A Smartphone App for Real-Time Assessment of Malaria Prophylaxis Adverse Events.

Background: Growth of international travel to malarial areas over the last decades has contributed to more travelers taking malaria prophylaxis. Travel-related symptoms may be wrongly attributed to malaria prophylaxis and hinder compliance. Here, we aimed to assess the frequency of real-time reporting of symptoms by travelers following malaria prophylaxis using a smartphone app. Method: Adult international travelers included in this single-center study (Barcelona, Spain) used the smartphone Trip Doctor® app developed by our group for real-time tracking of symptoms and adherence to prophylaxis. Results: Six hundred four (n = 604) international travelers were included in the study; 74.3% (449) used the app daily, and for one-quarter of travelers, malaria prophylaxis was prescribed. Participants from the prophylaxis group traveled more to Africa (86.7% vs. 4.3%; p < 0.01) and to high travel medical risk countries (60.8% vs. 18%; p < 0.01) and reported more immunosuppression (30.8% vs. 23.1% p < 0.01). Regarding symptoms, no significant intergroup differences were observed, and no relationship was found between the total number of malarial pills taken and reported symptoms. Conclusions: In our cohort, the number of symptoms due to malaria prophylaxis was not significantly higher than in participants for whom prophylaxis was not prescribed, and the overall proportion of symptoms is higher compared with other studies.

Mobile based surveillance platform for detecting Zika virus among Spanish Delegates attending the Rio de Janeiro Olympic Games.

BACKGROUND: Zika virus has created a major epidemic in Central and South America, especially in Brazil, during 2015-16. The infection is strongly associated with fetal malformations, mainly microcephaly, and neurological symptoms in adults. During the preparation of the Rio de Janeiro Olympic Games in 2016, members of Olympic Delegations worldwide expressed their concern about the health consequences of being infected with Zika virus. A major risk highlighted by the scientific community was the impact on the spreading of the virus into new territories immediately after the Games. OBJECTIVES: To detect real-time incidence of symptoms compatible with arboviral diseases and other tropical imported diseases among the Spanish Olympic Delegation (SOD) attending the Rio Olympic Games in 2016. METHODS: We developed a surveillance platform based on a mobile application installed in participant's smartphones that monitored the health status of the SOD through a daily interactive check of the user health status including geo-localization data. The results were evaluated by a study physician on-call through a web-based platform monitoring system. Participants presenting severe symptoms or those compatible with Zika infection prompted an alarm in the system triggering specialized medical assistance and allowing early detection and control of the introduction of arboviral diseases in Spain. SUMMARY OF THE RESULTS: The system was downloaded by 189 participants and used by 143 of them (76%). Median age was 38 years (IQR 16), and 134 (71%) were male. Mean duration of travel was 19 days (+/-9SD). During the Games the highest accumulated incidence observed was for headache: 6.06% cough: 5.30% and conjunctivitis: 3.03%. The incidence rate of cough during the Olympic Games was 1.1% per day per person, followed by headache 0.8% and 0.4% conjunctivitis or diarrhea. In our cohort we observed that non-athletes experienced more incidence of symptoms, except for incidence of cough which was the same in the two groups (1.1%). No participants reported symptoms fulfilling Zika definition case. CONCLUSION: Our system did not find cases fulfilling Zika definition amongst participants of the SOD during the Games, consistent with limited cases of Zika in Rio during the Games. The app showed good usability and the web based monitoring platform allowed to manage infectious cases in real-time. The overall system has proven to serve as a real-time surveillance platform for detecting symptoms that could be present in tropical imported diseases, especially arboviral diseases, contributing to the preparedness for the introduction of vector borne-diseases in non-endemic countries.

Counting malaria parasites with a two-stage EM based algorithm using crowsourced data.

Malaria eradication of the worldwide is currently one of the main WHO's global goals. In this work, we focus on the use of human-machine interaction strategies for low-cost fast reliable malaria diagnostic based on a crowdsourced approach. The addressed technical problem consists in detecting spots in images even under very harsh conditions when positive objects are very similar to some artifacts. The clicks or tags delivered by several annotators labeling an image are modeled as a robust finite mixture, and techniques based on the Expectation-Maximization (EM) algorithm are proposed for accurately counting malaria parasites on thick blood smears obtained by microscopic Giemsa-stained techniques. This approach outperforms other traditional methods as it is shown through experimentation with real data.