Background: Health infodemic undermines public health response, results in poor observance of public health measures and costs lives. Health campaigns will not produce intended results without controlling misinformation. This study aimed to analyzed the correlation between infodemic, COVID-19 stress and media trust. Subjects and Method: This was a cross sectional study conducted using online structured questionnaire, from December 2020 to January 2021. A total of 470 participants among African twitter community were randomly selected for this study. The dependent variables were COVID-19 stress and media trust. The independent variable was while Infodemic serve. The data was analysed using Pearson's product moment correlation coefficient test. Results: COVID-19 stress (r= 0.369; p<0.001) and media trust (r= 0.301; p<0.001) were correlated with infodemic and it was statistically significant. Conclusion: infodemic is correlated with COVID-19 stress and media trust.

Background: Health infodemic undermines public health response, results in poor observance of public health measures and costs lives. Health campaigns will not produce intended results without controlling misinformation. This study aimed to analyzed the correlation between infodemic, COVID-19 stress and media trust. Subjects and Method: This was a cross sectional study conducted using online structured questionnaire, from December 2020 to January 2021. A total of 470 participants among African twitter community were randomly selected for this study. The dependent variables were COVID-19 stress and media trust. The independent variable was while Infodemic serve. The data was analysed using Pearson's product moment correlation coefficient test. Results: COVID-19 stress (r= 0.369; p<0.001) and media trust (r= 0.301; p<0.001) were correlated with infodemic and it was statistically significant. Conclusion: infodemic is correlated with COVID-19 stress and media trust.

Background: Communication about COVID-19 pandemic has a huge impact on coordination, control and mitigation efforts against the disease. Patterns and trends of COVID-19 pandemic conversations amongst African tweeps between the year 2019 and 2020 was studied. This study aimed to determine the impact of Twitter COVID-19 information dissemination on attitudes, behaviour and decision making during the pandemic. Subjects and Method: This was a cohort study with combined quantitative and qualitative approach. This study was conducted in Africa, from December 2019 to December 2020. The quantitative approach was founded on data mining and data analytics research approach, applying measurements in terms of counts, numbers and frequencies while qualitative approach was founded on Natural Language Processing (NPL) algorithm to extract themes/topics and further applying sentiment analysis to a body of large textual data. Results: A total number of 24,251 tweets was recorded, out of which 9, 016 (37.2%) of the tweets were positive, indicating positive attitude towards COVID-19 related information, control, treatment and regulations. A number of 7, 024 (29%) of tweets were considered neutral, indicating a neutral opinion on conversations related to COVID-19, while 8, 211 (33.9%) were considered negative tweets. South Africa is the most frequently used word and frequently used hashtag followed by Nigeria. Result further revealed four clear topics of discussion which are: a) Africa coronavirus, b) First sub-Saharan pandemic variant, c) Total number of confirmed new deaths, and d) COVID-19 cases in Africa. Besides, it was observed that most health authorities and health partners in Africa are not actively participating on Twitter. Conclusion: Health information dissemination on social media must be moderated through censorship, otherwise fake news and misinformation would persist to aggravate the spread of diseases and cause deaths. In order to protect the public against false information, public health institutions, governments and partners in health should establish an active presence on social media to share factual information, and timely debunk misinformation.

To examine challenges to health-literacy with a focus on non-communicable diseases (NCDs) in Tanzania and to develop a Chatbot for NCDs in Swahili language. Objective: To examine the challenges of health information dissemination in Tanzania; to analyse the opportunities offered by Chatbot to address the challenges of health information dissemination in Tanzania; to assess the readiness of users on the acceptance of an interactive Chatbot for NCDs in Swahili Language. Method: Survey was conducted amongst a sample of 100 participants at Wazo ward, Dares-Salam; Chatbot was designed in Swahili and integrated into Facebook Messenger to eliminate the cost of the internet from the end-users. Results: Challenges to health-literacy includes: inaccurate, inconsistent, untrustworthy, unreliable, untimely, contradictory, and confusing information; popular media such as TV programs and the Internet presents no opportunity for feedback, requests, or clarifications. Others are language barrier; high cost of internet, poor connectivity, the ratio of healthcare providers to patients, poverty, and traditional beliefs. There is a general acceptance of receiving health information through messaging apps. Conclusions: Chatbot for NCDs in Swahili language ‘afyaBot' could be strategic in affording Tanzanians access to adequate and timely information on NCDs, improve health-literacy, and promoting good health. Stakeholders and public policymakers in the health sector will find the study useful.

Current health awareness campaign strategies, efforts, and methods on NCDs have not produced improved outcomes in reducing the burden, spread, and deaths linked to NCDs in Tanzania. To support, compliment, and improve health literacy on NCDs and promote good healthcare, we designed and developed an interactive health Chatbot ‘afyaBot’ in Swahili language that can respond to user's requests concerning NCDs symptoms, prevention, management, and cure. The Chatbot was designed using Botsociety; a special tool for designing Chatbot prototypes; BotMan framework for coding the Chatbot logic and Google Dialogflow platform that offers high-level Natural Language Processing capabilities. The Chatbot was integrated into the Facebook Messenger platform which offers free public API access that eliminates the cost of the internet from the consumers. The Chatbot was tested for accuracy, usability, user experience, responsiveness, reliability, maintainability, and portability. The results of implementation were satisfactory and provide insights useful to stakeholders in the health sector. The interactive Chatbot was designed to provide real-time information on NCDs, create awareness, and educate users on preventive, control, and treatment measures of NCDs. It will likewise assist healthcare providers to collect accurate timely health data for monitoring, planning, and research purpose.