Arbovirus emergence in the temperate city of Córdoba, Argentina, 2009-2018

The distribution of arbovirus disease transmission is expanding from the tropics and subtropics into temperate regions worldwide. The temperate city of Córdoba, Argentina has been experiencing the emergence of dengue virus, transmitted by the mosquito Aedes aegypti, since 2009, when autochthonous transmission of the virus was first recorded in the city. The aim of this work is to characterize the emergence of dengue and related arboviruses (Zika and chikungunya) in Córdoba since 2009. Herein, we present a data set with all known information about transmission of dengue, Zika, and chikungunya viruses in Córdoba, Argentina from 2009-2018, including what information is known of dengue virus (DENV) serotypes in circulation and origins of imported cases. The data presented in this work will assist researchers in investigating drivers of arbovirus emergence and transmission in Córdoba, Argentina and contribute to a better understanding of the global problem of the expanding distribution of arbovirus disease transmission.Fil: Robert, Michael A.. University Of The Sciences; Estados UnidosFil: Tinunin, Daniela T.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Benitez, Elisabet Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Ludueña Almeida, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Romero, Moory. State University of New York; Estados UnidosFil: Stewart-Ibarra, Anna M.. State University of New York; Estados UnidosFil: Estallo, Elizabet Lilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentin

Ingested insecticide to control Aedes aegypti: developing a novel dried attractive toxic sugar bait device for intra-domiciliary control

© 2020 The Author(s). Background: Illnesses transmitted by Aedes aegypti (Linnaeus, 1762) such as dengue, chikungunya and Zika comprise a considerable global burden; mosquito control is the primary public health tool to reduce disease transmission. Current interventions are inadequate and insecticide resistance threatens the effectiveness of these options. Dried attractive bait stations (DABS) are a novel mechanism to deliver insecticide to Ae. aegypti. The DABS are a high-contrast 28 inch2 surface coated with dried sugar-boric acid solution. Aedes aegypti are attracted to DABS by visual cues only, and the dried sugar solution elicits an ingestion response from Ae. aegypti landing on the surface. The study presents the development of the DABS and tests of their impact on Ae. aegypti mortality in the laboratory and a series of semi-field trials. Methods: We conducted multiple series of laboratory and semi-field trials to assess the survivability of Ae. aegypti mosquitoes exposed to the DABS. In the laboratory experiments, we assessed the lethality, the killing mechanism, and the shelf life of the device through controlled experiments. In the semi-field trials, we released laboratory-reared female Ae. aegypti into experimental houses typical of peri-urban tropical communities in South America in three trial series with six replicates each. Laboratory experiments were conducted in Quito, Ecuador, and semi-field experiments were conducted in Machala, Ecuador, an area with abundant wild populations of Ae. aegypti and endemic arboviral transmission. Results: In the laboratory, complete lethality was observed after 48 hours regardless of physiological status of the mosquito. The killing mechanism was determined to be through ingestion, as the boric acid disrupted the gut of the mosquito. In experimental houses, total mosquito mortality was greater in the treatment house for all series of experiments (P \u3c 0.0001). Conclusions: The DABS devices were effective at killing female Ae. aegypti under a variety of laboratory and semi-field conditions. DABS are a promising intervention for interdomiciliary control of Ae. aegypti and arboviral disease prevention.[Figure not available: see fulltext.

Building urban climate resilience through public health: Identifying strategies for integrated public health governance in Duran, Ecuador

In the poster it is presented the Case Study of Duran, Ecuador, a coastal city of 235.000 inhabitants which are exposed to annual flooding events that increase the risk of vector-borne and other infectious diseases. Duran is an in-dustrial satellite city of Guayaquil, the largest city of Ecuador, with a rapid population growth that lead to a large area of informal settle-ments on the city. Applying an integrated climate risk management and urban health focus, it is assessed the Duran strategies for reducing vulnerability to flooding, landslides and heat waves through a collabo-rative inter-sectoral approach among the health, urban, and scientific actors. Stakeholder engagement between municipality and researchers are providing evidence and building knowledge to implement “low re-gret” adaptation strategies and community active participation

Could the Recent Zika Epidemic Have Been Predicted?

Given knowledge at the time, the recent 2015–2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes-borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus. We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower—but still of potential use to decision-makers—for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics

Household and climate factors influence Aedes aegypti presence in the arid city of Huaquillas, Ecuador

Funding: This study was funded by NSF EEID DEB 1518681 to SJR, EAM, AMS. EAM was also supported by NIH R35GM133439, NSF DEB-2011147, the Terman Award, the Helman Faculty Fellowship, and the Stanford Center for Innovation in Global Health.Arboviruses transmitted by Aedes aegypti (e.g., dengue, chikungunya, Zika) are of major public health concern on the arid coastal border of Ecuador and Peru. This high transit border is a critical disease surveillance site due to human movement-associated risk of transmission. Local level studies are thus integral to capturing the dynamics and distribution of vector populations and social-ecological drivers of risk, to inform targeted public health interventions. Our study examines factors associated with household-level Ae. aegypti presence in Huaquillas, Ecuador, while accounting for spatial and temporal effects. From January to May of 2017, adult mosquitoes were collected from a cohort of households (n = 63) in clusters (n = 10), across the city of Huaquillas, using aspirator backpacks. Household surveys describing housing conditions, demographics, economics, travel, disease prevention, and city services were conducted by local enumerators. This study was conducted during the normal arbovirus transmission season (January-May), but during an exceptionally dry year. Household level Ae. aegypti presence peaked in February, and counts were highest in weeks with high temperatures and a week after increased rainfall. Univariate analyses with proportional odds logistic regression were used to explore household social-ecological variables and female Ae. aegypti presence. We found that homes were more likely to have Ae. aegypti when households had interruptions in piped water service. Ae. aegypti presence was less likely in households with septic systems. Based on our findings, infrastructure access and seasonal climate are important considerations for vector control in this city, and even in dry years, the arid environment of Huaquillas supports Ae. aegypti breeding habitat.Publisher PDFPeer reviewe

Ingested insecticide to control Aedes aegypti : developing a novel dried attractive toxic sugar bait device for intra-domiciliary control

Laboratory work was Funded by Pontificia Universidad Católica del Ecuadorʼs Internal Research Grant L13234, awarded to MN. Semi-field work was funded by a seed grant from the Syracuse University, awarded to DL.Background: Illnesses transmitted by Aedes aegypti (Linnaeus, 1762) such as dengue, chikungunya and Zika comprise a considerable global burden; mosquito control is the primary public health tool to reduce disease transmission. Current interventions are inadequate and insecticide resistance threatens the effectiveness of these options. Dried attractive bait stations (DABS) are a novel mechanism to deliver insecticide to Ae. aegypti. The DABS are a high-contrast 28 inch2 surface coated with dried sugar-boric acid solution. Aedes aegypti are attracted to DABS by visual cues only, and the dried sugar solution elicits an ingestion response from Ae. aegypti landing on the surface. The study presents the development of the DABS and tests of their impact on Ae. aegypti mortality in the laboratory and a series of semi-field trials. Methods: We conducted multiple series of laboratory and semi-field trials to assess the survivability of Ae. aegypti mosquitoes exposed to the DABS. In the laboratory experiments, we assessed the lethality, the killing mechanism, and the shelf life of the device through controlled experiments. In the semi-field trials, we released laboratory-reared female Ae. aegypti into experimental houses typical of peri-urban tropical communities in South America in three trial series with six replicates each. Laboratory experiments were conducted in Quito, Ecuador, and semi-field experiments were conducted in Machala, Ecuador, an area with abundant wild populations of Ae. aegypti and endemic arboviral transmission. Results: In the laboratory, complete lethality was observed after 48 hours regardless of physiological status of the mosquito. The killing mechanism was determined to be through ingestion, as the boric acid disrupted the gut of the mosquito. In experimental houses, total mosquito mortality was greater in the treatment house for all series of experiments (P < 0.0001). Conclusions: The DABS devices were effective at killing female Ae. aegypti under a variety of laboratory and semi-field conditions. DABS are a promising intervention for interdomiciliary control of Ae. aegypti and arboviral disease prevention.Publisher PDFPeer reviewe

Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study

Background: Over the last 5 years (2013–2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016. Methods and findings: Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure–lag–response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure–lag–response functions of these indicators—rather than linear effects for individual lags—more appropriately described the climate–disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika. Conclusion: We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region

Severity Index for Suspected Arbovirus (SISA) : machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Funding: This study was supported, in part, by the Department of Defense Global Emerging Infection Surveillance (https://health.mil/Military-Health-Topics/Combat-Support/Armed-Forces-Health-Surveillance-Branch/Global-Emerging-Infections-Surveillance-and-Response) grant (P0220_13_OT) and the Department of Medicine of SUNY Upstate Medical University (http://www.upstate.edu/medicine/). D.F., M.H. and P.H. were supported by the Ben Kean Fellowship from the American Society for Tropical Medicine and Hygeine (https://www.astmh.org/awards-fellowships-medals/benjamin-h-keen-travel-fellowship-in-tropical-medi). S.J.R and A.M.S-I were supported by NSF DEB EEID 1518681, NSF DEB RAPID 1641145 (https://www.nsf.gov/), A.M.S-I was additionally supported by the Prometeo program of the National Secretary of Higher Education, Science, Technology, and Innovation of Ecuador (http://prometeo.educacionsuperior.gob.ec/).Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.Publisher PDFPeer reviewe

Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

Funding: J.M.C., A.D.L., E.F.L., and E.A.M. were supported by a Stanford Woods Institute for the Environment—Environmental Ventures Program grant (PIs: E.A.M., A.D.L., and E.F.L.). E.A.M. was also supported by a Hellman Faculty Fellowship and a Terman Award. A.D.L., B.A.N., F.M.M., E.N.G.S., M.S.S., A.R.K., R.D., A.A., and H.N.N. were supported by a National Institutes of Health R01 grant (AI102918; PI: A.D.L.). E.A.M., A.M.S.I., and S.J.R. were supported by a National Science Foundation (NSF) Ecology and Evolution of Infectious Diseases (EEID) grant (DEB-1518681), and A.M.S.I. and S.J.R. were also supported by an NSF DEB RAPID grant (1641145). E.A.M. was also supported by a National Institute of General Medical Sciences Maximizing Investigators’ Research Award grant (R35GM133439) and an NSF and Fogarty International Center EEID grant (DEB-2011147).Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28-85% for vectors, 44-88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.Publisher PDFPeer reviewe