Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia.

BACKGROUND: Dengue dynamics result from the complex interactions between the virus, the host and the vector, all being under the influence of the environment. Several studies explored the link between weather and dengue dynamics and some investigated the impact of climate change on these dynamics. Most attempted to predict incidence rate at a country scale or assess the environmental suitability at a global or regional scale. Here, we propose a new approach which consists in modeling the risk of dengue outbreak at a local scale according to climate conditions and study the evolution of this risk taking climate change into account. We apply this approach in New Caledonia, where high quality data are available. METHODS: We used a statistical estimation of the effective reproduction number (Rt) based on case counts to create a categorical target variable : epidemic week/non-epidemic week. A machine learning classifier has been trained using relevant climate indicators in order to estimate the probability for a week to be epidemic under current climate data and this probability was then estimated under climate change scenarios. RESULTS: Weekly probability of dengue outbreak was best predicted with the number of days when maximal temperature exceeded 30.8°C and the mean of daily precipitation over 80 and 60 days prior to the predicted week respectively. According to scenario RCP8.5, climate will allow dengue outbreak every year in New Caledonia if the epidemiological and entomological contexts remain the same. CONCLUSION: We identified locally relevant climatic factor driving dengue outbreaks in New Caledonia and assessed the inter-annual and seasonal risk of dengue outbreak under different climate change scenarios up to the year 2100. We introduced a new modeling approach to estimate the risk of dengue outbreak depending on climate conditions. This approach is easily reproducible in other countries provided that reliable epidemiological and climate data are available.

Development of a bedside score to predict dengue severity.

BACKGROUND: In 2017, New Caledonia experienced an outbreak of severe dengue causing high hospital burden (4379 cases, 416 hospital admissions, 15 deaths). We decided to build a local operational model predictive of dengue severity, which was needed to ease the healthcare circuit. METHODS: We retrospectively analyzed clinical and biological parameters associated with severe dengue in the cohort of patients hospitalized at the Territorial Hospital between January and July 2017 with confirmed dengue, in order to elaborate a comprehensive patient's score. Patients were compared in univariate and multivariate analyses. Predictive models for severity were built using a descending step-wise method. RESULTS: Out of 383 included patients, 130 (34%) developed severe dengue and 13 (3.4%) died. Major risk factors identified in univariate analysis were: age, comorbidities, presence of at least one alert sign, platelets count < 30 × 109/L, prothrombin time < 60%, AST and/or ALT > 10 N, and previous dengue infection. Severity was not influenced by the infecting dengue serotype nor by previous Zika infection. Two models to predict dengue severity were built according to sex. Best models for females and males had respectively a median Area Under the Curve = 0.80 and 0.88, a sensitivity = 84.5 and 84.5%, a specificity = 78.6 and 95.5%, a positive predictive value = 63.3 and 92.9%, a negative predictive value = 92.8 and 91.3%. Models were secondarily validated on 130 patients hospitalized for dengue in 2018. CONCLUSION: We built robust and efficient models to calculate a bedside score able to predict dengue severity in our setting. We propose the spreadsheet for dengue severity score calculations to health practitioners facing dengue outbreaks of enhanced severity in order to improve patients' medical management and hospitalization flow.

Viral evolution sustains a dengue outbreak of enhanced severity.

Compared to the previous 2013-2014 outbreak, dengue 2016-2017 outbreak in New Caledonia was characterized by an increased number of severe forms associated with hepatic presentations. In this study, we assessed the virological factors associated with this enhanced severity. Whole-genome sequences were retrieved from dengue virus (DENV)-1 strains collected in 2013-2014 and from severe and non-severe patients in 2016-2017. Fitness, hepatic tropism and cytopathogenicity of DENV 2016-2017 strains were compared to those of 2013-2014 strains using replication kinetics in the human hepatic cell line HuH7. Whole-genome sequencing identified four amino acid substitutions specific to 2016-2017 strains and absent from 2013-2014 strains. Three of these mutations occurred in predicted T cell epitopes, among which one was also a B cell epitope. Strains retrieved from severe forms did not exhibit specific genetic features. DENV strains from 2016-2017 exhibited a trend towards reduced replicative fitness and cytopathogenicity in vitro compared to strains from 2013-2014. Overall, the 2016-2017 dengue outbreak in New Caledonia was associated with a viral genetic evolution which had limited impact on DENV hepatic tropism and cytopathogenicity. These mutations, however, may have modified DENV strains antigenicity, altering the anti-DENV immune response in some patients, in turn favoring the development of severe forms.Trial registration: ClinicalTrials.gov identifier: NCT04615364.

Dengue in New Caledonia: Knowledge and Gaps.

Arboviruses are viruses transmitted to humans by the bite of infected mosquito vectors. Over the last decade, arbovirus circulation has increasingly been detected in New Caledonia (NC), a French island territory located in the subtropical Pacific region. Reliable epidemiological, entomological, virological and climate data have been collected in NC over the last decade. Here, we describe these data and how they inform arboviruses' epidemiological profile. We pinpoint areas which remain to be investigated to fully understand the peculiar epidemiological profile of arbovirus circulation in NC. Further, we discuss the advantages of conducting studies on arboviruses dynamics in NC. Overall, we show that conclusions drawn from observations conducted in NC may inform epidemiological risk assessments elsewhere and may be vital to guide surveillance and response, both in New Caledonia and beyond.

Zika virus outbreak in New Caledonia and Guillain-Barré syndrome: a case-control study.

Zika virus (ZIKV) infection has been associated with neurologic disorders including Guillain-Barré syndrome (GBS). In New Caledonia during the ZIKV outbreak (2014-2015), case-control and retrospective studies have been performed to assess the link between ZIKV and GBS. Among the 15 cases included, 33% had evidence of a recent ZIKV infection compared to only 3.3% in the 30 controls involved. All patients were Melanesian, had facial diplegia and similar neurophysiological pattern consistent with acute inflammatory demyelinating polyneuropathy, and recovered well. Furthermore, during the peak of ZIKV transmission, we observed a number of GBS cases higher than the calculated upper limit, emphasizing the fact that ZIKV is now a major trigger of GBS.

Comparison of bioassays with different exposure time patterns: the added value of dynamic modelling in predictive ecotoxicology.

The purpose of this study was to compare Daphnia magna responses to cadmium between two toxicity experiments performed in static and flow-through conditions. As a consequence of how water was renewed, the two experiments were characterised by two different exposure time patterns for daphnids, time-varying and constant, respectively. Basing on survival, growth and reproduction, we addressed the questions of organism development and sensitivity to cadmium. Classical analysis methods are not designed to deal with the time dimension and therefore not suitable to compare effects of different exposure time patterns. We used instead a dynamic modelling framework taking all timepoints and the time course of exposure into account, making comparable the results obtained from our two experiments. This modelling framework enabled us to detect an improvement of organism development in flow-through conditions compared to static ones and infer similar sensitivity to cadmium for both exposure time patterns.

A new perspective on the Dunnett procedure: filling the gap between NOEC/LOEC and ECx concepts.

The no-observed-effect concentration (NOEC) is known to be based on a wrong usage of hypothesis tests, and the use of confidence intervals is preferred. The purpose of the present study is to provide an easy and proper way to interpret ecotoxicological tests based on simultaneous confidence intervals associated with the commonly used Dunnett procedure, and to show how these intervals may allow one to infer ECx values (effective concentrations).