SUMMARY In global health, critical challenges have arisen from infectious diseases, including the emergence and reemergence of old and new infectious diseases. Emergence and reemergence are accelerated by rapid human development, including numerous changes in demographics, populations, and the environment. This has also led to zoonoses in the changing human-animal ecosystem, which are impacted by a growing globalized society where pathogens do not recognize geopolitical borders. Within this context, neglected tropical infectious diseases have historically lacked adequate attention in international public health efforts, leading to insufficient prevention and treatment options. This subset of 17 infectious tropical diseases disproportionately impacts the world's poorest, represents a significant and underappreciated global disease burden, and is a major barrier to development efforts to alleviate poverty and improve human health. Neglected tropical diseases that are also categorized as emerging or reemerging infectious diseases are an even more serious threat and have not been adequately examined or discussed in terms of their unique risk characteristics. This review sets out to identify emerging and reemerging neglected tropical diseases and explore the policy and innovation environment that could hamper or enable control efforts. Through this examination, we hope to raise awareness and guide potential approaches to addressing this global health concern.

Background The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. Objective The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. Methods Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. Results A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. Conclusions The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

Background The coronavirus disease (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. The pandemic is historic in scope and precedent given its emergence in an increasingly digital era. Importantly, there have been concerns about the accuracy of COVID-19 case counts due to issues such as lack of access to testing and difficulty in measuring recoveries. Objective The aims of this study were to detect and characterize user-generated conversations that could be associated with COVID-19-related symptoms, experiences with access to testing, and mentions of disease recovery using an unsupervised machine learning approach. Methods Tweets were collected from the Twitter public streaming application programming interface from March 3-20, 2020, filtered for general COVID-19-related keywords and then further filtered for terms that could be related to COVID-19 symptoms as self-reported by users. Tweets were analyzed using an unsupervised machine learning approach called the biterm topic model (BTM), where groups of tweets containing the same word-related themes were separated into topic clusters that included conversations about symptoms, testing, and recovery. Tweets in these clusters were then extracted and manually annotated for content analysis and assessed for their statistical and geographic characteristics. Results A total of 4,492,954 tweets were collected that contained terms that could be related to COVID-19 symptoms. After using BTM to identify relevant topic clusters and removing duplicate tweets, we identified a total of 3465 (<1%) tweets that included user-generated conversations about experiences that users associated with possible COVID-19 symptoms and other disease experiences. These tweets were grouped into five main categories including first- and secondhand reports of symptoms, symptom reporting concurrent with lack of testing, discussion of recovery, confirmation of negative COVID-19 diagnosis after receiving testing, and users recalling symptoms and questioning whether they might have been previously infected with COVID-19. The co-occurrence of tweets for these themes was statistically significant for users reporting symptoms with a lack of testing and with a discussion of recovery. A total of 63% (n=1112) of the geotagged tweets were located in the United States. Conclusions This study used unsupervised machine learning for the purposes of characterizing self-reporting of symptoms, experiences with testing, and mentions of recovery related to COVID-19. Many users reported symptoms they thought were related to COVID-19, but they were not able to get tested to confirm their concerns. In the absence of testing availability and confirmation, accurate case estimations for this period of the outbreak may never be known. Future studies should continue to explore the utility of infoveillance approaches to estimate COVID-19 disease severity.

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BackgroundYouth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens.ObjectiveIn order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies.MethodsTweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning.ResultsA total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72% of the generic and 12.28% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription.ConclusionsThis study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online.

Background: Pharmaceutical corruption is a serious challenge in global health. Digital technologies that can detect and prevent fraud and corruption are particularly important to address barriers to access to medicines, such as medicines availability and affordability, stockouts, shortages, diversion, and infiltration of substandard and falsified medicines. Objectives: To better understand how digital technologies are used to combat corruption, increase transparency, and detect fraud in pharmaceutical procurement systems to improve population health outcomes. Methods: We conducted a multidisciplinary review of the health/medicine, engineering, and computer science literature. Our search queries included keywords associated with medicines procurement and digital technology in combination with terms associated with transparency and anti-corruption initiatives. Our definition of 'digital technology' focused on Internetbased communications, including online portals and management systems, supply chain tools, and electronic databases. Results: We extracted 37 articles for in-depth review based on our inclusion criteria focused on the utilization of digital technology to improve medicines procurement. The vast majority of articles focused on electronic data transfer and/or e-procurement systems with fewer articles discussing emerging technologies such as machine learning and blockchain distributed ledger solutions. In the context of e-procurement, slow adoption, justifying cost-savings, and need for technical standards setting were identified as key challenges for current and future utilization. Conclusions: Though there is a significant promise for digital technologies, particularly e-procurement, overall adoption of solutions that can enhance transparency, accountability and concomitantly combat corruption, is still underdeveloped. Future efforts should focus on tying cost-saving measurements with anti-corruption indicators, prioritizing centralization of e-procurement systems, establishing regulatory harmonization with standards setting, and incorporating additional anti-corruption technologies into procurement processes for improving access to medicines and to reach the overall goal of Universal Health Coverage. ARTICLE HISTORY

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