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Jibon MJN, Ruku SMRP, Islam ARMT, Khan MN, Mallick J, Bari ABMM, Senapathi V. Impact of climate change on vector-borne diseases: Exploring hotspots, recent trends and future outlooks in Bangladesh. Acta Trop 2024; 259:107373. [PMID: 39214233 DOI: 10.1016/j.actatropica.2024.107373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 08/10/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
Climate change is a significant risk multiplier and profoundly influences the transmission dynamics, geographical distribution, and resurgence of vector-borne diseases (VBDs). Bangladesh has a noticeable rise in VBDs attributed to climate change. Despite the severity of this issue, the interconnections between climate change and VBDs in Bangladesh have yet to be thoroughly explored. To address this research gap, our review meticulously examined existing literature on the relationship between climate change and VBDs in Bangladesh. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, we identified 3849 records from SCOPUS, Web of Science, and Google Scholar databases. Ultimately, 22 research articles meeting specific criteria were included. We identified that the literature on the subject matter of this study is non-contemporaneous, with 68% of studies investing datasets before 2014, despite studies on climate change and dengue nexus having increased recently. We pinpointed Dhaka and Chittagong Hill Tracts as the dengue and malaria research hotspots, respectively. We highlighted that the 2023 dengue outbreak illustrates a possible shift in dengue-endemic areas in Bangladesh. Moreover, dengue cases surged by 317% in 2023 compared to 2019 records, with a corresponding 607% increase in mortality compared to 2022. A weak connection was observed between dengue incidents and climate drivers, including the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). However, no compelling evidence supported an association between malaria cases, and Sea Surface Temperature (SST) in the Bay of Bengal, along with the NINO3 phenomenon. We observed minimal microclimatic and non-climatic data inclusion in selected studies. Our review holds implications for policymakers, urging the prioritization of mitigation measures such as year-round surveillance and early warning systems. Ultimately, it calls for resource allocation to empower researchers in advancing the understanding of VBD dynamics amidst changing climates.
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Affiliation(s)
| | | | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh; Department of Development Studies, Daffodil International University, Dhaka 1216, Bangladesh.
| | - Md Nuruzzaman Khan
- Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Bangladesh
| | - Javed Mallick
- Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia
| | - A B M Mainul Bari
- Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | - Venkatramanan Senapathi
- PG and Research Department of Geology, National College (Autonomous), Tiruchirappalli, Tamil Nadu 620001, India
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Judson SD, Dowdy DW. Modeling zoonotic and vector-borne viruses. Curr Opin Virol 2024; 67:101428. [PMID: 39047313 PMCID: PMC11292992 DOI: 10.1016/j.coviro.2024.101428] [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: 02/02/2024] [Accepted: 07/06/2024] [Indexed: 07/27/2024]
Abstract
The 2013-2016 Ebola virus disease epidemic and the coronavirus disease 2019 pandemic galvanized tremendous growth in models for emerging zoonotic and vector-borne viruses. Therefore, we have reviewed the main goals and methods of models to guide scientists and decision-makers. The elements of models for emerging viruses vary across spectrums: from understanding the past to forecasting the future, using data across space and time, and using statistical versus mechanistic methods. Hybrid/ensemble models and artificial intelligence offer new opportunities for modeling. Despite this progress, challenges remain in translating models into actionable decisions, particularly in areas at highest risk for viral disease outbreaks. To address this issue, we must identify gaps in models for specific viruses, strengthen validation, and involve policymakers in model development.
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Affiliation(s)
- Seth D Judson
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
| | - David W Dowdy
- Division of Infectious Disease Epidemiology, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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Hossain S, Islam MM, Hasan MA, Chowdhury PB, Easty IA, Tusar MK, Rashid MB, Bashar K. Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach. Heliyon 2023; 9:e16053. [PMID: 37215791 PMCID: PMC10192530 DOI: 10.1016/j.heliyon.2023.e16053] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 05/03/2023] [Accepted: 05/03/2023] [Indexed: 05/24/2023] Open
Abstract
Background In Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. Methods A total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg-1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. Results The mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. Conclusion The findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.
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Affiliation(s)
- Sorif Hossain
- Department of Statistics, Noakhali Science and Technology University, Bangladesh
| | - Md. Momin Islam
- Department of Meteorology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md. Abid Hasan
- Department of Oceanography, Noakhali Science and Technology University, Bangladesh
| | | | - Imtiaj Ahmed Easty
- Department of Oceanography, Noakhali Science and Technology University, Bangladesh
| | - Md. Kamruzzaman Tusar
- Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Bangladesh
| | | | - Kabirul Bashar
- Department of Zoology, Jahangirnagar University, Bangladesh
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Islam MA, Hasan MN, Tiwari A, Raju MAW, Jannat F, Sangkham S, Shammas MI, Sharma P, Bhattacharya P, Kumar M. Correlation of Dengue and Meteorological Factors in Bangladesh: A Public Health Concern. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5152. [PMID: 36982061 PMCID: PMC10049245 DOI: 10.3390/ijerph20065152] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Dengue virus (DENV) is an enveloped, single-stranded RNA virus, a member of the Flaviviridae family (which causes Dengue fever), and an arthropod-transmitted human viral infection. Bangladesh is well known for having some of Asia's most vulnerable Dengue outbreaks, with climate change, its location, and it's dense population serving as the main contributors. For speculation about DENV outbreak characteristics, it is crucial to determine how meteorological factors correlate with the number of cases. This study used five time series models to observe the trend and forecast Dengue cases. Current data-based research has also applied four statistical models to test the relationship between Dengue-positive cases and meteorological parameters. Datasets were used from NASA for meteorological parameters, and daily DENV cases were obtained from the Directorate General of Health Service (DGHS) open-access websites. During the study period, the mean of DENV cases was 882.26 ± 3993.18, ranging between a minimum of 0 to a maximum of 52,636 daily confirmed cases. The Spearman's rank correlation coefficient between climatic variables and Dengue incidence indicated that no substantial relationship exists between daily Dengue cases and wind speed, temperature, and surface pressure (Spearman's rho; r = -0.007, p > 0.05; r = 0.085, p > 0.05; and r = -0.086, p > 0.05, respectively). Still, a significant relationship exists between daily Dengue cases and dew point, relative humidity, and rainfall (r = 0.158, p < 0.05; r = 0.175, p < 0.05; and r = 0.138, p < 0.05, respectively). Using the ARIMAX and GA models, the relationship for Dengue cases with wind speed is -666.50 [95% CI: -1711.86 to 378.86] and -953.05 [-2403.46 to 497.36], respectively. A similar negative relation between Dengue cases and wind speed was also determined in the GLM model (IRR = 0.98). Dew point and surface pressure also represented a negative correlation in both ARIMAX and GA models, respectively, but the GLM model showed a positive association. Additionally, temperature and relative humidity showed a positive correlation with Dengue cases (105.71 and 57.39, respectively, in the ARIMAX, 633.86, and 200.03 in the GA model). In contrast, both temperature and relative humidity showed negative relation with Dengue cases in the GLM model. In the Poisson regression model, windspeed has a substantial significant negative connection with Dengue cases in all seasons. Temperature and rainfall are significantly and positively associated with Dengue cases in all seasons. The association between meteorological factors and recent outbreak data is the first study where we are aware of the use of maximum time series models in Bangladesh. Taking comprehensive measures against DENV outbreaks in the future can be possible through these findings, which can help fellow researchers and policymakers.
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Affiliation(s)
- Md. Aminul Islam
- COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
- Advanced Molecular Lab, Department of Microbiology, President Abdul Hamid Medical College, Karimganj 2310, Bangladesh
| | - Mohammad Nayeem Hasan
- Department of Statistics, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Ananda Tiwari
- Department of Health Security, Expert Microbiology Research Unit, Finnish Institute for Health and Welfare, 70701 Kuopio, Finland
| | - Md. Abdul Wahid Raju
- Department of Statistics, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Fateha Jannat
- Department of Public Health, North East University, Sylhet 3100, Bangladesh
| | - Sarawut Sangkham
- Department of Environmental Health, School of Public Health, University of Phayao, Muang District, Phayao 56000, Thailand
| | - Mahaad Issa Shammas
- Department of Civil and Environmental Engineering, College of Engineering, Dhofar University, P.O. Box 2509, Salalah PC 211, Oman
| | - Prabhakar Sharma
- School of Ecology and Environment Studies, Nalanda University, Rajgir 803116, India
| | - Prosun Bhattacharya
- COVID-19 Research, Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, Teknikringen 10B, SE 10044 Stockholm, Sweden
| | - Manish Kumar
- Sustainability Cluster, University of Petroleum and Energy Studies, Dehradun 248007, India
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterey, Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico
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Kamal ASMM, Al-Montakim MN, Hasan MA, Mitu MMP, Gazi MY, Uddin MM, Mia MB. Relationship between Urban Environmental Components and Dengue Prevalence in Dhaka City-An Approach of Spatial Analysis of Satellite Remote Sensing, Hydro-Climatic, and Census Dengue Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3858. [PMID: 36900868 PMCID: PMC10001735 DOI: 10.3390/ijerph20053858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Dengue fever is a tropical viral disease mostly spread by the Aedes aegypti mosquito across the globe. Each year, millions of people have dengue fever, and many die as a result. Since 2002, the severity of dengue in Bangladesh has increased, and in 2019, it reached its worst level ever. This research used satellite imagery to determine the spatial relationship between urban environmental components (UEC) and dengue incidence in Dhaka in 2019. Land surface temperature (LST), urban heat-island (UHI), land-use-land-cover (LULC), population census, and dengue patient data were evaluated. On the other hand, the temporal association between dengue and 2019 UEC data for Dhaka city, such as precipitation, relative humidity, and temperature, were explored. The calculation indicates that the LST in the research region varies between 21.59 and 33.33 degrees Celsius. Multiple UHIs are present within the city, with LST values ranging from 27 to 32 degrees Celsius. In 2019, these UHIs had a higher incidence of dengue. NDVI values between 0.18 and 1 indicate the presence of vegetation and plants, and the NDWI identifies waterbodies with values between 0 and 1. About 2.51%, 2.66%, 12.81%, and 82% of the city is comprised of water, bare ground, vegetation, and settlement, respectively. The kernel density estimate of dengue data reveals that the majority of dengue cases were concentrated in the city's north edge, south, north-west, and center. The dengue risk map was created by combining all of these spatial outputs (LST, UHI, LULC, population density, and dengue data) and revealed that UHIs of Dhaka are places with high ground temperature and lesser vegetation, waterbodies, and dense urban characteristics, with the highest incidence of dengue. The average yearly temperature in 2019 was 25.26 degrees Celsius. May was the warmest month, with an average monthly temperature of 28.83 degrees Celsius. The monsoon and post-monsoon seasons (middle of March to middle of September) of 2019 sustained higher ambient temperatures (>26 °C), greater relative humidity (>80%), and at least 150 mm of precipitation. The study reveals that dengue transmits faster under climatological circumstances characterized by higher temperatures, relative humidity, and precipitation.
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Affiliation(s)
- A. S. M. Maksud Kamal
- Department of Disaster Science and Climate Resilience, University of Dhaka, Dhaka 1000, Bangladesh
| | - Md. Nahid Al-Montakim
- Geoinformatics Laboratory, Department of Geology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Md. Asif Hasan
- Geoinformatics Laboratory, Department of Geology, University of Dhaka, Dhaka 1000, Bangladesh
| | | | - Md. Yousuf Gazi
- Geoinformatics Laboratory, Department of Geology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Md. Mahin Uddin
- Geoinformatics Laboratory, Department of Geology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Md. Bodruddoza Mia
- Geoinformatics Laboratory, Department of Geology, University of Dhaka, Dhaka 1000, Bangladesh
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Dey SK, Rahman MM, Howlader A, Siddiqi UR, Uddin KMM, Borhan R, Rahman EU. Prediction of dengue incidents using hospitalized patients, metrological and socio-economic data in Bangladesh: A machine learning approach. PLoS One 2022; 17:e0270933. [PMID: 35857776 PMCID: PMC9299345 DOI: 10.1371/journal.pone.0270933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/21/2022] [Indexed: 11/20/2022] Open
Abstract
Dengue fever is a severe disease spread by Aedes mosquito-borne dengue viruses (DENVs) in tropical areas such as Bangladesh. Since its breakout in the 1960s, dengue fever has been endemic in Bangladesh, with the highest concentration of infections in the capital, Dhaka. This study aims to develop a machine learning model that can use relevant information about the factors that cause Dengue outbreaks within a geographic region. To predict dengue cases in 11 different districts of Bangladesh, we created a DengueBD dataset and employed two machine learning algorithms, Multiple Linear Regression (MLR) and Support Vector Regression (SVR). This research also explores the correlation among environmental factors like temperature, rainfall, and humidity with the rise and decline trend of Dengue cases in different cities of Bangladesh. The entire dataset was divided into an 80:20 ratio, with 80 percent used for training and 20% used for testing. The research findings imply that, for both the MLR with 67% accuracy along with Mean Absolute Error (MAE) of 4.57 and SVR models with 75% accuracy along with Mean Absolute Error (MAE) of 4.95, the number of dengue cases reduces throughout the winter season in the country and increases mainly during the rainy season in the next ten months, from August 2021 to May 2022. Importantly, Dhaka, Bangladesh's capital, will see the maximum number of dengue patients during this period. Overall, the results of this data-driven analysis show that machine learning algorithms have enormous potential for predicting dengue epidemics.
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Affiliation(s)
- Samrat Kumar Dey
- School of Science and Technology (SST), Bangladesh Open University (BOU), Gazipur, Bangladesh
| | - Md. Mahbubur Rahman
- Department of Computer Science and Engineering (CSE), Military Institute of Science and Technology (MIST), Dhaka, Bangladesh
| | - Arpita Howlader
- Department of Computer and Communication Engineering (CCE), Patuakhali Science and Technology University (PSTU), Dumki, Patuakhali, Bangladesh
| | - Umme Raihan Siddiqi
- Department of Physiology, Shaheed Suhrawardy Medical College (ShSMC), Dhaka, Bangladesh
| | | | - Rownak Borhan
- Department of Computer Science and Engineering (CSE), Dhaka International University (DIU), Dhaka, Bangladesh
| | - Elias Ur Rahman
- Department of Computer Science and Engineering (CSE), Dhaka International University (DIU), Dhaka, Bangladesh
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