Chikungunya Viral Fitness Measures within the Vector and Subsequent Transmission Potential

<div><p>Given the recent emergence of chikungunya in the Americas, the accuracy of forecasting and prediction of chikungunya transmission potential in the U.S. requires urgent assessment. The La Reunion-associated sub-lineage of chikungunya (with a valine substitution in the envelope protein) was shown to increase viral fitness in the secondary vector, <i>Ae. albopictus</i>. Subsequently, a majority of experimental and modeling efforts focused on this combination of a sub-lineage of the East-Central-South African genotype (ECSA-V) – <i>Ae. albopictus</i>, despite the Asian genotype being the etiologic agent of recent chikungunya outbreaks world-wide. We explore a collection of data to investigate relative transmission efficiencies of the three major genotypes/sub-lineages of chikungunya and found difference in the extrinsic incubation periods to be largely overstated. However, there is strong evidence supporting the role of <i>Ae. albopictus</i> in the expansion of chikungunya that our R0 calculations cannot attribute to fitness increases in one vector over another. This suggests other ecological factors associated with the <i>Ae. albopictus-</i>ECSA-V cycle may drive transmission intensity differences. With the apparent bias in literature, however, we are less prepared to evaluate transmission where <i>Ae. aegypti</i> plays a significant role. Holistic investigations of CHIKV transmission cycle(s) will allow for more complete assessment of transmission risk in areas affected by either or both competent vectors.</p></div

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<p>Bunyamwera (BUNV), Batai (BATV), and Ngari (NRIV) are mosquito-borne viruses of the Bunyamwera serogroup in the Orthobunyavirus genus of the Bunyaviridae family. These three viruses have been found to cause disease in both livestock animals, avian species, and humans. Thus, these viruses pose a potential threat to human public health, animal health, and food security. This is especially the case in the developing nations, where BUNV and NRIV are found, mainly in Africa. BUNV and BATV are fairly well characterized, while NRIV is not well characterized owing to only sporadic detection in human and animal populations in Africa. Reassortment is common among bunyaviruses, but NRIV is believed to be the only natural reassortant of the Bunyamwera serogroup. It resulted from a combination of BUNV S and L segments and the BATV M segment. This indicates at least some level co-circulation of BUNV and BATV, which have no historically been reported to overlap in geographic distributions. But as these viruses are undercharacterized, there remains a gap in the understanding of how such reassortment could occur, and the consequences of such. Due to their combined wide range of hosts and vectors, geographic distributions, potential severity of associated diseases, and potential for transmissibility between vertebrate hosts, these viruses represent a significant gap in knowledge with important One Health implications. The goal of this review is to report available knowledge of and identify potential future directions for study of these viruses. As these are collectively understudied viruses, there is a relative paucity of data; however, we use available studies to discuss different perspectives in an effort to promote a better understanding of these three viruses and the public and One Health threat(s) they may pose.</p

R0 values calculated based on differences in viral efficiency among (sub) lineage:mosquito combinations.

<p>R0 values calculated based on differences in viral efficiency among (sub) lineage:mosquito combinations.</p

Scatterplot depicting the data points from mosquito infection experiments the Asian (red dots) versus ECSA (green triangles) genotype where circles indicate an overlap where both Asian and ECSA data point exists.

<p>Scatterplot depicting the data points from mosquito infection experiments the Asian (red dots) versus ECSA (green triangles) genotype where circles indicate an overlap where both Asian and ECSA data point exists.</p

Values, definitions and sources for parameters used in modeling efforts.

<p>Values, definitions and sources for parameters used in modeling efforts.</p

The average proportion of mosquitoes with disseminated infections from all 23 studies (black dots) were used to fit the average rate of dissemination, estimated by the cumulative exponential distribution (red line).

<p>The average proportion of mosquitoes with disseminated infections from all 23 studies (black dots) were used to fit the average rate of dissemination, estimated by the cumulative exponential distribution (red line).</p

Scatterplot depicting the data points from mosquito infection experiments ECSA-A (red dots) versus ECSA-V (blue squares) with those data of non-determined lineage (green triangles).

<p>Circles indicate where there is an overlap of at least two sub-lineages.</p

Short Report: Serological Evidence of Under-Reported Dengue Circulation in Sierra Leone

<div><p>Dengue virus (DENV) is thought to have emerged from a sylvatic cycle in Africa but has since become adapted to an urban-centric transmission cycle. These urban areas include villages in West Africa where DENV is not often routinely considered for patients presenting with febrile illnesses, as other endemic diseases (malaria, Lassa fever, e.g.) present with similar non-specific symptoms. Thus, dengue is likely under diagnosed in the region. These plaque reduction neutralization test-50 (PRNT50) screening results of patients presenting with fevers of unknown origin (FUO) at a clinic in Kenema, Sierra Leone indicate that all four serotypes of DENV likely circulate in areas surrounding Kenema. Using a more conservative PRNT80 cut-off value, our results still indicate the presence of antibody to all four serotypes in the region. Identifying alternate etiologies of FUOs in this region will assist clinicians in plan-of-care decisions as well as follow-up priorities. This is particularly relevant given the Ebola outbreak in the region, where diagnosis has a range of downstream effects ranging from correct allocation of medical resources, appropriate isolation of patients, and ultimately, a better informed public health sector.</p></div

117 of 149 patients were positive for 1, 2, 3 or 4 serotypes of DENV as assessed by the PRNT50; patient serum was then further assessed for neutralizing capabilities at the PRNT80 level, often resulting in a lower order combination neutralization or no neutralization at all.

<p>117 of 149 patients were positive for 1, 2, 3 or 4 serotypes of DENV as assessed by the PRNT50; patient serum was then further assessed for neutralizing capabilities at the PRNT80 level, often resulting in a lower order combination neutralization or no neutralization at all.</p

Modeling Mosquito-Borne Disease Spread in U.S. Urbanized Areas: The Case of Dengue in Miami

<div><p>Expansion of mosquito-borne pathogens into more temperate regions of the world necessitates tools such as mathematical models for understanding the factors that contribute to the introduction and emergence of a disease in populations naïve to the disease. Often, these models are not developed and analyzed until after a pathogen is detected in a population. In this study, we develop a spatially explicit stochastic model parameterized with publicly available U.S. Census data for studying the potential for disease spread in Urbanized Areas of the United States. To illustrate the utility of the model, we specifically study the potential for introductions of dengue to lead to autochthonous transmission and outbreaks in a population representative of the Miami Urbanized Area, where introductions of dengue have occurred frequently in recent years. We describe seasonal fluctuations in mosquito populations by fitting a population model to trap data provided by the Miami-Dade Mosquito Control Division. We show that the timing and location of introduced cases could play an important role in determining both the probability that local transmission occurs as well as the total number of cases throughout the entire region following introduction. We show that at low rates of clinical presentation, small outbreaks of dengue could go completely undetected during a season, which may confound mitigation efforts that rely upon detection. We discuss the sensitivity of the model to several critical parameter values that are currently poorly characterized and motivate the collection of additional data to strengthen the predictive power of this and similar models. Finally, we emphasize the utility of the general structure of this model in studying mosquito-borne diseases such as chikungunya and Zika virus in other regions.</p></div