Risk Assessment of Dengue Transmission in Bangladesh Using a Spatiotemporal Network Model and Climate Data.
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ABSTRACT:
SUBMITTER: Riad MH
PROVIDER: S-EPMC8045636 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Approximately 70% of the global burden of dengue disease occurs on the Asian continent, where many large urban centres provide optimal environments for sustained endemic transmission and periodic epidemic cycles. Jakarta, the capital of Indonesia, is a densely populated megacity with hyperendemic dengue transmission. Characterization of the spatiotemporal distribution of dengue transmission intensity is of key importance for optimal implementation of novel control and prevention programmes, including vaccination. In this paper we use mathematical models to provide the first detailed description of spatial and temporal variability in dengue transmission intensity in Jakarta. METHODOLOGY/PRINCIPAL FINDINGS:We applied catalytic models in a Bayesian framework to age-stratified dengue case notification data to estimate dengue force of infection and reporting probabilities in 42 subdistricts of Jakarta. The model was fitted to yearly and average annual data covering a 10-year period between 2008 and 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129-0.131) per year in Jakarta province, ranging from 0.090 (95%CrI: 0.077-0.103) to 0.164 (95%CrI: 0.153-0.174) across subdistricts. Annual average transmission intensity in Jakarta province during the 10-year period ranged from 0.012 (95%CrI: 0.011-0.013) in 2017 to 0.124 (95%CrI: 0.121-0.128) in 2016. CONCLUSIONS/SIGNIFICANCE:While the absolute number of dengue case notifications cannot be relied upon as a measure of endemicity, the age-distribution of reported dengue cases provides valuable insights into the underlying nature of transmission. Our estimates from yearly and average annual case notification data represent the first detailed estimates of dengue transmission intensity in Jakarta's subdistricts. These will be important to consider when assessing the population-level impact and cost-effectiveness of potential control and prevention programmes in Jakarta province, such as the controlled release of Wolbachia-carrying mosquitoes and vaccination.
| S-EPMC7080271 | biostudies-literature
Project description:Bangladesh is one of the most vulnerable countries to climate change impacts also struck by the COVID-19 pandemic. The lockdown measures were ineffective with no sign of flattening the curve. Therefore, the high risk of transmission is evident with an increasing number of affected people. Under this circumstance, a multiple hazards scenario can be developed in this country due to climatic hazards such as cyclones, floods, landslides, heat waves, and the outbreak of infectious diseases such as dengue, cholera, and diarrhoea. The country experiences simultaneously the global pandemic, exceptionally prolonged flood along with the recovery stage from the damages due to the cyclone (Amphan). Therefore, these multiple factors have been putting pressure on losing millions of homes, livelihoods, and agricultural crops. This study aimed to assess the potential impact of a simultaneous strike of climatic hazards and infectious disease outbreaks and their possible strategic management in Bangladesh under different scenarios. A mixed methodological approach was followed in this study including a questionnaire survey, in-depth discussion with experts, and extensive literature review to assess the multi-hazard scenario in a resource-limited setting with high population density. A set of statistical techniques were used to analyze the responses (n = 1590) from different social groups (healthcare professionals, academicians, students, Government and NGO officials, and businessman) under three scenarios. The results revealed the high possibility of aggravating the impact of COVID-19 pandemic if there is a climatic hazard such as flood, cyclone have appeared. The majority of the respondents agreed that the situation will become more devastating if there is another outbreak of diseases such as dengue, cholera, and diarrhoea. The poor and fragile healthcare system of this country cannot bear such unprecedented pressure. The lack of risk assessment and communication, lack of sectoral coordination might restrict the contingency plan of the government. Therefore, considering the unprecedented worst cases a stringent strategic plan for emergency response, short term and long-term management should have to be formulated. Resilience building through proactive planning and implementation of integrated, inclusive and sustainable strategies will be effective to ensure the health and socio-economic security for multi-hazard threats in the country.
| S-EPMC7561529 | biostudies-literature
Project description:Emerging and re-emerging dengue fever has posed serious problems to public health officials in many tropical and subtropical countries. Continuous traveling in seasonally varying areas makes it more difficult to control the spread of dengue fever. In this work, we consider a two-patch dengue model that can capture the movement of host individuals between and within patches using a residence-time matrix. A previous two-patch dengue model without seasonality is extended by adding host demographics and seasonal forcing in the transmission rates. We investigate the effects of human movement and seasonality on the two-patch dengue transmission dynamics. Motivated by the recent Peruvian dengue data in jungle/rural areas and coast/urban areas, our model mimics the seasonal patterns of dengue outbreaks in two patches. The roles of seasonality and residence-time configurations are highlighted in terms of the seasonal reproduction number and cumulative incidence. Moreover, optimal control theory is employed to identify and evaluate patch-specific control measures aimed at reducing dengue prevalence in the presence of seasonality. Our findings demonstrate that optimal patch-specific control strategies are sensitive to seasonality and residence-time scenarios. Targeting only the jungle (or endemic) is as effective as controlling both patches under weak coupling or symmetric mobility. However, focusing on intervention for the city (or high density areas) turns out to be optimal when two patches are strongly coupled with asymmetric mobility.
| S-EPMC5354280 | biostudies-literature
Project description:Urbanization is one of the major drivers of dengue epidemics globally. In Kenya, an intriguing pattern of urban dengue virus epidemics has been documented in which recurrent epidemics are reported from the coastal city of Mombasa, whereas no outbreaks occur in the two major inland cities of Kisumu and Nairobi. In an attempt to understand the entomological risk factors underlying the observed urban dengue epidemic pattern in Kenya, we evaluated vector density, human feeding patterns, vector genetics, and prevailing environmental temperature to establish how these may interact with one another to shape the disease transmission pattern. We determined that (i) Nairobi and Kisumu had lower vector density and human blood indices, respectively, than Mombasa, (ii) vector competence for dengue-2 virus was comparable among Ae. aegypti populations from the three cities, with no discernible association between susceptibility and vector cytochrome c oxidase subunit 1 gene variation, and (iii) vector competence was temperature-dependent. Our study suggests that lower temperature and Ae. aegypti vector density in Nairobi may be responsible for the absence of dengue outbreaks in the capital city, whereas differences in feeding behavior, but not vector competence, temperature, or vector density, contribute in part to the observed recurrent dengue epidemics in coastal Mombasa compared to Kisumu.
| S-EPMC6728053 | biostudies-literature
Project description:BACKGROUND:Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction. METHODS AND FINDINGS:We make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM's Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach. CONCLUSIONS:We demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.
| S-EPMC6283552 | biostudies-literature
Project description:Dengue is a climate-sensitive mosquito-borne disease with increasing geographic extent and human incidence. Although the climate-epidemic association and outbreak risks have been assessed using both statistical and mathematical models, local mosquito population dynamics have not been incorporated in a unified predictive framework. Here, we use mosquito surveillance data from 2005 to 2015 in China to integrate a generalized additive model of mosquito dynamics with a susceptible-infected-recovered (SIR) compartmental model of viral transmission to establish a predictive model linking climate and seasonal dengue risk. The findings illustrate that spatiotemporal dynamics of dengue are predictable from the local vector dynamics, which in turn, can be predicted by climate conditions. On the basis of the similar epidemiology and transmission cycles, we believe that this integrated approach and the finer mosquito surveillance data provide a framework that can be extended to predict outbreak risk of other mosquito-borne diseases as well as project dengue risk maps for future climate scenarios.
| S-EPMC6397594 | biostudies-literature
Project description:BackgroundDengue fever, a mosquito-borne viral disease, is a rapidly emerging public health problem in Ecuador and throughout the tropics. However, we have a limited understanding of the disease transmission dynamics in these regions. Previous studies in southern coastal Ecuador have demonstrated the potential to develop a dengue early warning system (EWS) that incorporates climate and non-climate information. The objective of this study was to characterize the spatiotemporal dynamics and climatic and social-ecological risk factors associated with the largest dengue epidemic to date in Machala, Ecuador, to inform the development of a dengue EWS.MethodsThe following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. We applied LISA and Moran's I to analyze the spatial distribution of the 2010 dengue cases, and developed multivariate logistic regression models through a multi-model selection process to identify census variables and entomological covariates associated with the presence of dengue at the neighborhood level. Using data aggregated at the city-level, we conducted a time-series (wavelet) analysis of weekly climate and dengue incidence (2003-2012) to identify significant time periods (e.g., annual, biannual) when climate co-varied with dengue, and to describe the climate conditions associated with the 2010 outbreak.ResultsWe found significant hotspots of dengue transmission near the center of Machala. The best-fit model to predict the presence of dengue included older age and female gender of the head of the household, greater access to piped water in the home, poor housing condition, and less distance to the central hospital. Wavelet analyses revealed that dengue transmission co-varied with rainfall and minimum temperature at annual and biannual cycles, and we found that anomalously high rainfall and temperatures were associated with the 2010 outbreak.ConclusionsOur findings highlight the importance of geospatial information in dengue surveillance and the potential to develop a climate-driven spatiotemporal prediction model to inform disease prevention and control interventions. This study provides an operational methodological framework that can be applied to understand the drivers of local dengue risk.
| S-EPMC4264610 | biostudies-literature
Project description:With the acceleration of global urbanization and climate change, dengue fever is spreading worldwide. Different levels of dengue fever have also occurred in China, especially in southern China, causing enormous economic losses. Unfortunately, there is no effective treatment for dengue, and the most popular dengue vaccine does not exhibit good curative effects. Therefore, we developed a Generalized Additive Mixed Model (GAMM) that gathered climate factors (mean temperature, relative humidity and precipitation) and Baidu search data during 2011-2015 in Guangzhou city to improve the accuracy of dengue fever prediction. Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). Finally, the GAMM was built and evaluated by comparing it with Generalized Additive Mode (GAM). Experimental results indicated that the GAMM (R2: 0.95 and RMSE: 34.1) has a superior prediction capability than GAM (R2: 0.86 and RMSE: 121.9). The study could help the government agencies and hospitals respond early to dengue fever outbreak.
| S-EPMC6936853 | biostudies-literature
Project description:BackgroundWhile floods can potentially increase the transmission of dengue, only few studies have reported the association of dengue epidemics with flooding. We estimated the effects of river levels and rainfall on the hospital admissions for dengue fever at 11 major hospitals in Dhaka, Bangladesh.MethodsWe examined time-series of the number of hospital admissions of dengue fever in relation to river levels from 2005 to 2009 using generalized linear Poisson regression models adjusting for seasonal, between-year variation, public holidays and temperature.ResultsThere was strong evidence for an increase in dengue fever at high river levels. Hospitalisations increased by 6.9% (95% CI: 3.2, 10.7) for each 0.1 metre increase above a threshold (3.9 metres) for the average river level over lags of 0-5?weeks. Conversely, the number of hospitalisations increased by 29.6% (95% CI: 19.8, 40.2) for a 0.1 metre decrease below the same threshold of the average river level over lags of 0-19?weeks.ConclusionsOur findings provide evidence that factors associated with both high and low river levels increase the hospitalisations of dengue fever cases in Dhaka.
| S-EPMC3528427 | biostudies-literature
Project description:BackgroundDengue virus (DENV) activity has been reported in Dhaka, Bangladesh since the early 1960s with the greatest burden of dengue fever and dengue hemorrhagic fever cases observed in 2000. Since this time, the intensity of dengue activity has varied from year to year, and its determining factors remained relatively unknown. In light of such gaps in knowledge, the main objectives of this study were to determine the magnitude of seroprevalence and seroconversion among the surveyed population, and establish the individual/household level risk factors for the presence of DENV antibodies among all age groups of target populations in the city of Dhaka.Methodology/principal findingsConsidering the lack of fine scale investigations on the factors driving dengue activity in Bangladesh, a prospective cohort study involving serological surveys was undertaken with participant interviews and blood donation across the city of Dhaka in 2012. Study participants were recruited from 12 of 90 wards and blood samples were collected during both the pre-monsoon (n = 1125) and post-monsoon (n = 600) seasons of 2012. The findings revealed that the seroprevalence in all pre-monsoon samples was 80.0% (900/1125) while the seropositivity in the pre-monsoon samples that had paired post-monsoon samples was 83.3% (503/600). Of the 97 paired samples that were negative at the pre-monsoon time point, 56 were positive at the post-monsoon time point. This resulted in a seroprevalence of 93.2% (559/600) among individuals tested during the post-monsoon period. Seroprevalence trended higher with age with children exhibiting a lower seropositivity as compared to adults. Results from this study also indicated that DENV strains were the only flaviviruses circulating in Dhaka in 2012. A multivariate analysis revealed that age, possession of indoor potted plants, and types of mosquito control measures were significant factors associated with DENV seroprevalence; while attendance in public/mass gatherings, and use of mosquito control measures were significantly associated with DENV seroconversion after adjusting for all other variables.Conclusions/significanceOur study suggests that there is a high level of endemic dengue virus circulation in the city of Dhaka which has resulted in significant DENV seroprevalence among its residents. Seropositivity increased with age, however, a substantial proportion of children are at risk for DENV infections. Our serological analysis also documents considerable DENV seroconversion among study participants which indicates that a large proportion of the population in the city of Dhaka were newly exposed to DENV during the study period (pre-and post-monsoon 2012). High levels of seroconversion suggest that there was an intense circulation of DENV in 2012 and this may have resulted in a significant risk for viral associated illness. Findings of our study further indicated that home-based interventions, such as removing indoor potted plants and increased bed net use, in addition to vector control measures in public parks, would reduce exposure to DENV and further decrease risk of viral associated disease.
| S-EPMC5380355 | biostudies-literature