Weather Factors Associated with Reduced Risk of Dengue Transmission in an Urbanized Tropical City.
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ABSTRACT:
SUBMITTER: Gui H
PROVIDER: S-EPMC8751148 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDengue virus (DENV) transmission is spatially heterogeneous. Hence, to stratify dengue prevalence in space may be an efficacious strategy to target surveillance and control efforts in a cost-effective manner particularly in Venezuela where dengue is hyperendemic and public health resources are scarce. Here, we determine hot spots of dengue seroprevalence and the risk factors associated with these clusters using local spatial statistics and a regression modeling approach.Methodology/principal findingsFrom August 2010 to January 2011, a community-based cross-sectional study of 2012 individuals in 840 households was performed in high incidence neighborhoods of a dengue hyperendemic city in Venezuela. Local spatial statistics conducted at household- and block-level identified clusters of recent dengue seroprevalence (39 hot spot households and 9 hot spot blocks) in all neighborhoods. However, no clusters were found for past dengue seroprevalence. Clustering of infection was detected at a very small scale (20-110m) suggesting a high disease focal aggregation. Factors associated with living in a hot spot household were occupation (being a domestic worker/housewife (P = 0.002), lower socio-economic status (living in a shack (P<0.001), sharing a household with <7 people (P = 0.004), promoting potential vector breeding sites (storing water in containers (P = 0.024), having litter outdoors (P = 0.002) and mosquito preventive measures (such as using repellent, P = 0.011). Similarly, low socio-economic status (living in crowded conditions, P<0.001), having an occupation of domestic worker/housewife (P = 0.012) and not using certain preventive measures against mosquitoes (P<0.05) were directly associated with living in a hot spot block.Conclusions/significanceOur findings contribute to a better comprehension of the spatial dynamics of dengue by assessing the relationship between disease clusters and their risk factors. These results can inform health authorities in the design of surveillance and control activities. Focalizing dengue control measures during epidemic and inter-epidemic periods to disease high risk zones at household and neighborhood-level may significantly reduce virus transmission in comparison to random interventions.
| S-EPMC5289626 | biostudies-literature
Project description:BackgroundDengue 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 findingsWe 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.ConclusionsWe 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:Using a novel analytical approach, weather dynamics and seasonal dengue virus transmission cycles were profiled for each Thailand province, 1983-2001, using monthly assessments of cases, temperature, humidity, and rainfall. We observed systematic differences in the structure of seasonal transmission cycles of different magnitude, the role of weather in regulating seasonal cycles, necessary versus optimal transmission "weather-space," basis of large epidemics, and predictive indicators that estimate risk. Larger epidemics begin earlier, develop faster, and are predicted at Onset change-point when case counts are low. Temperature defines a viable range for transmission; humidity amplifies the potential within that range. This duality is central to transmission. Eighty percent of 1.2 million severe dengue cases occurred when mean temperature was 27-29.5°C and mean humidity was > 75%. Interventions are most effective when applied early. Most cases occur near Peak, yet small reductions at Onset can substantially reduce epidemic magnitude. Monitoring the Quiet-Phase is fundamental in effectively targeting interventions pre-emptively.
| S-EPMC3854883 | biostudies-literature
Project description:Global urbanization is leading to an inexorable spread of several major diseases that need to be stemmed. Dengue is one of these major diseases spreading in cities today, with its principal mosquito vector superbly adapted to the urban environment. Current mosquito control strategies are proving inadequate, especially in the face of such urbanisation and novel, evidence-based targeted approaches are needed. Through combined epidemiological and entomological approaches, we aimed to identify a novel sanitation strategy to alleviate the burden of dengue through how the dengue virus spreads through the community. We combined surveillance case mapping, prospective serological studies, year-round mosquito surveys, socio-economic and Knowledge Attitudes and Practices surveys across Delhi. We identified lack of access to tap water (≤98%) as an important risk factor for dengue virus IgG sero-positivity (adjusted Odds Ratio 4.69, 95% C.I. 2.06-10.67) and not poverty per se. Wealthier districts had a higher dengue burden despite lower mosquito densities than the Intermediary income communities (adjusted Odds Ratio 2.92, 95% C.I. 1.26-6.72). This probably reflects dengue being introduced by people travelling from poorer areas to work in wealthier houses. These poorer, high density areas, where temperatures are also warmer, also had dengue cases during the winter. Control strategies based on improved access to a reliable supply of tap water plus focal intervention in intra-urban heat islands prior to the dengue season could not only lead to a reduction in mosquito abundance but also eliminate the reservoir of dengue virus clearly circulating at low levels in winter in socio-economically disadvantaged areas.
| S-EPMC7877620 | biostudies-literature
Project description:This study investigated a model to assess the role of climate fluctuations on dengue (DENV) dynamics from 2010 to 2019 in four Brazilian municipalities. The proposed transmission model was based on a preexisting SEI-SIR model, but also incorporates the vector vertical transmission and the vector's egg compartment, thus allowing rainfall to be introduced to modulate egg-hatching. Temperature and rainfall satellite data throughout the decade were used as climatic model inputs. A sensitivity analysis was performed to understand the role of each parameter. The model-simulated scenario was compared to the observed dengue incidence and the findings indicate that the model was able to capture the observed seasonal dengue incidence pattern with good accuracy until 2016, although higher deviations were observed from 2016 to 2019. The results further demonstrate that vertical transmission fluctuations can affect attack transmission rates and patterns, suggesting the need to investigate the contribution of vertical transmission to dengue transmission dynamics in future assessments. The improved understanding of the relationship between different environment variables and dengue transmission achieved by the proposed model can contribute to public health policies regarding mosquito-borne diseases.
| S-EPMC8466780 | biostudies-literature
Project description:Understanding trends in dengue disease burden and risk factors for severe disease can inform health service allocation, clinical management, and planning for vaccines and therapeutics. Dengue admissions at three tertiary hospitals in Ho Chi Minh City, Vietnam, increased between 1996 and 2009, peaking at 22,860 in 2008. Children aged 6-10 years had highest risk of dengue shock syndrome (DSS); however, mortality was highest in younger children and decreased with increasing age (odds ratio [OR] = 0.52, 95% confidence interval [CI] = 0.36-0.75 in 6- to 10- year-old children and OR = 0.27, 95% CI = 0.16-0.44 in 11- to 15-year-old children compared with 1- to 5-year-old children). Males were overrepresented among dengue cases; however, girls had higher risk of DSS (OR = 1.19, 95% CI = 1.14-1.24) and death (OR = 1.57, 95% CI = 1.14-2.17). Young children with dengue had greatest risk of death and should be targeted in dengue vaccine and drug trials. The increased risk of severe outcomes in girls warrants further attention in studies of pathogenesis, health-seeking behavior, and clinical care.
| S-EPMC3005500 | biostudies-literature
Project description:BackgroundDengue is one of the most aggressively expanding mosquito-transmitted viruses. The human burden approaches 400 million infections annually. Complex transmission dynamics pose challenges for predicting location, timing, and magnitude of risk; thus, models are needed to guide prevention strategies and policy development locally and globally. Weather regulates transmission-potential via its effects on vector dynamics. An important gap in understanding risk and roadblock in model development is an empirical perspective clarifying how weather impacts transmission in diverse ecological settings. We sought to determine if location, timing, and potential-intensity of transmission are systematically defined by weather.Methodology/principal findingsWe developed a high-resolution empirical profile of the local weather-disease connection across Peru, a country with considerable ecological diversity. Applying 2-dimensional weather-space that pairs temperature versus humidity, we mapped local transmission-potential in weather-space by week during 1994-2012. A binary classification-tree was developed to test whether weather data could classify 1828 Peruvian districts as positive/negative for transmission and into ranks of transmission-potential with respect to observed disease. We show that transmission-potential is regulated by temperature-humidity coupling, enabling epidemics in a limited area of weather-space. Duration within a specific temperature range defines transmission-potential that is amplified exponentially in higher humidity. Dengue-positive districts were identified by mean temperature >22°C for 7+ weeks and minimum temperature >14°C for 33+ weeks annually with 95% sensitivity and specificity. In elevated-risk locations, seasonal peak-incidence occurred when mean temperature was 26-29°C, coincident with humidity at its local maximum; highest incidence when humidity >80%. We profile transmission-potential in weather-space for temperature-humidity ranging 0-38°C and 5-100% at 1°C x 2% resolution.Conclusions/significanceLocal duration in limited areas of temperature-humidity weather-space identifies potential locations, timing, and magnitude of transmission. The weather-space profile of transmission-potential provides needed data that define a systematic and highly-sensitive weather-disease connection, demonstrating separate but coupled roles of temperature and humidity. New insights regarding natural regulation of human-mosquito transmission across diverse ecological settings advance our understanding of risk locally and globally for dengue and other mosquito-borne diseases and support advances in public health policy/operations, providing an evidence-base for modeling, predicting risk, and surveillance-prevention planning.
| S-EPMC4519153 | biostudies-literature
Project description:BACKGROUND:The incidence of dengue is rising steadily in Malaysia since the first major outbreak in 1973. Despite aggressive measures taken by the relevant authorities, Malaysia is still facing worsening dengue crisis over the past few years. There is an urgent need to evaluate dengue cases for better understanding of clinic-laboratory spectrum in order to combat this disease. METHODS:A retrospective analysis of dengue patients admitted to a tertiary care teaching hospital during the period of six years (2008 - 2013) was performed. Patient's demographics, clinical and laboratory findings were recorded via structured data collection form. Patients were categorized into dengue fever (DF) and dengue hemorrhagic fever (DHF). Appropriate statistical methods were used to compare these two groups in order to determine difference in clinico-laboratory characteristics and to identify independent risk factors of DHF. RESULTS:A total 667 dengue patients (30.69?±?16.13 years; Male: 56.7 %) were reviewed. Typical manifestations of dengue like fever, myalgia, arthralgia, headache, vomiting, abdominal pain and skin rash were observed in more than 40 % patients. DHF was observed in 79 (11.8 %) cases. Skin rash, dehydration, shortness of breath, pleural effusion and thick gall bladder were more significantly (P?<?0.05) associated with DHF than DF. Multivariate regression analysis demonstrated presence of age?>?40 years (OR: 4.1, P?<?0.001), secondary infection (OR: 2.7, P?=?0.042), diabetes mellitus (OR: 2.8, P?=?0.041), lethargy (OR: 3.1, P?=?0.005), thick gallbladder (OR: 1.7, P?=?0.029) and delayed hospitalization (OR: 2.3, P?=?0.037) as independent predictors of DHF. Overall mortality was 1.2 % in our study. CONCLUSIONS:Current study demonstrated that DF and DHF present significantly different clinico-laboratory profile. Older age, secondary infection, diabetes mellitus, lethargy, thick gallbladder and delayed hospitalization significantly predict DHF. Prior knowledge of expected clinical profile and predictors of DHF/DSS development would provide information to identify individuals at higher risk and on the other hand, give sufficient time to clinicians for reducing dengue related morbidity and mortality.
| S-EPMC4590689 | biostudies-other
Project description:We examined the spatial distribution pattern and meteorological drivers of dengue fever (DF) in Guangdong Province, China. Annual incidence of DF was calculated for each county between 2005 and 2011 and the geographical distribution pattern of DF was examined using Moran's I statistic and excess risk maps. A time-stratified case-crossover study was used to investigate the short-term relationship between DF and meteorological factors and the Southern Oscillation Index (SOI). High-epidemic DF areas were restricted to the Pearl River Delta region and the Han River Delta region, Moran's I of DF distribution was significant from 2005 to 2006 and from 2009 to 2011. Daily vapour pressure, mean and minimum temperatures were associated with increased DF risk. Maximum temperature and SOI were negatively associated with DF transmission. The risk of DF was non-randomly distributed in the counties in Guangdong Province. Meteorological factors could be important predictors of DF transmission.
| S-EPMC9161228 | biostudies-literature
Project description:Macroscopic descriptions of populations commonly assume that encounters between individuals are well mixed; i.e. each individual has an equal chance of coming into contact with any other individual. Relaxing this assumption can be challenging though, due to the difficulty of acquiring detailed knowledge about the non-random nature of encounters. Here, we fitted a mathematical model of dengue virus transmission to spatial time-series data from Pakistan and compared maximum-likelihood estimates of 'mixing parameters' when disaggregating data across an urban-rural gradient. We show that dynamics across this gradient are subject not only to differing transmission intensities but also to differing strengths of nonlinearity due to differences in mixing. Accounting for differences in mobility by incorporating two fine-scale, density-dependent covariate layers eliminates differences in mixing but results in a doubling of the estimated transmission potential of the large urban district of Lahore. We furthermore show that neglecting spatial variation in mixing can lead to substantial underestimates of the level of effort needed to control a pathogen with vaccines or other interventions. We complement this analysis with estimates of the relationships between dengue transmission intensity and other putative environmental drivers thereof.
| S-EPMC4614486 | biostudies-literature