universidad CES, Grupo de Epidemiología y Bioestadística, Medellín, Colombia
- Fundación Universitaria San Martín, Grupo GEINCRO, Sabaneta, Colombia
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10
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Santos CY, Tuboi S, de Jesus Lopes de Abreu A, Abud DA, Lobao Neto AA, Pereira R, Siqueira JB. A machine learning model to assess potential misdiagnosed dengue hospitalization. Heliyon 2023; 9:e16634. [PMID: 37313173 PMCID: PMC10258378 DOI: 10.1016/j.heliyon.2023.e16634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 06/15/2023] Open
Abstract
Dengue, like other arboviruses with broad clinical spectra, can easily be misdiagnosed as other infectious diseases due to the overlap of signs and symptoms. During large outbreaks, severe dengue cases have the potential to overwhelm the health care system and understanding the burden of dengue hospitalizations is therefore important to better allocate medical care and public health resources. A machine learning model that used data from the Brazilian public healthcare system database and the National Institute of Meteorology (INMET) was developed to estimate potential misdiagnosed dengue hospitalizations in Brazil. The data was modeled into a hospitalization level linked dataset. Then, Random Forest, Logistic Regression and Support Vector Machine algorithms were assessed. The algorithms were trained by dividing the dataset in training/test set and performing a cross validation to select the best hyperparameters in each algorithm tested. The evaluation was done based on accuracy, precision, recall, F1 score, sensitivity, and specificity. The best model developed was Random Forest with an accuracy of 85% on the final reviewed test. This model shows that 3.4% (13,608) of all hospitalizations in the public healthcare system from 2014 to 2020 could have been dengue misdiagnosed as other diseases. The model was helpful in finding potentially misdiagnosed dengue and might be a useful tool to help public health decision makers in planning resource allocation.
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Affiliation(s)
- Claudia Yang Santos
- Takeda Pharmaceuticals Brazil, Av. das Nações Unidas 14401, São Paulo, SP, Brazil
| | - Suely Tuboi
- Takeda Pharmaceuticals Brazil, Av. das Nações Unidas 14401, São Paulo, SP, Brazil
| | | | - Denise Alves Abud
- Takeda Pharmaceuticals Brazil, Av. das Nações Unidas 14401, São Paulo, SP, Brazil
| | | | - Ramon Pereira
- IQVIA Brazil, Rua Verbo Divino 2001, São Paulo, SP, Brazil
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11
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Lessa CLS, Hodel KVS, Gonçalves MDS, Machado BAS. Dengue as a Disease Threatening Global Health: A Narrative Review Focusing on Latin America and Brazil. Trop Med Infect Dis 2023; 8:241. [PMID: 37235289 PMCID: PMC10221906 DOI: 10.3390/tropicalmed8050241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/10/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
Arboviruses constitute the largest known group of viruses. These viruses are the etiological agents of pathologies known as arboviruses, with dengue being one of the most prevalent. Dengue has resulted in important socioeconomic burdens placed on different countries around the world, including those in Latin America, especially Brazil. Thus, this work intends to carry out a narrative-based review of the literature, conducted using a study of the secondary data developed through a survey of scientific literature databases, and to present the situation of dengue, particularly its distribution in these localities. Our findings from the literature demonstrate the difficulties that managers face in controlling the spread of and planning a response against dengue, pointing to the high cost of the disease for public coffers, rendering the resources that are already limited even scarcer. This can be associated with the different factors that affect the spread of the disease, including ecological, environmental, and social factors. Thus, in order to combat the disease, it is expected that targeted and properly coordinated public policies need to be adopted not only in specific localities, but also globally.
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Affiliation(s)
- Carlos Letacio Silveira Lessa
- Postgraduate Program in Industrial Management and Technology, SENAI CIMATEC University Center, Salvador 41650-010, Brazil
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador 40296-710, Brazil
| | - Katharine Valéria Saraiva Hodel
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), SENAI CIMATEC University Center, Salvador 41650-010, Brazil
| | - Marilda de Souza Gonçalves
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador 40296-710, Brazil
- Anemia Research Laboratory, Department of Clinical and Toxicological Analysis, Faculty of Pharmacy, Federal University of Bahia, Salvador 40170-115, Brazil
| | - Bruna Aparecida Souza Machado
- Postgraduate Program in Industrial Management and Technology, SENAI CIMATEC University Center, Salvador 41650-010, Brazil
- SENAI Institute of Innovation (ISI) in Health Advanced Systems (CIMATEC ISI SAS), SENAI CIMATEC University Center, Salvador 41650-010, Brazil
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12
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Bonilla-Aldana DK, Jimenez-Diaz SD, Barboza JJ, Rodriguez-Morales AJ. Mapping the Spatiotemporal Distribution of Bovine Rabies in Colombia, 2005-2019. Trop Med Infect Dis 2022; 7:tropicalmed7120406. [PMID: 36548660 PMCID: PMC9784067 DOI: 10.3390/tropicalmed7120406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/09/2022] [Accepted: 11/21/2022] [Indexed: 11/30/2022] Open
Abstract
Introduction: Rabies is caused by a virus belonging to the genus Lyssavirus and family Rhabdoviridae, which can infect any mammal including humans. Hematophagous, fructivorous, and insectivorous bats have become the main reservoir of sylvatic rabies in Latin America. In the sylvatic cycle, hematophagous bats are usually the main reservoir. In contrast, dogs and cats fulfil this critical role in the urban cycle. However, in rural areas, the most affected animals are bovines. They show clinical signs such as behavioural changes, hypersalivation, muscle tremors, spasms caused by extensive damage to the central nervous system, and death from respiratory paralysis. Objective: To describe the spatiotemporal distribution of bovine rabies in Colombia from 2005 to 2019. Methods: Retrospective cross-sectional descriptive observational study, based on the monthly reports of the Colombian Agricultural Institute (ICA) on the surveillance of bovine rabies in Colombia from 2005 to 2019, retrieved from its official website. The data were converted to databases in Microsoft Access 365®. Multiple epidemiological maps were developed with the GIS software Kosmo RC1® 3.0 coupled to the shape files (.shp) of all the country’s municipalities. Results: During the study period, 4888 cases of rabies were confirmed in cattle, ranging from a peak of 542 cases (11.1%) in 2014 to 43 in 2019 (0.88%). From 2014 to 2019, there has been a significant reduction in the annual national number of cases (r2 = 0.9509, p < 0.05). In 2019, 32.6% of the cases occurred in January, and 48.8% occurred in the department of Sucre. In 2009, the maximum number of spatial clusters (13) occurred in the Orinoquia region, where other clusters were also identified in 2005, 2006 and 2008. In 2018, 98 outbreaks were identified that led to the death of cattle and other animals, 28.6% of them in the department of Sucre. In the first half of 2019, of 38 outbreaks, 55.2% were identified in Sucre. Conclusions: It is necessary to review the current national program for the prevention and control of rabies in cattle, incorporating concepts from the ecology of bats, as well as the prediction of contagion waves of geographical and temporal spread in the context of the OneHealth Approach. Sylvatic rabies remains a threat in Colombia that requires further study.
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Affiliation(s)
| | - S. Daniela Jimenez-Diaz
- Faculty of Veterinary Medicine, Fundación Universitaria Autónoma de las Américas, Pereira 660003, Risaralda, Colombia
| | - Joshuan J. Barboza
- Vicerrectorado de Investigación, Universidad Norbert Wiener, Lima 15046, Peru
- Correspondence: (J.J.B.); (A.J.R.-M.); Tel.: +51-992108520 (J.J.B.)
| | - Alfonso J. Rodriguez-Morales
- Faculty of Health Sciences, Universidad Científica del Sur, Lima 15024, Peru
- Grupo de Investigación Biomedicina, Faculty of Medicine, Fundación Universitaria Autónoma de las Américas, Pereira 660003, Risaralda, Colombia
- Institución Universitaria Visión de las Américas, Pereira 660003, Risaralda, Colombia
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut P.O. Box 13-5053, Lebanon
- Correspondence: (J.J.B.); (A.J.R.-M.); Tel.: +51-992108520 (J.J.B.)
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