BACKGROUND:Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly.OBJECTIVE:LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev's capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev.METHODS:Based on the user's query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling.RESULTS:LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5%). This represents a total work saved over sampling of 56%.CONCLUSIONS:We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest.

AIMS OF THE STUDY:Remdesivir has shown benefits against COVID-19. However, it remains unclear whether, to what extent, and among whom remdesivir can reduce COVID-19-related mortality. We explored whether the treatment response to remdesivir differed by patient characteristics.METHODS:We analysed data collected from a hospital surveillance study conducted in 21 referral hospitals in Switzerland between 2020 and 2022. We applied model-based recursive partitioning to group patients by the association between treatment levels and mortality. We included either treatment (levels: none, remdesivir within 7 days of symptom onset, remdesivir after 7 days, or another treatment), age and sex, or treatment only as regression variables. Candidate partitioning variables included a range of risk factors and comorbidities (and age and sex unless included in regression). We repeated the analyses using local centring to correct the results for the propensity to receive treatment.RESULTS:Overall (n = 21,790 patients), remdesivir within 7 days was associated with increased mortality (adjusted hazard ratios 1.28-1.54 versus no treatment). The CURB-65 score caused the most instability in the regression parameters of the model. When adjusted for age and sex, patients receiving remdesivir within 7 days of onset had higher mortality than those not treated in all identified eight patient groups. When age and sex were included as partitioning variables instead, the number of groups increased to 19-20; in five to six of those branches, mortality was lower among patients who received early remdesivir. Factors determining the groups where remdesivir was potentially beneficial included the presence of oncological comorbidities, male sex, and high age.CONCLUSIONS:Some subgroups of patients, such as individuals with oncological comorbidities or elderly males, may benefit from remdesivir.

ImportanceWith the ongoing COVID-19 pandemic, it is crucial to assess the current burden of disease of community-acquired SARS-CoV-2 Omicron variant in hospitalized patients to tailor appropriate public health policies. Comparisons with better-known seasonal influenza infections may facilitate such decisions.ObjectiveTo compare the in-hospital outcomes of patients hospitalized with the SARS-CoV-2 Omicron variant with patients with influenza.Design, Setting, and ParticipantsThis cohort study was based on a national COVID-19 and influenza registry. Hospitalized patients aged 18 years and older with community-acquired SARS-CoV-2 Omicron variant infection who were admitted between January 15 and March 15, 2022 (when B.1.1.529 Omicron predominance was >95%), and hospitalized patients with influenza A or B infection from January 1, 2018, to March 15, 2022, where included. Patients without a study outcome by August 30, 2022, were censored. The study was conducted at 15 hospitals in Switzerland.ExposuresCommunity-acquired SARS-CoV-2 Omicron variant vs community-acquired seasonal influenza A or B.Main Outcomes and MeasuresPrimary and secondary outcomes were defined as in-hospital mortality and admission to the intensive care unit (ICU) for patients with the SARS-CoV-2 Omicron variant or influenza. Cox regression (cause-specific and Fine-Gray subdistribution hazard models) was used to account for time-dependency and competing events, with inverse probability weighting to adjust for confounders with right-censoring at day 30.ResultsOf 5212 patients included from 15 hospitals, 3066 (58.8%) had SARS-CoV-2 Omicron variant infection in 14 centers and 2146 patients (41.2%) had influenza A or B in 14 centers. Of patients with the SARS-CoV-2 Omicron variant, 1485 (48.4%) were female, while 1113 patients with influenza (51.9%) were female (P = .02). Patients with the SARS-CoV-2 Omicron variant were younger (median [IQR] age, 71 [53-82] years) than those with influenza (median [IQR] age, 74 [59-83] years; P < .001). Overall, 214 patients with the SARS-CoV-2 Omicron variant (7.0%) died during hospitalization vs 95 patients with influenza (4.4%; P < .001). The final adjusted subdistribution hazard ratio (sdHR) for in-hospital death for SARS-CoV-2 Omicron variant vs influenza was 1.54 (95% CI, 1.18-2.01; P = .002). Overall, 250 patients with the SARS-CoV-2 Omicron variant (8.6%) vs 169 patients with influenza (8.3%) were admitted to the ICU (P = .79). After adjustment, the SARS-CoV-2 Omicron variant was not significantly associated with increased ICU admission vs influenza (sdHR, 1.08; 95% CI, 0.88-1.32; P = .50).Conclusions and RelevanceThe data from this prospective, multicenter cohort study suggest a significantly increased risk of in-hospital mortality for patients with the SARS-CoV-2 Omicron variant vs those with influenza, while ICU admission rates were similar.

Background Literature Reviews (LRs) identify, evaluate, and synthesise relevant papers to a particular research question to advance understanding and support decision making. However, LRs, especially traditional systematic reviews are slow, resource intensive, and are outdated quickly. Objective Using recent Natural Language Processing (NLP) and Unsupervised Machine Learning (UML) methods, this paper presents a tool named LiteRev that supports researchers in conducting LRs. Methods Based on the user’s query, LiteRev can perform an automated search on different open-access databases and retrieve relevant metadata on the resulting papers. Papers (abstracts or full texts) are text processed and represented as a Term Frequency-Inverse Document Frequency (TF-IDF) matrix. Using dimensionality reduction (PaCMAP) and clustering (HDBSCAN) techniques, the corpus is divided into different topics described by a list of keywords. The user can select one or several topics of interest, enter additional keywords to refine their search, or provide key papers to the research question. Based on these inputs, LiteRev performs an iterative nearest neighbours search, and suggests a list of potentially interesting papers. The user can tag the relevant ones and trigger a new search until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using True and False Predictive Values, recall and Work Saved over Sampling. Results We extracted, text processed and represented into a TF-IDF matrix 631 unique papers from PubMed. The topic modelling module identified 5 main topics and 16 topics (ranging from 13 to 98 papers) and extracted the 10 most important keywords for each. Then, based on 18 key papers, we were able to identify 2 topics of interest with 7 key papers in each of them. Finally, we ran the k-nearest neighbours module and LiteRev suggested first a list of 110 papers for screening, among which 45 papers were confirmed as relevant. From these 45 papers, LiteRev suggested 26 additional papers, out of which 8 were confirmed as relevant. At the end of the iterative process (4 iterations), 193 papers out of 613 papers in total (31.5% of the whole corpus) were suggested by LiteRev. After title/abstract screening, LiteRev identified 64 out of the 87 relevant papers (i.e., recall of 73.6%). After full text screening, LiteRev identified 42 out of the 48 relevant papers (i.e., recall of 87.5%, and Work Saved over Sampling of 56.0%). Conclusions We presented LiteRev, an automation tool that uses NLP and UML methods to streamline and accelerate LRs and to support researchers in getting quick and in-depth overviews on any topic of interest.

Background: Since the onset of the COVID-19 pandemic, the disease has frequently been compared with seasonal influenza, but this comparison is based on little empirical data. Aim: This study compares in-hospital outcomes for patients with community-acquired COVID-19 and patients with community-acquired influenza in Switzerland. Methods: This retrospective multi-centre cohort study includes patients >18 years admitted for COVID-19 or influenza A/B infection determined by RT-PCR. Primary and secondary outcomes were in-hospital mortality and intensive care unit (ICU) admission for patients with COVID-19 or influenza. We used Cox regression (cause-specific and Fine-Gray subdistribution hazard models) to account for time-dependency and competing events with inverse probability weighting to adjust for confounders. Results: In 2020, 2,843 patients with COVID-19 from 14 centres were included. Between 2018 and 2020, 1,381 patients with influenza from seven centres were included; 1,722 (61%) of the patients with COVID-19 and 666 (48%) of the patients with influenza were male (p<0.001). The patients with COVID-19 were younger (median 67 years; interquartile range (IQR): 54-78) than the patients with influenza (median 74 years; IQR: 61-84) (p<0.001). A larger percentage of patients with COVID-19 (12.8%) than patients with influenza (4.4%) died in hospital (p< 0.001). The final adjusted subdistribution hazard ratio for mortality was 3.01 (95% CI: 2.22-4.09; p< 0.001) for COVID-19 compared with influenza and 2.44 (95% CI: 2.00-3.00, p <0.001) for ICU admission. Conclusion: Community-acquired COVID-19 was associated with worse outcomes compared with community-acquired influenza, as the hazards of ICU admission and in-hospital death were about two-fold to three-fold higher.

INTRODUCTION:High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics. METHODS:We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies. RESULTS:Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer's index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be tested to find 55.7% of all males and 50.5% of all females living with HIV. CONCLUSIONS:We trained a gradient boosting trees algorithm to find 95% of PLHIV with a precision twice higher than with general population testing by using only a limited number of socio-behavioural characteristics. We also successfully identified people at high risk of infection who may be offered pre-exposure prophylaxis or voluntary medical male circumcision. These findings can inform the implementation of new high-yield HIV tests and help develop very precise strategies based on low-resource settings constraints.

Introduction: The assessment of COVID-19 associated mortality is crucial to evaluate the impact of the pandemic and to assess the effectiveness of measures. Objectives: We aimed to investigate trends in COVID-19 related mortality over time in Switzerland, using data from the COVID-19 Hospitalbased Surveillance (CH-SUR) database. Methods: Considering four different time periods of COVID-19 epidemic, we calculated crude and adjusted mortality rates and performed competing risks survival analyses for all patients and for patients admitted to intensive care (ICU). Results: Overall, 16,967 COVID-19 related hospitalizations and 2,307 deaths of adult patients were recorded. Crude hospital mortality rates were 15.6% in the 1st and 14.4% in the 2nd wave;for ICU patients it was 24% and 31.3%, respectively. The overall adjusted risk of death was lower for hospitalised patients during the 2nd compared to the 1st wave (HR 0.75, 95% CI 0.73 - 0.77). In contrast, patients admitted to ICU as well as patients with invasive ventilation presented a higher risk of death during the 2nd wave (HR 1.62, 95% CI 1.54-1.70 and HR 2.10, 95% CI 1.99-2.20, respectively). Conclusion: Our findings may be explained by various changes in the COVID-19 patient management in Swiss hospitals, e.g. with the use of effective drugs against complications or with different guidelines for ICU admission and invasive ventilation use.

This article explored the differences in HIV testing in the elimination of mother-to-child transmission of HIV (EMTCT) between women with and without disabilities aged 16-55 years, reported being pregnant and receiving the social cash transfers (SCT) social safety nets in Luapula province, Zambia. We tested for associations between HIV testing in EMTCT and disability using logistic regression analyses. We calculated a functional score for each woman to determine if they had mild, moderate or severe difficulties and controlled for age, intimate partner sexual violence, and the SCT receipt. Of 1692 women, 29.8% (504) reported a disability, 724 (42.8%) mild, 203 (12.0%) moderate, and 83 (4.9%) severe functional difficulties (adjusted odds ratio [aOR] 1.33; 95% confidence interval [CI] 1.04-1.70). Women with moderate (aOR 2.04; 95% CI 1.44-2.88) or mild difficulties (aOR 1.66; 95% CI 1.32-2.08) or with a disability in cognition (aOR 1.67 95% CI 1.22-2.29) reported testing more for HIV than women without disabilities; Women with a disability in hearing (aOR 0.36 CI 0.16-0.80) reported testing less for HIV. Disability is common among women receiving the SCT in the study area accessing HIV testing in the EMTCT setting. HIV testing in EMTCT is challenging for women with disabilities in hearing.

BACKGROUND: When the periods of time during and after the first wave of the ongoing SARS-CoV-2/COVID-19 pandemic in Europe are compared, the associated COVID-19 mortality seems to have decreased substantially. Various factors could explain this trend, including changes in demographic characteristics of infected persons and the improvement of case management. To date, no study has been performed to investigate the evolution of COVID-19 in-hospital mortality in Switzerland, while also accounting for risk factors. METHODS: We investigated the trends in COVID-19-related mortality (in-hospital and in-intermediate/intensivecare) over time in Switzerland, from February 2020 to June 2021, comparing in particular the first and the second wave. We used data from the COVID-19 Hospital-based Surveillance (CH-SUR) database. We performed survival analyses adjusting for well-known risk factors of COVID-19 mortality (age, sex and comorbidities) and accounting for competing risk. RESULTS: Our analysis included 16,984 patients recorded in CH-SUR, with 2201 reported deaths due to COVID-19 (13.0%). We found that overall in-hospital mortality was lower during the second wave of COVID-19 than in the first wave (hazard ratio [HR] 0.70, 95% confidence interval [CI] 0.63- 0.78; p <0.001), a decrease apparently not explained by changes in demographic characteristics of patients. In contrast, mortality in intermediate and intensive care significantly increased in the second wave compared with the first wave (HR 1.25, 95% CI 1.05-1.49; p = 0.029), with significant changes in the course of hospitalisation between the first and the second wave. CONCLUSION: We found that, in Switzerland, COVID-19 mortality decreased among hospitalised persons, whereas it increased among patients admitted to intermediate or intensive care, when comparing the second wave to the first wave. We put our findings in perspective with changes over time in case management, treatment strategy, hospital burden and non-pharmaceutical interventions. Further analyses of the potential effect of virus variants and of vaccination on mortality would be crucial to have a complete overview of COVID-19 mortality trends throughout the different phases of the pandemic.

BACKGROUND SARS-CoV-2/COVID-19, which emerged in China in late 2019, rapidly spread across the world with several million victims in 213 countries. Switzerland was severely hit by the virus, with 43,000 confirmed cases as of 1 September 2020. AIM In cooperation with the Federal Office of Public Health, we set up a surveillance database in February 2020 to monitor hospitalised patients with COVID-19, in addition to their mandatory reporting system. METHODS Patients hospitalised for more than 24 hours with a positive polymerase chain-reaction test, from 20 Swiss hospitals, are included. Data were collected in a customised case report form based on World Health Organisation recommendations and adapted to local needs. Nosocomial infections were defined as infections for which the onset of symptoms was more than 5 days after the patient’s admission date. RESULTS As of 1 September 2020, 3645 patients were included. Most patients were male (2168, 59.5%), and aged between 50 and 89 years (2778, 76.2%), with a median age of 68 (interquartile range 54–79). Community infections dominated with 3249 (89.0%) reports. Comorbidities were frequently reported, with hypertension (1481, 61.7%), cardiovascular diseases (948, 39.5%) and diabetes (660, 27.5%) being the most frequent in adults; respiratory diseases and asthma (4, 21.1%), haematological and oncological diseases (3, 15.8%) were the most frequent in children. Complications occurred in 2679 (73.4%) episodes, mostly respiratory diseases (2470, 93.2% in adults; 16, 55.2% in children), and renal (681, 25.7%) and cardiac (631, 23.8%) complications for adults. The second and third most frequent complications in children affected the digestive system and the liver (7, 24.1%). A targeted treatment was given in 1299 (35.6%) episodes, mostly with hydroxychloroquine (989, 76.1%). Intensive care units stays were reported in 578 (15.8%) episodes. A total of 527 (14.5%) deaths were registered, all among adults. CONCLUSION The surveillance system has been successfully initiated and provides a robust set of data for Switzerland by including about 80% (compared with official statistics) of SARS-CoV-2/COVID-19 hospitalised patients, with similar age and comorbidity distributions. It adds detailed information on the epidemiology, risk factors and clinical course of these cases and, therefore, is a valuable addition to the existing mandatory reporting.

BACKGROUND As clinical signs of COVID-19 differ widely among individuals, from mild to severe, the definition of risk groups has important consequences for recommendations to the public, control measures and patient management, and needs to be reviewed regularly. AIM The aim of this study was to explore risk factors for in-hospital mortality and intensive care unit (ICU) admission for hospitalised COVID-19 patients during the first epidemic wave in Switzerland, as an example of a country that coped well during the first wave of the pandemic. METHODS This study included all (n = 3590) adult polymerase chain reaction (PCR)-confirmed hospitalised patients in 17 hospitals from the hospital-based surveillance of COVID-19 (CH-Sur) by 1 September 2020. We calculated univariable and multivariable (adjusted) (1) proportional hazards (Fine and Gray) survival regression models and (2) logistic regression models for in-hospital mortality and admission to ICU, to evaluate the most common comorbidities as potential risk factors. RESULTS AND DISCUSSION We found that old age was the strongest factor for in-hospital mortality after having adjusted for gender and the considered comorbidities (hazard ratio [HR] 2.46, 95% confidence interval [CI] 2.33−2.59 and HR 5.6 95% CI 5.23−6 for ages 65 and 80 years, respectively). In addition, male gender remained an important risk factor in the multivariable models (HR 1.47, 95% CI 1.41−1.53). Of all comorbidities, renal disease, oncological pathologies, chronic respiratory disease, cardiovascular disease (but not hypertension) and dementia were also risk factors for in-hospital mortality. With respect to ICU admission risk, the pattern was different, as patients with higher chances of survival might have been admitted more often to ICU. Male gender (OR 1.91, 95% CI 1.58−2.31), hypertension (OR 1.3, 95% CI 1.07−1.59) and age 55–79 years (OR 1.15, 95% CI 1.06−1.26) are risk factors for ICU admission. Patients aged 80+ years, as well as patients with dementia or with liver disease were admitted less often to ICU. CONCLUSION We conclude that increasing age is the most important risk factor for in-hospital mortality of hospitalised COVID-19 patients in Switzerland, along with male gender and followed by the presence of comorbidities such as renal diseases, chronic respiratory or cardiovascular disease, oncological malignancies and dementia. Male gender, hypertension and age between 55 and 79 years are, however, risk factors for ICU admission. Mortality and ICU admission need to be considered as separate outcomes when investigating risk factors for pandemic control measures and for hospital resources planning.

AbstractBackgroundCoronavirus disease 19 (COVID-19) has frequently been colloquially compared to the seasonal influenza, but comparisons based on empirical data are scarce.AimsTo compare in-hospital outcomes for patients admitted with community-acquired COVID-19 to patients with community-acquired influenza in Switzerland.MethodsPatients >18 years, who were admitted with PCR proven COVID-19 or influenza A/B infection to 14 participating Swiss hospitals were included in a prospective surveillance. Primary and secondary outcomes were the in-hospital mortality and intensive care unit (ICU) admission between influenza and COVID-19 patients. We used Cox regression (cause-specific models, and Fine & Gray subdistribution) to account for time-dependency and competing events with inverse probability weighting to account for confounders.ResultsIn 2020, 2843 patients with COVID-19 were included from 14 centers and in years 2018 to 2020, 1361 patients with influenza were recruited in 7 centers. Patients with COVID-19 were predominantly male (n=1722, 61% vs. 666 influenza patients, 48%, p<0.001) and were younger than influenza patients (median 67 years IQR 54-78 vs. median 74 years IQR 61-84, p<0.001). 363 patients (12.8%) died in-hospital with COVID-19 versus 61 (4.4%) patients with influenza (p<0.001). The final, adjusted subdistribution Hazard Ratio for mortality was 3.01 (95% CI 2.22-4.09, p<0.001) for COVID-19 compared to influenza, and 2.44 (95% CI, 2.00-3.00, p<0.001) for ICU admission.ConclusionEven in a national healthcare system with sufficient human and financial resources, community-acquired COVID-19 was associated with worse outcomes compared to community-acquired influenza, as the hazards of in-hospital death and ICU admission were ∼3-fold higher.

BACKGROUND:Demographic and sociobehavioral factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide in-depth insight into the local behavior and may help to improve HIV prevention.OBJECTIVE:To obtain a comprehensive overview of the sociobehavioral factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature using a newly programmed tool for automatizing part of the systematic review process.METHODS:Due to the choice of broad search terms ("HIV AND Malawi"), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorize open-access articles published from January 1, 1987 to October 1, 2019 in the PubMed, PubMed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest.RESULTS:Our tool extracted 22,709 unique articles; 16,942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics, of which 20 were kept after manual screening. We retrieved 7 more publications after examining the references so that 27 publications were finally included in the review. Reducing the 16,942 articles to 519 potentially relevant articles using the software took 5 days. Several factors contributing to the risk of HIV infection were identified, including religion, gender and relationship dynamics, beliefs, and sociobehavioral attitudes.CONCLUSIONS:Our software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a "seed dataset" of relevance that could be further developed. It identified known factors and factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries.

Implemented in Switzerland in November 2016, Grippenet provides Internet-based participatory surveillance of influenza-like illness (ILI). The aim of this research is to test the feasibility of such a system and its ability to detect risk factors and to assess ILI-related behaviors. Participants filled in a web-based socio-demographic and behavioral questionnaire upon registration, and a weekly symptoms survey during the influenza season. ILI incidence was calculated weekly, and risk factors associated to ILI were analyzed at the end of each season. From November 2016 to May 2019, 1247 participants were included. The crossing of the Sentinel System (Sentinella) epidemic threshold was associated with an increase or decrease of Grippenet ILI incidence, within the same week or earlier. The number of active users varied according to ILI incidence. Factors associated with ILI were: ages 0–4 compared with 5–14 (adjusted odds ratio (AOR) 0.6, 95% confidence interval (CI) 0.19–0.99), 15–29 (AOR 0.29, 95% CI 0.15–0.60), and 65+ (AOR 0.38, 95% CI 0.16–0.93); female sex (male AOR 0.81, 95% CI 0.7–0.95); respiratory allergies (AOR 1.58, 95% CI 1.38–1.96), not being vaccinated (AOR 2.4, 95% CI 1.9–3.04); and self-employment (AOR 1.97, 95% CI 1.33–3.03). Vaccination rates were higher than those of the general population but not high enough to meet the Swiss recommendations. Approximately, 36.2% to 42.5% of users who reported one or more ILIs did not seek medical attention. These results illustrate the potential of Grippenet in complementing Sentinella for ILI monitoring in Switzerland.