Despite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic. Epidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005–2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1–2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively. There was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model. With the continuously high levels of worldwide dengue transmission, predicting dengue outbreaks in advance of their occurrence or identifying specific locations where outbreak risks are highest is of critical importance. However, only few studies have been conducted in dengue non-endemic countries to evaluate the association of vector index with the occurrence of dengue cases; and the establishment of an early warning signal would significantly enhance the public health intervention. Our study here provided the proof-of-concept results, utilizing a two-stage model to identify the best set of lag effects of meteorological and entomological variables, explaining dengue epidemics based on the data obtained from Taiwan, which is a dengue-non-endemic country. Each of the vector indices when combined with the meteorological factors has better performance compared to the prediction using AI, BI, CI and HI alone, with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. Because of the complex interplays between the size of human hosts and movement, environmental factors and dynamic changes of mosquito population and density, each country should consider its own individual data and situation and apply this two-stage model to find the optimal predictive models for allocating public health resources and prevention strategies.

Studies indicate that asymptomatic infections do indeed occur frequently for both seasonal and pandemic influenza, accounting for about one-third of influenza infections. Studies carried out during the 2009 pH1N1 pandemic have found significant antibody response against seasonal H1N1 and H3N2 vaccine strains in schoolchildren receiving only pandemic H1N1 monovalent vaccine, yet reported either no symptoms or only mild symptoms.

Background: Although the previous study demonstrated the envelope protein of dengue viruses is under purifying selection pressure, little is known about the genetic differences of full-length viral genomes of DENV-3. In our study, complete genomic sequencing of DENV-3 strains collected from different geographical locations and isolation years were determined and the sequence diversity as well as selection pressure sites in the DENV genome other than within the E gene were also analyzed. Results: Using maximum likelihood and Bayesian approaches, our phylogenetic analysis revealed that the Taiwan's indigenous DENV-3 isolated from 1994 and 1998 dengue/DHF epidemics and one 1999 sporadic case were of the three different genotypes – I, II, and III, each associated with DENV-3 circulating in Indonesia, Thailand and Sri Lanka, respectively. Sequence diversity and selection pressure of different genomic regions among DENV-3 different genotypes was further examined to understand the global DENV-3 evolution. The highest nucleotide sequence diversity among the fully sequenced DENV-3 strains was found in the nonstructural protein 2A (mean ± SD: 5.84 ± 0.54) and envelope protein gene regions (mean ± SD: 5.04 ± 0.32). Further analysis found that positive selection pressure of DENV-3 may occur in the non-structural protein 1 gene region and the positive selection site was detected at position 178 of the NS1 gene. Conclusion: Our study confirmed that the envelope protein is under purifying selection pressure although it presented higher sequence diversity. The detection of positive selection pressure in the non-structural protein along genotype II indicated that DENV-3 originated from Southeast Asia needs to monitor the emergence of DENV strains with epidemic potential for better epidemic prevention and vaccine development.

Dengue fever is caused by four different serotypes of dengue virus (DENV) which is the leading cause of worldwide arboviral diseases in humans. Virus-like particles (VLPs) containing flavivirus prM/E proteins have been demonstrated to be a potential vaccine candidate; however, the structure of dengue VLP is poorly understood. Herein VLP derived from DENV serotype-2 were engineered becoming highly matured (mD2VLP) and showed variable size distribution with diameter of ~31 nm forming the major population under cryo-electron microscopy examination. Furthermore, mD2VLP particles of 31 nm diameter possess a T = 1 icosahedral symmetry with a groove located within the E-protein dimers near the 2-fold vertices that exposed highly overlapping, cryptic neutralizing epitopes. Mice vaccinated with mD2VLP generated higher cross-reactive (CR) neutralization antibodies (NtAbs) and were fully protected against all 4 serotypes of DENV. Our results highlight the potential of ‘epitope-resurfaced’ mature-form D2VLPs in inducing quaternary structure-recognizing broad CR NtAbs to guide future dengue vaccine design.

IgM antibody- and IgG antibody-capture enzyme-linked immunosorbent assays (MAC/GAC-ELISAs) targeted at envelope protein (E) of dengue viruses (DENV), West Nile virus, and Japanese encephalitis virus (JEV) are widely used as serodiagnostic tests for presumptive confirmation of viral infection. Antibodies directed against the flavivirus nonstructural protein 1 (NS1) have been proposed as serological markers of natural infections among vaccinated populations. The aim of the current study is to optimize an IgM and IgG antibody-capture ELISA (MAC/GAC-ELISA) to detect anti-NS1 antibodies and compare it with anti-E MAC/GAC-ELISA. Plasmids to express premembrane/envelope (prM/E) or NS1 proteins of six medically important flaviviruses, including dengue viruses (DENV-1 to DENV-4), West Nile virus (WNV), and Japanese encephalitis virus (JEV), were constructed. These plasmids were used for the production of prM/E-containing virus-like particles (VLPs) and secreted NS1 (sNS1) from COS-1 cells. Archived clinical specimens from patients with confirmed DENV, JEV, and WNV infections, along with naive sera, were subjected to NS1-MAC/GAC-ELISAs before or after depletion of anti-prM/E antibodies by preabsorption with or without VLPs. Human serum specimens from previously confirmed DENV infections showed significantly enhanced positive-to-negative (P/N) ratios for NS1-MAC/GAC-ELISAs after the depletion of anti-prM/E antibodies. No statistical differences in sensitivities and specificities were found between the newly developed NS1- and VLP-MAC/GAC-ELISAs. Further application of the assays to WNV- and JEV-infected serum panels showed similar results. A novel approach to perform MAC/GAC-ELISAs for NS1 antibody detection was successfully developed with great potential to differentiate antibodies elicited by the tetravalent chimeric yellow fever-17D/dengue vaccine or DENV infection.

Emergence and intercontinental spread of highly pathogenic avian influenza A (HPAI) H5Nx virus clade 2.3.4.4 has resulted in substantial economic losses to the poultry industry in Asia, Europe, and North America. The long-distance migratory birds have been suggested to play a major role in the global spread of avian influenza viruses during this wave of panzootic outbreaks since 2013. Poultry farm epidemics caused by multiple introduction of different HPAI novel subtypes of clade 2.3.4.4 viruses also occurred in Taiwan between 2015 and 2017. The mandatory and active surveillance detected H5N3 and H5N6 circulation in 2015 and 2017, respectively, while H5N2 and H5N8 were persistently identified in poultry farms since their first arrival in 2015. This study intended to assess the importance of various ecological factors contributed to the persistence of HPAI during three consecutive years. We used satellite technology to identify the location of waterfowl flocks. Four risk factors consistently showed strong association with the spatial clustering of H5N2 and H5N8 circulations during 2015 and 2017, including high poultry farm density (aOR:17.46, 95%CI: 5.91–74.86 and 8.23, 95% CI: 2.12–54.86 in 2015 and 2017, respectively), poultry heterogeneity index (aOR of 12.28, 95%CI: 5.02–31.14 and 2.79, 95%CI: 1.00–7.69, in 2015 and 2017, respectively), non-registered waterfowl flock density (aOR: 6.8, 95%CI: 3.41–14.46 and 9.17, 95%CI: 3.73–26.20, in 2015 and 2017, respectively) and higher percentage of cropping land coverage (aOR of 1.36, 95%CI: 1.10–1.69 and 1.04, 95%CI: 1.02–1.07, in 2015 and 2017, respectively). Our study highlights the application of remote sensing and clustering analysis for the identification and characterization of environmental factors in facilitating and contributing to the persistent circulation of certain subtypes of H5Nx in poultry farms in Taiwan.