The complex relationship between weather and dengue virus transmission in Thailand

Current and lagged associations of meteorological variables and Aedes mosquito indices with dengue incidence in the Philippines

BACKGROUND

Dengue is an increasing health burden that has spread throughout the tropics and sub-tropics. There is currently no effective vaccine and control is only possible through integrated vector management. Early warning systems (EWS) to alert potential dengue outbreaks are currently being explored but despite showing promise are yet to come to fruition. This study addresses the association of meteorological variables with both mosquito indices and dengue incidences and assesses the added value of additionally using mosquito indices for predicting dengue incidences.

METHODOLOGY/PRINCIPAL FINDINGS

Entomological surveys were carried out monthly for 14 months in six sites spread across three environmentally different cities of the Philippines. Meteorological and dengue data were acquired. Non-linear generalized additive models were fitted to test associations of the meteorological variables with both mosquito indices and dengue cases. Rain and the diurnal temperature range (DTR) contributed most to explaining the variation in both mosquito indices and number of dengue cases. DTR and minimum temperature also explained variation in dengue cases occurring one and two months later and may offer potentially useful variables for an EWS. The number of adult mosquitoes did associate with the number of dengue cases, but contributed no additional value to meteorological variables for explaining variation in dengue cases.

CONCLUSIONS/SIGNIFICANCE

The use of meteorological variables to predict future risk of dengue holds promise. The lack of added value of using mosquito indices confirms several previous studies and given the onerous nature of obtaining such information, more effort should be placed on improving meteorological information at a finer scale to evaluate efficacy in early warning of dengue outbreaks.

'Seven Plus One' a unique approach to assess the knowledge, attitude and practices for dengue prevention and control among frontline workers of a teaching institution in Rishikesh, Uttarakhand

Background

Dengue which is an arboviral disease transmitted by Aedes aegypti mosquito, drastically affected communities worldwide. It has been showing consistently a rising trend in developing country like India.

Study Objectives

To assess the knowledge, attitude and practices (KAPs) related to the prevention and control of dengue fever (DF) among frontline staff members at a medical teaching institution in Rishikesh, Uttarakhand.

Materials and Methods

A community-based cross-sectional study was conducted at a medical teaching institution in Rishikesh, Uttarakhand. Out of 830 frontline workers only 208 workers were recruited in a study by the simple random selection method. A semi-structured interview questionnaire was applied to assess the KAPs of participants. Attitude was assessed by four-point Likert scaling. Statistical analysis was done using SPSS version 23 for Windows.

Results

A total of 208 participants were interviewed. Majority were male (70.2%) with the age group of 31 to 45 years (71.2%). Majority (50.5%) of participants were educated up to higher secondary and working as a security guard (78.8%). Most (72.6%) of the respondents said the rainy season was the most common outbreak season for dengue. Most (63.6%) of the participants were aware that dengue is transmitted by mosquitoes. The majority (49%) of participants strongly agreed that dengue is a fatal disease.

Conclusion

There is an urgent need to prevent and control the epidemics of dengue by adoption of seven plus one model which contributes in reducing the overall burden in healthcare delivery system.

Detecting the impacts of humidity, rainfall, temperature, and season on chikungunya, dengue and Zika viruses in Aedes albopictus mosquitoes from selected sites in Cebu city, Philippines

BACKGROUND

Aedes albopictus is the secondary vector for dengue virus (DENV) in the Philippines, and also harbors chikungunya (CHIKV) and Zika (ZIKV) viruses. This study aimed to determine the minimum infection rates (MIRs) of CHIKV, DENV serotypes, and ZIKV in Ae. albopictus collected from selected two-site categories by altitude (highland [H] and lowland [L] sites) in Cebu city, Philippines during the wet (WS) and dry seasons (DS) of 2021-2022, and to explore the relationships between these arboviral MIRs and the local weather.

METHODS

The viral RNA extracts in pooled and reared adult Ae. albopictus collected during the DS and WS from two-site categories were subjected to RT-PCR to amplify and detect gene loci specific for CHIKV, DENV-1 to DENV-4, and ZIKV and analyzed with the weather data.

RESULTS

The range of CHIKV MIRs was higher in the WS (13.61-107.38 infected individuals per 1,000 mosquitoes) than in the DS (13.22-44.12), but was similar between the two-site categories. Rainfall (RF) influenced the CHIKV MIR. The MIR ranges of both DENV-2 (WS: H = 0, L = 0; DS: H = 0-5.92; L = 0-2.6) and DENV-4 (WS: H = 0, L = 0-2.90; DS: H = 2.96-6.13, L = 0-15.63) differed by season but not between the two-site categories. Relative humidity (RH), RF, and temperature did not influence DENVs' MIRs. The MIR range of ZIKV was similar in both seasons (WS: 11.36-40.27; DS: 0-46.15) and two-site categories (H = 0-90.91, L = 0-55.56). RH and temperature influenced ZIKV MIR.

CONCLUSIONS

RF influenced CHIKV MIR in Ae. albopictus, whereas RH and temperature influenced that of ZIKV. Season influenced the MIRs of CHIKV and DENVs but not in ZIKV. Ae. albopictus were co-infected with CHIKV, DENVs, and ZIKV in both highland and lowland sites in Cebu city. Recommendations include all-year-round implementation of the Philippine Department of Health's  4S enhanced strategy and installation of water pipelines in rural highlands for vector and disease control. Our findings are relevant to protect public health in the tropics in this climate change.

Non-linear associations between meteorological factors, ambient air pollutants and major mosquito-borne diseases in Thailand

BACKGROUND

Transmission intensity for mosquito-borne diseases are highly heterogenous and multi-factorial. Understanding risk factors associated to disease transmission allow the optimization of vector control. This study sets out to understand and compare the combined anthropogenic and environmental risk factors of four major mosquito-borne diseases, dengue, malaria, chikungunya and Japanese encephalitis in Thailand.

METHODS

An integrated analysis of mosquito-borne diseases, meteorological and ambient air pollutants of 76 provinces of Thailand was conducted over 2003-2021. We explored the use of generalized linear models and generalized additive models to consider both linear and non-linear associations between meteorological factors, ambient air pollutants and mosquito-borne disease incidence. Different assumptions on spatio-temporal dependence and nonlinearity were considered through province-specific and panel models, as well as different spline functions. Disease-specific model evidence was assessed to select best-fit models for epidemiological inference downstream.

RESULTS

Analyses indicated several findings which can be generally applied to all diseases explored: (1) higher AH above mean values was positively associated with disease case counts (2) higher total precipitation above mean values was positively associated with disease case counts (3) extremely high temperatures were negatively associated with disease case counts (4) higher SO2 and PM2.5 surface concentrations were negatively associated with disease case counts. However, the relationships between disease and RH, non-extreme temperatures and CO surface concentration were more mixed, with directions of associations changing across the different diseases considered.

CONCLUSIONS

This study found protective and enhancing effects of meteorological and ambient air pollutant factors on mosquito-borne diseases burdens in Thailand. Further studies should employ these factors to understand and predict risk factors associated with mosquito-borne disease transmission.

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data

Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

The spatio-temporal distribution of acute encephalitis syndrome and its association with climate and landcover in Vietnam

BACKGROUND

Acute encephalitis syndrome (AES) differs in its spatio-temporal distribution in Vietnam with the highest incidence seen during the summer months in the northern provinces. AES has multiple aetiologies, and the cause remains unknown in many cases. While vector-borne disease such as Japanese encephalitis and dengue virus and non-vector-borne diseases such as influenza and enterovirus show evidence of seasonality, associations with climate variables and the spatio-temporal distribution in Vietnam differs between these. The aim of this study was therefore to understand the spatio-temporal distribution of, and risk factors for AES in Vietnam to help hypothesise the aetiology.

METHODS

The number of monthly cases per province for AES, meningitis and diseases including dengue fever; influenza-like-illness (ILI); hand, foot, and mouth disease (HFMD); and Streptococcus suis were obtained from the General Department for Preventive Medicine (GDPM) from 1998-2016. Covariates including climate, normalized difference vegetation index (NDVI), elevation, the number of pigs, socio-demographics, JEV vaccination coverage and the number of hospitals were also collected. Spatio-temporal multivariable mixed-effects negative binomial Bayesian models with an outcome of the number of cases of AES, a combination of the covariates and harmonic terms to determine the magnitude of seasonality were developed.

RESULTS

The national monthly incidence of AES declined by 63.3% over the study period. However, incidence increased in some provinces, particularly in the Northwest region. In northern Vietnam, the incidence peaked in the summer months in contrast to the southern provinces where incidence remained relatively constant throughout the year. The incidence of meningitis, ILI and S. suis infection; temperature, relative humidity with no lag, NDVI at a lag of one month, and the number of pigs per 100,000 population were positively associated with the number of cases of AES in all models in which these covariates were included.

CONCLUSIONS

The positive correlation of AES with temperature and humidity suggest that a number of cases may be due to vector-borne diseases, suggesting a need to focus on vaccination campaigns. However, further surveillance and research are recommended to investigate other possible aetiologies such as S. suis or Orientia tsutsugamushi.

A second local dengue fever outbreak: A field experience from Muscat Governorate in Oman, 2022

Background

Dengue fever is an infectious disease of global health concern. This study aimed to describe the epidemiology and field experience of a locally transmitted outbreak of dengue fever in Muscat Governorate, Oman from mid-March to mid-April 2022, and the multi-sectoral approach to control the outbreak.

Methods

Data were collected from an electronic e-notification system, active surveillance and contact investigations.

Results

Of 250 suspected and probable cases, 169 were confirmed as dengue fever with DENV-2 serotype. Of these, 108 (63.9%) were male and 94 (55.6%) were Omani. The mean age was 39 years (standard deviation 13 years). Fever was the most common symptom and occurred in 100% of cases. Haemorrhagic manifestations occurred in 10% (n=17) of cases. Hospitalization was required for 93 cases (55.1%). The field investigation included 3444 houses and other suspected sites. Breeding sites for Aedes aegypti were identified in 565 (18.5%) sites visited. Interventions to control the outbreak included environmental and entomological assessment of the affected houses and surrounding areas (400 m radius of each house).

Conclusion

Outbreaks are expected to continue, with the possibility of severe cases due to antibody-dependent enhancement. More data are required to understand the genetics, geographical spread and behaviour of A. aegypti in Oman.

Long-term projections of the impacts of warming temperatures on Zika and dengue risk in four Brazilian cities using a temperature-dependent basic reproduction number

For vector-borne diseases the basic reproduction number [Formula: see text], a measure of a disease's epidemic potential, is highly temperature-dependent. Recent work characterizing these temperature dependencies has highlighted how climate change may impact geographic disease spread. We extend this prior work by examining how newly emerging diseases, like Zika, will be impacted by specific future climate change scenarios in four diverse regions of Brazil, a country that has been profoundly impacted by Zika. We estimated a [Formula: see text], derived from a compartmental transmission model, characterizing Zika (and, for comparison, dengue) transmission potential as a function of temperature-dependent biological parameters specific to Aedes aegypti. We obtained historical temperature data for the five-year period 2015-2019 and projections for 2045-2049 by fitting cubic spline interpolations to data from simulated atmospheric data provided by the CMIP-6 project (specifically, generated by the GFDL-ESM4 model), which provides projections under four Shared Socioeconomic Pathways (SSP). These four SSP scenarios correspond to varying levels of climate change severity. We applied this approach to four Brazilian cities (Manaus, Recife, Rio de Janeiro, and São Paulo) that represent diverse climatic regions. Our model predicts that the [Formula: see text] for Zika peaks at 2.7 around 30°C, while for dengue it peaks at 6.8 around 31°C. We find that the epidemic potential of Zika will increase beyond current levels in Brazil in all of the climate scenarios. For Manaus, we predict that the annual [Formula: see text] range will increase from 2.1-2.5, to 2.3-2.7, for Recife we project an increase from 0.4-1.9 to 0.6-2.3, for Rio de Janeiro from 0-1.9 to 0-2.3, and for São Paulo from 0-0.3 to 0-0.7. As Zika immunity wanes and temperatures increase, there will be increasing epidemic potential and longer transmission seasons, especially in regions where transmission is currently marginal. Surveillance systems should be implemented and sustained for early detection.

Estimating the impact of the COVID-19 pandemic on dengue in Brazil

Atypical dengue prevalence was observed in 2020 in many dengue-endemic countries, including Brazil. Evidence suggests that the pandemic disrupted not only dengue dynamics due to changes in mobility patterns, but also several aspects of dengue surveillance, such as care seeking behavior, care availability, and monitoring systems. However, we lack a clear understanding of the overall impact on dengue in different parts of the country as well as the role of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in 2020 using an interrupted time series design with forecasts from a neural network and a structural Bayesian time series model. We also decomposed the gap into the impacts of climate conditions, pandemic-induced changes in reporting, human susceptibility, and human mobility. We find that there is considerable variation across the country in both overall pandemic impact on dengue and the relative importance of individual drivers. Increased understanding of the causal mechanisms driving the 2020 dengue season helps mitigate some of the data gaps caused by the COVID-19 pandemic and is critical to developing effective public health interventions to control dengue in the future.

Distinguishing SARS-CoV-2 Infection and Non-SARS-CoV-2 Viral Infections in Adult Patients through Clinical Score Tools

This study aimed to determine distinguishing predictors and develop a clinical score to differentiate COVID-19 and common viral infections (influenza, respiratory syncytial virus (RSV), dengue, chikungunya (CKV), and zika (ZKV)). This retrospective study enrolled 549 adults (100 COVID-19, 100 dengue, 100 influenza, 100 RSV, 100 CKV, and 49 ZKV) during the period 2017−2020. CKV and ZKV infections had specific clinical features (i.e., arthralgia and rash); therefore, these diseases were excluded. Multiple binary logistic regression models were fitted to identify significant predictors, and two scores were developed differentiating influenza/RSV from COVID-19 (Flu-RSV/COVID) and dengue from COVID-19 (Dengue/COVID). The five independent predictors of influenza/RSV were age > 50 years, the presence of underlying disease, rhinorrhea, productive sputum, and lymphocyte count < 1000 cell/mm3. Likewise, the five independent predictors of dengue were headache, myalgia, no cough, platelet count < 150,000/mm3, and lymphocyte count < 1000 cell/mm3. The Flu-RSV/COVID score (cut-off value of 4) demonstrated 88% sensitivity and specificity for predicting influenza/RSV (AUROC = 0.94). The Dengue/COVID score (cut-off value of 4) achieved 91% sensitivity and 94% specificity for differentiating dengue and COVID-19 (AUROC = 0.98). The Flu-RSV/COVID and Dengue/COVID scores had a high discriminative ability for differentiating influenza/RSV or dengue infection and COVID-19. The further validation of these scores is needed to ensure their utility in clinical practice.

Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus

Dengue is an important vector-borne disease transmitted by the mosquitoes Aedes aegypti and Ae. albopictus. In the absence of an effective vaccine, vector control has become the key intervention tool in controlling the disease. Vector densities are significantly affected by the changing weather patterns of a region. The present study was conducted in three selected localities, i.e., urban Bandaranayakapura, semiurban Galgamuwa, and rural Buluwala in the Kurunegala district of Sri Lanka to assess spatial and temporal distribution of dengue vector mosquitoes and to predict vector prevalence with respect to changing weather parameters. Monthly ovitrap surveys and larval surveys were conducted from January to December 2019 and continued further in the urban area up to December 2021. Aedes aegypti was found moderately in the urban area and to a lesser extent in semiurban but not in the rural area. Aedes albopictus had the preference for rural over urban areas. Aedes aegypti preferred indoor breeding, while Ae. albopictus preferred both indoor and outdoor. For Ae. albopictus, ovitrap index (OVI), premise index (PI), container index (CI), and Breteau index (BI) correlated with both the rainfall (RF) and relative humidity (RH) of the urban site. Correlations were stronger between OVI and RH and also between BI and RF. Linear regression analysis was fitted, and a prediction model was developed using BI and RF with no lag period (R ² (sq) = 86.3%; F = 53.12; R ² (pred) = 63.12%; model: Log10 (BI) = 0.153 + 0.286 ^∗ Log10 (RF); RMSE = 1.49). Another prediction model was developed using OVI and RH with one month lag period (R ² (sq) = 70.21%; F = 57.23; model: OVI predicted = 15.1 + 0.528 ^∗ Lag 1 month RH; RMSE = 2.01). These two models can be used to monitor the population dynamics of Ae. albopictus in urban settings to predict possible dengue outbreaks.

Molecular surveillance of arboviruses circulation and co-infection during a large chikungunya virus outbreak in Thailand, October 2018 to February 2020

A large national outbreak of chikungunya virus (CHIKV) was recently reported in Thailand. While dengue virus (DENV) infection tends to occur year-round with an upsurge in the rainy season, Zika virus (ZIKV) also circulates in the country. The overlap in the distribution of these viruses increased the probability of co-infections during the heightened CHIKV activity. By examining 1806 patient serum samples submitted for CHIKV diagnostics from October 2018-February 2020 (511 CHIKV-negatives and 1295 CHIKV-positives), we used real-time reverse transcription-polymerase chain reaction to identify DENV and ZIKV individually. A total of 29 ZIKV and 36 DENV single-infections were identified. Interestingly, 13 co-infection cases were observed, of which 8 were CHIKV/DENV, 3 were CHIKV/ZIKV, and 2 were DENV/ZIKV. There were six DENV genotypes (13 DENV-1 genotype I, 10 DENV-2 Asian I, 10 DENV-2 Cosmopolitan, 6 DENV-3 genotype I, 2 DENV-3 genotype III, and 5 DENV-4 genotype I). Additionally, ZIKV strains identified in this study either clustered with strains previously circulating in Thailand and Singapore, or with strains previously reported in China, French Polynesia, and the Americas. Our findings reveal the co-infection and genetic diversity patterns of mosquito-borne viruses circulating in Thailand.

Etiologies of Acute Undifferentiated Febrile Illnesses in and near Iquitos from 1993 to 1999 in the Amazon River Basin of Peru

The objective of this study was to determine the etiology of febrile illnesses among patients from October 1, 1993 through September 30, 1999, in the urban community of Iquitos in the Amazon River Basin of Peru. Epidemiological and clinical data as well as blood samples were obtained from consenting patients at hospitals, health clinics and private residences. Samples were tested for arboviruses in cell cultures and for IgM and IgG antibodies by ELISA. Blood smears were examined for malaria, and sera were tested for antibodies to Leptospira spp. by ELISA and microscopic agglutination. Among 6,607 febrile patients studied, dengue viruses caused 14.6% of the cases, and Venezuelan equine encephalitis virus caused 2.5%, Oropouche virus 1.0%, Mayaro virus 0.4%, and other arboviruses caused 0.2% of the cases. Also, 22.9% of 4,844 patients tested were positive for malaria, and of 400 samples tested, 9% had evidence of acute leptospirosis. Although the study was not designed to assess the importance of these pathogens as a cause of human morbidity in the total population, these results indicate that arboviruses, leptospirosis, and malaria were the cause of approximately 50% of the febrile cases. Although the arboviruses that were diagnosed can produce asymptomatic infections, our findings increased the overall understanding of the relative health burden of these infections, as well as baseline knowledge needed for designing and implementing further studies to better assess the health impact and threat of these pathogens in the Amazon Basin of Peru.

An assessment of remotely sensed environmental variables on Dengue epidemiology in Central India

In recent decades, dengue has been expanding rapidly in the tropical cities. Even though environmental factors and landscape features profoundly impact dengue vector abundance and disease epidemiology, significant gaps exist in understanding the role of local environmental heterogeneity on dengue epidemiology in India. In this study, we assessed the role of remotely sensed climatic factors (rainfall, temperature and humidity) and landscape variables (land use pattern, vegetation and built up density) on dengue incidence (2012–2019) in Bhopal city, Central India. Dengue hotspots in the city were assessed through geographical information system based spatial statistics. Dengue incidence increased from 0.59 cases in 2012 to 9.11 cases in 2019 per 10,000 inhabitants, and wards located in Southern Bhopal were found to be dengue hotspots. Distributed lag non-linear model combined with quasi Poisson regression was used to assess the exposure-response association, relative risk (RR), and delayed effects of environmental factors on dengue incidence. The analysis revealed a non-linear relationship between meteorological variables and dengue cases. The model shows that the risk of dengue cases increases with increasing mean temperature, rainfall and absolute humidity. The highest RR of dengue cases (~2.0) was observed for absolute humidity ≥60 g/m³ with a 5–15 week lag. Rapid urbanization assessed by an increase in the built-up area (a 9.1% increase in 2020 compared to 2014) could also be a key factor driving dengue incidence in Bhopal city. The study sheds important insight into the synergistic effects of both the landscape and climatic factors on the transmission dynamics of dengue. Furthermore, the study provides key baseline information on the climatic variables that can be used in the micro-level dengue prediction models in Bhopal and other cities with similar climatic conditions.

Dengue, a viral disease transmitted by infected Aedes mosquitoes, is a major public health concern globally. In addition to its increased incidence in recent years, dengue is also spreading to new geographical regions. Local environmental factors are known to modify the mosquito vector density that directly impacts dengue virus transmission. Understanding the influence of environmental factors (meteorological conditions and landscape features) on dengue epidemiology in local settings is important for focused dengue intervention. Here, by utilizing dengue incidence and remotely sensed environmental data from 2012–2019, we have assessed the role of environmental factors in driving dengue virus transmission in the city of Bhopal in Central India. During the study period, a 14.5 fold increase in dengue incidence was observed in Bhopal city, which is way higher than the 2.3 fold increase reported at the national level. The risk of dengue virus transmission was higher with higher temperature and absolute humidity. An increase in built-up area, a proxy for urbanization, was found to be another predictor of increased dengue incidence in Bhopal. These findings can provide a stepping-stone for the development of dengue prediction models and the identification of dengue hotspots in order to improve vector control of this disease in cities with similar environmental conditions.

Species-specific climate Suitable Conditions Index and dengue transmission in Guangdong, China

Background

Optimal climatic conditions for dengue vector mosquito species may play a significant role in dengue transmission. We previously developed a species-specific Suitable Conditions Index (SCI) for Aedes aegypti and Aedes albopictus, respectively. These SCIs rank geographic locations based on their climatic suitability for each of these two dengue vector species and theoretically define parameters for transmission probability. The aim of the study presented here was to use these SCIs together with socio-environmental factors to predict dengue outbreaks in the real world.

Methods

A negative binomial regression model was used to assess the relationship between vector species-specific SCI and autochthonous dengue cases after accounting for potential confounders in Guangdong, China. The potential interactive effect between the SCI for Ae. albopictus and the SCI for Ae. aegypti on dengue transmission was assessed.

Results

The SCI for Ae. aegypti was found to be positively associated with autochthonous dengue transmission (incidence rate ratio: 1.06, 95% confidence interval: 1.03, 1.09). A significant interaction effect between the SCI of Ae. albopictus and the SCI of Ae. aegypti was found, with the SCI of Ae. albopictus significantly reducing the effect of the SCI of Ae. aegypti on autochthonous dengue cases. The difference in SCIs had a positive effect on autochthonous dengue cases.

Conclusions

Our results suggest that dengue fever is more transmittable in regions with warmer weather conditions (high SCI for Ae. aegypti). The SCI of Ae. aegypti would be a useful index to predict dengue transmission in Guangdong, China, even in dengue epidemic regions with Ae. albopictus present. The results also support the benefit of the SCI for evaluating dengue outbreak risk in terms of vector sympatry and interactions in the absence of entomology data in future research.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05453-x.

RP, G. S, K. AAP, K. SM, A. SP, P. R, V. S, Dasgupta S, Krishnan J, Ishtiaq F. Island biogeography and human practices drive ecological connectivity in mosquito species richness in the Lakshadweep Archipelago

Mosquitoes are globally distributed and adapted to a broad range of environmental conditions. As obligatory hosts of many infectious pathogens, mosquito abundance and distribution are primarily determined by the presence and quality of larval habitats. To understand the dynamics and productivity of larval habitats in changing island environments, we conducted a four-month mosquito survey across ten inhabited islands in the Lakshadweep archipelago. Using fine-resolution larval habitat mapping, we recorded 7890 mosquitoes representing 13 species and 7 genera. Of these, four species comprised 95% of the total collections—Aedes albopictus (Stegomyia) was the dominant species followed by Armigeres subalbatus, Culex quinquefasciatus and Malaya genurostris. We found larval species richness was positively associated with the island area and mosquito larval richness (Chao1 estimator) was higher in artificial habitats than in natural habitats. Furthermore, mosquito species composition did not deteriorate with distance between islands. Mosquito abundance by species was associated with microclimatic variables—pH and temperature. We detected co-existence of multiple species at a micro-habitat level with no evidence of interactions like competition or predation. Our study analyzed and identified the most productive larval habitats –discarded plastic container and plastic drums contributing to high larval indices predicting dengue epidemic across the Lakshadweep islands. Our data highlight the need to devise vector control strategies by removal of human-induced plastic pollution (household waste) which is a critical driver of disease risk.

Effects of Guangzhou seasonal climate change on the development of Aedes albopictus and its susceptibility to DENV-2

The susceptibility of Asian tiger mosquitoes to DENV-2 in different seasons was observed in simulated field environments as a reference to design dengue fever control strategies in Guangzhou. The life table experiments of mosquitoes in four seasons were carried out in the field. The susceptibility of Ae. albopictus to dengue virus was observed in both environments in Guangzhou in summer and winter. Ae. albopictus was infected with dengue virus by oral feeding. On day 7 and 14 after infection, the viral load in the head, ovary, and midgut of the mosquito was detected using real-time fluorescent quantitative PCR. Immune-associated gene expression in infected mosquitoes was performed using quantitative real-time reverse transcriptase PCR. The hatching rate and pupation rate of Ae. albopictus larvae in different seasons differed significantly. The winter hatching rate of larvae was lower than that in summer, and the incubation time was longer than in summer. In the winter field environment, Ae. albopictus still underwent basic growth and development processes. Mosquitoes in the simulated field environment were more susceptible to DENV-2 than those in the simulated laboratory environment. In the midgut, viral RNA levels on day 7 in summer were higher than those on day 7 in winter (F = 14.459, P = 0.01); ovarian viral RNA levels on day 7 in summer were higher than those on day 7 in winter (F = 8.656, P < 0.001), but there was no significant difference in the viral load at other time points (P > 0.05). Dicer-2 mRNA expression on day 7 in winter was 4.071 times than that on day 7 in summer: the viral load and Dicer-2 expression correlated moderately. Ae. albopictus could still develop and transmit dengue virus in winter in Guangzhou. Mosquitoes under simulated field conditions were more susceptible to DENV-2 than those under simulated laboratory conditions.

Prediction of dengue fever outbreaks using climate variability and Markov chain Monte Carlo techniques in a stochastic susceptible-infected-removed model

The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019–2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.

Determining Perceived Self-Efficacy for Preventing Dengue Fever in Two Climatically Diverse Mexican States: A Cross-Sectional Study

Knowledge of dengue fever and perceived self-efficacy toward dengue prevention does not necessarily translate to the uptake of mosquito control measures. Understanding how these factors (knowledge and self-efficacy) influence mosquito control measures in Mexico is limited. Our study sought to bridge this knowledge gap by assessing individual-level variables that affect the use of mosquito control measures. A cross-sectional survey with 623 participants was administered online in Mexico from April to July 2021. Multiple linear regression and multiple logistic regression models were used to explore factors that predicted mosquito control scale and odds of taking measures to control mosquitoes in the previous year, respectively. Self-efficacy (β = 0.323, p-value = < 0.0001) and knowledge about dengue reduction scale (β = 0.316, p-value =< 0.0001) were the most important predictors of mosquito control scale. The linear regression model explained 24.9% of the mosquito control scale variance. Increasing age (OR = 1.064, p-value =< 0.0001) and self-efficacy (OR = 1.020, p-value = 0.0024) were both associated with an increase in the odds of taking measures against mosquitoes in the previous year. There is a potential to increase mosquito control awareness and practices through the increase in knowledge about mosquito reduction and self-efficacy in Mexico.

Influence of climate variables on dengue fever occurrence in the southern region of Thailand

The 3-5year epidemic cycle of dengue fever in Thailand makes it a major re-emerging public health problem resulting in being a burden in endemic areas. Although the Thai Ministry of Public Health adopted the WHO dengue control strategy, all dengue virus serotypes continue to circulate. Health officers and village health volunteers implement some intervention options but there is a need to ascertain most appropriate (or a combination of) interventions regarding the environment and contextual factors that may undermine the effectiveness of such interventions. This study aims to understand the dengue-climate relationship patterns at the district level in the southern region of Thailand from 2002 to 2018 by examining the statistical association between dengue incidence rate and eight environmental patterns, testing the hypothesis of equal incidence of these. Data on environmental variables and dengue reported cases in Nakhon Si Thammarat province situated in the south of Thailand from 2002 to 2018 were analysed to (1) detect the environmental factors that affect the risk of dengue infectious disease; to (2) determine if disease risk is increasing or decreasing over time; and to (3) identify the high-risk district areas for dengue cases that need to be targeted for interventions. To identify the predictors that have a high and significant impact on reported dengue infection, three steps of analysis were used. First, we used Partial Least Squares (PLS) Regression and Poisson Regression, a variant of the Generalized Linear Model (GLM). Negative co-efficient in correspondence with the PLS components suggests that sea-level pressure, wind speed, and pan evaporation are associated with dengue occurrence rate, while other variables were positively associated. Using the Akaike information criterion in the stepwise GLM, the filtered predictors were temperature, precipitation, cloudiness, and sea level pressure with the standardized coefficients showing that the most influential variable is cloud cover (three times more than temperature and precipitation). Also, dengue occurrence showed a constant negative response to the average increase in sea-level pressure values. In southern Thailand, the predictors that have been locally determined to drive dengue occurrence are temperature, rainfall, cloud cover, and sea-level pressure. These explanatory variables should have important future implications for epidemiological studies of mosquito-borne diseases, particularly at the district level. Predictive indicators guide effective and dynamic risk assessments, targeting pre-emptive interventions.

The impacts of precipitation patterns on dengue epidemics in Guangzhou city

Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67-55.50 mm per week, Pre_LMD of 3-4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control.

Impact of Climate Variability and Incidence on Dengue Hemorrhagic Fever in Palembang City, South Sumatra, Indonesia

Dengue hemorrhagic fever (DHF) is a dengue virus infection transmitted by Aedes spp. Climate has a profound influence on mosquito breeding. Palembang has the highest rate of DHF in South Sumatra. This study aimed to investigate the relationship between the components of climate factors and the incidence of DHF in Palembang. This study was cross-sectional, with an observational analytic approach. The Palembang City Health Office compiled data on DHF incidence rates from 2016 to 2020. Climatic factor data (rainfall, number of rainy days, temperature, humidity, wind speed, sun irradiance) were collected from the Climatology Station Class I Palembang - BMKG Station and Task Force that same year. The Spearman test was used to conduct the correlation test. Between 2016 and 2020, there were 3,398 DHF patients. From January to May, DHF increased. There was a significant correlation between rainfall (r = 0.320; p = 0.005), number of rainy days (r = 0.295; p = 0.020), temperature (r = 0.371; p = 0.040), and humidity (r = 0.221; p = 0.024), wind speed (r= 0.76; p = 0.492), and sunlight (r = 0.008; p = 0.865). Rainfall, the number of rainy days, and temperature were three climatic factors determining the increase in dengue incidence in Palembang.

Health impacts of daily weather fluctuations: Empirical evidence from COVID-19 in U.S. counties

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

Ecological, Social, and Other Environmental Determinants of Dengue Vector Abundance in Urban and Rural Areas of Northeastern Thailand

Aedes aegypti is the main vector of dengue globally. The variables that influence the abundance of dengue vectors are numerous and complex. This has generated a need to focus on areas at risk of disease transmission, the spatial-temporal distribution of vectors, and the factors that modulate vector abundance. To help guide and improve vector-control efforts, this study identified the ecological, social, and other environmental risk factors that affect the abundance of adult female and immature Ae. aegypti in households in urban and rural areas of northeastern Thailand. A one-year entomological study was conducted in four villages of northeastern Thailand between January and December 2019. Socio-demographic; self-reported prior dengue infections; housing conditions; durable asset ownership; water management; characteristics of water containers; knowledge, attitudes, and practices (KAP) regarding climate change and dengue; and climate data were collected. Household crowding index (HCI), premise condition index (PCI), socio-economic status (SES), and entomological indices (HI, CI, BI, and PI) were calculated. Negative binomial generalized linear models (GLMs) were fitted to identify the risk factors associated with the abundance of adult females and immature Ae. aegypti. Urban sites had higher entomological indices and numbers of adult Ae. aegypti mosquitoes than rural sites. Overall, participants’ KAP about climate change and dengue were low in both settings. The fitted GLM showed that a higher abundance of adult female Ae. aegypti was significantly (p < 0.05) associated with many factors, such as a low education level of household respondents, crowded households, poor premise conditions, surrounding house density, bathrooms located indoors, unscreened windows, high numbers of wet containers, a lack of adult control, prior dengue infections, poor climate change adaptation, dengue, and vector-related practices. Many of the above were also significantly associated with a high abundance of immature mosquito stages. The GLM model also showed that maximum and mean temperature with four-and one-to-two weeks of lag were significant predictors (p < 0.05) of the abundance of adult and immature mosquitoes, respectively, in northeastern Thailand. The low KAP regarding climate change and dengue highlights the engagement needs for vector-borne disease prevention in this region. The identified risk factors are important for the critical first step toward developing routine Aedes surveillance and reliable early warning systems for effective dengue and other mosquito-borne disease prevention and control strategies at the household and community levels in this region and similar settings elsewhere.

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. 

The Burden of Dengue in Children by Calculating Spatial Temperature: A Methodological Approach Using Remote Sensing Techniques

BACKGROUND

Dengue fever is one of the most important arboviral diseases. Surface temperature versus dengue burden in tropical environments can provide valuable information that can be adapted in future measurements to improve health policies.

METHODS

A methodological approach using Daymet-V3 provided estimates of daily weather parameters. A Python code developed by us extracted the median temperature from the urban regions of Colima State (207.3 km2) in Mexico. JointPoint regression models computed the mean temperature-adjusted average annual percentage of change (AAPC) in disability-adjusted life years (DALY) rates (per 100,000) due to dengue in Colima State among school-aged (5-14 years old) children.

RESULTS

Primary outcomes were average temperature in urban areas and cumulative dengue burden in DALYs in the school-aged population. A model from 1990 to 2017 medium surface temperature with DALY rates was performed. The increase in DALYs rate was 64% (95% CI, 44-87%), and it seemed to depend on the 2000-2009 estimates (AAPC = 185%, 95% CI 18-588).

CONCLUSION

From our knowledge, this is the first study to evaluate surface temperature and to model it through an extensive period with health economics calculations in a specific subset of the Latin-American endemic population for dengue epidemics.

Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand

Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions.

Global, regional, and national dengue burden from 1990 to 2017: A systematic analysis based on the global burden of disease study 2017

BACKGROUND

Dengue is one of the most common vector-borne diseases globally, however, its burden is poorly quantified. Hence, we aimed to report the dengue burden in 195 countries and territories between 1990 and 2017, using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017.

METHODS

Following the methodology framework and analytical strategies used in the Global Burden of Disease Study 2017, we analysed the incidence, mortality, and disability-adjusted life years (DALYs) of dengue in geographically defined populations worldwide between 1990 and 2017. We also determined the association between development levels and dengue burden. All estimates were reported as numbers and rates per 100 000 population, with 95% uncertainty intervals.

FINDINGS

Globally, the total number of dengue cases increased from 23 283 274 (95% UI 453 180.7-51 840 670) in 1990 to 104 771 911 (95% UI 63 759 019-158 870 031) in 2017. The age-standardised incidence rate increased from 431.6 (8.4-961.0) per 100 000 population in 1990 to 1371.3 (834.5-2079.3) per 100 000 population in 2017. In addition, the number of deaths due to dengue increased from approximately16 957 (7 613-30 091) in 1990 to 40 467 (17 620-49 778) in 2017. Meanwhile, the global age-standardised death rate increased from 0.31 (0.14-0•56) per 100 000 population in 1990 to 0.53 (0.23-0•65) per 100 000 population in 2017. Overall, there were 2 922 630 DALYs (1 629 424-3 967 492) attributed to dengue in 2017 globally, an increase of 107.6% since 1990 (1 407 571 DALYs [624 016.4-2 510 025]), and the age-standardised DALY rate increased from 26.10 (11.57-46.53) per 100 000 population to 38.25 (21.33-51.93) per 100 000 population between 1990 and 2017. The association between socio-demographic index (SDI) and dengue-related DALYs suggested that the lowest age-standardised DALY rates were found in countries in the low and high-SDI quintile in 2017, and from 1990 to 2017, the age-standardized DALY rate tended to increase in regions with the lowest SDI but declined in regions with the highest SDI. There was a nonlinear association between the socio-demographic index and the healthcare access and quality index and age-standardised DALY rates.

INTERPRETATION

Dengue is a major public health challenge worldwide. While there is remarkable international variation in its incidence, the dengue burden is increasing globally. The results of this study could be useful for policy makers to implement cost-effective interventions and reduce the dengue burden, particularly in countries with high incidence or increasing burden.

FUNDING

This work was supported by a grant from the National Natural Science Foundation of China (NSFC) (grant numbers 81,800,041 and 82,000,078).

Comparative Analyses of Historical Trends in Confirmed Dengue Illnesses Detected at Public Hospitals in Bangkok and Northern Thailand, 2002-2018

Dengue is a re-emerging global public health problem, the most common arbovirus causing human disease in the world, and a major cause of hospitalization in endemic countries causing significant economic burden. Data were analyzed from passive surveillance of hospital-attended dengue cases from 2002 to 2018 at Phramongkutklao Hospital (PMKH) located in Bangkok, Thailand, and Kamphaeng Phet Provincial Hospital (KPPH) located in the lower northern region of Thailand. At PMKH, serotype 1 proved to be the most common strain of the virus, whereas at KPPH, serotypes 1, 2, and 3 were the most common strains from 2006 to 2008, 2009 to 2012, and 2013 to 2015, respectively. The 11–17 years age-group made up the largest proportion of patients impacted by dengue illnesses during the study period at both sites. At KPPH, dengue virus (DENV)-3 was responsible for most cases of dengue fever (DF), whereas it was DENV-1 at PMKH. In cases where dengue hemorrhagic fever was the clinical diagnosis, DENV-2 was the predominant serotype at KPPH, whereas at PMKH, it was DENV-1. The overall disease prevalence remained consistent across the two study sites with DF being the predominant clinical diagnosis as the result of an acute secondary dengue infection, representing 40.7% of overall cases at KPPH and 56.8% at PMKH. The differences seen between these sites could be a result of climate change increasing the length of dengue season and shifts in migration patterns of these populations from rural to urban areas and vice versa.

Coronavirus Disease of 2019: a Mimicker of Dengue Infection?

At the beginning of 2020, the national health system and medical communities are faced with unprecedented public health challenges. A novel strain of coronavirus, later identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally, marking another pandemic of coronaviruses. This viral disease is responsible for devastating pneumonia, named coronavirus disease of 2019 (COVID-19), and projected to persist until the end of the year. In tropical countries, however, concerns arise regarding the similarities of COVID-19 with other infectious diseases due to the same chief complaint, which is fever. One of the infectious disease of a primary concern is dengue infection, which its peak season is approaching. Others report that there are cases of serological cross-reaction of COVID-19 and dengue infection. In this comprehensive review, we underscore the importance of knowing similar clinical presentations of both diseases and emphasize why excluding COVID-19 in the differentials in the setting of a pandemic is imprudent.

Site Occupancy by Aedes aegypti in a Subtropical City is Most Sensitive to Control during Autumn and Winter Months

The Aedes aegypti mosquito inhabits most tropical and subtropical regions of the globe, where it transmits arboviral diseases of substantial public health relevance, such as dengue fever. In subtropical regions, Ae. aegypti often presents an annual abundance cycle driven by weather conditions. Because different population states may show varying responses to control, we are interested in studying what time of the year is most appropriate for control. To do so, we developed two dynamic site-occupancy models based on more than 200 weeks of mosquito trapping data from nearly 900 sites in a subtropical Brazilian city. Our phenomenological, Markovian models, fitted to data in a Bayesian framework, accounted for failure to detect mosquitoes in two alternative ways and for temporal variation in dynamic rates of local extinction and colonization of new sites. Infestation varied from nearly full cover of the city area in late summer, to between 10% and 67% of sites occupied in winter depending on the model. Sensitivity analysis reveals that changes in dynamic rates should have the greatest impact on site occupancy during autumn and early winter months, when the mosquito population is declining. We discuss the implications of this finding to the timing of mosquito control.

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.

Temperature impacts on dengue emergence in the United States: Investigating the role of seasonality and climate change

Tropical mosquito-borne viruses have been expanding into more temperate regions in recent decades. This is partly due to the coupled effects of temperature on mosquito life history traits and viral infection dynamics and warming surface temperatures, resulting in more suitable conditions for vectors and virus transmission. In this study, we use a deterministic ordinary differential equations model to investigate how seasonal and diurnal temperature fluctuations affect the potential for dengue transmission in six U.S. cities. We specifically consider temperature-dependent mosquito larval development, adult mosquito mortality, and the extrinsic incubation period of the virus. We show that the ability of introductions to lead to outbreaks depends upon the relationship between a city's temperature profile and the time of year at which the initial case is introduced. We also investigate how the potential for outbreaks changes with predicted future increases in mean temperatures due to climate change. We find that climate change will likely lead to increases in suitability for dengue transmission and will increase the periods of the year in which introductions may lead to outbreaks, particularly in cities that typically have mild winters and warm summers, such as New Orleans, Louisiana, and El Paso, Texas. We discuss our results in the context of temperature heterogeneity within and across cities and how these differences may impact the potential for dengue emergence given present day and predicted future temperatures.

Spatial and temporal patterns of dengue incidence in northeastern Thailand 2006-2016

Background

Dengue, a viral disease transmitted by Aedes mosquitoes, is an important public health concern throughout Thailand. Climate variables are potential predictors of dengue transmission. Associations between climate variables and dengue have usually been performed on large-scale first-level national administrative divisions, i.e. provinces. Here we analyze data on a finer spatial resolution in one province, which is often more relevant for effective disease control design. The objective of this study was to investigate the effect of seasonal variations, monthly climate variability, and to identify local clusters of symptomatic disease at the sub-district level based on reported dengue cases.

Methods

Data on dengue cases were retrieved from the national communicable disease surveillance system in Thailand. Between 2006 and 2016, 15,167 cases were recorded in 199 sub-districts of Khon Kaen Province, northeastern Thailand. Descriptive analyses included demographic characteristics and temporal patterns of disease and climate variables. The association between monthly disease incidence and climate variations was analyzed at the sub-district level using Bayesian Poisson spatial regression. A hotspot analysis was used to assess the spatial patterns (clustered/dispersed/random) of dengue incidence.

Results

Dengue was predominant in the 5–14 year-old age group (51.1%). However, over time, dengue incidence in the older age groups (> 15 years) gradually increased and was the most affected group in 2013. Dengue outbreaks coincide with the rainy season. In the spatial regression model, maximum temperature was associated with higher incidence. The hotspot analysis showed clustering of cases around the urbanized area of Khon Kaen city and in rural areas in the southwestern portion of the province.

Conclusions

There was an increase in the number of reported dengue cases in older age groups over the study period. Dengue incidence was highly seasonal and positively associated with maximum ambient temperature. However, climatic variables did not explain all the spatial variation of dengue in the province. Further analyses are needed to clarify the detailed effects of urbanization and other potential environmental risk factors. These results provide useful information for ongoing prediction modeling and developing of dengue early warning systems to guide vector control operations.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4379-3) contains supplementary material, which is available to authorized users.

Integrated vector management with additional pre-transmission season thermal fogging is associated with a reduction in dengue incidence in Makassar, Indonesia: Results of an 8-year observational study

Dengue virus transmission is endemic in Makassar, Indonesia, with the majority of cases reported soon after the start of the annual rainy season. Before 2006, larval source reduction, larvaciding, and reactive routine, outdoor, insecticide fogging campaigns did not result in a reduction in seasonal dengue incidence. Beginning in 2006, village volunteers conducted comprehensive surveys for immature Aedes during the dry season, when vector populations were at their lowest. Based on this pre-season vector data, a single additional pre-emptive outdoor fogging with Malathion was conducted once annually before the rains began in villages with a pre-defined proportion of sampled houses positive for Aedes immatures. This additional procedure was associated with reduced temporal larval indices as well as an 83% reduction in reported cases during the transmission season over the 8-year period of implementation. Two cities adjacent to Makassar experienced substantial but smaller reductions in dengue incidence; while other cities further from the intervention area did not. This represents the first time an integrated intervention strategy has been coupled with substantially reduced dengue transmission in Indonesia.

Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach

Dengue fever is a mosquito-borne viral disease prevalent in more than one hundred tropical and subtropical countries. Annually, an estimated 390 million infections occur worldwide. It is transmitted by the bite of an Aedes mosquito infected with the virus. It has become a major public health challenge in recent years for many countries, including Sri Lanka. It is known that climate factors such as rainfall, temperature, and relative humidity influence the generation of mosquito offspring, thus increasing dengue incidences. Identifying the climate factors that affect the spread of dengue fever would be helpful in order for the relevant authorities to take necessary actions. The objective of this study is to build a model for predicting the likelihood of having high dengue incidences based on climate factors. A logistic regression approach was utilized for model formulation. This study found a significant association between high numbers of dengue incidences and rainfall. Furthermore, it was observed that the influence of rainfall on dengue incidences was expected to be visible after some lag period.

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.

Public perception and attitude towards dengue prevention activity and response to dengue early warning in Malaysia

An early warning system for dengue is meant to predict outbreaks and prevent dengue cases by aiding timely decision making and deployment of interventions. However, only a system which is accepted and utilised by the public would be sustainable in the long run. This study aimed to explore the perception and attitude of the Malaysian public towards a dengue early warning system. The sample consisted of 847 individuals who were 18 years and above and living/working in the Petaling District, an area adjacent to Kuala Lumpur, Malaysia. A questionnaire consisting of personal information and three sub-measures of; i) perception, ii) attitude towards dengue early warning and iii) response towards early warning; was distributed to participants. We found that most of the respondents know about dengue fever (97.1%) and its association with climate factors (90.6%). Most of them wanted to help reduce the number of dengue cases in their area (91.5%). A small percentage of the respondents admitted that they were not willing to be involved in public activities, and 64% of them admitted that they did not check dengue situations or hotspots around their area regularly. Despite the high awareness on the relationship between climate and dengue, about 45% of respondents do not know or are not sure how this can be used to predict dengue. Respondents would like to know more about how climate data can be used to predict a dengue outbreak (92.7%). Providing more information on how climate can influence dengue cases would increase public acceptability and improve response towards climate-based warning system. The most preferred way of communicating early warning was through the television (66.4%). This study shows that the public in Petaling District considers it necessary to have a dengue warning system to be necessary, but more education is required.

Arbovirus-Mosquito Vector-Host Interactions and the Impact on Transmission and Disease Pathogenesis of Arboviruses

Hundreds of viruses, designated as arboviruses, are transmitted by arthropod vectors in complex transmission cycles between the virus, vertebrate host, and the vector. With millions of human and animal infections per year, it is critical to improve our understanding of the interactions between the biological and environmental factors that play a critical role in pathogenesis, disease outcomes, and transmission of arboviruses. This review focuses on mosquito-borne arboviruses and discusses current knowledge of the factors and underlying mechanisms that influence infection and transmission of arboviruses and discusses critical factors and pathways that can potentially become targets for intervention and therapeutics.

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.

Epidemiology and outcomes of dengue in kidney transplant recipients: A 20-year retrospective analysis and comparative literature review

BACKGROUND

Kidney transplant (KT) recipients in dengue-endemic areas are at risk of exposure. We investigated the epidemiology and outcomes from dengue in KT recipients at our transplant center and conducted a literature review.

MATERIALS AND METHODS

We conducted a 20-year retrospective study of KT recipients who were diagnosed with laboratory-confirmed dengue from January 1997 to September 2017 according to the 2009 World Health Organization (WHO) classification. We analyzed clinical characteristics and treatment outcomes.

RESULTS

There were 13 (0.7%) dengue cases among 1917 KT recipients with a median age of 39 years (interquartile ranges [IQR], 22-46); 54% were males. Cases occurred with a median onset of 24 months (IQR, 6-122) after KT. Dengue was diagnosed via dengue NS1 antigen (85%), IgM antibodies (38.5%), or RT-PCR (15.4%). Patients were classified as having dengue without warning sign (30.8%), with warning sign (53.8%), or severe dengue (15.4%). All patients resolved without complications, except one had hemophagocytic lymphohistiocytosis. Ten (76.9%) patients experienced eGFR reduction with a median of 13.7 mL/min/1.73 m² (IQR, 8.3-20.5); eight (80%) had a full allograft function recovery.

CONCLUSIONS

Dengue in KT recipients in endemic areas is uncommon. Although a transient decline in allograft function can occur, the overall clinical and allograft outcomes seem to be favorable.

Risk prediction system for dengue transmission based on high resolution weather data

BACKGROUND

Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction.

METHODS AND FINDINGS

We make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM's Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach.

CONCLUSIONS

We demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.

Early warning and response system (EWARS) for dengue outbreaks: Recent advancements towards widespread applications in critical settings

Background

Dengue outbreaks are increasing in frequency over space and time, affecting people’s health and burdening resource-constrained health systems. The ability to detect early emerging outbreaks is key to mounting an effective response. The early warning and response system (EWARS) is a toolkit that provides countries with early-warning systems for efficient and cost-effective local responses. EWARS uses outbreak and alarm indicators to derive prediction models that can be used prospectively to predict a forthcoming dengue outbreak at district level.

Methods

We report on the development of the EWARS tool, based on users’ recommendations into a convenient, user-friendly and reliable software aided by a user’s workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico.

Findings

34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion.

Conclusion

EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities.

Long-term epidemiological dynamics of dengue in Barbados - one of the English-speaking Caribbean countries

Using the dengue surveillance program, we prospectively collected data on all the suspected and confirmed cases of dengue in Barbados from 2006 to 2015. Data were analysed for demographic, seasonal and temporal dynamics of this disease in this country. The overall mean annual incidence rate of suspected and confirmed dengue over the study period was 0.49% (range 0.15%-0.99%) and 0.16% (range 0.05%-0.48%), respectively. There was a significant correlation between the mean monthly number of confirmed cases, the mean monthly rainfall and the mean monthly relative humidity percentage. Dengue in this population is predominantly an infection affecting children and young adults. The median age of the patients with both, suspected and confirmed dengue was 25 years and the highest proportion of cases was seen in the age group 0-15 years. The annual incidence rates of both the suspected and the confirmed cases showed an upward trend during the study period and this upward trend was more pronounced among children.

Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014

Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000-2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010-2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.

Influenza and dengue virus co-infection impairs monocyte recruitment to the lung, increases dengue virus titers, and exacerbates pneumonia

Co-infections of influenza virus and bacteria are known to cause severe disease, but little information exists on co-infections with other acute viruses. Seasonal influenza and dengue viruses (DENV) regularly co-circulate in tropical regions. The pandemic spread of influenza virus H1N1 (hereafter H1N1) in 2009 led to additional severe disease cases that were co-infected with DENV. Here, we investigated the impact of co-infection on immune responses and pathogenesis in a new mouse model. Co-infection of otherwise sublethal doses of a Nicaraguan clinical H1N1 isolate and two days later with a virulent DENV2 strain increased systemic DENV titers and caused 90% lethality. Lungs of co-infected mice carried both viruses, developed severe pneumonia, and expressed a unique pattern of host mRNAs, resembling only partial responses against infection with either virus alone. A large number of monocytes were recruited to DENV-infected but not to co-infected lungs, and depletion and adoptive transfer experiments revealed a beneficial role of monocytes. Our study shows that co-infection with influenza and DENV impairs host responses, which fail to control DENV titers and instead, induce severe lung damage. Further, our findings identify key inflammatory pathways and monocyte function as targets for future therapies that may limit immunopathology in co-infected patients.

Synchrony of Dengue Incidence in Ho Chi Minh City and Bangkok

Background

Ho Chi Minh City and Bangkok are highly dengue endemic. The extent to which disease patterns are attributable to local versus regional dynamics remains unclear. To address this gap we compared key transmission parameters across the locations.

Methods and Principal Findings

We used 2003–2009 age-stratified case data to inform catalytic transmission models. Further, we compared the spatial clustering of serotypes within each city. We found that annual case numbers were highly consistent across the two cities (correlation of 0.77, 95% CI: 0.74–0.79) as was the annual force of infection (correlation of 0.57, 95% CI: 0.46–0.68). Serotypes were less similar with serotype-specific correlations ranging from 0.65 for DENV1 to -0.14 for DENV4. Significant spatial clustering of serotypes was observed in HCMC at distances <500m, similar to previous observations from Bangkok.

Discussions

Dengue dynamics are comparable across these two hubs. Low correlation in serotype distribution suggests that similar built environments, vector populations and climate, rather than viral flow drives these observations.

All four serotypes of dengue have circulated endemically throughout Southeast Asia for decades. However, despite the enormous burden of disease, there remains poor understanding of the similarity in disease patterns across the region. We analyzed data from over 100,000 cases of dengue from two of the largest cities in the region, Bangkok and Ho Chi Minh City between 2001 and 2009. We use basic statistical methods to reconstruct the annual probability of infection in the two cities during this time period using methods that are robust to differences in reporting mechanisms. We find that both the epidemic curves and annual probabilities of infection were highly correlated across the cities, however, serotype-specific correlations were far more variable. Finally, we used geocoded case homes from Ho Chi Minh to demonstrate that cases in the city clustered at spatial scales (<500m) similar to that previously observed in Bangkok. These findings show that dengue dynamics are highly comparable across these two urban hubs; however, the low correlation in serotype distribution suggests that similar built environments and climate, rather than viral flow drives these observations.