Social Stressors, Arboviral Infection, and Immune Dysregulation in the Coastal Lowland Region of Ecuador: A Mixed Methods Approach in Ecological Perspective.

Aedes aegypti, the mosquito that transmits arboviral diseases such as dengue (DENV), chikungunya (CHIKV), and Zika viruses (ZIKV), is present in tropical and subtropical regions of the world. Individuals at risk of mosquito-borne disease (MBD) in the urban tropics face daily challenges linked to their socio-environment conditions, such as poor infrastructure, poverty, crowding, and limited access to adequate healthcare. These daily demands induce chronic stress events and dysregulated immune responses. We sought to investigate the role of socio-ecologic risk factors in distress symptoms and their impact on biological responses to MBD in Machala, Ecuador. Between 2017 and 2019, individuals (≥ 18 years) with suspected arbovirus illness (DENV, ZIKV, and CHIKV) from sentinel clinics were enrolled (index cases, N = 28). Cluster investigations of the index case households and people from four houses within a 200-m radius of index home (associate cases, N = 144) were conducted (total N = 172). Hair samples were collected to measure hair cortisol concentration (HCC) as a stress biomarker. Blood samples were collected to measure serum cytokines concentrations of IL-10, IL-8, TNF-α, and TGF-ß. Univariate analyses were used to determine the association of socio-health metrics related to perceived stress scores (PSS), HCC, and immune responses. We found that housing conditions influence PSS and HCC levels in individuals at risk of MBD. Inflammatory cytokine distribution was associated with the restorative phase of immune responses in individuals with low-moderate HCC. These data suggest that cortisol may dampen pro-inflammatory responses and influence activation of the restorative phase of immune responses to arboviral infections.

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

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.

Effects of Political Instability in Venezuela on Malaria Resurgence at Ecuador-Peru Border, 2018.

Mass migration from Venezuela has increased malaria resurgence risk across South America. During 2018, migrants from Venezuela constituted 96% of imported malaria cases along the Ecuador-Peru border. Plasmodium vivax predominated (96%). Autochthonous malaria cases emerged in areas previously malaria-free. Heightened malaria control and a response to this humanitarian crisis are imperative.