Background:Dengue fever, caused by a mosquito-transmitted virus, is an increasing health concern in the Americas. Meteorological variables such as temperature and precipitation can affect disease d...

/pdf/an-analysis-of-the-potential-impact-of-climate-change-on-3skbk6mbxz.pdf

An Analysis of the Potential Impact of Climate Change on Dengue Transmission in the Southeastern United States

Background: The World Health Organization estimates that the global number of dengue infections range between 80–100 million per year, with some studies estimating approximately three times higher numbers. Furthermore, the geographic range of dengue virus transmission is extending with the disease now occurring more frequently in areas such as southern Europe. Ae. aegypti, one of the most prominent dengue vectors, is endemic to the far north-east of Australia and the city of Cairns frequently experiences dengue outbreaks which sometimes lead to large epidemics. Method: A spatially-explicit, individual-based mathematical model that accounts for the spread of dengue infection as a result of human movement and mosquito dispersion is presented. The model closely couples the four key sub-models necessary for representing the overall dynamics of the physical system, namely those describing mosquito population dynamics, human movement, virus transmission and vector control. Important features are the use of high quality outbreak data and mosquito trapping data for calibration and validation and a strategy to derive local mosquito abundance based on vegetation coverage and census data. Results: The model has been calibrated using detailed 2003 dengue outbreak data from Cairns, together with census and mosquito trapping data, and is shown to realistically reproduce a further dengue outbreak. The simulation results replicating the 2008/2009 Cairns epidemic support several hypotheses (formulated previously) aimed at explaining the large-scale epidemic which occurred in 2008/2009; specifically, while warmer weather and increased human movement had only a small effect on the spread of the virus, a shorter virus strain-specific extrinsic incubation time can explain the observed explosive outbreak of 2008/2009. Conclusion: The proof-of-concept simulation model described in this study has potential as a tool for understanding factors contributing to dengue spread as well as planning and optimizing dengue control, including reducing the Ae. aegypti vector population and for estimating the effectiveness and cost-effectiveness of future vaccination programmes. This model could also be applied to other vector borne viral diseases such as chikungunya, also spread by Ae. aegypti and, by re-parameterisation of the vector sub-model, to dengue and chikungunya viruses spread by Aedes albopictus.

/pdf/a-spatial-simulation-model-for-dengue-virus-infection-in-56zmk05ref.pdf

A spatial simulation model for dengue virus infection in urban areas

Dengue has a negative impact in low- and lower middle-income countries, but also affects upper middle- and high-income countries. Despite the efforts at controlling this disease, it is unclear why dengue remains an issue in affluent countries. A better understanding of dengue epidemiology and its burden, and those of chikungunya virus and Zika virus which share vectors with dengue, is required to prevent the emergence of these diseases in high-income countries in the future. The purpose of this review was to assess the relative burden of dengue in four high-income countries and to appraise the similarities and differences in dengue transmission. We searched PubMed, ISI Web of Science, and Google Scholar using specific keywords for articles published up to 05 May 2016. We found that outbreaks rarely occur where only Aedes albopictus is present. The main similarities between countries uncovered by our review are the proximity to dengue-endemic countries, the presence of a competent mosquito vector, a largely nonimmune population, and a lack of citizens’ engagement in control of mosquito breeding. We identified important epidemiological and environmental issues including the increase of local transmission despite control efforts, population growth, difficulty locating larval sites, and increased human mobility from neighboring endemic countries. Budget cuts in health and lack of practical vaccines contribute to an increased risk. To be successful, dengue-control programs for high-income countries must consider the epidemiology of dengue in other countries and use this information to minimize virus importation, improve the control of the cryptic larval habitat, and engage the community in reducing vector breeding. Finally, the presence of a communicable disease center is critical for managing and reducing future disease risks.

/pdf/public-health-responses-to-and-challenges-for-the-control-of-1b1y2hnzr1.pdf

Public health responses to and challenges for the control of dengue transmission in high-income countries: four case studies

The effect of Wolbachia on dengue outbreaks when dengue is repeatedly introduced.

Dengue fever in Saudi Arabia: A review of environmental and population factors impacting emergence and spread.

The impact of weather variation on dengue transmission in Cairns, Australia, was determined by applying a process-based dengue simulation model (DENSiM) that incorporated local meteorologic, entomologic, and demographic data. Analysis showed that inter-annual weather variation is one of the significant determinants of dengue outbreak receptivity. Cross-correlation analyses showed that DENSiM simulated epidemics of similar relative magnitude and timing to those historically recorded in reported dengue cases in Cairns during 1991–2009, (r = 0.372, P < 0.01). The DENSiM model can now be used to study the potential impacts of future climate change on dengue transmission. Understanding the impact of climate variation on the geographic range, seasonality, and magnitude of dengue transmission will enhance development of adaptation strategies to minimize future disease burden in Australia.

/pdf/weather-driven-variation-in-dengue-activity-in-australia-2amtxhqcrc.pdf

Weather-Driven Variation in Dengue Activity in Australia Examined Using a Process-Based Modeling Approach

Abstract   During the SARS-COV2 pandemic (COVID-19) many studies have examined changes in women's stress, depression/anxiety, reproduction, plus post-vaccination cycle/flow changes1. Although there are retrospective basal temperature data2, we lack menstrual cycle and ovulation information during the pandemic. This analysis compared two independent, similar-design, convenience/community-based, single-cycle cohort studies 13 years apart: Menstruation Ovulation Study (MOS3, n=301, 2007-8) and MOS2 (n=112) during the pandemic. With MOS as a control, and given evidence subclinicalovulatory disturbances (SOD; short luteal phase/anovulatory menstrual cycles of normal lengths) occur before changed cycle lengths4 5, we hypothesized MOS2 would have increased SOD but similar cycle lengths as MOS. Methods In both studies, recruitment of menstruating women ages 19-35 years, not using systemic or combined hormonal contraceptives (CHC) used posters/eblasts/social media. In MOS, ovulation was assessed by 3-fold increased follicular (FP)-to-luteal (LP/premenstrual) urinary progesterone (PdG); in MOS2, by validated Quantitative Basal Temperature© (QBT)6 7 (normal LP=10+ days). We performed the same interviewer-administered (CaMos©) questionnaire for demographics, SES, and reproduction, measured anthropomorphic variables, plus collected daily Menstrual Cycle Diary© (Diary) for all. FP and LP/premenstrual PdG or salivary progesterone (Ps) samples were respectively collected. Participants in MOS2 were not different from MOS in: average age 29, menarche age 12.5, BMI 24, living situation and education (≥75% university graduates). Cohorts also differed: MOS2 women were less likely to to be White (56% vs 76%), work fulltime, ever use CHC (68% vs 79%) or to be parous (8% vs 20%); they were younger at starting CHC (17.9 vs 18.6 years). Results MOS2 and MOS had similar cycle (30.3 vs 29.9 days, P =.306) and flow lengths (median 6.0 days; P=.055). MOS2 recorded significantly more SOD cycles (>50% anovulatory) vs MOS3 (63% vs 10%; P<.001). MOS2 Diary analyses by Principal Components Analysis showed significantly increased anxiety/depression/frustration (negative moods) and "outside stresses" plus sleep problems and headaches vs MOS (all P<.001). Discussion This is the first evidence that ovulatory disturbances without cycle length changes may be associated with the multidimensional stresses women experience during the pandemic. Increased SOD may also relate to greater nulliparity, younger CHC teen use and more non-White women in MOS2, as well higher prevalence of negative moods, outside stresses and sleep problems. Salivary progesterone, cortisol and estradiol levels remain pending. Prevalent SOD cycles, if persistent/recurrent, risk increased infertility, bone loss5, early heart attacks, and breast and endometrial cancers8. In this 'experiment of nature,' the pandemic was associated with disturbed ovulation rather than menstrual cycles. 1Edelman AB Ob Gyn 2022; 2Nguyen JK PLOS ONE 2021; 3Prior JC IJERPH 2018; 4Prior JC NEJM 1990; 5Li D Epidiol Rev 2014; 6Prior JC Clin Invest. Med 1990; 7Bedford JL Eur J ObGyn Reprod Biol 2009; 8Prior JC DDDM 2020. Presentation: Sunday, June 12, 2022 12:15 p.m. - 12:30 p.m.

/pdf/or15-6-epidemic-of-subclinical-ovulatory-disturbances-during-ndypiv9p.pdf

OR15-6 Epidemic of Subclinical Ovulatory Disturbances During SARS-COV2 Pandemic—an Experiment of Nature

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Weather-Driven Variation in Dengue Activity in Australia Examined Using a Process-Based Modeling Approach

OR15-6 Epidemic of Subclinical Ovulatory Disturbances During SARS-COV2 Pandemic—an Experiment of Nature