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IntroductionCough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms.Methods and analysisParticipants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions.Ethics and disseminationEthics approval was obtained from the ethics committee of the Centre Hospitalier de l’Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences.Trial registration numberNCT04762693.

Research QuestionWhat is the impact of the duration of cough monitoring on its accuracy in detecting changes in the cough frequency?Materials and MethodsThis is a statistical analysis of a prospective cohort study. Participants were recruited in the city of Pamplona (Northern Spain) and their cough frequency was passively monitored using smartphone-based acoustic artificial intelligence software. Differences in cough frequency were compared using a one-tailed Mann-Whitney U test and a randomisation routine to simulate 24-h monitoring.Results616 participants were monitored for an aggregated duration of over 9 person-years and registered 62 325 coughs. This empiric analysis found that an individual's cough patterns are stochastic, following a binomial distribution. When compared to continuous monitoring, limiting observation to 24 h can lead to inaccurate estimates of change in cough frequency, particularly in persons with low or small changes in rate.InterpretationDetecting changes in an individual's rate of coughing is complicated by significant stochastic variability within and between days. Assessing change based solely on intermittent sampling, including 24-h, can be misleading. This is particularly problematic in detecting small changes in individuals who have a low rate and/or high variance in cough pattern.

Research questionCan smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of COVID-19 and other respiratory infections?MethodsThis was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine, significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average (ARIMA) analysis, and its strength determined by calculating its auto-correlation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated with a satisfaction questionnaire and through focused group discussions.ResultsWe followed up 616 participants and collected over 62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference=+0.77 coughs h−1, p=0.00001) There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF=0.43). Technical issues affected uptake and regular use of the system.InterpretationArtificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring.

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Background Malaria is expanding rapidly across Venezuela, spreading outwards from traditional high transmission regions in the southeast of the country, but the lack of official data make it impossible to understand the reasons for this expansion and to estimate its real magnitude. This study aims to evaluate the epidemiological characteristics driving the re-emergence of malaria in Mérida, a state in the west of Venezuela, where no cases have been reported since 2003, and also to study the clinical presentation of the disease in patients presenting with malaria. Methods Thirty-three patients who presented with anemia and fever and with a microscopic diagnosis of malaria were examined and interviewed. Data were collected in standardized forms and analyzed. One-way analysis of variance was used to study differences among patients infected with different parasites. Results Twenty-two patients were from the Zulia state and eleven were from the Mérida state, mainly from the lowlands south of Lake Maracaibo. Six of these patients traveled to the Bolívar state between 2017 and 2019. Thirteen patients presented with the WHO criteria for severe malaria. Conclusions: Domestic migration to the southeast of Venezuela may have played an important role in the expansion of malaria in previously existing endemic areas of transmission and also in the increase in the number of cases of severe malaria.

Syndromic surveillance for respiratory disease is limited by an inability to monitor its protean manifestation, cough. Advances in artificial intelligence provide the ability to passively monitor cough at individual and community levels. We hypothesized that changes in the aggregate number of coughs recorded among a sample could serve as a lead indicator for population incidence of respiratory diseases, particularly that of COVID-19. We enrolled over 900 people from the city of Pamplona (Spain) between 2020 and 2021 and used artificial intelligence cough detection software to monitor their cough. We collected nine person-years of cough aggregated data. Coughs per hour surged around the time cohort subjects sought medical care. There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population. We propose that a clearer correlation with COVID-19 incidence could be achieved with better penetration and compliance with cough monitoring.

IntroductionChagas disease is caused by the protozoan parasite Trypanosoma cruzi, and it is the most important neglected tropical disease in the Americas. Two drugs are available to treat the infection, but their efficacy in the chronic stage of the disease, when most cases are diagnosed, is reduced. Their tolerability is also hindered by common adverse effects, making the development of safer and efficacious alternatives a pressing need. T. cruzi is unable to synthesize purines de novo, relying on a purine salvage pathway to acquire these from its host, making it an attractive target for the development of new drugs. MethodsWe evaluated the anti-parasitic activity of 23 purine analogs with different substitutions in the complementary chains of their purine rings. We sequentially screened the compounds' capacity to inhibit parasite growth, their toxicity in Vero and HepG2 cells, and their specific capacity to inhibit the development of amastigotes. We then used in-silico docking to identify their likely targets.ResultsEight compounds showed specific anti-parasitic activity, with IC50 values ranging from 2.42 to 8.16 μM. Adenine phosphoribosyl transferase, and hypoxanthine-guanine phosphoribosyl transferase, are their most likely targets. DiscussionOur results illustrate the potential role of the purine salvage pathway as a target route for the development of alternative treatments against T. cruzi infection, highlithing the apparent importance of specific substitutions, like the presence of benzene groups in the C8 position of the purine ring, consistently associated with a high and specific anti-parasitic activity.