Epidemiology of dengue fever in Hanoi from 2002 to 2010 and its meteorological determinants

Influence of air pollution and climate variability on dengue in Singapore: a time-series analysis

The associations between respiratory and cardiovascular health outcomes with air quality have been well examined. Less conclusive are the studies assessing the relationship between air quality and dengue, a mosquito-borne illness which continues to pose risk to more than half the world's population. We examined this relationship in Singapore, a tropical city-state located in the South-East Asian region where the burden of dengue is among the highest across the globe. We analyzed the short-term associations between all laboratory-confirmed dengue reports and variations in PM2.5, PM10, CO, O3, NO2 and SO2 in Singapore from 2009 to 2019 using the Distributed Lag Non-linear Model (DLNM) framework. PM2.5 (RR90th percentile: 1.28, 95% CI 1.11,1.49), PM10 (RR90th percentile: 1.30, 95% CI 1.12, 1.51), and CO (RR90th percentile: 1.30, 95% CI 1.06, 1.61), were positively associated with dengue up to a period of 48 days with observed maximum threshold effects, while O3 and NO2 were negatively associated with dengue. There was a positive, non-linear association between ambient temperature and dengue. We observed strong evidence of ambient temperature modifying the association between particulate matter and dengue risk. Variations in the concentrations of these air pollutant types may inform short-term dengue control resource augmentation plans.

Climate warming is expanding dengue burden in the Americas and Asia

Climate change is expected to pose significant threats to public health, particularly including vector-borne diseases. Despite dramatic recent increases in the burden of dengue that many anecdotally connect with climate change, the effect of past and future anthropogenic climate change on dengue remains poorly quantified. To assess the link between climate warming and dengue we assembled a dataset covering 21 countries in Asia and the Americas, and found a nonlinear relationship between temperature and dengue incidence with the largest impact of warming at lower temperatures (below about 20°C), peak incidence at 27.8°C, and subsequent declines at higher temperatures. Using this inferred temperature response, we estimate that historical climate change has increased dengue incidence by 18% (11 - 27%) on average across our study countries, and that future warming could further increase it by 49% (16 - 136%) to 76% (27 - 239%) by mid-century for low or high emissions scenarios, respectively, with some cooler regions projected to see dengue doubling due to warming and other currently hot regions seeing no impact or even small declines. Under the highest emissions scenario, we estimate that 262 million people are currently living in places in these 21 countries where dengue incidence is expected to more than double due to climate change by mid-century. These insights highlight the major impacts of anthropogenic warming on dengue burden across most of its endemic range, providing a foundation for public health planning and the development of strategies to mitigate future risks due to climate change.

Review of the aetiologies of central nervous system infections in Vietnam

Central nervous system (CNS) infections are an important cause of morbidity and mortality in Vietnam, with many studies conducted to determine the aetiology. However, the cause remains unknown in a large proportion of cases. Although a systematic review of the aetiologies of CNS infections was conducted in the Mekong region, there are no known published reviews of the studies specifically in Vietnam. Here, we review the cause of CNS infections in Vietnam while also considering the potential aetiologies where a cause was not identified, based on the literature from the region. In particular, we focus on the most common pathogens in adults and children including Streptococcus suis which is associated with the consumption of raw pig products, and Japanese encephalitis virus, a mosquito-borne pathogen. We also discuss pathogens less commonly known to cause CNS infections in Vietnam but have been detected in neighbouring countries such as Orientia tsutsugamushi, Rickettsia typhi and Leptospira species and how these may contribute to the unknown causes in Vietnam. We anticipate that this review may help guide future public health measures to reduce the burden of known pathogens and broaden testing to help identify additional aetiologies.

Extreme precipitation, exacerbated by anthropogenic climate change, drove Peru's record-breaking 2023 dengue outbreak

Anthropogenic forcing is increasing the likelihood and severity of certain extreme weather events, which may catalyze outbreaks of climate-sensitive infectious diseases. Extreme precipitation events can promote the spread of mosquito-borne illnesses by creating vector habitat, destroying infrastructure, and impeding vector control. Here, we focus on Cyclone Yaku, which caused heavy rainfall in northwestern Peru from March 7th - 20th, 2023 and was followed by the worst dengue outbreak in Peru's history. We apply generalized synthetic control methods to account for baseline climate variation and unobserved confounders when estimating the causal effect of Cyclone Yaku on dengue cases across the 56 districts with the greatest precipitation anomalies. We estimate that 67 (95% CI: 30 - 87) % of cases in cyclone-affected districts were attributable to Cyclone Yaku. The cyclone significantly increased cases for over six months, causing 38,209 (95% CI: 17,454 - 49,928) out of 57,246 cases. The largest increases in dengue incidence due to Cyclone Yaku occurred in districts with a large share of low-quality roofs and walls in residences, greater flood risk, and warmer temperatures above 24°C. Analyzing an ensemble of climate model simulations, we found that extremely intense March precipitation in northwestern Peru is 42% more likely in the current era compared to a preindustrial baseline due to climate forcing. In sum, extreme precipitation like that associated with Cyclone Yaku has become more likely with climate change, and Cyclone Yaku caused the majority of dengue cases across the cyclone-affected districts.

The 2023 report of the MJA-Lancet Countdown on health and climate change: sustainability needed in Australia's health care sector

The MJA-Lancet Countdown on health and climate change in Australia was established in 2017 and produced its first national assessment in 2018 and annual updates in 2019, 2020, 2021 and 2022. It examines five broad domains: health hazards, exposures and impacts; adaptation, planning and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. In this, the sixth report of the MJA-Lancet Countdown, we track progress on an extensive suite of indicators across these five domains, accessing and presenting the latest data and further refining and developing our analyses. Our results highlight the health and economic costs of inaction on health and climate change. A series of major flood events across the four eastern states of Australia in 2022 was the main contributor to insured losses from climate-related catastrophes of $7.168 billion - the highest amount on record. The floods also directly caused 23 deaths and resulted in the displacement of tens of thousands of people. High red meat and processed meat consumption and insufficient consumption of fruit and vegetables accounted for about half of the 87 166 diet-related deaths in Australia in 2021. Correction of this imbalance would both save lives and reduce the heavy carbon footprint associated with meat production. We find signs of progress on health and climate change. Importantly, the Australian Government released Australia's first National Health and Climate Strategy, and the Government of Western Australia is preparing a Health Sector Adaptation Plan. We also find increasing action on, and engagement with, health and climate change at a community level, with the number of electric vehicle sales almost doubling in 2022 compared with 2021, and with a 65% increase in coverage of health and climate change in the media in 2022 compared with 2021. Overall, the urgency of substantial enhancements in Australia's mitigation and adaptation responses to the enormous health and climate change challenge cannot be overstated. Australia's energy system, and its health care sector, currently emit an unreasonable and unjust proportion of greenhouse gases into the atmosphere. As the Lancet Countdown enters its second and most critical phase in the leadup to 2030, the depth and breadth of our assessment of health and climate change will be augmented to increasingly examine Australia in its regional context, and to better measure and track key issues in Australia such as mental health and Aboriginal and Torres Strait Islander health and wellbeing.

Climate change and human health in Vietnam: a systematic review and additional analyses on current impacts, future risk, and adaptation

This study aims to investigate climate change's impact on health and adaptation in Vietnam through a systematic review and additional analyses of heat exposure, heat vulnerability, awareness and engagement, and projected health costs. Out of 127 reviewed studies, findings indicated the wider spread of infectious diseases, and increased mortality and hospitalisation risks associated with extreme heat, droughts, and floods. However, there are few studies addressing health cost, awareness, engagement, adaptation, and policy. Additional analyses showed rising heatwave exposure across Vietnam and global above-average vulnerability to heat. By 2050, climate change is projected to cost up to USD1-3B in healthcare costs, USD3-20B in premature deaths, and USD6-23B in work loss. Despite increased media focus on climate and health, a gap between public and government publications highlighted the need for more governmental engagement. Vietnam's climate policies have faced implementation challenges, including top-down approaches, lack of cooperation, low adaptive capacity, and limited resources.

The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review

Background: Climate change poses a real challenge and has contributed to causing the emergence and re-emergence of many communicable diseases of public health importance. Here, we reviewed scientific studies on the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and synthesized the key findings on communicable disease projection in the event of global warming. Method: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow checklist. Four databases (Web of Science, Ovid MEDLINE, Scopus, EBSCOhost) were searched for articles published from 2005 to 2020. The eligible articles were evaluated using a modified scale of a checklist designed for assessing the quality of ecological studies. Results: A total of 38 studies were included in the review. Precipitation and temperature were most frequently associated with the selected climate-sensitive communicable diseases. A climate change scenario simulation projected that dengue, malaria, and cholera incidence would increase based on regional climate responses. Conclusion: Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases.

The effects of maximum ambient temperature and heatwaves on dengue infections in the tropical city-state of Singapore - A time series analysis

BACKGROUND

Global incidence of dengue has surged rapidly over the past decade. Each year, an estimated 390 million infections occur worldwide, with Asia-Pacific countries bearing about three-quarters of the global dengue disease burden. Global warming may influence the pattern of dengue transmission. While previous studies have shown that extremely high temperatures can impede the development of the Aedes mosquito, the effect of such extreme heat over a sustained period, also known as heatwaves, has not been investigated in a tropical climate setting.

AIM

We examined the short-term relationships between maximum ambient temperature and heatwaves and reported dengue infections in Singapore, via ecological time series analysis, using data from 2009 to 2018.

METHODS

We studied the effect of two measures of extreme heat - (i) heatwaves and (ii) maximum ambient temperature. We used a negative binomial regression, coupled with a distributed lag nonlinear model, to examine the immediate and lagged associations of extreme temperature on dengue infections, on a weekly timescale. We adjusted for long-term trend, seasonality, rainfall and absolute humidity, public holidays and autocorrelation.

RESULTS

We observed an overall inhibitive effect of heatwaves on the risk of dengue infections, and a parabolic relationship between maximum temperature and dengue infections. A 1 °C increase in maximum temperature from 31 °C was associated with a 13.1% (Relative Risk (RR): 0.868, 95% CI: 0.798, 0.946) reduction in the cumulative risk of dengue infections over six weeks. Weeks with 3 heatwave days were associated with a 28.3% (RR: 0.717, 95% CI: 0.608, 0.845) overall reduction compared to weeks with no heatwave days. Adopting different heatwaves specifications did not substantially alter our estimates.

CONCLUSION

Extreme heat was associated with decreased dengue incidence. Findings from this study highlight the importance of understanding the temperature dependency of vector-borne diseases in resource planning for an anticipated climate change scenario.

Risk factors of dengue fever in an urban area in Vietnam: a case-control study

Background

Dengue is a mosquito-borne flavivirus present in many metropolitan cities of tropical countries.

Methods

During and after the dengue season (September 2018 to January 2019), we conducted a case-control study in order to determine the risk factors for dengue fever in Hanoi city, Vietnam. 98 dengue patients and 99 patients with other acute infections, such as Hepatitis B virus infection, were recruited at Department of Infectious Disease of Bach Mai national hospital in Hanoi. Patients were interviewed using a structured questionnaire covering demographic, housing, environmental factors and knowledge, attitude, and practice on dengue prevention and control. Univariate analysis and multivariable logistic regression were used to determine the risk factors of dengue status.

Results

The mean score of knowledge items and practice items was only 7.9 out of total 19 points and 3.9 out of total 17 points, respectively. While the mean score of attitude items was 4.8 out of total 6 points. Multivariable logistic regression indicated that older patients had lesser risk of getting dengue infection as compared to younger adults aged 16–30, and patients living in peri-urban districts were less likely to suffer of dengue fever than patients living in central urban districts (OR = 0.31; 95% CI 0.13–0.75). This study could not find any association with occupation, water storage habit, knowledge, attitude, or practice on dengue prevention.

Conclusions

All patients had a relatively low level of knowledge and practice on dengue prevention and control. However, the attitude of the participants was good. We found that age group and living district were the risk factors correlated with the dengue status. Communication programs on raising dengue awareness should be repeated all year round and target particular groups of adolescents, younger adults, landlords and migrants from other provinces to improve their knowledge and encourage them to implement preventive measures against dengue fever.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10687-y.

Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

The effects of climate on vector-borne disease systems are highly context-dependent. Here, the authors incorporate laboratory-measured physiological traits of the mosquito Aedes aegypti into climate-driven mechanistic models to predict number, timing, and duration of outbreaks in Ecuador and Kenya.

First Full-Length Genome Sequence of Dengue Virus Serotype 2 Circulating in Vietnam in 2017

INTRODUCTION

Dengue hemorrhagic fever is caused by four serotypes of dengue viruses transmitted by mosquitoes. In Vietnam, dengue outbreaks occur every year, and all four serotypes have been found circulating with the dominant one varying over time. However, in 2017 an unusual dengue fever outbreak occurred in the North of Vietnam, predominantly caused by DENV1 (92%) and DENV2 (7.3%). The objective of the present study was to obtain and characterize the full-length genome sequence of seven DENV2 strains in 2017 epidemic.

MATERIALS AND METHODS

Whole-genome sequencing of seven DENV2 isolates from the 2017 outbreak were obtained using the Illumina MiSeq next generation sequencer system. Complete genome sequences were then analyzed to find out genetic variants and genetic relationships between these DENV2 with other strains that circulated in Vietnam previously and other regions of the world.

RESULTS

The complete genome sequence of seven DENV2 isolates in the 2017 dengue outbreak comprised 10,696 nucleotides with an open reading frame coding for 3392 amino acids. The genome analysis showed only a small number of amino acid changes which were obtained in all genes, in which a few amino acids substitutions were distributed over the positions such as G156 (NS1), V106 (NS2A), and L258/T260 (NS5). The phylogenetic analysis revealed that the DENV2 isolates in the 2017 outbreak were most closely related to the dengue virus from India in 2006, suggesting that the causative virus originated from the DENV2 that caused dengue hemorrhagic fever in 2006 in India.

CONCLUSION

The first complete genome sequences of seven DENV2 isolates in the 2017 dengue outbreak in Northern Vietnam were successfully obtained. The genetic and phylogenetic data indicated that these DENV2 isolates were not causative virus circulating in Vietnam previously but originated from India in 2006. These data are emerging and providing valuable information for the management and surveillance of dengue in Vietnam.

Dengue virus infection and pregnancy outcomes during the 2017 outbreak in Ouagadougou, Burkina Faso: A retrospective cohort study

Introduction

Dengue fever is a re-emerging pathology in Burkina Faso. It affects everyone and pregnant women are not left out. The objective of this study was to estimate the burden of dengue fever and to assess its effects on pregnancy outcomes in hospitalized pregnant women during the 2017 outbreak in Ouagadougou, Burkina Faso.

Method

This was a retrospective cohort study including febrile pregnant women from five health facilities in Ouagadougou. The study was carried out from July 1st to December 31st, 2017. A logistic stepwise regression was performed to identify the pregnancy adverse outcomes risk factors.

Results

Our study included 424 pregnant women at a mean age of 27.1 years old (Standard deviation: 6.23 years). Overall 28.54% (121/424) were infected with dengue virus. During follow-up, 29.01% (123/424) presented an adverse pregnancy outcome. Adjusted for gestational age and clinical symptoms, the risk of adverse pregnancy outcome was twice as high among dengue infected women as compared to uninfected women with an adjusted Odds Ratio (aOR) = 2.09 (1.08–4.05). The risk of the adverse pregnancy outcome was higher in the third trimester of pregnancy with aOR = 1.66 (1.02–2.72) in dengue fever infected women.

Conclusion

Dengue fever is a risk factor for adverse pregnancy outcomes, especially in the third trimester in Burkina Faso. The implementation of effective anti-vectorial control interventions and better management of dengue fever during pregnancy are needed to improve pregnancy outcomes.

Climate Change and Inpatient Dermatology

PURPOSE OF REVIEW

Climate change represents a major existential threat facing the global community, and it has already begun to affect human health in a multitude of ways. This review highlights and discusses the implications that climate change has already had and is expected to have for inpatient dermatologists.

RECENT FINDINGS

There are a variety of conditions affected by climate changes. The distribution and frequencies of infectious diseases and their vectors are changing in line with variations in climate conditions. Increased temperatures have already been associated with exacerbation of existing skin conditions, such as atopic dermatitis, and recent evidence suggests that higher temperatures will also magnify the effects of harmful ultraviolet radiation. Extreme weather events that result from climate change are followed by an array of dermatologic conditions that may be unusual for the given location. Inpatient dermatologists should be prepared to manage these potentially unfamiliar dermatologic consequences of climate change.

SUMMARY

Climate change will have widespread effects on the medical field, and inpatient dermatologists will be faced with their own unique set of challenges and practice variations. Practitioners should be familiar with the ongoing and predicted effects of climate change in their locations so that they can readily identify and treat associated conditions, and they should adjust their practice to reduce their carbon footprint and serve as a model for patients to do the same.

A Mixed-Methods Approach to Understanding Knowledge of Mosquito-Borne Infections and Barriers for Protection in Hanoi, Vietnam

Dengue is a growing problem in Hanoi, with cyclical epidemics of increasing frequency and magnitude. In June 2019, we conducted a cross-sectional survey using mixed methods to investigate how inhabitants of Hanoi perceive and respond to the risk of mosquito-borne diseases (MBD). A total of 117 participants recruited using a stratified random sampling method were interviewed in three districts of Hanoi. Knowledge and practices (KP) regarding MBDs were assessed using a pre-tested questionnaire. Inferential statistics were used to identify factors associated with KP scores and describe the relationship between variables. Additionally, a "risk-mapping" exercise was conducted in a subsample through semi-structured interviews and analyzed qualitatively and quantitatively using the System Effects platform. Factors significantly associated with knowledge scores were education and family history of MBDs. While knowledge and practice scores were found to be positively correlated in the statistical analysis, this was not corroborated by our observations on the field. The results also revealed gaps in knowledge about MBDs and vectors and highlighted a general feeling of powerlessness which prevented the adoption of protective behaviors. Therefore, educational interventions which provide concrete tools to empower communities should have a positive impact on improving vector control.

Impact of Climate Variability and Abundance of Mosquitoes on Dengue Transmission in Central Vietnam

Dengue fever is an important arboviral disease in many countries. Its incidence has increased during the last decade in central Vietnam. Most dengue studies in Vietnam focused on the northern area (Hanoi) and southern regions but not on central Vietnam. Dengue transmission dynamics and relevant environmental risk factors in central Vietnam are not understood. This study aimed to evaluate spatiotemporal patterns of dengue fever in central Vietnam and effects of climatic factors and abundance of mosquitoes on its transmission. Dengue and mosquito surveillance data were obtained from the Department of Vector Control and Border Quarantine at Nha Trang Pasteur Institute. Geographic Information System and satellite remote sensing techniques were used to perform spatiotemporal analyses and to develop climate models using generalized additive models. During 2005-2018, 230,458 dengue cases were reported in central Vietnam. Da Nang and Khanh Hoa were two major hotspots in the study area. The final models indicated the important role of Indian Ocean Dipole, multivariate El Niño-Southern Oscillation index, and vector index in dengue transmission in both regions. Regional climatic variables and mosquito population may drive dengue transmission in central Vietnam. These findings provide important information for developing an early dengue warning system in central Vietnam.

Whole genome sequencing and genetic variations in several dengue virus type 1 strains from unusual dengue epidemic of 2017 in Vietnam

Background

Dengue hemorrhagic fever is an acute viral infection transmitted by mosquitoes. In the 2017, a dengue epidemic occurred in Hanoi in a short time interval and many cases were serious with associated mortality. This was the largest and unusual dengue fever outbreak in the North of Vietnam over the past 20 years. The objective of the present study was to understand the genetic characteristics of the DENV-1 strain in the 2017 epidemic and its relationship with previous viruses in Vietnam and the rest of the world.

Methods

Complete genomes of 72 DENV-1 from patients in the 2017 epidemic were sequenced using NGS. The full genome sequences were then analyzed to find out the genetic variants in the groups of 72 strains, followed by their comparison with other strains that caused disease in Vietnam previously and several other regions of the world, revealing a genetic relationship between them.

Results

The complete genome sequence of 72 DENV-1 strains comprised 10,697 nucleotides with an open reading frame coding for 3392 amino acids. The genomic analysis revealed different amino acid substitutions in all genes, especially varying at position S75 (Capsid), M125 (PrM), D54 (E), T147, V180 (NS1), G45, Y126, I154 (NS2A), A94 (NS2B), M298 (NS3), K47, V68 (NS4A), I29 (NS4B), and R166, E536, G614, T821 (NS5). The genetic analysis suggested that the viruses were most closely related to the causative virus of the dengue outbreak in Vietnam and Cambodia from 2006 to 2008. These results indicated that DENV-1 from the dengue epidemic 2017 in Northern Vietnam originated from the virus that caused the dengue outbreak during the 2007 to 2008 period in Vietnam.

Conclusion

The present study is the first of its kind to describe complete genome sequence as well as genetic variants and phylogenetic analysis of DENV-1 associated with the unusual dengue epidemic of 2017 in northern Vietnam. These results provide detailed evidence to elucidate the origin, circulation, and genetic evolution of DENV in Vietnam.

Barriers to Engaging Communities in a Dengue Vector Control Program: An Implementation Research in an Urban Area in Hanoi City, Vietnam

The dengue situation in the urban setting of Hanoi city, Vietnam, is emerging, focusing on inner districts. Previous studies showed that a dengue vector control program in Hanoi was not effective because of the lack of adequate engagement of the local government authorities, health sector, and community. This implementation research aimed to explore barriers to implementing community engagement in a dengue vector control program in an urban district of Hanoi city. Ten in-depth interviews and 14 focus group discussions were conducted at Lang Thuong, Khuong Thuong, Tho Quan, and Kim Lien wards in Dong Da district, Hanoi city. Data collection was implemented from April to June 2017. All discussions were recorded and transcribed verbatim. Data were analyzed using the content analysis approach. Secondary data from the dengue vector control program reports were used to support the qualitative evidence. We found that the barriers to implementing effective community engagement were as follows: 1) lack of interest and an attitude of dependency on action from the health sector of local people's committee, 2) lack of enthusiasm of mass organizations and community leaders, 3) overburdened workloads and lack of communication skills from health sector, 4) low awareness and readiness from community, 5) lack of detailed policy guidelines and low enforcement of related policy, and 6) limited budget. Recommended actions should be made to improve the community engagement in the current resource-limited context of Vietnam by both top-down and bottom-up approaches.

Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008-2016)

Introduction

In order to improve the prediction accuracy of dengue fever incidence, we constructed a prediction model with interactive effects between meteorological factors, based on weekly dengue fever cases in Guangdong, China from 2008 to 2016.

Methods

Dengue fever data were derived from statistical data from the China National Notifiable Infectious Disease Reporting Information System. Daily meteorological data were obtained from the China Integrated Meteorological Information Sharing System. The minimum temperature for transmission was identified using data fitting and the Ross-Macdonald model. Correlations and interactive effects were examined using Spearman’s rank correlation and multivariate analysis of variance. A probit regression model to describe the incidence of dengue fever from 2008 to 2016 and forecast the 2017 incidence was constructed, based on key meteorological factors, interactive effects, mosquito-vector factors, and other important factors.

Results

We found the minimum temperature suitable for dengue transmission was ≥18°C, and as 97.91% of cases occurred when the minimum temperature was above 18 °C, the data were used for model training and construction. Epidemics of dengue are related to mean temperature, maximum/minimum and mean atmospheric pressure, and mean relative humidity. Moreover, interactions occur between mean temperature, minimum atmospheric pressure, and mean relative humidity. Our weekly probit regression prediction model is 0.72. Prediction of dengue cases for the first 41 weeks of 2017 exhibited goodness of fit of 0.60.

Conclusion

Our model was accurate and timely, with consideration of interactive effects between meteorological factors.

Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk

BACKGROUND

Dengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001-2012 to determine seasonal trends, develop risk maps and an incidence forecasting model.

METHODS

The data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001-2009) and validation (2010-2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil's coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010-2012 were also used to generate risk maps.

RESULTS

The mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil's coefficient of inequality of 0.22 was generated.

CONCLUSIONS

The study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil's coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country.

Urban livestock-keeping and dengue in urban and peri-urban Hanoi, Vietnam

Urban livestock provides an important source of food and income, but it may increase the risks for disease transmission. Vectors, such as mosquitoes, might increase and thereby cause an enhanced transmission of infectious diseases, such as dengue fever; considered the most important mosquito-borne viral disease globally. This cross-sectional study evaluated the awareness of dengue fever and investigated how the presence of dengue vectors is affected by the keeping of livestock in urban households in the city of Hanoi, Vietnam. From February to March 2018, during the season of lowest occurrence of dengue in Hanoi, 140 households were interviewed, of which 69 kept livestock. A general trend was observed; respondents living in the Dan Phuong district, a peri-urban district, had better knowledge and practice regarding dengue as compared to the urban Ha Dong district. In total, 3899 mosquitoes were collected and identified, of which 52 (1.33%) were Aedes species. A significant difference between the two districts was observed, with more households in Ha Dong having Aedes spp. mosquitoes (p = 0.02) and a higher incidence of dengue fever (p = 0.001). There was no significant association between livestock-rearing and the presence of Aedes spp. mosquitoes (p = 0.955), or between livestock-rearing and the incidence of dengue fever (p = 0.08). In conclusion, this study could not find any indication that households keeping livestock were at higher risk of dengue virus infections in Hanoi during the season of lowest occurrence of dengue, but clearly indicated the need of more information provided to urban inhabitants, particularly on personal protection.

Urban transmission of mosquito-borne flaviviruses - a review of the risk for humans in Vietnam

Vietnam is a tropical country where mosquito-borne diseases are common. This review explores the transmission of mosquito-borne flaviviruses in urban areas of Vietnam. It concludes that urban transmission has mainly been studied for Dengue virus, and so far, much less for Japanese encephalitis virus. Dengue is the most common flavivirus in Vietnam. Due to fast urbanization and favorable climatic conditions, the viral transmission concentrates mainly to large cities with high population density including Ha Noi, Nha Trang and Ho Chi Minh. Human cases of Japanese encephalitis have been controlled by an expanded immunization program. However, this virus is still circulating throughout the country, also in cities due to the pig rearing practices in urban and peri-urban areas. Zika virus is an additional major concern because it has long circulated in the Northern area and is now increasingly diagnosed in urban areas of the Central, Central Highlands and Southern regions using the same mosquito vectors as Dengue virus. There was alarge outbreak of Zika disease from 2016 to early 2017, with most infections observed in Ho Chi Minh city, the largest town in Vietnam. Other flaviviruses circulate in Vietnam but have not been investigated in terms of urban transmission.

Knowledge, Attitude and Practice about Dengue Fever among Patients Experiencing the 2017 Outbreak in Vietnam

There is a gap in the literature on the understanding of the general Vietnamese population toward dengue fever (DF). This study aimed to explore knowledge, attitudes, practice (KAP) of dengue fever among Vietnamese participants and the potential associated factors. A cross-sectional study was conducted among 330 patients at the Bach Mai Hospital in Northern Vietnam. A Tobit regression model was utilized to investigate the associated factors. The average knowledge score was 4.6/19. Respondents perceived their risk of DF infection to be very low (39.5%) to low (20.7%) and had a neutral attitude about the necessity of hospitalization when being infected with DF (60.9%). A total of 17.6%, 9.8% and 6.6% of respondents reported frequently changing water, properly disposing of waste and covering water storage containers to eliminate larvae. Gender, education level, duration of illness and travel history were correlated with knowledge. Occupation, the presence of DF in the neighborhood, mosquito density at home and DF symptom severity were associated with attitudes. Occupation, mosquito density at home, type of patient, knowledge and attitudes were associated with practices. To enhance the KAP towards DF, further efforts should first be directed to improve knowledge through education, especially at the school level and people in less developed areas.

Climatic factors influencing dengue incidence in an epidemic area of Nepal

Objective

Geographic expansion of dengue incidence has drawn a global interest to identify the influential factors that instigate the spread of this disease. The objective of this study was to find the environmental factors linked to dengue incidence in a dengue epidemic area of Nepal by negative binomial models using climatic factors from 2010 to 2017.

Results

Minimum temperature at lag 2 months, maximum temperature and relative humidity without lag period significantly affected dengue incidence. Rainfall was not associated with dengue incidence in Chitwan district of Nepal. The incident rate ratio (IRR) of dengue case rise by more than 1% for every unit increase in minimum temperature at lag 2 months, maximum temperature and relative humidity, but decrease by .759% for maximum temperature at lag 3 months. Considering the effect of minimum temperature of previous months on dengue incidence, the vector control and dengue management program should be implemented at least 2 months ahead of dengue outbreak season.

Electronic supplementary material

The online version of this article (10.1186/s13104-019-4185-4) contains supplementary material, which is available to authorized users.

Cost-of-Illness and the Health-Related Quality of Life of Patients in the Dengue Fever Outbreak in Hanoi in 2017

Dengue fever (DF) outbreaks occur intermittently in Vietnam, and the most recent epidemic happened in 2017. However, attempts to measure the burden of DF in relation to the quality of life and the cost of treatment for patients during an epidemic period are constrained. This study explored the health-related quality of life (HRQOL) and the cost of illness among patients with dengue fever in Vietnam. A cross-sectional study was conducted in Bach Mai Hospital from September to November 2017. The EuroQol-5 dimensions-5 levels (EQ-5D-5L) was used to measure HRQOL. Cost-of illness was measured by collecting data about the direct medical cost, the non-medical cost, and the indirect cost. Among 225 patients, most of the participants experienced problems regarding mobility (62.3%), self-care (71.8%), usual activities (64.6%), and anxiety/depression (64.1%). The mean EQ-5D index was 0.66 (SD = 0.24). The median cost of illness for inpatient and outpatient groups were US $110.10 (IQR = US $4.40–1200.00) and US $36.10 (IQR = US $1.80–816.30), respectively. Indirect costs accounted for a major proportion in both groups. Lower-skilled workers and those with a higher severity of the disease had significantly lower HRQOL. Meanwhile, people who were inpatients, had comorbidities, had higher incomes, and who experienced a longer disease duration, had a higher cost of treatment. In conclusion, high costs and severe health deterioration, especially in psychological dimensions, were found in patients with DF in Vietnam. Strengthening primary health care services and communication campaigns are necessary to relieve the burden of diseases and could possibly contribute to effective DF control and prevention strategies.

A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka

Background

Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike’s information criterion, Bayesian information criterion and residual analysis.

Results

The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity.

Conclusions

The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month’s dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2828-2) contains supplementary material, which is available to authorized users.

Feasibility and willingness to pay for dengue vaccine in the threat of dengue fever outbreaks in Vietnam

BACKGROUND

The escalation of dengue fever (DF) cases in recent years and the occurrence of a large-scale DF outbreak in 2017 underline the importance of dengue vaccines in Vietnam. Given the potential benefits of the dengue vaccines and the need for copayment by the private sector, this study aims to evaluate the willingness to pay (WTP) for the dengue vaccines in patients with DF in Northern Vietnam.

METHODS

A cross-sectional study was conducted on 330 in-and-out patients with DF admitted to the Bach Mai Hospital. We used the contingent valuation method to evaluate the WTP for dengue vaccines. Socioeconomic and clinical characteristics were also investigated. Multivariate interval and logistic regression models were used to estimate the average amount of WTP and identify the factors associated with the WTP.

RESULTS

Around 77.3% patients were willing to pay an average amount of US$ 67.4 (95% CI=57.4-77.4) for the vaccine. People of higher ages, those having health insurance, those traveling in the past 15 days or suffering from anxiety/depression were less likely to be willing to pay for the dengue vaccine. However, people having a longer duration of DF or having problems with mobility were positively associated with WTP for the dengue vaccine. Patients educated to more than high school levels (Coeff.=31.31; 95% CI=3.26-59.35), those in the richest quintile (Coeff.=62.76; 95% CI=25.40; 100.13), or those having a longer duration of the disease (Coeff.=6.18; 95% CI=0.72-11.63) were willing to pay a higher amount.

CONCLUSION

This study highlights a relatively high rate and amount of WTP for the dengue vaccine among patients with DF. Psychological counseling services as well as educational campaigns should be undertaken to improve the WTP for the vaccine. Moreover, government subsidies should be given to increase the coverage of the vaccine in the future, especially for the poor.

Bayesian dynamic modeling of time series of dengue disease case counts

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

Surveillance of dengue vectors using spatio-temporal Bayesian modeling

Background

At present, dengue control focuses on reducing the density of the primary vector for the disease, Aedes aegypti, which is the only vulnerable link in the chain of transmission. The use of new approaches for dengue entomological surveillance is extremely important, since present methods are inefficient. With this in mind, the present study seeks to analyze the spatio-temporal dynamics of A. aegypti infestation with oviposition traps, using efficient computational methods. These methods will allow for the implementation of the proposed model and methodology into surveillance and monitoring systems.

Methods

The study area includes a region in the municipality of Rio de Janeiro, characterized by high population density, precarious domicile construction, and a general lack of infrastructure around it. Two hundred and forty traps were distributed in eight different sentinel areas, in order to continually monitor immature Aedes aegypti and Aedes albopictus mosquitoes. Collections were done weekly between November 2010 and August 2012. The relationship between egg number and climate and environmental variables was considered and evaluated through Bayesian zero-inflated spatio-temporal models. Parametric inference was performed using the Integrated Nested Laplace Approximation (INLA) method.

Results

Infestation indexes indicated that ovipositing occurred during the entirety of the study period. The distance between each trap and the nearest boundary of the study area, minimum temperature and accumulated rainfall were all significantly related to the number of eggs present in the traps. Adjusting for the interaction between temperature and rainfall led to a more informative surveillance model, as such thresholds offer empirical information about the favorable climatic conditions for vector reproduction. Data were characterized by moderate time (0.29 – 0.43) and spatial (21.23 – 34.19 m) dependencies. The models also identified spatial patterns consistent with human population density in all sentinel areas. The results suggest the need for weekly surveillance in the study area, using traps allocated between 18 and 24 m, in order to understand the dengue vector dynamics.

Conclusions

Aedes aegypti, due to it short generation time and strong response to climate triggers, tend to show an eruptive dynamics that is difficult to predict and understand through just temporal or spatial models. The proposed methodology allowed for the rapid and efficient implementation of spatio-temporal models that considered zero-inflation and the interaction between climate variables and patterns in oviposition, in such a way that the final model parameters contribute to the identification of priority areas for entomological surveillance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-015-0219-6) contains supplementary material, which is available to authorized users.