A study of the correlation between dengue and weather in Kandy City, Sri Lanka (2003 -2012) and lessons learned

Informing climate-health adaptation options through mapping the needs and potential for integrated climate-driven early warning forecasting systems in South Asia-A scoping review

BACKGROUND

Climate change is widely recognised to threaten human health, wellbeing and livelihoods, including through its effects on the emergence, spread and burdens of climate-and water-sensitive infectious diseases. However, the scale and mechanisms of the impacts are uncertain and it is unclear whether existing forecasting capacities will foster successful local-level adaptation planning, particularly in climate vulnerable regions in developing countries. The purpose of this scoping review was to characterise and map priority climate- and water-sensitive diseases, map existing forecasting and surveillance systems in climate and health sectors and scope out the needs and potential to develop integrated climate-driven early warning forecasting systems for long-term adaptation planning and interventions in the south Asia region.

METHODS

We searched Web of Science Core Collection, Scopus and PubMed using title, abstract and keywords only for papers focussing on climate-and water-sensitive diseases and explicit mention of either forecasting or surveillance systems in south Asia. We conducted further internet search of relevant national climate adaptation plans and health policies affecting disease management. We identified 187 studies reporting on climate-sensitive diseases and information systems in the south Asia context published between 1992 and 2024.

RESULTS

We found very few robust, evidenced-based forecasting systems for climate- and water- sensitive infectious diseases, which suggests limited operationalisation of decision-support tools that could inform actions to reduce disease burdens in the region. Many of the information systems platforms identified focussed on climate-sensitive vector-borne disease systems, with limited tools for water-sensitive diseases. This reveals an opportunity to develop tools for these neglected disease groups. Of the 34 operational platforms identified across the focal countries, only 13 (representing 38.2%) are freely available online and all were developed and implemented by the human health sector. Tools are needed for other south Asian countries (Afghanistan, Sri Lanka, Bhutan) where the risks of infectious diseases are predicted to increase substantially due to climate change, drought and shifts in human demography and use of ecosystems.

CONCLUSION

Altogether, the findings highlight clear opportunities to invest in the co-development and implementation of contextually relevant climate-driven early warning tools and research priorities for disease control and adaptation planning.

Estimating dynamics of dengue disease in Colombo district of Sri Lanka with environmental impact by quantifying the per-capita vector density

Dengue is a vector-borne disease transmitted to humans by vectors of genus Aedes causing a global threat to health, social, and economic sectors in many of the tropical countries including Sri Lanka. In Sri Lanka, the tropical climate, marked by seasonal weather primarily influenced by monsoons, fosters optimal conditions for the virus to spread efficiently. This heightened transmission results in increased per-capita vector density. In this work, we investigate the dynamic influence of environmental conditions on dengue emergence in Colombo district - the geographical region with the highest recorded dengue threat in Sri Lanka. An iterative approach is employed to dynamically estimate dengue cases leveraging the Markov chain Monte Carlo simulations, utilizing the dynamics of four seasons per year influenced by monsoon weather patterns governing in the region. The developed algorithm allows to estimate the risk of dengue outbreaks in 2017 and 2019 with high precision, facilitating accurate forecasts of upcoming disease emergence patterns for better preparedness. The uncertainty quantification not only validated the accuracy of outbreak estimates but also showcased the model's capacity to capture extreme cases and revealed undisclosed external factors such as human mobility and environmental pollution that might affect dengue transmission in the Colombo district of Sri Lanka.

Risk of arboviral transmission and insecticide resistance status of Aedes mosquitoes during a yellow fever outbreak in Ghana

BACKGROUND

In late 2021, Ghana was hit by a Yellow Fever outbreak that started in two districts in the Savannah region and spread to several other Districts in three regions. Yellow fever is endemic in Ghana. However, there is currently no structured vector control programme for Aedes the arboviral vector in Ghana. Knowledge of Aedes bionomics and insecticide susceptibility status is important to control the vectors. This study therefore sought to determine Aedes vector bionomics and their insecticide resistance status during a yellow fever outbreak.

METHODS

The study was performed in two yellow fever outbreak sites (Wenchi, Larabanga) and two non-outbreak sites (Kpalsogu, Pagaza) in Ghana. Immature Aedes mosquitoes were sampled from water-holding containers in and around human habitations. The risk of disease transmission was determined in each site using stegomyia indices. Adult Aedes mosquitoes were sampled using Biogents Sentinel (BG) traps, Human Landing Catch (HLC), and Prokopack (PPK) aspirators. Phenotypic resistance to permethrin, deltamethrin and pirimiphos-methyl was determined with WHO susceptibility tests using Aedes mosquitoes collected as larvae and reared into adults. Knockdown resistance (kdr) mutations were detected using allele-specific multiplex PCR.

RESULTS

Among the 2,664 immature Aedes sampled, more than 60% were found in car tyres. Larabanga, an outbreak site, was classified as a high-risk zone for the Yellow Fever outbreak (BI: 84%, CI: 26.4%). Out of 1,507 adult Aedes mosquitoes collected, Aedes aegypti was the predominant vector species (92%). A significantly high abundance of Aedes mosquitoes was observed during the dry season (61.2%) and outdoors (60.6%) (P < 0.001). Moderate to high resistance to deltamethrin was observed in all sites (33.75% to 70%). Moderate resistance to pirimiphos-methyl (65%) was observed in Kpalsogu. Aedes mosquitoes from Larabanga were susceptible (98%) to permethrin. The F1534C kdr, V1016I kdr and V410 kdr alleles were present in all the sites with frequencies between (0.05-0.92). The outbreak sites had significantly higher allele frequencies of F1534C and V1016I respectively compared to non-outbreak sites (P < 0.001).

CONCLUSION

This study indicates that Aedes mosquitoes in Ghana pose a significant risk to public health. Hence there is a need to continue monitoring these vectors to develop an effective control strategy.

Risk of Arboviral Transmission and Insecticide Resistance Status of Aedes Mosquitoes during a Yellow Fever Outbreak in Ghana

Background

In late 2021, Ghana was hit by a Yellow Fever outbreak that started in two (2) districts in the Savannah region and spread to several other Districts in (3) regions (Oti, Bono and Upper West).Yellow fever is endemic in Ghana. However, there is currently no structured vector control programme for the yellow vector, Aedes mosquitoes in Ghana. Knowledge of Aedes bionomics and insecticide susceptibility status is important to control the vectors. This study therefore sought todetermine Aedes vector bionomics and their insecticide resistance status during a yellow fever outbreak.

Methods

The study was performed in two yellow fever outbreak sites (Wenchi, Larabanga) and two non-outbreak sites (Kpalsogu, Pagaza) in Ghana. Immature Aedes mosquitoes were sampled from water-holding containers in and around human habitations. The risk of disease transmission was determined in each site using stegomyia indices. Adult Aedes mosquitoes were sampled using Biogents Sentinel (BG) traps, Human Landing Catch (HLC), and Prokopack (PPK) aspirators. Phenotypic resistance was determined with WHO susceptibility tests using Aedes mosquitoes collected as larvae and reared into adults. Knockdown resistance (kdr) mutations were detected using allele-specific multiplex PCR.

Results

Of the 2,664 immature Aedes sampled, more than 60% were found in car tyres. Larabanga, an outbreak site, was classified as a high-risk zone for the Yellow Fever outbreak (BI: 84%, CI: 26.4%). Out of 1,507 adult Aedes mosquitoes collected, Aedes aegypti was the predominant vector species (92%). A significantly high abundance of Aedes mosquitoes was observed during the dry season (61.2%) and outdoors (60.6%) (P < 0.001). Moderate to high resistance to deltamethrin was observed in all sites (33.75% to 70%). Moderate resistance to pirimiphos-methyl (65%) was observed in Kpalsogu. Aedesmosquitoes from Larabanga were susceptible (98%) to permethrin. The F1534C kdr, V1016I kdr and V410 kdr alleles were present in all the sites with frequencies between (0.05-0.92). The outbreak sites had significantly higher allele frequencies of F1534C and V1016I respectively compared to non-outbreak sites (P < 0.001).

Conclusion

This study indicates that Aedes mosquitoes in Ghana pose a significant risk to public health, and there is a need for continuous surveillance to inform effective vector control strategies.

Cross-correlation and time series analysis of rabies in different animal species in Nepal from 2005 to 2018

Rabies is a fatal zoonotic disease, resulting in human and livestock deaths. In Nepal, animal rabies has posed a significant challenge to public health. Because animals are the primary source of rabies in humans, a better understanding of rabies epidemiology in animals is necessary. The objectives of this study were to determine the correlation between rabies occurrences in dogs and livestock animals and to detect the trends and change points of the disease using longitudinal data. The nationwide rabies dataset from 2005 to 2018 was analyzed using cross-correlation, multiple change points, and time series methods. Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) were applied to the time series data. The results show a positive correlation between canine rabies and livestock rabies occurrences. Three significant change points were detected in the time series data, demonstrating that the occurrences were high in the initial years but stabilized before peaking to an upward trend in the final years of the study period. Nonetheless, there was no seasonality pattern in rabies occurrences. The most suitable models were ARIMA (2,1,2) and NNAR (5,1,4) (12). Based on the study findings, both locals and tourists in Nepal need to have enhanced awareness of the potential dangers posed by rabies in canines and livestock. This study offers much-needed insight into the patterns and epidemiology of animal rabies which will be helpful for policymakers in drafting rabies control plans for Nepal.

Low level of dengue infection and transmission risk in Hong Kong: an integrated analysis of temporal seroprevalence results and corresponding meteorological data

Hong Kong is an Asia-Pacific City with low incidence but periodic local outbreaks of dengue. A mixed-method assessment of the risk of expansion of dengue endemicity in such setting was conducted. Archived blood samples of healthy adult blood donors were tested for anti-dengue virus IgG at 2 time-points of 2014 and 2018/2019. Data on the monthly notified dengue cases, meteorological and vector (ovitrap index) variables were collected. The dengue virus (DENV) IgG seroprevalence of healthy adults in 2014 was 2.2% (95%C.I. = 1.8-2.8%, n = 3827) whereas that in 2018/2019 was 1.7% (95%C.I. = 1.2-2.3%, n = 2320). Serotyping on 42 sera in 2018/2019 showed that 22 (52.4%) were DENV-2. In 2002-2019, importation accounted for 95.3% of all reported cases. By wavelet analysis, local cases were in weak or no association with meteorological and vector variables. Without strong association between local cases and meteorological/vector variables, there was no evidence of increasing level of dengue infection in Hong Kong.

Association between environmental factors and dengue incidence in Lao People's Democratic Republic: a nationwide time-series study

BACKGROUND

Dengue fever is a vector-borne disease of global public health concern, with an increasing number of cases and a widening area of endemicity in recent years. Meteorological factors influence dengue transmission. This study aimed to estimate the association between meteorological factors (i.e., temperature and rainfall) and dengue incidence and the effect of altitude on this association in the Lao People's Democratic Republic (Lao PDR).

METHODS

We used weekly dengue incidence and meteorological data, including temperature and rainfall, from 18 jurisdictions in Lao PDR from 2015 to 2019. A two-stage distributed lag nonlinear model with a quasi-Poisson distribution was used to account for the nonlinear and delayed associations between dengue incidence and meteorological variables, adjusting for long-term time trends and autocorrelation.

RESULTS

A total of 55,561 cases were reported in Lao PDR from 2015 to 2019. The cumulative relative risk for the 90th percentile of weekly mean temperature (29 °C) over 22 weeks was estimated at 4.21 (95% confidence interval: 2.00-8.84), relative to the 25th percentile (24 °C). The cumulative relative risk for the weekly total rainfall over 12 weeks peaked at 82 mm (relative risk = 1.76, 95% confidence interval: 0.91-3.40) relative to no rain. However, the risk decreased significantly when heavy rain exceeded 200 mm. We found no evidence that altitude modified these associations.

CONCLUSIONS

We found a lagged nonlinear relationship between meteorological factors and dengue incidence in Lao PDR. These findings can be used to develop climate-based early warning systems and provide insights for improving vector control in the country.

Associations between Dengue Incidence, Ecological Factors, and Anthropogenic Factors in Singapore

Singapore experiences endemic dengue. Vector control remains the primary means to reduce transmission due to the lack of available therapeutics. Resource limitations mean that vector-control tools need to be optimized, which can be achieved by studying risk factors related to disease transmission. We developed a statistical modelling framework which can account for a high-resolution and high-dimensional set of covariates to delineate spatio-temporal characteristics that are associated with dengue transmission from 2014 to 2020 in Singapore. We applied the proposed framework to two distinct datasets, stratified based on the primary type of housing within each spatial unit. Generalized additive models reveal non-linear exposure responses between a large range of ecological and anthropogenic factors as well as dengue incidence rates. At values below their mean, lesser mean total daily rainfall (Incidence rate ratio (IRR): 3.75, 95% CI: 1.00-14.05, Mean: 4.40 mm), decreased mean windspeed (IRR: 3.65, 95% CI: 1.87-7.10, Mean: 4.53 km/h), and lower building heights (IRR: 2.62, 95% CI: 1.44-4.77, Mean: 6.5 m) displayed positive associations, while higher than average annual NO2 concentrations (IRR: 0.35, 95% CI: 0.18-0.66, Mean: 13.8 ppb) were estimated to be negatively associated with dengue incidence rates. Our study provides an understanding of associations between ecological and anthropogenic characteristics with dengue transmission. These findings help us understand high-risk areas of dengue transmission, and allows for land-use planning and formulation of vector control policies.

Role of air pollutants in dengue fever incidence: evidence from two southern cities in Taiwan

Air pollution may be involved in spreading dengue fever (DF) besides rainfalls and warmer temperatures. While particulate matter (PM), especially those with diameter of 10 μm (PM10) or 2.5 μm or less (PM25), and NO2 increase the risk of coronavirus 2 infection, their roles in triggering DF remain unclear. We explored if air pollution factors predict DF incidence in addition to the classic climate factors. Public databases and DF records of two southern cities in Taiwan were used in regression analyses. Month order, PM10 minimum, PM2.5 minimum, and precipitation days were retained in the enter mode model, and SO2 minimum, O3 maximum, and CO minimum were retained in the stepwise forward mode model in addition to month order, PM10 minimum, PM2.5 minimum, and precipitation days. While PM2.5 minimum showed a negative contribution to the monthly DF incidence, other variables showed the opposite effects. The sustain of month order, PM10 minimum, PM2.5 minimum, and precipitation days in both regression models confirms the role of classic climate factors and illustrates a potential biological role of the air pollutants in the life cycle of mosquito vectors and dengue virus and possibly human immune status. Future DF prevention should concern the contribution of air pollution besides the classic climate factors.

Mathematical modeling of Dengue virus serotypes propagation in Mexico

The Dengue virus (DENV) constitutes a major vector borne virus disease worldwide. Prediction of the DENV spread dynamics, prevalence and infection rates are crucial elements to guide the public health services effort towards meaningful actions. The existence of four DENV serotypes further complicates the virus proliferation forecast. The different serotypes have varying clinical impacts, and the symptomatology of the infection is dependent on the infection history of the patient. Therefore, changes in the prevalent DENV serotype found in one location have a profound impact on the regional public health. The prediction of the spread and intensity of infection of the individual DENV serotypes in specific locations would allow the authorities to plan local pesticide spray to control the vector as well as the purchase of specific antibody therapy. Here we used a mathematical model to predict serotype-specific DENV prevalence and overall case burden in Mexico.

Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction

Dengue is a major public health problem in Myanmar. The country aims to reduce morbidity by 50% and mortality by 90% by 2025 based on 2015 data. To support efforts to reach these goals it is important to have a detailed picture of the epidemiology of dengue, its relationship to meteorological factors and ideally to predict ahead of time numbers of cases to plan resource allocations and control efforts. Health facility-level data on numbers of dengue cases from 2012 to 2017 were obtained from the Vector Borne Disease Control Unit, Department of Public Health, Myanmar. A detailed analysis of routine dengue and dengue hemorrhagic fever (DHF) incidence was conducted to examine the spatial and temporal epidemiology. Incidence was compared to climate data over the same period. Dengue was found to be widespread across the country with an increase in spatial extent over time. The temporal pattern of dengue cases and fatalities was episodic with annual outbreaks and no clear longitudinal trend. There were 127,912 reported cases and 632 deaths from 2012 and 2017 with peaks in 2013, 2015 and 2017. The case fatality rate was around 0.5% throughout. The peak season of dengue cases was from May to August in the wet season but in 2014 peak dengue season continued until November. The strength of correlation of dengue incidence with different climate factors (total rainfall, maximum, mean and minimum temperature and absolute humidity) varied between different States and Regions. Monthly incidence was forecasted 1 month ahead using the Auto Regressive Integrated Moving Average (ARIMA) method at country and subnational levels. With further development and validation, this may be a simple way to quickly generate short-term predictions at subnational scales with sufficient certainty to use for intervention planning.

Paradox between adequate sanitation and rainfall in dengue fever cases

BACKGROUND

Dengue fever is a tropical disease and a major public health concern, and almost half of the world's population lives in areas at risk of contracting this disease. Climate change is identified by WHO and other international health authorities as one of the primary factors that contribute to the rapid spread of dengue fever.

METHODS

We evaluated the effect of sanitation on the cross-correlation between rainfall and the first symptoms of dengue in the city of Mato Grosso do Sul, which is in a state in the Midwest region of Brazil, and employed the time-lagged detrended cross-correlation analysis (DCCAC) method.

RESULTS

Co-movements were obtained through the time-phased DCCAC to analyze the effects of climatic variables on arboviruses. The use of a time-lag analysis was more robust than DCCAC without lag to present the behavior of dengue cases in relation to accumulated precipitation. Our results show that the cross-correlation between rain and dengue increased as the city implemented actions to improve basic sanitation in the city.

CONCLUSION

With climate change and the increase in the global average temperature, mosquitoes are advancing beyond the tropics, and our results show that cities with improved sanitation have a high correlation between dengue and annual precipitation. Public prevention and control policies can be targeted according to the period of time and the degree of correlation calculated to structure vector control and prevention work in places where sanitation conditions are adequate.

Time series analysis of leishmaniasis incidence in Sri Lanka: evidence for humidity-associated fluctuations

Leishmaniasis is a vector-borne disease of which the transmission is highly influenced by climatic factors, whereas the nature and magnitude differ between geographical regions. The effects of climatic variables on leishmaniasis in Sri Lanka are poorly investigated. The present study focused on time-series analysis of leishmaniasis cases reported from Sri Lanka with selected climatic variables. Variance stabilized time series of leishmaniasis patients of entire Sri Lanka and major districts from 2014 to 2018 was fitted to autoregressive integrated moving average (ARIMA) models. All the possible models were generated by assigning different values for autoregression and moving average terms using a function written in R statistical program. The top ten models with the lowest Akaike information criterion (AIC) values were selected by writing another function. These models were further evaluated using RMSE and MAPE error parameters to select the optimal model for each area. Cross-autocorrelation analyses were performed to assess the associations between climate and the leishmaniasis incidence. Most associated lags of each variable were integrated into the optimal models to determine the true effects imposed. The optimal models varied depending on the area. SARIMA (0,1,1) (1,0,0)₁₂ was optimal for the country level. All the forecasts were within the 95% confidence intervals. Humidity was the most notable factor associated with leishmaniasis, which could be attributed to the positive impacts on sand fly activity. Rainfall showed a negative impact probably as a result of flooding of sand fly larval habitats. The ARIMA-based models performed well for the prediction of leishmaniasis in the short term.

A Privacy-Preserved Internet-of-Medical-Things Scheme for Eradication and Control of Dengue Using UAV

Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.

Association between Climate Factors and Dengue Fever in Asuncion, Paraguay: A Generalized Additive Model

Dengue fever has been endemic in Paraguay since 2009 and is a major cause of public-health-management-related burdens. However, Paraguay still lacks information on the association between climate factors and dengue fever. We aimed to investigate the association between climatic factors and dengue fever in Asuncion. Cumulative dengue cases from January 2014 to December 2020 were extracted weekly, and new cases and incidence rates of dengue fever were calculated. Climate factor data were aggregated weekly, associations between dengue cases and climate factors were analyzed, and variables were selected to construct our model. A generalized additive model was used, and the best model was selected based on Akaike information criteria. Piecewise regression analyses were performed for non-linear climate factors. Wind and relative humidity were negatively associated with dengue cases, and minimum temperature was positively associated with dengue cases when the temperature was less than 21.3 °C and negatively associated with dengue when greater than 21.3 °C. Additional studies on dengue fever in Asuncion and other cities are needed to better understand dengue fever.

Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease

Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the "big n" problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January-July and a decrease in the period August-December.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10109-021-00368-0.

Weather Factors Associated with Reduced Risk of Dengue Transmission in an Urbanized Tropical City

This study assessed the impact of weather factors, including novel predictors-pollutant standards index (PSI) and wind speed-on dengue incidence in Singapore between 2012 and 2019. Autoregressive integrated moving average (ARIMA) model was fitted to explore the autocorrelation in time series and quasi-Poisson model with a distributed lag non-linear term (DLNM) was set up to assess any non-linear association between climatic factors and dengue incidence. In DLNM, a PSI level of up to 111 was positively associated with dengue incidence; incidence reduced as PSI level increased to 160. A slight rainfall increase of up to 7 mm per week gave rise to higher dengue risk. On the contrary, heavier rainfall was protective against dengue. An increase in mean temperature under around 28.0 °C corresponded with increased dengue cases whereas the association became negative beyond 28.0 °C; the minimum temperature was significantly positively associated with dengue incidence at around 23-25 °C, and the relationship reversed when temperature exceed 27 °C. An overall positive association, albeit insignificant, was observed between maximum temperature and dengue incidence. Wind speed was associated with decreasing relative risk (RR). Beyond prevailing conclusions on temperature, this study observed that extremely poor air quality, high wind speed, minimum temperature ≥27 °C, and rainfall volume beyond 12 mm per week reduced the risk of dengue transmission in an urbanized tropical environment.

Prediction of Dengue Incidence in the Northeast Malaysia Based on Weather Data Using the Generalized Additive Model

Introduction

Dengue, a vector-borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east-coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time-based estimate of possible dengue incidence increase following weather-related changes, thereby highlighting potential dengue outbreaks.

Method

All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10′N, longitude 102°18′E) was analysed with dengue data.

Result

A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities.

Conclusion

Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.

Association between climate variables and dengue incidence in Nakhon Si Thammarat Province, Thailand

The tropical climate of Thailand encourages very high mosquito densities in certain areas and is ideal for dengue transmission, especially in the southern region where the province Nakhon Si Thammarat is located. It has the longest dengue fever transmission duration that is affected by some important climate predictors, such as rainfall, number of rainy days, temperature and humidity. We aimed to explore the relationship between weather variables and dengue and to analyse transmission hotspots and coldspots at the district-level. Poisson probability distribution of the generalized linear model (GLM) was used to examine the association between the monthly weather variable data and the reported number of dengue cases from January 2002 to December 2018 and geographic information system (GIS) for dengue hotspot analysis. Results showed a significant association between the environmental variables and dengue incidence when comparing the seasons. Temperature, sea-level pressure and wind speed had the highest coefficients, i.e. β=0.17, β= -0.12 and β= -0.11 (P<0.001), respectively. The risk of dengue incidence occurring during the rainy season was almost twice as high as that during monsoon. Statistically significant spatial clusters of dengue cases were observed all through the province in different years. Nabon was identified as a hotspot, while Pak Phanang was a coldspot for dengue fever incidence, explained by the fact that the former is a rubber-plantation hub, while the agricultural plains of the latter lend themselves to the practice of pisciculture combined with rice farming. This information is imminently important for planning apt sustainable control measures for dengue epidemics.

Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand

Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon's Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson's correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVIWADI was significantly correlated on average in 19% (4-40%) of districts in Laos (mean r = 0.5) and 27% (15-53%) of subdistricts in Thailand (mean r = 0.85). The DVISE was validated in 22% (12-40%) of districts in Laos and in 13% (3-38%) of subdistricts in Thailand. The DVIBWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0-28%) of Lao districts. The DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.

Climate Variability, Dengue Vector Abundance and Dengue Fever Cases in Dhaka, Bangladesh: A Time-Series Study

Numerous studies on climate change and variability have revealed that these phenomena have noticeable influence on the epidemiology of dengue fever, and such relationships are complex due to the role of the vector—the Aedes mosquitoes. By undertaking a step-by-step approach, the present study examined the effects of climatic factors on vector abundance and subsequent effects on dengue cases of Dhaka city, Bangladesh. Here, we first analyzed the time-series of Stegomyia indices for Aedes mosquitoes in relation to temperature, rainfall and relative humidity for 2002–2013, and then in relation to reported dengue cases in Dhaka. These data were analyzed at three sequential stages using the generalized linear model (GLM) and generalized additive model (GAM). Results revealed strong evidence that an increase in Aedes abundance is associated with the rise in temperature, relative humidity, and rainfall during the monsoon months, that turns into subsequent increases in dengue incidence. Further we found that (i) the mean rainfall and the lag mean rainfall were significantly related to Container Index, and (ii) the Breteau Index was significantly related to the mean relative humidity and mean rainfall. The relationships of dengue cases with Stegomyia indices and with the mean relative humidity, and the lag mean rainfall were highly significant. In examining longitudinal (2001–2013) data, we found significant evidence of time lag between mean rainfall and dengue cases.

Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand

This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area.

The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004-2019

Background

Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI.

Methods

We acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected.

Results

Wavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods.

Conclusions

Results of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41182-021-00325-z.

CORRELATION BETWEEN CLIMATE FACTORS WITH DENGUE HEMORRHAGIC FEVER CASES IN SURABAYA 2007 – 2017

Dengue Hemorrhagic Fever (DHF) is a disease caused by dengue virus. DHF is mediated by the mosquito vector, the Aedes mosquito. The proliferation of dengue vector is influenced by many factors, one of which is climate factors. DHF is one of the main public health problems in Indonesia. Cases of dengue were first discovered in 1968 in the city of Jakarta and Surabaya. Currently Surabaya is one of the dengue endemic areas in Indonesia. . The case of DHF in the city of Surabaya can be said to be still quite high compared with another city in Indonesia, although there is a decrease in the number from year to year. When examined, many factors influence the high number of dengue cases in Surabaya, one of which is climate factor. Climate factors play a role in the proliferation of DHF vectors. Therefore, this study aims to examine for 10 years, namely in 2007 - 2017 whether there is a correlation between climate factors with dengue cases in the city of Surabaya., which in this study the climate factors used are rainfall, average temperature, and average air humidity. This research uses an analytical method namely Spearman on the SPSS software version 20. The results obtained that the case of DHF in the city of Surabaya has no relationship with climatic factors such as rainfall and average temperature with a significance value of the relationship p> 0.05. While the climate factor that has a relationship with DHF cases in Surabaya City is air humidity with a significance value of p <0.05 and has a positive relationship with the value of r = + 0.190. It can be concluded that not all climate factors have a relationship with the DHF case in Surabaya in 2007 - 2017, which has a relationship with the DHF case is air humidity. 

Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images

Background

Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images.

Methods

The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting.

Results

The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city.

Conclusions

Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.

Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis

Dengue is an important emerging vector-borne disease in Bhutan. This study aimed to quantify the spatial and temporal patterns of dengue and their relationship to environmental factors in dengue-affected areas at the sub-district level. A multivariate zero-inflated Poisson regression model was developed using a Bayesian framework with spatial and spatiotemporal random effects modelled using a conditional autoregressive prior structure. The posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. A total of 708 dengue cases were notified through national surveillance between January 2016 and June 2019. Individuals aged ≤14 years were found to be 53% (95% CrI: 42%, 62%) less likely to have dengue infection than those aged >14 years. Dengue cases increased by 63% (95% CrI: 49%, 77%) for a 1°C increase in maximum temperature, and decreased by 48% (95% CrI: 25%, 64%) for a one-unit increase in normalized difference vegetation index (NDVI). There was significant residual spatial clustering after accounting for climate and environmental variables. The temporal trend was significantly higher than the national average in eastern sub-districts. The findings highlight the impact of climate and environmental variables on dengue transmission and suggests prioritizing high-risk areas for control strategies.

The interrelationship between meteorological parameters and leptospirosis incidence in Hambantota district, Sri Lanka 2008-2017 and practical implications

Background

Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals pass leptospires to the environment with their urine. Leprospires' survival in the environment to infect a new host depends on meteorological factors. El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) modulate the weather in Sri Lanka.

Objectives

The determination of interrelationship between the LI in the Hambantota District, and local meteorological parameters, ENSO and IOD.

Methods

We acquired notified leptospirosis cases in the Hambantota District and population data. We calculated weekly leptospirosis incidences for 2008 to 2017.Weather data from two weather stations was obtained, averaged and converted into weekly data. We plotted time series graphs and observed the correlation between seven aggregated weather parameters and LI. We estimated cross-correlations between those weather parameters and LI. As our principal analysis we determined correlation between LI and seven local weather parameters, Nino 3.4, Nino4 and Dipole Mode Index (DMI) indices using wavelet analysis.

Results

Our wavelet analysis results showed troughs of minimum, maximum, mean temperatures, soil temperature, the evaporation rate, the duration of sunshine were followed by peaks in LI and peaks of rainfall followed by peaks of LI, all after lag periods. Our time series graphs and cross-correlation determination results are generally in agreement with these results. However there was no significant correlation between rainfall and LI in the cross-correlation analysis. There were peaks of LI following both peaks and troughs of DMI. There was no clear correlation between both Nino indices and LI.

Discussion

This may be the first long-term study demonstrating soil temperature, evaporation rate and IOD are correlating with LI. The correlation pattern of LI with temperature parameters differs from similar past studies and we explain the reasons. We propose ways to control high LI we observed after periods of weather favorable for transmission of leptospirosis.

Analysis and forecast of dengue incidence in urban Colombo, Sri Lanka

BACKGROUND

Understanding the dynamical behavior of dengue transmission is essential in designing control strategies. Mathematical models have become an important tool in describing the dynamics of a vector borne disease. Classical compartmental models are well-known method used to identify the dynamical behavior of spread of a vector borne disease. Due to use of fixed model parameters, the results of classical compartmental models do not match realistic nature. The aim of this study is to introduce time in varying model parameters, modify the classical compartmental model by improving its predictability power.

RESULTS

In this study, per-capita vector density has been chosen as the time in varying model parameter. The dengue incidences, rainfall and temperature data in urban Colombo are analyzed using Fourier mathematical analysis tool. Further, periodic pattern of the reported dengue incidences and meteorological data and correlation of dengue incidences with meteorological data are identified to determine climate data-driven per-capita vector density parameter function. By considering that the vector dynamics occurs in faster time scale compares to host dynamics, a two dimensional data-driven compartmental model is derived with aid of classical compartmental models. Moreover, a function for per-capita vector density is introduced to capture the seasonal pattern of the disease according to the effect of climate factors in urban Colombo.

CONCLUSIONS

The two dimensional data-driven compartmental model can be used to predict weekly dengue incidences upto 4 weeks. Accuracy of the model is evaluated using relative error function and the model can be used to predict more than 75% accurate data.

Epidemiological Analysis of the 2019 Dengue Epidemic in Bhutan

Bhutan experienced its largest and first nation-wide dengue epidemic in 2019. The cases in 2019 were greater than the total number of cases in all the previous years. This study aimed to characterize the spatiotemporal patterns and effective reproduction number of this explosive epidemic. Weekly notified dengue cases were extracted from the National Early Warning, Alert, Response and Surveillance (NEWARS) database to describe the spatial and temporal patterns of the epidemic. The time-varying, temperature-adjusted cohort effective reproduction number was estimated over the course of the epidemic. The dengue epidemic occurred between 29 April and 8 December 2019 over 32 weeks, and included 5935 cases. During the epidemic, dengue expanded from six to 44 subdistricts. The effective reproduction number was <3 for most of the epidemic period, except for a ≈1 month period of explosive growth, coinciding with the monsoon season and school vacations, when the effective reproduction number peaked >30 and after which the effective reproduction number declined steadily. Interventions were only initiated 6 weeks after the end of the period of explosive growth. This finding highlights the need to reinforce the national preparedness plan for outbreak response, and to enable the early detection of cases and timely response.

Transmissibility of COVID-19 in 11 major cities in China and its association with temperature and humidity in Beijing, Shanghai, Guangzhou, and Chengdu

BACKGROUND

The new coronavirus disease COVID-19 began in December 2019 and has spread rapidly by human-to-human transmission. This study evaluated the transmissibility of the infectious disease and analyzed its association with temperature and humidity to study the propagation pattern of COVID-19.

METHODS

In this study, we revised the reported data in Wuhan based on several assumptions to estimate the actual number of confirmed cases considering that perhaps not all cases could be detected and reported in the complex situation there. Then we used the equation derived from the Susceptible-Exposed-Infectious-Recovered (SEIR) model to calculate R0 from January 24, 2020 to February 13, 2020 in 11 major cities in China for comparison. With the calculation results, we conducted correlation analysis and regression analysis between R0 and temperature and humidity for four major cities in China to see the association between the transmissibility of COVID-19 and the weather variables.

RESULTS

It was estimated that the cumulative number of confirmed cases had exceeded 45 000 by February 13, 2020 in Wuhan. The average R0 in Wuhan was 2.7, significantly higher than those in other cities ranging from 1.8 to 2.4. The inflection points in the cities outside Hubei Province were between January 30, 2020 and February 3, 2020, while there had not been an obvious downward trend of R0 in Wuhan. R0 negatively correlated with both temperature and humidity, which was significant at the 0.01 level.

CONCLUSIONS

The transmissibility of COVID-19 was strong and importance should be attached to the intervention of its transmission especially in Wuhan. According to the correlation between R0 and weather, the spread of disease will be suppressed as the weather warms.

Transmissibility of COVID-19 in 11 major cities in China and its association with temperature and humidity in Beijing, Shanghai, Guangzhou, and Chengdu

BACKGROUND

The new coronavirus disease COVID-19 began in December 2019 and has spread rapidly by human-to-human transmission. This study evaluated the transmissibility of the infectious disease and analyzed its association with temperature and humidity to study the propagation pattern of COVID-19.

METHODS

In this study, we revised the reported data in Wuhan based on several assumptions to estimate the actual number of confirmed cases considering that perhaps not all cases could be detected and reported in the complex situation there. Then we used the equation derived from the Susceptible-Exposed-Infectious-Recovered (SEIR) model to calculate R₀ from January 24, 2020 to February 13, 2020 in 11 major cities in China for comparison. With the calculation results, we conducted correlation analysis and regression analysis between R₀ and temperature and humidity for four major cities in China to see the association between the transmissibility of COVID-19 and the weather variables.

RESULTS

It was estimated that the cumulative number of confirmed cases had exceeded 45 000 by February 13, 2020 in Wuhan. The average R₀ in Wuhan was 2.7, significantly higher than those in other cities ranging from 1.8 to 2.4. The inflection points in the cities outside Hubei Province were between January 30, 2020 and February 3, 2020, while there had not been an obvious downward trend of R₀ in Wuhan. R₀ negatively correlated with both temperature and humidity, which was significant at the 0.01 level.

CONCLUSIONS

The transmissibility of COVID-19 was strong and importance should be attached to the intervention of its transmission especially in Wuhan. According to the correlation between R₀ and weather, the spread of disease will be suppressed as the weather warms.

Climatological, virological and sociological drivers of current and projected dengue fever outbreak dynamics in Sri Lanka

The largest ever Sri Lankan dengue outbreak of 2017 provides an opportunity for investigating the relative contributions of climatological, epidemiological and sociological drivers on the epidemic patterns of this clinically important vector-borne disease. To do so, we develop a climatologically driven disease transmission framework for dengue virus using spatially resolved temperature and precipitation data as well as the time-series susceptible-infected-recovered (SIR) model. From this framework, we first demonstrate that the distinct climatological patterns encountered across the island play an important role in establishing the typical yearly temporal dynamics of dengue, but alone are unable to account for the epidemic case numbers observed in Sri Lanka during 2017. Using a simplified two-strain SIR model, we demonstrate that the re-introduction of a dengue virus serotype that had been largely absent from the island in previous years may have played an important role in driving the epidemic, and provide a discussion of the possible roles for extreme weather events and human mobility patterns on the outbreak dynamics. Lastly, we provide estimates for the future burden of dengue across Sri Lanka using the Coupled Model Intercomparison Phase 5 climate projections. Critically, we demonstrate that climatological and serological factors can act synergistically to yield greater projected case numbers than would be expected from the presence of a single driver alone. Altogether, this work provides a holistic framework for teasing apart and analysing the various complex drivers of vector-borne disease outbreak dynamics.

Entomological surveillance with viral tracking demonstrates a migrated viral strain caused dengue epidemic in July, 2017 in Sri Lanka

Dengue is the most important mosquito-borne viral infection disease in Sri Lanka triggering extensive economic and social burden in the country. Even after numerous source reduction programmes, more than 30,000 incidences are reporting in the country every year. The last and greatest dengue epidemic in the country was reported in July, 2017 with more than 300 dengue related deaths and the highest number of dengue incidences were reported from the District of Gampaha. There is no Dengue Virus (DENV) detection system in field specimens in the district yet and therefore the aim of the study is development of entomological surveillance approach through vector survey programmes together with molecular and phylogenetic methods to identify detection of DENV serotypes circulation in order to minimize adverse effects of imminent dengue outbreaks. Entomological surveys were conducted in five study areas in the district for 36 months and altogether, 10,616 potential breeding places were investigated and 423 were positive for immature stages of dengue vector mosquitoes. During adult collections, 2,718 dengue vector mosquitoes were collected and 4.6% (n = 124) were Aedes aegypti. While entomological indices demonstrate various correlations with meteorological variables and reported dengue incidences, the mosquito pools collected during the epidemic in 2017 were positive for DENV. The results of the phylogenetic analysis illustrated that Envelope (E) gene sequences derived from the isolated DENV belongs to the Clade Ib of Cosmopolitan genotype of the DENV serotype 2 which has been the dominant stain in South-East Asian evidencing that a recent migration of DENV strain to Sri Lanka.

The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

Background

In Thailand, dengue fever is one of the most well-known public health problems. The objective of this study was to examine the epidemiology of dengue and determine the seasonal pattern of dengue and its associate to climate factors in Bangkok, Thailand, from 2003 to 2017.

Methods

The dengue cases in Bangkok were collected monthly during the study period. The time-series data were extracted into the trend, seasonal, and random components using the seasonal decomposition procedure based on loess. The Spearman correlation analysis and artificial neuron network (ANN) were used to determine the association between climate variables (humidity, temperature, and rainfall) and dengue cases in Bangkok.

Results

The seasonal-decomposition procedure showed that the seasonal component was weaker than the trend component for dengue cases during the study period. The Spearman correlation analysis showed that rainfall and humidity played a role in dengue transmission with correlation efficiency equal to 0.396 and 0.388, respectively. ANN showed that precipitation was the most crucial factor. The time series multivariate Poisson regression model revealed that increasing 1% of rainfall corresponded to an increase of 3.3% in the dengue cases in Bangkok. There were three models employed to forecast the dengue case, multivariate Poisson regression, ANN, and ARIMA. Each model displayed different accuracy, and multivariate Poisson regression was the most accurate approach in this study.

Conclusion

This work demonstrates the significance of weather in dengue transmission in Bangkok and compares the accuracy of the different mathematical approaches to predict the dengue case. A single model may insufficient to forecast precisely a dengue outbreak, and climate factor may not only indicator of dengue transmissibility.

Epidemiological study on dengue in southern Brazil under the perspective of climate and poverty

Social and epidemiological aspects of dengue were evaluated in an important metropolitan area in southern Brazil, from August 2012 to September 2014. Demographic, clinical, serological data were collected from patients with acute dengue symptoms treated at public health system units (HSUs). A systematic approach to analyze the spatial and temporal distribution of cases was developed, considering the temporal cross-correlation between dengue and weather, and the spatial correlation between dengue and income over the city's census tracts. From the 878 patients with suggestive symptoms, 249 were diagnosed as positive dengue infection (28%). Considering the most statistically significant census tracts, a negative correlation was found between mean income and dengue (r = -0.65; p = 0.02; 95% CI: -0.03 to -0.91). The occurrence of dengue followed a seasonal distribution, and it was found to be three and four months delayed in relation to precipitation and temperature, respectively. Unexpectedly, the occurrence of symptomatic patients without dengue infection followed the same seasonal distribution, however its spatial distribution did not correlate with income. Through this methodology, we have found evidence that suggests a relation between dengue and poverty, which enriches the debate in the literature and sheds light on an extremely relevant socioeconomic and public health issue.

Developing a dengue prediction model based on climate in Tawau, Malaysia

Dengue is fast becoming the most urgent health issue in Malaysia, recording close to a 10-fold increase in cases over the last decade. With much uncertainty hovering over the recently introduced tetravalent vaccine and no effective antiviral drugs, vector control remains the most important strategy in combating dengue. This study analyses the relationship between weather predictors including its lagged terms, and dengue incidence in the District of Tawau over a period of 12 years, from 2006 to 2017. A forecasting model purposed to predict future outbreaks in Tawau was then developed using this data. Monthly dengue incidence data, mean temperature, maximum temperature, minimum temperature, mean relative humidity and mean rainfall over a period of 12 years from 2006 to 2017 in Tawau were retrieved from Tawau District Health Office and the Malaysian Meteorological Department. Cross-correlation analysis between weather predictors, lagged terms of weather predictors and dengue incidences established statistically significant cross-correlation between lagged periods of weather predictors-namely maximum temperature, mean relative humidity and mean rainfall with dengue incidence at time lags of 4-6 months. These variables were then employed into 3 different methods: a multivariate Poisson regression model, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a SARIMA with external regressors for selection. Three models were selected but the SARIMA with external regressors model utilising maximum temperature at a lag of 6 months (p-value:0.001), minimum temperature at a lag of 4 months (p-value:0.01), mean relative humidity at a lag of 2 months (p-value:0.001), and mean rainfall at a lag of 6 months (p-value:0.001) produced an AIC of 841.94, and a log-likelihood score of -413.97 establishing it as the best fitting model of the methodologies utilised. In validating the models, they were utilised to develop forecasts with the model selected with the highest accuracy of predictions being the SARIMA model predicting 1 month in advance (MAE: 7.032, MSE: 83.977). This study establishes the effect of weather on the intensity and magnitude of dengue incidence as has been previously studied. A prediction model remains a novel method of evidence-based forecasting in Tawau, Sabah. The model developed in this study, demonstrated an ability to forecast potential dengue outbreaks 1 to 4 months in advance. These findings are not dissimilar to what has been previously studied in many different countries- with temperature and humidity consistently being established as powerful predictors of dengue incidence magnitude. When used in prognostication, it can enhance- decision making and allow judicious use of resources in public health setting. Nevertheless, the model remains a work in progress- requiring larger and more diverse data.

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006-2016: a spatiotemporal analysis

OBJECTIVES

To describe and quantify spatiotemporal trends of dengue fever at district level in Sumatra and Kalimantan, Indonesia in relation to forest cover and climatic factors.

METHODS

A spatial ecological study design was used to analyse monthly surveillance data of notified dengue fever cases from January 2006 to December 2016 in the 154 districts of Sumatra and 56 districts of Kalimantan. A multivariate, zero-inflated Poisson regression model was developed with a conditional autoregressive prior structure with posterior parameters estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling.

RESULTS

There were 230 745 cases in Sumatra and 132 186 cases in Kalimantan during the study period. In Sumatra, the risk of dengue fever decreased by 9% (95% credible interval [CrI] 8.5-9.5%) for a 1% increase in forest cover and by 12.2% (95% CrI 11.9-12.6%) for a 1% increase in relative humidity. In Kalimantan, dengue fever risk fell by 17.6% (95% CrI 17.1-18.1%) for a 1% increase in relative humidity and rose by 7.6% (95% CrI 6.9-8.4%) for a 1 °C increase in minimum temperature. There was no significant residual spatial clustering in Sumatra after accounting for climate and demographic variables. In Kalimantan, high residual risk areas were primarily centred in North and East of the island.

CONCLUSIONS

Dengue fever in Sumatra and Kalimantan was highly seasonal and associated with climate factors and deforestation. Incorporation of climate indicators into risk-based surveillance might be warranted for dengue fever in Indonesia.

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.

Effect of meteorological factors on the seasonal prevalence of dengue vectors in upland hilly and lowland Terai regions of Nepal

Background

The expansion of dengue vectors from lowland plains to the upland hilly regions of Nepal suggests the likelihood of increased risk of dengue. Our objective was to assess the effects of meteorological variables on vector indices and populations of dengue vectors in two different ecological regions of Nepal. An entomological survey was conducted in Kathmandu and Lalitpur (upland) and Chitwan (lowland) of Nepal in three different seasons from July 2015 to May 2016. The effect of meteorological variables on vector indices (house index, container index and Breteau index) and Aedes spp. population abundance was analyzed. A gamma regression was used to fit the models for vector indices and a negative binomial regression was used to model Aedes spp. population abundance.

Results

Monsoon season showed higher values for vector indices and vector populations compared to post-monsoon and pre-monsoon. Overall, the factor temperature-rainfall effect had a more significant influence on vector indices compared to relative humidity. The regression models showed that relative humidity has a greater impact in Chitwan than in Kathmandu. Variation was observed in the effect of predictor variables on Aedes aegypti and Ae. albopictus abundance.

Conclusions

Temperature and rainfall contribute to the vector indices in the upland hilly region while relative humidity contributes in the lowland plains. Since vector prevalence is not only linked to meteorological factors, other factors such as water storage practices, waste disposal, sanitary conditions and vector control strategy should also be considered. We recommend strengthening and scaling up dengue vector surveillance and control programmes for monsoon season in both upland and lowland regions in Nepal.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3304-3) contains supplementary material, which is available to authorized users.

Prediction of dengue outbreaks in Mexico based on entomological, meteorological and demographic data

Dengue virus has shown a complex pattern of transmission across Latin America over the last two decades. In an attempt to explain the permanence of the disease in regions subjected to drought seasons lasting over six months, various hypotheses have been proposed. These include transovarial transmission, forest reservoirs and asymptomatic human virus carriers. Dengue virus is endemic in Mexico, a country in which half of the population is seropositive. Seropositivity is a risk factor for Dengue Hemorrhagic Fever upon a second encounter with the dengue virus. Since Dengue Hemorrhagic Fever can cause death, it is important to develop epidemiological mathematical tools that enable policy makers to predict regions potentially at risk for a dengue epidemic. We formulated a mathematical model of dengue transmission, considering both human behavior and environmental conditions pertinent to the transmission of the disease. When data on past human population density, temperature and rainfall were entered into this model, it provided an accurate picture of the actual spread of dengue over recent years in four states (representing two climactic conditions) in Mexico.

An Analysis of a Dengue Outbreak at a Large Hospital and Epidemiological Evidence for Nosocomial Dengue

Reports on dengue outbreaks at hospitals are extremely rare. Here the authors analyze a dengue outbreak at the Teaching Hospital-Kandy (THK), Sri Lanka. Our hypothesis was that the present outbreak of dengue was due to nosocomial infections. Our objectives were to illustrate epidemiological evidence for nosocomial dengue infections among THK workers and comparison of dengue incidence of hospital workers of wards that treat dengue patients with workers of other wards, to ascertain whether most nosocomial dengue incidences occur closer to where dengue patients are treated and vector larvae were detected, and to draw the attention of the medical community to the significance of hospital outbreaks, making suggestions on how to improve dengue preventive work at the THK. We calculated weekly dengue incidences for the hospital workers and for the surrounding Kandy district population, plotted epicurves, and compared them. We also compared these with the temporal changes of numbers of patients who were admitted for other illnesses and then diagnosed with dengue and the numbers of containers with vector mosquito larvae found on hospital premises. Dengue incidence of the hospital workers for the 24-week study period (2388 per 100000 population) was significantly high when compared to incidence of the district (151 per 100000 population). Peaks of dengue incidence in hospital workers, the numbers of patients hospitalized for other illnesses contracting dengue, and numbers of containers with vector larvae occurred in the same week. The peak dengue incidence of the Kandy district happened six weeks later. There was no evidence to indicate blood contact causing dengue among hospital workers. The outbreak was controlled while dengue was rising in the district. This evidence indicates a probable nosocomial dengue outbreak. This outbreak adversely affected hospital workers, patients, and the community. We propose some measures to prevent such outbreaks.

Meteorological factors affecting dengue incidence in Davao, Philippines

Background

Dengue fever is a major public health concern in the Philippines, and has been a significant cause of hospitalizations and deaths among young children. Previous literature links climate change to dengue, and with increasingly unpredictable changing climate patterns, there is a need to understand how these meteorological variables affect dengue incidence in a highly endemic area.

Methods

Weekly dengue incidences (2011–2015) in Davao Region, Philippines were obtained from the Department of Health. Same period of weekly local meteorological variables were obtained from the National Climatic Data Center (NCDC) and the National Oceanic and Atmospheric Administration (NOAA). Wavelet coherence analysis was used to determine the presence of non-stationary relationships, while a quasi-Poisson regression combined with distributed lag nonlinear model (DLNM) was used to analyze the association between meteorological variables and dengue incidences.

Results

Significant periodicity was detected in the 7 to 14-week band between the year 2011–2012 and a 26-week periodicity from the year 2013–2014. Overall cumulative risks were particularly high for rainfall at 32 mm (RR: 1.67, 95% CI: 1.07–2.62), while risks were observed to increase with increasing dew point. On the other hand, lower average temperature of 26 °C has resulted to an increased RR of dengue (RR: 1.96, 95% CI: 0.47–8.15) while higher temperature from 27 °C to 31 °C has lower RR.

Conclusions

The observed possible threshold levels of these meteorological variables can be integrated into an early warning system to enhance dengue prediction for better vector control and management in the future.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5532-4) contains supplementary material, which is available to authorized users.

The association between local meteorological changes and exacerbation of acute wheezing in Kandy, Sri Lanka

BACKGROUND

Severe wheezing is a common medical emergency. Past studies have demonstrated associations between exacerbation of wheezing and meteorological factors and atmospheric pollution. There are no past studies from Sri Lanka that analyzed correlation between daily multiple meteorological variables and exacerbation of wheezing.

OBJECTIVES

To determine the correlations between daily counts of patients nebulized at the Outpatient Department (OPD) of Teaching Hospital - Kandy (THK) and local meteorological variables, and to explore the utility of that information.

DESIGN

We considered daily counts of patients nebulized at the OPD of THK as an indicator of exacerbations of wheezing in the population catered to by this hospital. We determined the correlations between daily counts of patients nebulized at OPD and the following meteorological variables for four years: daily rainfall, minimum temperature, maximum temperature, diurnal temperature range, difference between maximum temperature and the temperature at 1800 hours, daytime humidity, nighttime humidity, barometric pressure and visibility. We utilized wavelet time series method for data analysis.

RESULTS

All nine meteorological parameters studied were correlated with the daily counts of patients nebulized with average lag periods ranging from 5 to 15 days. Peaks of daily rainfall, maximum temperature, diurnal temperature range, difference between maximum temperature and the temperature at 1800 hours and daytime humidity were followed by peaks of counts of patients nebulized (positive correlations). Troughs of minimum temperature, nighttime humidity, barometric pressure and visibility were followed by peaks of patients nebulized (negative correlations).

CONCLUSIONS

The THK shall expect more patients with acute wheezing after extremes of weather. Minimum temperature has been consistently correlated with the exacerbation of respiratory symptoms in the past studies in other countries as well. Hence, prescribing the inhalation of more drugs on unusually cold days (prophylactically) may help prevent acute exacerbation of wheezing in patients on treatment for asthma and COPD.

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.

A dengue infection without bleeding manifestations in an adult with immune thrombocytopenic purpura

BACKGROUND

Dengue is the most prevalent and fast spreading arboviral infection affecting people. No specific drug is available to treat dengue. Thrombocytopenia with potential of serious hemorrhages is one of the hall mark features of dengue. Immune thrombocytopenic purpura is an autoimmune disease causing thrombocytopenia. If a patient with that gets dengue, we expect severe thrombocytopenia with bleeding manifestations. Only a handful of such cases were reported before, and they were managed in different ways.

CASE PRESENTATION

A 30-year-old Sinhalese man recently diagnosed of immune thrombocytopenic purpura and on prednisolone was presented on the fourth day of fever, head ache, arthralgia, myalgia, and nausea. We started standard symptomatic dengue management and continued prednisolone. Dengue IgM and IgG antibody tests became positive. He was monitored by physical signs and serial full blood counts as the mainstay of monitoring. The patient never developed clinical bleeding manifestations and recovered.

CONCLUSIONS

Considering the huge population at risk of dengue, generating more evidence on the topic and formulation of effective, simple guidelines to manage dengue in children and adults with immune thrombocytopenic purpura is going to be beneficial for many patients in the future.

Maintenance of demographic and hematological profiles in a long-lasting dengue fever outbreak: implications for management

Background

Dengue fever (DF) outbreaks present regionally specific epidemiological and clinical characteristics. In certain medium-sized cities (100 000–250 000 inhabitants) of São Paulo State, Brazil, and after reaching an incidence of 150 cases/100 000 inhabitants (“epidemiological threshold”), clinical diagnosis indicated dengue virus (DENV) infection. During this period, other seasonally infectious diseases with symptoms and physical signs mimicking DF can simultaneously occur, with the consequential overcrowding of health care facilities as the principal drawbacks. Confirmation of clinical diagnosis of DF with serological tests may help in avoiding faulty diagnosis in patients, who might later undergo dengue hemorrhagic fever (DHF) and the dengue-shock syndrome (DSS). Furthermore, demographic and hematological profiles of patients are useful in detecting specific early characteristics associated to DF, DHF and DSS.

Methods

From March to June, 2007, 456 patients from Marilia in northwest São Paulo State who had only been diagnosed for DF by clinical criteria, underwent serologic testing for non-structural 1 (NS1) DENV antigens. Individual results were used in comparative analysis according to demographic (gender, age) and hematological (leukocyte and platelet counts, percentage of atypical lymphocytes) profiles. Temporal patterns were evaluated by subdividing data according to time of initial attendance, using recorded variables as predictors of DENV infection in logistic regression models and ROC curves.

Results

Serologic DENV detection was positive in 70.6 % of the patients. Lower leukocyte and platelet counts were the most important factors in predicting DENV infection (respective medians DENV + = 3 715 cells/ml and DENV- = 6 760 cells/ml, and DENV + = 134 896 cells/ml and DENV- = 223 872 cells/ml). Furthermore, all demographic and hematological profiles presented a conservative temporal pattern throughout this long-lasting outbreak.

Conclusions

As consistency throughout the epidemic facilitated defining the conservation pattern throughout the early stages, this was useful for improving management during the remaining period.

Electronic supplementary material

The online version of this article (doi:10.1186/s40249-016-0177-y) contains supplementary material, which is available to authorized users.

The correlation between dengue incidence and diurnal ranges of temperature of Colombo district, Sri Lanka 2005-2014

BACKGROUND

Meteorological factors affect dengue transmission. Mechanisms of the way in which different diurnal temperatures, ranging around different mean temperatures, influence dengue transmission were published after 2011.

OBJECTIVE

We endeavored to determine the correlation between dengue incidence and diurnal temperature ranges (DTRs) in Colombo district, Sri Lanka, and to explore the possibilities of using our findings to improve control of dengue.

DESIGN

We calculated the weekly dengue incidence in Colombo during 2005-2014, after data on all of the reported dengue patients and estimated mid-year populations were collected. We obtained daily maximum and minimum temperatures from two Colombo weather stations, averaged, and converted them into weekly data. Weekly averages of DTR versus dengue incidence graphs were plotted and correlations observed. The count of days per week with a DTR of >7.5°C and <7.5°C were also calculated. Wavelet time series analysis was performed to determine the correlation between dengue incidence and DTR.

RESULTS

We obtained a negative correlation between dengue incidence and a DTR>7.5°C with an 8-week lag period, and a positive correlation between dengue incidence and a DTR<7.5°C, also with an 8-week lag.

CONCLUSIONS

Large DTRs were negatively correlated with dengue transmission in Colombo district. We propose to take advantage of that in local dengue control efforts. Our results agree with previous studies on the topic and with a mathematical model of relative vectorial capacity of Aedes aegypti. Global warming and declining DTR are likely to favor a rise of dengue, and we suggest a simple method to mitigate this.

The interrelationship between dengue incidence and diurnal ranges of temperature and humidity in a Sri Lankan city and its potential applications

BACKGROUND

Temperature, humidity, and other weather variables influence dengue transmission. Published studies show how the diurnal fluctuations of temperature around different mean temperatures influence dengue transmission. There are no published studies about the correlation between diurnal range of humidity and dengue transmission.

OBJECTIVE

The goals of this study were to determine the correlation between dengue incidence and diurnal fluctuations of temperature and humidity in the Sri Lankan city of Kandy and to explore the possibilities of using that information for better control of dengue.

DESIGN

We calculated the weekly dengue incidence in Kandy during the period 2003-2012, after collecting data on all of the reported dengue patients and estimated midyear populations. Data on daily maximum and minimum temperatures and night-time and daytime humidity were obtained from two weather stations, averaged, and converted into weekly data. The number of days per week with a diurnal temperature range (DTR) of >10°C and <10°C and the number of days per week with a diurnal humidity range (DHR) of >20 and <15% were calculated. Wavelet time series analysis was performed to determine the correlation between dengue incidence and diurnal ranges of temperature and humidity.

RESULTS

There were negative correlations between dengue incidence and a DTR >10°C and a DHR >20% with 3.3-week and 4-week lag periods, respectively. Additionally, positive correlations between dengue incidence and a DTR <10°C and a DHR <15% with 3- and 4-week lag periods, respectively, were discovered.

CONCLUSIONS

These findings are consistent with the results of previous entomological studies and theoretical models of DTR and dengue transmission correlation. It is important to conduct similar studies on diurnal fluctuations of humidity in the future. We suggest ways and means to use this information for local dengue control and to mitigate the potential effects of the ongoing global reduction of DTR on dengue incidence.