Novel chikungunya and dengue vaccines-travel medicine applications

Recent epidemics of dengue and chikungunya have highlighted the urgent need for vaccines to reduce the risk of infection in travellers. Given challenges tracking chikungunya outbreaks in real-time and the widespread resurgence of dengue, broader indications for the use of the new chikungunya and dengue vaccines should be considered.

Evaluating the mosquito host range of Getah virus and the vector competence of selected medically important mosquitoes in Getah virus transmission

BACKGROUND

The Getah virus (GETV) is a mosquito-borne Alphavirus (family Togaviridae) that is of significant importance in veterinary medicine. It has been associated with major polyarthritis outbreaks in animals, but there are insufficient data on its clinical symptoms in humans. Serological evidence of GETV exposure and the risk of zoonotic transmission makes GETV a potentially medically relevant arbovirus. However, minimal emphasis has been placed on investigating GETV vector transmission, which limits current knowledge of the factors facilitating the spread and outbreaks of GETV.

METHODS

To examine the range of the mosquito hosts of GETV, we selected medically important mosquitoes, assessed them in vitro and in vivo and determined their relative competence in virus transmission. The susceptibility and growth kinetics of GETVs in various mosquito-derived cell lines were also determined and quantified using plaque assays. Vector competency assays were also conducted, and quantitative reverse transcription-PCR and plaque assays were used to determine the susceptibility and transmission capacity of each mosquito species evaluated in this study.

RESULTS

GETV infection in all of the investigated mosquito cell lines resulted in detectable cytopathic effects. GETV reproduced the fastest in Culex tritaeniorhynchus- and Aedes albopictus-derived cell lines, as evidenced by the highest exponential titers we observed. Regarding viral RNA copy numbers, mosquito susceptibility to infection, spread, and transmission varied significantly between species. The highest vector competency indices for infection, dissemination and transmission were obtained for Cx. tritaeniorhynchus. This is the first study to investigate the ability of Ae. albopictus and Anopheles stephensi to transmit GETV, and the results emphasize the role and capacity of other mosquito species to transmit GETV upon exposure to GETV, in addition to the perceived vectors from which GETV has been isolated in nature.

CONCLUSIONS

This study highlights the importance of GETV vector competency studies to determine all possible transmission vectors, especially in endemic regions.

Climate-driven mosquito-borne viral suitability index: measuring risk transmission of dengue, chikungunya and Zika in Mexico

Background

Climate variability influences the population dynamics of the Aedes aegypti mosquito that transmits the viruses that cause dengue, chikungunya and Zika. In recent years these diseases have grown considerably. Dengue is now the fastest-growing mosquito-transmitted disease worldwide, putting 40 per cent of the global population at risk. With no effective antiviral treatments or vaccines widely available, controlling mosquito population remains one of the most effective ways to prevent epidemics. This paper analyses the temporal and spatial dynamics of dengue in Mexico during 2000–2020 and that of chikungunya and Zika since they first appeared in the country in 2014 and 2015, respectively. This study aims to evaluate how seasonal climatological variability affects the potential risk of transmission of these mosquito-borne diseases. Mexico is among the world’s most endemic countries in terms of dengue. Given its high incidence of other mosquito-borne diseases and its size and wide range of climates, it is a good case study.

Methods

We estimate the recently proposed mosquito-borne viral suitability index P, which measures the transmission potential of mosquito-borne pathogens. This index mathematically models how humidity, temperature and precipitation affect the number of new infections generated by a single infected adult female mosquito in a host population. We estimate this suitability index across all Mexico, at small-area level, on a daily basis during 2000–2020.

Results

We find that the index P predicted risk transmission is strongly correlated with the areas and seasons with a high incidence of dengue within the country. This correlation is also high enough for chikungunya and Zika in Mexico. We also show the index P is sensitive to seasonal climatological variability, including extreme weather shocks.

Conclusions

The paper shows the dynamics of dengue, chikungunya and Zika in Mexico are strongly associated with seasonal climatological variability and the index P. This potential risk of transmission index, therefore, is a valuable tool for surveillance for mosquito-borne diseases, particularly in settings with varied climates and limited entomological capacity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-022-00317-0.

A general modeling framework for exploring the impact of individual concern and personal protection on vector-borne disease dynamics

BACKGROUND

As climate variability and extreme weather events associated with climate change become more prevalent, public health authorities can expect to face an expanding spectrum of vector-borne diseases with increasing incidence and geographical spread. Common interventions include the use of larvicides and adulticides, as well as targeted communications to increase public awareness regarding the need for personal protective measures, such as mosquito repellant, protective clothing, and mosquito nets. Here, we propose a simplified compartmental model of mosquito-borne disease dynamics that incorporates the use of personal protection against mosquito bites influenced by two key individual-level behavioral drivers-concern for being bitten by mosquitos as a nuisance and concern for mosquito-borne disease transmission.

METHODS

We propose a modified compartmental model that describes the dynamics of vector-borne disease spread in a naïve population while considering the public demand for community-level control and, importantly, the effects of personal-level protection on population-level outbreak dynamics. We consider scenarios at low, medium, and high levels of community-level vector control, and at each level, we consider combinations of low, medium, and high levels of motivation to use personal protection, namely concern for disease transmission and concern for being bitten in general.

RESULTS

When there is very little community-level vector control, nearly the entire population is quickly infected, regardless of personal protection use. When vector control is at an intermediate level, both concerns that motivate the use of personal protection play an important role in reducing disease burden. When authorities have the capacity for high-level community vector control through pesticide use, the motivation to use personal protection to reduce disease transmission has little additional effect on the outbreak.

CONCLUSIONS

While results show that personal-level protection alone is not enough to significantly impact an outbreak, personal protective measures can significantly reduce the severity of an outbreak in conjunction with community-level control. Furthermore, the model provides insight for targeting public health messaging to increase the use of personal protection based on concerns related to being bitten by mosquitos or vector-borne disease transmission.

Text mining in mosquito-borne disease: A systematic review

Mosquito-borne diseases are emerging and re-emerging across the globe, especially after the COVID19 pandemic. The recent advances in text mining in infectious diseases hold the potential of providing timely access to explicit and implicit associations among information in the text. In the past few years, the availability of online text data in the form of unstructured or semi-structured text with rich content of information from this domain enables many studies to provide solutions in this area, e.g., disease-related knowledge discovery, disease surveillance, early detection system, etc. However, a recent review of text mining in the domain of mosquito-borne disease was not available to the best of our knowledge. In this review, we survey the recent works in the text mining techniques used in combating mosquito-borne diseases. We highlight the corpus sources, technologies, applications, and the challenges faced by the studies, followed by the possible future directions that can be taken further in this domain. We present a bibliometric analysis of the 294 scientific articles that have been published in Scopus and PubMed in the domain of text mining in mosquito-borne diseases, from the year 2016 to 2021. The papers were further filtered and reviewed based on the techniques used to analyze the text related to mosquito-borne diseases. Based on the corpus of 158 selected articles, we found 27 of the articles were relevant and used text mining in mosquito-borne diseases. These articles covered the majority of Zika (38.70%), Dengue (32.26%), and Malaria (29.03%), with extremely low numbers or none of the other crucial mosquito-borne diseases like chikungunya, yellow fever, West Nile fever. Twitter was the dominant corpus resource to perform text mining in mosquito-borne diseases, followed by PubMed and LexisNexis databases. Sentiment analysis was the most popular technique of text mining to understand the discourse of the disease and followed by information extraction, which dependency relation and co-occurrence-based approach to extract relations and events. Surveillance was the main usage of most of the reviewed studies and followed by treatment, which focused on the drug-disease or symptom-disease association. The advance in text mining could improve the management of mosquito-borne diseases. However, the technique and application posed many limitations and challenges, including biases like user authentication and language, real-world implementation, etc. We discussed the future direction which can be useful to expand this area and domain. This review paper contributes mainly as a library for text mining in mosquito-borne diseases and could further explore the system for other neglected diseases.

Development and characterization of infectious clones of two strains of Usutu virus

Usutu virus (USUV; genus Flavivirus; family Flaviviridae) is a mosquito-borne, positive-sense RNA virus that is currently causing significant die-offs in numerous bird species throughout Europe and has caused infections in humans. Currently, there are no molecular clones for USUV, hence, hindering studies on the pathogenesis and transmission of USUV. Here, we demonstrate the development and characterization of infectious clones for two modern strains of USUV isolated from Europe and Africa. We show that the infectious clone-derived viruses replicated similarly to the parental strains in mammalian and insect cells. Additionally, we observed similar levels of replication and disease in two mouse models. These clones will aid the study of USUV infection, transmission, diagnostics, and vaccines.

Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico

BACKGROUND

This study examines the impact of climate, socio-economic and demographic factors on the incidence of dengue in regions of the United States and Mexico. We select factors shown to predict dengue at a local level and test whether the association can be generalized to the regional or state level. In addition, we assess how different indicators perform compared to per capita gross domestic product (GDP), an indicator that is commonly used to predict the future distribution of dengue.

METHODS

A unique spatial-temporal dataset was created by collating information from a variety of data sources to perform empirical analyses at the regional level. Relevant regions for the analysis were selected based on their receptivity and vulnerability to dengue. A conceptual framework was elaborated to guide variable selection. The relationship between the incidence of dengue and the climate, socio-economic and demographic factors was modelled via a Generalized Additive Model (GAM), which also accounted for the spatial and temporal auto-correlation.

RESULTS

The socio-economic indicator (representing household income, education of the labour force, life expectancy at birth, and housing overcrowding), as well as more extensive access to broadband are associated with a drop in the incidence of dengue; by contrast, population growth and inter-regional migration are associated with higher incidence, after taking climate into account. An ageing population is also a predictor of higher incidence, but the relationship is concave and flattens at high rates. The rate of active physicians is associated with higher incidence, most likely because of more accurate reporting. If focusing on Mexico only, results remain broadly similar, however, workforce education was a better predictor of a drop in the incidence of dengue than household income.

CONCLUSIONS

Two lessons can be drawn from this study: first, while higher GDP is generally associated with a drop in the incidence of dengue, a more granular analysis reveals that the crucial factors are a rise in education (with fewer jobs in the primary sector) and better access to information or technological infrastructure. Secondly, factors that were shown to have an impact of dengue at the local level are also good predictors at the regional level. These indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations on a larger scale.

Modeling and prediction of dengue occurrences in Kolkata, India, based on climate factors

Dengue is one of the most serious vector-borne infectious diseases in India, particularly in Kolkata and its neighbouring districts. Dengue viruses have infected several citizens of Kolkata since 2012 and it is amplifying every year. It has been derived from earlier studies that certain meteorological variables and climate change play a significant role in the spread and amplification of dengue infections in different parts of the globe. In this study, our primary objective is to identify the relative contribution of the putative drivers responsible for dengue occurrences in Kolkata and project dengue incidences with respect to the future climate change. The regression model was developed using maximum temperature, minimum temperature, relative humidity and rainfall as key meteorological factors on the basis of statistically significant cross-correlation coefficient values to predict dengue cases. Finally, climate variables from the Coordinated Regional Climate Downscaling Experiment (CORDEX) for South Asia region were input into the statistical model to project the occurrences of dengue infections under different climate scenarios such as Representative Concentration Pathways (RCP4.5 and RCP8.5). It has been estimated that from 2020 to 2100, dengue cases will be higher from September to November with more cases in RCP8.5 (872 cases per year) than RCP4.5 (531 cases per year). The present research further concludes that from December to February, RCP8.5 leads to suitable warmer weather conditions essential for the survival and multiplication of dengue pathogens resulting more than two times dengue cases in RCP8.5 than in RCP4.5. Furthermore, the results obtained will be useful in developing early warning systems and provide important evidence for dengue control policy-making and public health intervention.

A generic arboviral model framework for exploring trade-offs between vector control and environmental concerns

Effective public health measures must balance potentially conflicting demands from populations they serve. In the case of infectious disease risks from mosquito-borne infections, such as Zika virus, public concern about the pathogen may be counterbalanced by public concern about environmental contamination from chemical agents used for vector control. Here we introduce a generic framework for modeling how the spread of an infectious pathogen might lead to varying public perceptions, and therefore tolerance, of both disease risk and pesticide use. We consider how these dynamics might impact the spread of a vector-borne disease. We tailor and parameterize our model for direct application to Zika virus as spread by Aedes aegypti mosquitoes, though the framework itself has broad applicability to any arboviral infection. We demonstrate how public risk perception of both disease and pesticides may drastically impact the spread of a mosquito-borne disease in a susceptible population. We conclude that models hoping to inform public health decision making about how best to mitigate arboviral disease risks should explicitly consider the potential public demand for, or rejection of, chemical control of mosquito populations.

Hydrological features and the ecological niches of mammalian hosts delineate elevated risk for Ross River virus epidemics in anthropogenic landscapes in Australia

Background

The current understanding of the landscape epidemiology of Ross River virus (RRV), Australia’s most common arthropod-borne pathogen, is fragmented due to gaps in surveillance programs and the relatively narrow focus of the research conducted to date. This leaves public health agencies with an incomplete understanding of the spectrum of infection risk across the diverse geography of the Australian continent. The current investigation sought to assess the risk of RRV epidemics based on abiotic and biotic landscape features in anthropogenic landscapes, with a particular focus on the influence of water and wildlife hosts.

Methods

Abiotic features, including hydrology, land cover and altitude, and biotic features, including the distribution of wild mammalian hosts, were interrogated using a Maxent model to discern the landscape suitability to RRV epidemics in anthropogenically impacted environments across Australia.

Results

Water-soil balance, proximity to controlled water reservoirs, and the ecological niches of four species (Perameles nasuta, Wallabia bicolor, Pseudomys novaehollandiae and Trichosurus vulpecula) were important features identifying high risk landscapes suitable for the occurrence of RRV epidemics.

Conclusions

These results help to delineate human infection risk and thus provide an important perspective for geographically targeted vector, wildlife, and syndromic surveillance within and across the boundaries of local health authorities. Importantly, our analysis highlights the importance of the hydrology, and the potential role of mammalian host species in shaping RRV epidemic risk in peri-urban space. This study offers novel insight into wildlife hosts and RRV infection ecology and identifies those species that may be beneficial to future targeted field surveillance particularly in ecosystems undergoing rapid change.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2776-x) contains supplementary material, which is available to authorized users.

Chikungunya virus pathogenesis: From bedside to bench

Chikungunya virus (CHIKV) is an arbovirus transmitted to humans by mosquito bite. A decade ago, the virus caused a major outbreak in the islands of the Indian Ocean, then reached India and Southeast Asia. More recently, CHIKV has emerged in the Americas, first reaching the Caribbean and now extending to Central, South and North America. It is therefore considered a major public health and economic threat. CHIKV causes febrile illness typically associated with debilitating joint pains. In rare cases, it may also cause central nervous system disease, notably in neonates. Joint symptoms may persist for months to years, and lead to arthritis. This review focuses on the spectrum of signs and symptoms associated with CHIKV infection in humans. It also illustrates how the analysis of clinical and biological data from human cohorts and the development of animal and cellular models of infection has helped to identify the tissue and cell tropisms of the virus and to decipher host responses in benign, severe or persistent disease. This article forms part of a symposium in Antiviral Research on "Chikungunya discovers the New World".