Dengue fever is a viral disease caused by one of four dengue stereotypes (Flavivirus: Flaviviridae) that are primarily transmitted by Aedes albopictus (Skuse) and Aedes aegypti (L.). To safeguard public health, it is crucial to conduct surveys that examine the factors favouring the presence of these species. Our study surveyed 42 councils across four towns within the Bhakkar district of Punjab Province, by inspecting man-made or natural habitats containing standing water. First, door-to-door surveillance teams from the district health department were assigned to each council to surveillance Aedes species and dengue cases. Second, data collection through surveillance efforts, and validation procedures were implemented, and the verified data was uploaded onto the Dengue Tracking System by Third Party Validation teams. Third, data were analysed to identify factors influencing dengue fever cases. The findings demonstrated the following: (1) Predominantly, instances were discerned among individuals who had a documented history of having travelled beyond the confines of the province. (2) Containers associated with evaporative air coolers and tyre shops were responsible for approximately 30% of the Aedes developmental sites. (4) Variability in temperature was responsible for approximately 45% of the observed differences in the quantity of recorded Aedes mosquito developmental sites. (5) Implementation of dengue prevention initiatives precipitated a 50% reduction in Aedes-positive containers, alongside a notable 70% decline in reported cases of dengue fever during the period spanning 2019 to 2020, while the majority of reported cases were of external origin. Aedes control measures substantially curtailed mosquito populations and lowered vector-virus interactions. Notably, local dengue transmission was eliminated through advanced and effective Aedes control efforts, emphasising the need for persistent surveillance and eradication of larval habitats in affected regions.

After the mass campaign of Measles and Rubella vaccination in 2003 in Iran, the cases of measles and rubella infection decreased but still, the cases of rash and fever were reported. It is worth noting that some other viral infections show signs similar to measles and rubella such as some arboviruses. Considering the epidemic outbreak of arbovirus infections in countries neighbouring Iran, we performed this study to estimate the possibility of chikungunya and dengue fever among measles and rubella IgM negative patients presenting with rash and fever from December 2016 to November 2017 in the National Measles Laboratory at Tehran University of Medical Sciences. Serum samples were selected at random from patients from eight provinces. The presence of DENV IgM and CHIKV IgM was examined by enzyme-linked immunosorbent assay. Of the 1306 sera tested, 210 were CHIKV seropositive and 82 were dengue seropositive. Statistical analysis demonstrated a significant increase in the CHIKV IgM antibody seropositivity rate in Kerman (OR = 2.07, 95% CI: 1.10–3.92; P = 0.024) and Fars (OR = 1.77, 95% CI: 1.06–2.93; P = 0.027). The DENV and CHIKV seropositivity rate in summer is higher than in other seasons (P < 0.01). Our seropositive samples suggest possible CHIKV and DENV infection in Iran. It is likely that these viruses are circulating in Iran and there is a need to study vector carriage of these two viruses.

Dengue infection in China has increased dramatically in recent years. Guangdong province (main city Guangzhou) accounted for more than 94% of all dengue cases in the 2014 outbreak. Currently, there is no existing effective vaccine and most efforts of control are focused on the vector itself. This study aimed to evaluate different dengue management strategies in a region where this disease is emerging. This work was done by establishing a dengue simulation model for Guangzhou to enable the testing of control strategies aimed at vector control and vaccination. For that purpose, the computer-based dengue simulation model (DENSiM) together with the Container-Inhabiting Mosquito Simulation Model (CIMSiM) has been used to create a working dengue simulation model for the city of Guangzhou. In order to achieve the best model fit against historical surveillance data, virus introduction scenarios were run and then matched against the actual dengue surveillance data. The simulation model was able to predict retrospective outbreaks with a sensitivity of 0.18 and a specificity of 0.98. This new parameterisation can now be used to evaluate the potential impact of different control strategies on dengue transmission in Guangzhou. The knowledge generated from this research would provide useful information for authorities regarding the historic patterns of dengue outbreaks, as well as the effectiveness of different disease management strategies.

Dengue fever (DF) has been a growing public-health concern in China since its emergence in Guangdong Province in 1978. Of all the regions that have experienced dengue outbreaks in mainland China, the city of Guangzhou is the most affected. This study aims to investigate the potential risk factors for dengue virus (DENV) transmission in Guangzhou, China, from 2006 to 2014. The impact of risk factors on DENV transmission was qualified by the q-values calculated using a novel spatial-temporal method, the GeoDetector model. Both climatic and socioeconomic factors were considered. The impacts on DF incidence of each single factor and the interaction of two factors were analysed. The results show that the number of days with rainfall of the month before last has the highest determinant power, with a q-value of 0.898 (P < 0.01); the q-values of the other factors related to temperature and precipitation were around 0.38–0.50. Integrating a Pearson correlation analysis, nonlinear associations were found between the DF incidence in Guangzhou and the climatic factors considered. The coupled impact of the different variables considered was enhanced compared with their individual effects. In addition, an increased number of tourists in the city were associated with a high incidence of DF. This study demonstrates that the number of rain days in a month has great influence on the DF incidence of the month after next; the temperature and precipitation have nonlinear impacts on the DF incidence in Guangzhou; both the domestic and overseas tourists coming to the city increase the risk of DENV transmission. These findings are useful in the risk assessment of DENV transmission, to predict DF outbreaks and to implement preventive DF reduction strategies.

PUFA might modulate inflammatory responses involved in the development of severe dengue. We aimed to examine whether serum PUFA concentrations in patients diagnosed with dengue fever (DF) were related to the risk of progression to dengue haemorrhagic fever/dengue shock syndrome (DHF/DSS). A secondary aim was to assess correlations between fatty acids (FA) and inflammatory biomarkers in patients with DF. We conducted a prospective case–control study nested within a cohort of patients who were diagnosed with DF and followed during the acute episode. We compared the distribution of individual FA (% of total FA) at onset of fever between 109 cases who progressed to DHF/DSS and 235 DF non-progressing controls using unconditional logistic regression. We estimated correlations between baseline FA and cytokine concentrations and compared FA concentrations between the acute episode and >1 year post-convalescence in a subgroup. DHA was positively related to progression to DHF/DSS (multivariable adjusted OR (AOR) for DHA in quintile 5 v. 1=5·34, 95 % CI 2·03, 14·1; Ptrend=0·007). Dihomo-γ-linolenic acid (DGLA) was inversely associated with progression (AOR for quintile 5 v. 1=0·30, 95 % CI 0·13, 0·69; Ptrend=0·007). Pentadecanoic acid concentrations were inversely related to DHF/DSS. Correlations of PUFA with cytokines at baseline were low. PUFA were lower during the acute episode than in a disease-free period. In conclusion, serum DHA in patients with DF predicts higher odds of progression to DHF/DSS whereas DGLA and pentadecanoic acid predict lower odds.

Dengue is the fastest spreading mosquito-transmitted disease in the world. In China, Guangzhou City is believed to be the most important epicenter of dengue outbreaks although the transmission patterns are still poorly understood. We developed an autoregressive integrated moving average model incorporating external regressors to examine the association between the monthly number of locally acquired dengue infections and imported cases, mosquito densities, temperature and precipitation in Guangzhou. In multivariate analysis, imported cases and minimum temperature (both at lag 0) were both associated with the number of locally acquired infections (P < 0.05). This multivariate model performed best, featuring the lowest fitting root mean squared error (RMSE) (0.7520), AIC (393.7854) and test RMSE (0.6445), as well as the best effect in model validation for testing outbreak with a sensitivity of 1.0000, a specificity of 0.7368 and a consistency rate of 0.7917. Our findings suggest that imported cases and minimum temperature are two key determinants of dengue local transmission in Guangzhou. The modelling method can be used to predict dengue transmission in non-endemic countries and to inform dengue prevention and control strategies.

Dengue virus circulates as four independent serotypes posing a major public health threat around the globe. In the recent years, frequent dengue outbreaks are being reported in many parts of the world including India. Among four serotypes, Den-4 is the least sampled and studied serotype until recent times, but the reported cases with Den-4 infections were mostly known to associate with severe dengue. In the past three decades, only one complete genome sequence of Den-4 has been published from India. Hence there is a deficit in information with reference to this serotype which would be required in deciphering its association with severe dengue. In this study, we have carried out the complete genome characterization of Den-4 virus, isolated from a dengue shock syndrome patient during the 2015 outbreak from Hyderabad, South India. Phylogenetic analysis revealed the circulation of genotype I (lineage C) which showed close relatedness to the reported virulent strains. The data also indicated few unique amino acid substitutions which are known to be important in virus replication and epitope presentation. This is the first report of complete genome characterization of Den-4 from South India, which may assist in shaping the genetic diversity of circulating strains in India.

Dengue fever (DF) is the most prevalent and rapidly spreading mosquito-borne disease globally. Control of DF is limited by barriers to vector control and integrated management approaches. This study aimed to explore the potential risk factors for autochthonous DF transmission and to estimate the threshold effects of high-order interactions among risk factors. A time-series regression tree model was applied to estimate the hierarchical relationship between reported autochthonous DF cases and the potential risk factors including the timeliness of DF surveillance systems (median time interval between symptom onset date and diagnosis date, MTIOD), mosquito density, imported cases and meteorological factors in Zhongshan, China from 2001 to 2013. We found that MTIOD was the most influential factor in autochthonous DF transmission. Monthly autochthonous DF incidence rate increased by 36·02-fold [relative risk (RR) 36·02, 95% confidence interval (CI) 25·26–46·78, compared to the average DF incidence rate during the study period] when the 2-month lagged moving average of MTIOD was >4·15 days and the 3-month lagged moving average of the mean Breteau Index (BI) was ⩾16·57. If the 2-month lagged moving average MTIOD was between 1·11 and 4·15 days and the monthly maximum diurnal temperature range at a lag of 1 month was <9·6 °C, the monthly mean autochthonous DF incidence rate increased by 14·67-fold (RR 14·67, 95% CI 8·84–20·51, compared to the average DF incidence rate during the study period). This study demonstrates that the timeliness of DF surveillance systems, mosquito density and diurnal temperature range play critical roles in the autochthonous DF transmission in Zhongshan. Better assessment and prediction of the risk of DF transmission is beneficial for establishing scientific strategies for DF early warning surveillance and control.

Dengue fever (DF) and leptospirosis are serious public problems in tropical regions, especially in Manila, the Philippines. In attempting to understand the causes of DF and leptospirosis seasonality, meteorological factors have been suspected, but quantitative correlation between seasonality and meteorological factors has not been fully investigated. In this study, we investigated correlation of temporal patterns of reported numbers of laboratory-confirmed cases of both DF and leptospirosis with meteorological conditions (temperature, relative humidity, rainfall) in Manila. We used time-series analysis combined with spectral analysis and the least squares method. A 1-year cycle explained underlying variations of DF, leptospirosis and meteorological data. There was a peak of the 1-year cycle in temperature during May, followed by maxima in rainfall, relative humidity and number of laboratory-confirmed DF and leptospirosis cases. This result suggests that DF and leptospirosis epidemics are correlated not only with rainfall but also relative humidity and temperature in the Philippines. Quantifying the correlation of DF and leptospirosis infections with meteorological conditions may prove useful in predicting DF and leptospirosis epidemics, and health services should plan accordingly.

Dengue is the world's most prevalent mosquito-borne disease, with more than 200 million people each year becoming infected. We used a mechanistic virus transmission model to determine whether climate warming would change dengue transmission in Australia. Using two climate models each with two carbon emission scenarios, we calculated future dengue epidemic potential for the period 2046–2064. Using the ECHAM5 model, decreased dengue transmission was predicted under the A2 carbon emission scenario, whereas some increases are likely under the B1 scenario. Dengue epidemic potential may decrease under climate warming due to mosquito breeding sites becoming drier and mosquito survivorship declining. These results contradict most previous studies that use correlative models to show increased dengue transmission under climate warming. Dengue epidemiology is determined by a complex interplay between climatic, human host, and pathogen factors. It is therefore naive to assume a simple relationship between climate and incidence, and incorrect to state that climate warming will uniformly increase dengue transmission, although in general the health impacts of climate change will be negative.

Immunoglobulin A (IgA)-based tests have been evaluated in different studies for their utility in diagnosing dengue infections. In most of the studies, the results were inconclusive because of a small sample size. Hence, a meta-analysis involving nine studies with 2096 samples was performed to assess the diagnostic accuracy of IgA-based tests in diagnosing dengue infections. The analysis was conducted using Meta-Disc software. The results revealed that IgA-based tests had an overall sensitivity, specificity, diagnostic odds ratio, and positive and negative likelihood ratios of 73·9%, 95·2%, 66·7, 22·0 and 0·25, respectively. Significant heterogeneity was observed between the studies. The type of test, infection status and day of sample collection influenced the diagnostic accuracy. The IgA-based diagnostic tests showed a greater accuracy when the samples were collected 4 days after onset of symptoms and for secondary infections. The results suggested that IgA-based tests had a moderate level of accuracy and are diagnostic of the disease. However, negative results cannot be used alone for dengue diagnosis. More prospective studies comparing the diagnostic accuracy of combinations of antigen-based tests with either IgA or IgM are needed and might be useful for suggesting the best strategy for dengue diagnosis.

We aimed to reparameterize and validate an existing dengue model, comprising an entomological component (CIMSiM) and a disease component (DENSiM) for application in Malaysia. With the model we aimed to measure the effect of importation rate on dengue incidence, and to determine the potential impact of moderate climate change (a 1 °C temperature increase) on dengue activity. Dengue models (comprising CIMSiM and DENSiM) were reparameterized for a simulated Malaysian village of 10 000 people, and validated against monthly dengue case data from the district of Petaling Jaya in the state of Selangor. Simulations were also performed for 2008-2012 for variable virus importation rates (ranging from 1 to 25 per week) and dengue incidence determined. Dengue incidence in the period 2010–2012 was modelled, twice, with observed daily weather and with a 1 °C increase, the latter to simulate moderate climate change. Strong concordance between simulated and observed monthly dengue cases was observed (up to r = 0·72). There was a linear relationship between importation and incidence. However, a doubling of dengue importation did not equate to a doubling of dengue activity. The largest individual dengue outbreak was observed with the lowest dengue importation rate. Moderate climate change resulted in an overall decrease in dengue activity over a 3-year period, linked to high human seroprevalence early on in the simulation. Our results suggest that moderate reductions in importation with control programmes may not reduce the frequency of large outbreaks. Moderate increases in temperature do not necessarily lead to greater dengue incidence.

Dengue fever/dengue haemorrhagic fever (DF/DHF) appears to be emerging in Hanoi in recent years. A case-control study was performed to investigate risk factors for the development of DF/DHF in Hanoi. A total of 73 patients with DF/DHF and 73 control patients were included in the study. The risk factor analysis indicated that living in rented housing, living near uncovered sewers, and living in a house discharging sewage directly into to ponds were all significantly associated with DF/DHF. People living in rented houses were 2·2 times more at risk of DF/DHF than those living in their own homes [adjusted odds ratio (aOR) 2·2, 95% confidence interval (CI) 1·1–4·6]. People living in an unhygienic house, or in a house discharging sewage directly to the ponds were 3·4 times and 4·3 times, respectively, more likely to be associated with DF/DHF (aOR 3·4, 95% CI 1–11·7; aOR 4·3, 95% CI 1·1–16·9). These results contribute to the understanding of the dynamics of dengue transmission in Hanoi, which is needed to implement dengue prevention and control programmes effectively and efficiently.

Dengue is regarded as the most important arboviral disease. Although sporadic cases have been reported, serotypes responsible for outbreaks have not been identified from central India over the last 20 years. We investigated two outbreaks of febrile illness, in August and November 2012, from Korea district (Chhattisgarh) and Narsinghpur district (Madhya Pradesh), respectively. Fever and entomological surveys were conducted in the affected regions. Molecular and serological tests were conducted on collected serum samples. Dengue-specific amplicons were sequenced and phylogenetic analyses were performed. In Korea and Narsinghpur districts 37·3% and 59% of cases were positive, respectively, for dengue infection, with adults being the worst affected. RT–PCR confirmed dengue virus serotype 1 genotype III as the aetiology. Ninety-six percent of infections were primary. This is the first time that dengue virus 1 outbreaks have been documented from central India. Introduction of the virus into the population and a conducive mosquitogenic environment favouring increased vector density caused the outbreak. Timely diagnosis and strengthening vector control measures are essential to avoid future outbreaks.

Models describing dengue epidemics are parametrized on disease incidence data and therefore high-quality data are essential. For Thailand, two different sources of long-term dengue data are available, the hard copy data from 1980 to 2005, where hospital admission cases were notified, and the electronic files, from 2003 to the present, where clinically classified forms of disease, i.e. dengue fever, dengue haemorrhagic fever, and dengue shock syndrome, are notified using separate files. The official dengue notification data, provided by the Bureau of Epidemiology, Ministry of Public Health in Thailand, were cross-checked with dengue data used in recent publications, where an inexact continuous time-series was observed to be consistently used since 2003, affecting considerably the model dynamics and its correct application. In this paper, numerical analysis and simulation techniques giving insights on predictability are performed to show the effects of model parametrization by using different datasets.

We examined the spatial distribution pattern and meteorological drivers of dengue fever (DF) in Guangdong Province, China. Annual incidence of DF was calculated for each county between 2005 and 2011 and the geographical distribution pattern of DF was examined using Moran's I statistic and excess risk maps. A time-stratified case-crossover study was used to investigate the short-term relationship between DF and meteorological factors and the Southern Oscillation Index (SOI). High-epidemic DF areas were restricted to the Pearl River Delta region and the Han River Delta region, Moran's I of DF distribution was significant from 2005 to 2006 and from 2009 to 2011. Daily vapour pressure, mean and minimum temperatures were associated with increased DF risk. Maximum temperature and SOI were negatively associated with DF transmission. The risk of DF was non-randomly distributed in the counties in Guangdong Province. Meteorological factors could be important predictors of DF transmission.

We present a stochastic dynamical model for the transmission of dengue that considers the co-evolution of the spatial dynamics of the vectors (Aedes aegypti) and hosts (human population), allowing the simulation of control strategies adapted to the actual evolution of an epidemic outbreak. We observed that imposing restrictions on the movement of infected humans is not a highly effective strategy. In contrast, isolating infected individuals with high levels of compliance by the human population is efficient even when implemented with delays during an ongoing outbreak. We also studied insecticide-spraying strategies assuming different (hypothetical) efficiencies. We observed that highly efficient fumigation strategies seem to be effective during an outbreak. Nevertheless, taking into account the controversial results on the use of spraying as a single control strategy, we suggest that carrying out combined strategies of fumigation and isolation during an epidemic outbreak should account for a suitable strategy for the attenuation of epidemic outbreaks.

Human behaviours, which are influenced by social, cultural, economic and political factors, can increase or decrease the risk of dengue infection, depending on the relationship with the insect vector. Because no vaccine is currently available, the spread of dengue can only be curtailed by controlling vector populations (Aedes aegypti and others) and by protecting individuals. This study tested the hypothesis that dengue-affected populations are likely to relax their vector-control habits if a potentially protective vaccine becomes available. The hypothesis was tested using two approaches: a mathematical model designed to describe dengue transmission and an empirical field test in which the local population of an endemic area was interviewed about their vector-control habits given the presence of a theoretical vaccine. The model demonstrated that depending on the level of vector-control reduction, there is a threshold in vaccine efficacy below which it is better not to introduce the vaccine. The interview showed that people who were informed that a very effective vaccine is available would reduce their vector-control habits significantly compared to a group that was informed that the vaccine is not very effective.

Sensitive surveillance systems are crucial for effective control of infectious disease outbreaks, and regional surveillance could provide valuable data to supplement global systems, improve sensitivity and timeliness of reporting, or capture otherwise undetected outbreaks. In New Zealand (NZ), there are no endemic arboviral diseases in humans, and the majority of dengue cases are imported from neighbouring Pacific Islands where comprehensive surveillance systems are under development. From 1997 to 2009, 679 cases of dengue were reported in NZ (74·2% acquired from the Pacific Islands), and the patterns of reported incidence of dengue acquired from different islands closely reflected local reported incidence in those areas. NZ is therefore in a unique position to provide early alerts on dengue outbreaks in the Pacific Islands. Such a strategy would reduce disease burden in both the Pacific Islands and NZ, and provide a model for transnational collaboration in disease surveillance with regional as well as global benefits.

Prior to 2009 dengue fever had not been reported in the Andaman and Nicobar archipelago. In 2009, a few patients with dengue fever-like illness were reported, some of whom tested positive for dengue antibodies. In 2010, 516 suspected cases were reported, including some with dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS); 80 (15·5%) were positive for dengue antibodies. DENV RNA was detected in five patients and PCR-based typing showed that three of these belonged to serotype 1 and two to serotype 2. This was confirmed by sequence typing. Two clones of dengue virus, one belonging to serotype 1 and the other to serotype 2 appeared to be circulating in Andaman. Emergence of severe diseases such as DHF and DSS might be due to recent introduction of a more virulent strain or because of the enhancing effect of sub-neutralizing levels of antibodies developed due to prior infections. There is a need to revise the vector-borne disease surveillance system in the islands.