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Lassa fever is a viral haemorrhagic disease endemic to west Africa. No large-scale studies exist from Nigeria, where the Lassa virus (LASV) is most diverse. LASV diversity, coupled with host genetic and environmental factors, might cause differences in disease pathophysiology. Small-scale studies in Nigeria suggest that acute kidney injury is an important clinical feature and might be a determinant of survival. We aimed to establish the demographic, clinical, and laboratory factors associated with mortality in Nigerian patients with Lassa fever, and hypothesised that LASV was the direct cause of intrinsic renal damage for a subset of the patients with Lassa fever. We did a retrospective, observational cohort study of consecutive patients in Nigeria with Lassa fever, who tested positive for LASV with RT-PCR, and were treated in Irrua Specialist Teaching Hospital. We did univariate and multivariate statistical analyses, including logistic regression, of all demographic, clinical, and laboratory variables available at presentation to identify the factors associated with patient mortality. Of 291 patients treated in Irrua Specialist Teaching Hospital between Jan 3, 2011, and Dec 11, 2015, 284 (98%) had known outcomes (died or survived) and seven (2%) were discharged against medical advice. Overall case-fatality rate was 24% (68 of 284 patients), with a 1·4 times increase in mortality risk for each 10 years of age (p=0·00017), reaching 39% (22 of 57) for patients older than 50 years. Of 284 patients, 81 (28%) had acute kidney injury and 104 (37%) had CNS manifestations and thus both were considered important complications of acute Lassa fever in Nigeria. Acute kidney injury was strongly associated with poor outcome (case-fatality rate of 60% [49 of 81 patients]; odds ratio [OR] 15, p<0·00001). Compared with patients without acute kidney injury, those with acute kidney injury had higher incidence of proteinuria (32 [82%] of 39 patients) and haematuria (29 [76%] of 38) and higher mean serum potassium (4·63 [SD 1·04] mmol/L) and lower blood urea nitrogen to creatinine ratio (8·6 for patients without clinical history of fluid loss), suggesting intrinsic renal damage. Normalisation of creatinine concentration was associated with recovery. Elevated serum creatinine (OR 1·3; p=0·046), aspartate aminotransferase (OR 1·5; p=0·075), and potassium (OR 3·6; p=0·0024) were independent predictors of death. Our study presents detailed clinical and laboratory data for Nigerian patients with Lassa fever and provides strong evidence for intrinsic renal dysfunction in acute Lassa fever. Early recognition and treatment of acute kidney injury might significantly reduce mortality. German Research Foundation, German Center for Infection Research, Howard Hughes Medical Institute, US National Institutes of Health, and World Bank.

Assessment of the response to the 2014-15 Ebola outbreak indicates the need for innovations in data collection, sharing, and use to improve case detection and treatment. Here we introduce a Machine Learning pipeline for Ebola Virus Disease (EVD) prognosis prediction, which packages the best models into a mobile app to be available in clinical care settings. The pipeline was trained on a public EVD clinical dataset, from 106 patients in Sierra Leone. We used a new tool for exploratory analysis, Mirador, to identify the most informative clinical factors that correlate with EVD outcome. The small sample size and high prevalence of missing records were significant challenges. We applied multiple imputation and bootstrap sampling to address missing data and quantify overfitting. We trained several predictors over all combinations of covariates, which resulted in an ensemble of predictors, with and without viral load information, with an area under the receiver operator characteristic curve of 0.8 or more, after correcting for optimistic bias. We ranked the predictors by their F1-score, and those above a set threshold were compiled into a mobile app, Ebola CARE (Computational Assignment of Risk Estimates). This method demonstrates how to address small sample sizes and missing data, while creating predictive models that can be readily deployed to assist treatment in future outbreaks of EVD and other infectious diseases. By generating an ensemble of predictors instead of relying on a single model, we are able to handle situations where patient data is partially available. The prognosis app can be updated as new data become available, and we made all the computational protocols fully documented and open-sourced to encourage timely data sharing, independent validation, and development of better prediction models in outbreak response.

The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata in a cross-sectional study of febrile patients and healthy controls in a low malaria burden area. Using 16S and untargeted sequencing, we detected viral, bacterial, or eukaryotic pathogens in 23% (38/163) of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15.5% and 3.8% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium , including one putative P. ovale infection. We developed a logistic regression model that can distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs (F1 score: 0.823). These results highlight the challenge and importance of improved diagnostics, especially for Borrelia , to support diagnosis and surveillance. Non-malarial febrile illnesses have a range of potential aetiologies which are difficult to diagnose and therefore treat. Here, the authors investigate the causes of acute febrile illness in a peri-urban area of Senegal with low malaria incidence using untargeted and targeted sequencing methods.

We performed a survey of US international travellers to evaluate their knowledge, attitudes and practices regarding mobile technologies related to health. We found that many international travellers carry smartphones and are interested in receiving health information from a mobile app when they travel abroad.

Abstract Background Although multiple prognostic models exist for Ebola virus disease mortality, few incorporate biomarkers, and none has used longitudinal point-of-care serum testing throughout Ebola treatment center care. Methods This retrospective study evaluated adult patients with Ebola virus disease during the 10th outbreak in the Democratic Republic of Congo. Ebola virus cycle threshold (Ct; based on reverse transcriptase polymerase chain reaction) and point-of-care serum biomarker values were collected throughout Ebola treatment center care. Four iterative machine learning models were created for prognosis of mortality. The base model used age and admission Ct as predictors. Ct and biomarkers from treatment days 1 and 2, days 3 and 4, and days 5 and 6 associated with mortality were iteratively added to the model to yield mortality risk estimates. Receiver operating characteristic curves for each iteration provided period-specific areas under curve with 95% CIs. Results Of 310 cases positive for Ebola virus disease, mortality occurred in 46.5%. Biomarkers predictive of mortality were elevated creatinine kinase, aspartate aminotransferase, blood urea nitrogen (BUN), alanine aminotransferase, and potassium; low albumin during days 1 and 2; elevated C-reactive protein, BUN, and potassium during days 3 and 4; and elevated C-reactive protein and BUN during days 5 and 6. The area under curve substantially improved with each iteration: base model, 0.74 (95% CI, .69–.80); days 1 and 2, 0.84 (95% CI, .73–.94); days 3 and 4, 0.94 (95% CI, .88–1.0); and days 5 and 6, 0.96 (95% CI, .90–1.0). Conclusions This is the first study to utilize iterative point-of-care biomarkers to derive dynamic prognostic mortality models. This novel approach demonstrates that utilizing biomarkers drastically improved prognostication up to 6 days into patient care. This is the first study to utilize iterative point-of-care biomarkers to derive a dynamic prognostic mortality model for Ebola virus disease. This model overcomes the limitations of previous models by extending prognostication to day 6 of patient care.

Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits of a more altruistic strategy, we built an epidemiological model that assesses the differential impact on case counts when institutions instead allocate a proportion of their tests to members’ close contacts in the larger community. We found that testing outside the institution benefits the institution in all plausible circumstances, with the optimal proportion of tests to use externally landing at 45% under baseline model parameters. Our results were robust to local prevalence, secondary attack rate, testing capacity, and contact reporting level, yielding a range of optimal community testing proportions from 18 to 58%. The model performed best under the assumption that community contacts are known to the institution; however, it still demonstrated a significant benefit even without complete knowledge of the contact network.

Global travel plays a role in the spread of infectious diseases. Existing travel surveillance programs collect data before and after trips, resulting in data incompleteness and recall bias. We developed the Travel Healthy mobile app to address these gaps, by enabling U.S. travelers to report daily symptom surveys including GPS location. The app offers traveler tools, including outbreak notices, a travel wallet, and a malaria medication reminder. We developed Travel Healthy following a user-centric approach. We recruited study participants through an online platform and at the Travelers’ Advice and Immunization Center at Massachusetts General Hospital, between July 2023 and August 2024. We analyzed demographic, GPS, and self-reported symptom data from the first 50 participants. Data were collected starting one day before the trip and ending three days after. A post-travel feedback survey was performed. Participants visited 204 locations in Asia, Africa, the Americas, and Europe. Mean age was 33 years and 66 % were female. The most common purposes of travel were leisure and/or business, with 46 (92 %) of participants listing these as traveling reasons. A total of 755 daily symptom surveys were entered, with 105 reporting symptoms, corresponding to 29 of the 50 (58 %) participants. Among all symptoms with GPS data, 58 % were upper respiratory symptoms, 25 % were gastrointestinal (clustered in South Asia), and 17 % were other. Post-travel questionnaires showed that participants found the application easy to use. This pilot study underscores the potential of participatory surveillance tools to complement traditional public health surveillance methods for travel-related illness. •The Travel Healthy app enables U.S. travelers to report daily symptom surveys, including GIS location.•Travel Healthy generates high-resolution disease surveillance during travel that would be hard to obtain otherwise.•We applied a user-centric approach to make the app easy to use and to provide useful features to travelers.•We report demographics and symptoms of the first 50 users, who visited 204 locations in Asia, Africa, the Americas, and Europe.

An unfolded state ensemble is generated by using a self-avoiding statistical coil model that is based on backbone conformational frequencies in a coil library, a subset of the Protein Data Bank. The model reproduces two apparently contradicting behaviors observed in the chemically denatured state for a variety of proteins, random coil scaling of the radius of gyration and the presence of significant amounts of local backbone structure (NMR residual dipolar couplings). The most stretched members of our unfolded ensemble dominate the residual dipolar coupling signal, whereas the uniformity of the sign of the couplings follows from the preponderance of polyproline II and β conformers in the coil library. Agreement with the NMR data substantially improves when the backbone conformational preferences include correlations arising from the chemical and conformational identity of neighboring residues. Although the unfolded ensembles match the experimental observables, they do not display evidence of native-like topology. By providing an accurate representation of the unfolded state, our statistical coil model can be used to improve thermodynamic and kinetic modeling of protein folding.

Background Ebola Virus Disease (EVD) causes high case fatality rates (CFRs) in young children, yet there are limited data focusing on predicting mortality in pediatric patients. Here we present machine learning-derived prognostic models to predict clinical outcomes in children infected with Ebola virus. Methods Using retrospective data from the Ebola Data Platform, we investigated children with EVD from the West African EVD outbreak in 2014-2016. Elastic net regularization was used to create a prognostic model for EVD mortality. In addition to external validation with data from the 2018-2020 EVD epidemic in the Democratic Republic of the Congo (DRC), we updated the model using selected serum biomarkers. Findings Pediatric EVD mortality was significantly associated with younger age, lower PCR cycle threshold (Ct) values, unexplained bleeding, respiratory distress, bone/muscle pain, anorexia, dysphagia, and diarrhea. These variables were combined to develop the newly described EVD Prognosis in Children (EPiC) predictive model. The area under the receiver operating characteristic curve (AUC) for EPiC was 0.77 (95% CI: 0.74-0.81) in the West Africa derivation dataset and 0.76 (95% CI: 0.64-0.88) in the DRC validation dataset. Updating the model with peak aspartate aminotransferase (AST) or creatinine kinase (CK) measured within the first 48 hours after admission increased the AUC to 0.90 (0.77-1.00) and 0.87 (0.74-1.00), respectively. Conclusion The novel EPiC prognostic model that incorporates clinical information and commonly used biochemical tests, such as AST and CK, can be used to predict mortality in children with EVD.

Rapid diagnostic tools for children with Ebola virus disease (EVD) are needed to expedite isolation and treatment. To evaluate a predictive diagnostic tool, we examined retrospective data (2014-2015) from the International Medical Corps Ebola Treatment Centers in West Africa. We incorporated statistically derived candidate predictors into a 7-point Pediatric Ebola Risk Score. Evidence of bleeding or having known or no known Ebola contacts was positively associated with an EVD diagnosis, whereas abdominal pain was negatively associated. Model discrimination using area under the curve (AUC) was 0.87, which outperforms the World Health Organization criteria (AUC 0.56). External validation, performed by using data from International Medical Corps Ebola Treatment Centers in the Democratic Republic of the Congo during 2018-2019, showed an AUC of 0.70. External validation showed that discrimination achieved by using World Health Organization criteria was similar; however, the Pediatric Ebola Risk Score is simpler to use.