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.

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

18Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. 19 Decisions regarding the clinical management of suspected arboviral infection are challenging in 20 resource-limited settings, particularly when deciding on patient hospitalization. The objective of 21 this study was to determine if hospitalization of individuals with suspected arboviral infections 22 could be predicted using subject intake data. Methodology/Principal Findings: Two prediction 23 models were developed using data from a surveillance study in Machala, a city in southern coastal 24Ecuador with a high burden of arboviral infections. Data was obtained from subjects who presented 25 at sentinel medical centers with suspected arboviral infection (November 2013 to September 26 2017. The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-27 used only demographic and symptom data. The second prediction model-called the Severity 28Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These 29 models were selected by comparing the prediction ability of seven machine learning algorithms; 30 the area under the receiver operating characteristic curve from the prediction of a test dataset was 31 used to select the final algorithm for each model. After eliminating those with missing data, the 32 SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best 33 prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the 34 best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed 35 that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both 36 SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in 37 our dataset. These algorithms will need to be tested and validated on new data from future patients. 38 Machine learning is a powerful prediction tool and provides an excellent option for new 39 management tools and clinical assessment of arboviral infection. 40 41 Keywords: machine learning, arboviral disease, dengue fever, undifferentiated febrile illness 42 43 Author Summary 44 Patient triage is a critical decision for clinicians; patients with suspected arbovirus infection are 45difficult to diagnose as symptoms can be vague and molecular testing can be expensive or 46 unavailable. Determining whether these patients should be hospitalized or not can be challenging, 47 especially in resource-limited settings. Our study included data from 543 subjects with a diagnosis 48 of suspected dengue, chikungunya, or Zika infection. Using a machine learning approach, we 49 tested the ability of seven algorithms to predict hospitalization status based on the signs, 50 symptoms, and laboratory data that would be available to a clinician at patient intake. Using only 51 signs and symptoms, we were able to predict hospitalization with high accuracy (94%). Including 52 laboratory data also resulted in hig...

Dengue virus (DENV) and chikungunya virus (CHIKV) are transmitted by the same mosquito vectors and now co-circulate in many parts of the world; however, coinfections and serial infections are not often diagnosed or reported. A 38-week pregnant woman was admitted to the hospital with a diagnosis of suspected DENV and CHIKV in southern coastal Ecuador. The pregnancy was complicated by mild polyhydramnios and fetal tachycardia, and a healthy newborn was born. The patient was positive for a recent secondary DENV infection (Immunoglobulin M and Immunoglobulin G positive) and an acute CHIKV infection (real-time reverse transcriptase polymerase chain reaction positive) (Asian genotype). The newborn was not tested for either virus. This case resulted in a benign clinical course with a favorable pregnancy outcome.