Dengue Outbreak Research Articles

A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City

The dengue virus is the most dangerous one that mosquitoes may spread to people. Despite attempts by the government, dengue outbreaks are becoming increasingly common in Bangladesh. Interventions in public health rely heavily on KAP (knowledge, attitude, and practice) studies. The primary goal of this research is to forecast the occurrence of dengue disease in the city of Dhaka using methods from machine learning and time series analysis and then to compare the models in order to find the one with the lowest MAPE. From January 2016 through July 2021, monthly data were retrieved for this study from WHO and the Directorate General of Health and Services (DGHS). According to the findings of this research, neural networks outperform time series analysis when it comes to making predictions. The best-fitted neural network (NN) model was found in model 04 with 05 hidden layers which produced the minimum error model with the value of error 0.003032557, and the values of RMSE and MAPE are 7.588889e − 06 and 1.15273, respectively, for the prediction of the dengue fever in Dhaka city. In contrast, the original dengue data in the time series analysis is not stationary. Take the difference and run the unit root test by the augmented Dickey–Fuller (ADF) test to make it stationary. The dengue data series is stationary at the first-order difference, as evidenced by the ACF and PACF, which show no noticeable spike in the first-order difference. The ARIMA (6, 1, 1) model with the lowest AIC = −251.8, RMSE = 0.0310797, and MAPE = 15.2892 is the best choice model for predicting the dengue death rate. Therefore, from these two models, the NN model gives better prediction performance with the lowest value of MAPE. So, the neural network gives better prediction performance than time series analysis. The NN model forecasted 12-month death rates of dengue fever that suggest the death rate in dengue fever falling month by month. This study is more innovative than any other research because this research approach is different from any other research approach. The model selection criteria are based on the most effective performance metrics MAPE, indicating the lowest error and better prediction performance. Therefore, from this research, the author suggests machine learning gives better prediction performance than time series analysis for any other prediction performance.

Awareness and Practice on Prevention and Control of Dengue among Community People of Lalitpur Metropolitan City

Background: Dengue is a mosquito-borne viral disease that has rapidly spread in many countries worldwide in recent years. Dengue is a major public health problem in many countries. Dengue is a rapidly emerging disease in Nepal that is endemic across most provinces. The incidence of dengue has increased in recent years due to the expansion of the Aedes aegypti vector. The increasing outbreak of dengue in Nepal is becoming a major concern for the public health. Objective: The objective of the study was to assess awareness level and practice on prevention and control of dengue among community people in Lalitpur metropolitan city. Method: A community-based cross-sectional study was conducted from January to June 2023 at ten different wards in Lalitpur metropolitan city. Sample populations of 368 residents of Lalitpur metropolitan city above the age of 18 years were selected for the study. Simple random probability sampling technique used for the sample selection. Statistical Package for the Social Sciences (SPSS) 16 version used to analyze the data. Result: Majority (98.1%) of respondents knew about dengue fever. Most of the respondents (47.1%) answered that dengue is caused by aedes mosquito. Majority (71.4%) of respondents stated that dirty water was breeding sites of dengue mosquito. Primary preventive measures used were cleanliness 34.1 percent followed by use of mosquito net/coil 31.4 percent. The community people had good awareness (79.3%) of dengue, but poor practice (56.79%) in prevention and control of dengue. According to this study, socio-demographic factors such as age, sex, and education level were significantly linked to the knowledge level of the participants. Conclusion: According to this study, the community people had a good awareness level of dengue (79.3%). However, their practice of prevention and control of dengue was found to be poor (56.79%).

Malaria and dengue outbreaks: A double health threat in southeastern Iran.

Malaria and dengue outbreaks: A double health threat in southeastern Iran.

Near coding-complete genome sequence of 12 dengue serotype 2 viruses from the 2023 outbreak in Bangladesh.

We report the near coding-complete genomes of 12 DENV serotype 2 strains collected during the 2023 dengue outbreak in Bangladesh. Analyses showed that all 12 strains were closely related and belonged to genotype II-Cosmopolitan.

Discrepancies in dengue burden estimates: a comparative analysis of reported cases and global burden of disease study, 2010-2019.

Dengue is a significant mosquito-borne disease. Several studies have utilized estimates from the Global Burden of Disease (GBD) study to assess the global, regional or national burden of dengue over time. However, our recent investigation suggests that GBD's estimates for dengue cases in Taiwan are unrealistically high. The current study extends the scope to compare reported dengue cases with GBD estimates across 30 high-burden countries and territories, aiming to assess the accuracy and interpretability of the GBD's dengue estimates. Data for this study were sourced from the GBD 2019 study and various national and international databases documenting reported dengue cases. The analysis targeted the top 30 countries and territories with the highest 10-year average of reported cases from 2010 to 2019. Discrepancies were quantified by computing absolute differences and ratios between the 10-year average of reported cases and GBD estimates. Coefficients of variation (CV) and estimated annual percentage changes (EAPCs) were calculated to assess variations and trends in the two data sources. Significant discrepancies were noted between reported data and GBD estimates in the number of dengue cases, incidence rates, and EAPCs. GBD estimates were substantially higher than reported cases for many entities, with the most notable differences found in China (570.0-fold), India (303.0-fold), Bangladesh (115.4-fold), Taiwan (85.5-fold) and Indonesia (23.2-fold). Furthermore, the GBD's estimates did not accurately reflect the extensive yearly fluctuations in dengue outbreaks, particularly in non-endemic regions such as Taiwan, China and Argentina, as evidenced by high CVs. This study reveals substantial discrepancies between GBD estimates and reported dengue cases, underscoring the imperative for comprehensive analysis in areas with pronounced disparities. The failure of GBD estimates to represent the considerable annual fluctuations in dengue outbreaks highlights the critical need for improvement in disease burden estimation methodologies for dengue.

A perspective on the worst ever dengue outbreak 2023 in Bangladesh: What makes this old enemy so deadly, and how can we combat it?

AbstractBackground and AimsBangladesh has been going through outbreaks of dengue fever cases every year since 2000. Yet this year's (2023) episode of dengue fever has crossed every line concerning fatality. Symptoms of the fever range from high fever, headaches, and muscle aches to deadly dengue hemorrhagic fever (DHF). The present review aims to assess the current pathogenicity and associated risk factors of recent dengue outbreaks in Bangladesh.MethodsTo perform this review work, we extracted relevant information from published articles available in PubMed, Scopus, and Google Scholar. We used dengue virus, dengue fever, and dengue outbreaks as keywords while searching for information.ResultsThis Aedes mosquito‐transmitted viral fever is more common in Bangladesh because of the tropical nature and immense burden of populations, resulting in convenient conditions for the reproduction of the vector. The rapid genetic transformation of this RNA virus and the resistance of its vector against insecticides have intensified the situation. The number of hospitalized patients has increased, and the case fatality rate has risen to 0.47%. Inadequate mosquito control measures, plenty of vector breeding sites, and a lack of public awareness have worsened the situation. Routine spraying of effective insecticides in high‐risk zones, regular inspection of potential mosquito breeding sites, and public awareness campaigns are the keys to limiting the spread of this virus. Also, the availability of detection kits, improved hospital settings, and trained health professionals are mandatory to keep disease fatalities under control.ConclusionDengue fever is a preventable disease. The successful development of a competent vaccine is now a prime need for preventing any future upsurge of the disease. Also, we recommend public awareness, vector control activities, and global collaboration to prevent spread.

High prevalence of dengue, Zika, and chikungunya viruses in blood donors during a dengue outbreak and an endemic period in Colombia.

Arboviruses pose a challenge in ensuring the supply of pathogen-free blood components because they are not routinely screened in blood banks, and blood components from infected asymptomatic donors could be transfused. This study aimed to detect and characterize arboviral infections in Colombian blood donors. In a cross-sectional study, the prevalence of dengue (DENV), Zika (ZIKV), and chikungunya (CHIKV) viruses and co-infections of blood donors were compared between an epidemic period (November 2019-February 2020, n = 462) and an endemic period (November 2021-August 2022, n = 1,119). Viral RNA from each donor serum was purified, and the viruses were detected using a previously standardized multiplex hemi-nested RT-PCR protocol. Subsequently, donors who tested positive were surveyed 15 days after the detection of the virus to identify clinical characteristics related to the arboviral infection. The prevalences of each virus were presented as percentages and compared between epidemic and endemic periods. Significantly higher prevalences were found in the epidemic period compared with the endemic period for DENV (14.5 vs. 1.9%), ZIKV (7.8 vs. 0.3%), CHIKV (8 vs. 3.3%), and co-infections (4.3 vs. 0.2%). The survey response rate of positive donors in the two periods was 83/175 (47%). In total, 57% of the donors surveyed were asymptomatic. Symptomatic donors most frequently reported headache (31%), malaise (13%), arthralgia (10%), and fever/chills (8%). The prevalence observed in epidemic and endemic periods was higher than that reported in other studies in the Americas. The high proportion of asymptomatic cases found, in addition to the mild and nonspecific manifestations among the symptomatic, may limit the effectiveness of the donor selection criteria used to mitigate the risk of transfusion-transmitted arboviruses.

A serotype-specific and tiled amplicon multiplex PCR method for whole genome sequencing of dengue virus

Dengue fever, a mosquito-borne viral disease of significant public health concern in tropical and subtropical regions, is caused by any of the four serotypes of the dengue virus (DENV1–4). Cutting-edge technologies like next-generation sequencing (NGS) are revolutionizing virology, enabling in-depth exploration of DENV's genetic diversity. Here, we present an optimized workflow for full-genome sequencing of DENV 1–4 utilizing tiled amplicon multiplex PCR and Illumina sequencing. Our assay, sequenced on the Illumina MiSeq platform, demonstrates its ability to recover the full-length dengue genome across various viral abundances in clinical specimens with high-quality base coverage. This high quality underscores its suitability for precise examination of intra-host diversity, enriching our understanding of viral evolution and holding potential for improved diagnostic and intervention strategies in regions facing dengue outbreaks.

Spatial analysis of dengue transmission in an endemic city in Brazil reveals high spatial structuring on local dengue transmission dynamics.

In the last decades, dengue has become one of the most widespread mosquito-borne arboviruses in the world, with an increasing incidence in tropical and temperate regions. The mosquito Aedes aegypti is the dengue primary vector and is more abundant in highly urbanized areas. Traditional vector control methods have showing limited efficacy in sustaining mosquito population at low levels to prevent dengue virus outbreaks. Considering disease transmission is not evenly distributed in the territory, one perspective to enhance vector control efficacy relies on identifying the areas that concentrate arbovirus transmission within an endemic city, i.e., the hotspots. Herein, we used a 13-month timescale during the SARS-Cov-2 pandemic and its forced reduction in human mobility and social isolation to investigate the spatiotemporal association between dengue transmission in children and entomological indexes based on adult Ae. aegypti trapping. Dengue cases and the indexes Trap Positive Index (TPI) and Adult Density Index (ADI) varied seasonally, as expected: more than 51% of cases were notified on the first 2months of the study, and higher infestation was observed in warmer months. The Moran's Eigenvector Maps (MEM) and Generalized Linear Models (GLM) revealed a strong large-scale spatial structuring in the positive dengue cases, with an unexpected negative correlation between dengue transmission and ADI. Overall, the global model and the purely spatial model presented a better fit to data. Our results show high spatial structure and low correlation between entomological and epidemiological data in Foz do Iguaçu dengue transmission dynamics, suggesting the role of human mobility might be overestimated and that other factors not evaluated herein could be playing a significant role in governing dengue transmission.

Comment on “Challenges and epidemiological implications of the first outbreak of dengue and chikungunya” published in EMHJ Volume 30 Number 1

Comment on “Challenges and epidemiological implications of the first outbreak of dengue and chikungunya” published in EMHJ Volume 30 Number 1

Molecular and Entomological Characterization of 2023 Dengue Outbreak in Dhading District, Central Nepal.

In 2023, Nepal faced its second largest dengue outbreak ever, following a record-breaking number of dengue cases in 2022, characterized by the expansion of infections into areas of higher altitudes. However, the characteristics of the 2023 circulating dengue virus (DENV) and the vector density remain poorly understood. Therefore, we performed DENV serotyping, clinical and laboratory assessment, and entomological analysis of the 2023 outbreak in central Nepal. A total of 396 fever cases in Dhading hospital suspected of being DENV positive were enrolled, and blood samples were collected and tested by different techniques including PCR. Of these, 278 (70.2%) had confirmed DENV infection. Multiple serotypes (DENV-1, -2, and -3) were detected. DENV-2 (97.5%) re-emerged after six years in Dhading while DENV-3 was identified for the first time. Dengue inpatients had significantly higher frequency of anorexia, myalgia, rash, diarrhea, nausea, vomiting, abdominal pain, and thrombocytopenia (p < 0.05). In this area, Aedes mosquitoes largely predominated (90.7%) with the majority being A. aegypti (60.7%). We also found high levels of Aedes index (20.0%) and container index (16.7%). We confirmed multiple DENV serotype circulation with serotype re-emergence and new serotype introduction, and high vector density in 2023. These findings call for the urgent initiation and scaling up of DENV molecular surveillance in human and mosquito populations for dengue control and prevention in Nepal.

Epidemiological investigation of dengue fever outbreak and its socioeconomic determinants in Banadir region, Somalia

BackgroundDengue has become an alarming global problem and is endemic in many countries, particularly in tropical and subtropical countries. The aim of this study was to investigate dengue fever outbreak in Banadir Region, Somalia, to understand the risk factors (time, place, personal characteristics).MethodsA descriptive cross-sectional study was undertaken to determine the levels of circulating anti-dengue virus antibodies and DENV NS1 antigen among Banadir Region residents, while a questionnaire survey was conducted to understand the clinical and demographic characteristics of the patients.ResultsA total of 735 febrile patients were studied, with 55.6% men and 44.3% women. The majority of the participants were children aged 14 years and younger. Among them, 10.8% tested positive for IgM antibodies against dengue virus (DENV), while the prevalence of DENV NS1 antigen was 11.8%. Fever and myalgia were the most common symptoms observed in the DENV-positive patients.ConclusionsA dengue fever outbreak has been confirmed in Banadir region, Somalia. This study provides information on the most affected districts and identifies risk factors contributing to DF outbreaks. The study recommends improving outbreak readiness and response, particularly in surveillance and laboratory diagnostics, by fostering intersectoral collaboration and establishing regulatory frameworks for financial and operational participation.

Demographic and clinical features of dengue fever infection in Pakistan: a cross-sectional epidemiological study

BackgroundDengue fever caused by dengue virus is a tropical disease and is among the deadliest vector-borne diseases. The humid and hot summers of Pakistan support the probation of the vectors responsible for the transmission of viral and other parasitic diseases.MethodologyA retrospective study, from 2012- 2019, of dengue infected individuals from the Punjab province of Pakistan was carried out to analyze epidemiology, clinical and laboratory findings of subjects with dengue virus infection. Data was derived from National Institute of Health (NIH) followed by Dengue control program of Pakistan, covering the incidence rate in 36 districts of Punjab and Islamabad Capital Territory (ICT) respectively. Patients data including the presence of dengue specific antigen or/and antibodies such as NS1 and IgG/IgM were observed. The study also included the analysis of demographic data, geographic data, and the month-wise distribution of dengue cases to examine seasonal trends.ResultsWe analyzed 25,682 dengue infected individuals. The statistical analysis revealed a significant association between genders in which male population was more affected by dengue than females. It was also noted that the middle age group was the most affected age group while the highest number of cases were reported in October. Rawalpindi and Lahore were the most affected cities in Punjab province while Islamabad represented the highest number of cases during the recent outbreak in 2019. The IgM and IgG antibodies were highly prevalent among the infected patients.ConclusionDengue is endemic in Pakistan, circulating throughout the year. Highest number of cases were observed in the month of October, September and November respectively. Association between climate change and vector-borne diseases need to be investigated in Pakistan as they significantly influence the timing and intensity of dengue and other disease outbreaks. Further exploration of hematological parameters is required to better diagnose and treat the disease. For the effective control of dengue outbreaks, awareness campaigns on sewage management and vector control along with social factors are strongly recommended for better control and eradication of the disease.

Medicinal Plants Used in Sri Lankan Traditional Medicine for Dengue Fever

Dengue fever, a mosquito-borne viral infectious disease caused by the dengue virus, is a significant global health concern, especially in tropical and subtropical regions. Despite preventive efforts, Sri Lanka faces recurring dengue outbreaks, with the Western province being the most affected. Current treatments primarily focus on supportive care, as specific antiviral therapies remain elusive. This review presents an overview of dengue, its clinical presentations, the dengue burden in Sri Lanka, and the potential of Sri Lankan medicinal plants used in traditional medicine for treating dengue. Several plants, such as Munronia pinnata, Azardirachta indica, Cissampelos pareira L., Carica papaya, Zingiber officinale, Curcuma longa, and Bambusa vulgaris, show antiviral properties against dengue. The utilization of these medicinal plants in dengue treatment could offer a promising avenue for further research and drug development.

Bangladesh in 2022 and 2023

Following the semicentennial celebrations and a global pandemic, Bangladesh seemed well on track to achieve the promises of furthering both democracy and development. But while development was visible in the completion of spectacular infrastructural projects, democracy continued to suffer, as the government plans to host the 12th parliamentary elections without the participation of the main opposition party. The war in Ukraine and a depleting foreign exchange reserve severely affected the country’s economic profile, while a dengue outbreak once more revealed the inadequacies of its public health system.

IJCM_369A: Notification of malaria and dengue outbreaks during COVID-19 pandemic: A secondary analysis of weekly IDSP reporting

Background: Like many other diseases, vector borne diseases related health activities got affected during COVID-19 due to disruption in health services. The preventive measures, vector surveillance, diagnostics, early diagnosis, and management are likely to have been affected. Objective: To explore the vector-borne disease trends as outbreaks from January 2021 to December 2023 using weekly reports of Integrated Disease Surveillance Programme (IDSP). Methodology: We used the secondary data of the weekly reports of IDSP weekly reports from Jan 2020 Dec 2023. We included dengue and malaria as two important diseases to represent the prevailing vector borne diseases in the country. The outbreak data collected were entered in MS Excel and reported as frequency and proportions to visualize the time trends during the COVID-19 pandemic. Results: The outbreaks of malaria were found to occur mainly in the second half of the year with 63% and 80% outbreaks in the years 2020 and 2021 respectively, coinciding with the onset of monsoons. Maximum number of outbreaks was reported in the year of 2021, followed by 2022 with number of outbreaks being 20 and 19 respectively. Maximum number of dengue outbreaks were reported in the year 2021 followed by year 2022 with the numbers being 114 and 81 respectively. The year 2020 had around half the number of outbreaks as of the year 2022 (42 outbreaks). The first half of the year 2023 reported 25 outbreaks. Conclusion: Both malaria and dengue had maximum number of outbreaks in the year 2021 followed by a slight dip in the year 2022. At the onset of pandemic, the number of outbreaks were quite less compared to the subsequent years, probably due to the lockdown imposed.

Upsurge of dengue outbreaks in several WHO regions: Public awareness, vector control activities, and international collaborations are key to prevent spread.

Dengue, the world's fastest-growing vector-borne disease, has skyrocketed in the 21st century. Dengue has harmed human health since its first known cases among Spanish soldiers in the Philippines to its 21st-century outbreaks in Southeast Asia, the Pacific, and the Americas. In light of the current circumstances, it is imperative to investigate its origin and prevalence, enabling the implementation of effective interventions to curb the upsurge. Our study examines the history of dengue outbreaks, and evolving impact on public health, aiming to offer valuable insights for a more resilient public health response worldwide. In this comprehensive review, we incorporated data from renowned databases such as PubMed, Google Scholar, and Scopus to provide a thorough analysis of dengue outbreaks. Recent dengue outbreaks are associated with rapid urbanization, international travel, climatic change, and socioeconomic factors. Rapid urbanization and poor urban design and sanitation have created mosquito breeding places for dengue vectors. Also, international travel and trade have spread the pathogen. Climate change in the past two decades has favored mosquito habitats and outbreaks. Socioeconomic differences have also amplified the impact of dengue outbreaks on vulnerable communities. Dengue mitigation requires vector control, community engagement, healthcare strengthening, and international cooperation. Climate change adaptation and urban planning are crucial. Although problems remain, a comprehensive vector control and community involvement plan may reduce dengue epidemics and improve public health in our interconnected world.

An eco-epidemiological analysis of dengue outbreak in Port Blair, South Andaman Island, India.

In Port Blair, the capital of Andaman & Nicobar Islands, in the Bay of Bengal, India, there was a sudden increase in fever cases resembling the symptoms of dengue in the monsoon period of 2022. Hence, an investigation was carried out to find out epidemiological and entomological reasons behind the outbreak. An entomological survey was carried out in 24 wards of Port Blair Municipal Council (PBMC) area, epidemiological data of last six years (January-December 2017-2021 and January-October 2022) and patient details of the year 2022 were collected. Both the epidemiological and entomological data were analyzed concerning time and place. During this outbreak period tyres (22.4%) came out as a major breeding habitat followed by small discarded materials (17.5%), metal drums (15.3%), and plastic drums (11.7%). In rainy season, peri-domestic breeding (55%) was more than the domestic breeding (45%) habitat. Ae. aegypti had a high preference for indoor large containers (100-1000 L) like plastic tank, metal drum and cement tank whereasAe. albopictus prefers to breed in medium sized plastic container (20-100 L) and outdoor water receptacles like tyres, flower pots, and domestic discarded materials. By source reduction, indoor space spray and outdoor fogging, vector density was controlled which curtailed the transmission and within eight weeks a decreasing trend of dengue cases was noticed. A regular entomological survey is crucial to know the seasonality and key breeding habitats of the vector for proper planning of vector control.

Atypical Dengue Outbreak in Odisha: Insights from the Entomological Investigations.

An outbreak of dengue fever struck Tikarapada village, Rayagada district (April 2020). Among 117 fever cases, 49 tested positive for the dengue NS1 antigen. To identify mosquito-related factors in the outbreak, a survey of 101 houses found Aedes breeding sites in 11.6% of containers. Aedes aegypti (79%) and Aedes albopictus (21%) emerged from collected pupae. The village exhibited high (house index = 24.8), (container index = 11.6), (pupal index = 32.7), and (Breteau index = 40.6) indices. The findings confirmed the presence of Aedes and ample breeding sites, suggesting their role in the outbreak. A report recommending integrated vector control measures was submitted to district and state health authorities.

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data.

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. 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. 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. 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.