METHODS: A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment.

RESULT: OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics.

Methods: We administered relevant translations of the BOLD-1 questionnaire with additional questions from ECRHS-II, performed spirometry and arranged specialist clinical review for a sub-group to confirm the diagnosis. Using random sampling, we piloted a community-based survey at five sites in four LMICs and noted any practical barriers to conducting the survey. Three clinicians independently used information from questionnaires, spirometry and specialist reviews, and reached consensus on a clinical diagnosis. We used lasso regression to identify variables that predicted the clinical diagnoses and attempted to develop an algorithm for detecting asthma and COPD.

Results: Of 508 participants, 55.9% reported one or more chronic respiratory symptoms. The prevalence of asthma was 16.3%; COPD 4.5%; and 'other chronic respiratory disease' 3.0%. Based on consensus categorisation (n = 483 complete records), "Wheezing in last 12 months" and "Waking up with a feeling of tightness" were the strongest predictors for asthma. For COPD, age and spirometry results were the strongest predictors. Practical challenges included logistics (participant recruitment; researcher safety); misinterpretation of questions due to local dialects; and assuring quality spirometry in the field.

Materials and Methods: A total of 111 subjects who fulfilled the inclusion and exclusion criteria were randomly included in the study. The subjects were recalled after 1 month of the commencement of fixed orthodontic treatment for the recording of baseline data including plaque index (PI), gingival index (GI), and modified papillary bleeding index (MPBI). After recording of the baseline data, the subjects were randomly allocated into each of the intervention groups, i.e., group A (manual tooth brush), group B (powered tooth brush), and group C (manual tooth brush combined with mouthwash) by lottery method. Further, all the subjects were recalled after 1 and 2 months for recording the data.

Results: Regarding plaque levels, it was seen that there was a highly statistically significant difference between the three groups (P = 0.001), with the manual tooth brush combined with chlorhexidine mouthwash group recording the lowest mean PI score of 0.5 ± 0.39. A comparison of the mean GI scores among the groups at the end of 2 months shows a highly statistically significant difference (P = 0.001). The mean MPBI scores at the end of 2 months were highly statistically significant among the three groups (P = 0.001), with the group C recording the lowest mean MPBI score of 0.3 ± 0.3.