Decolonizing global AI governance: assessment of the state of decolonized AI governance in Sub-Saharan Africa.

Global artificial intelligence (AI) governance must prioritize equity, embrace a decolonial mindset, and provide the Global South countries the authority to spearhead solution creation. Decolonization is crucial for dismantling Western-centric cognitive frameworks and mitigating biases. Integrating a decolonial approach to AI governance involves recognizing persistent colonial repercussions, leading to biases in AI solutions and disparities in AI access based on gender, race, geography, income and societal factors. This paradigm shift necessitates deliberate efforts to deconstruct imperial structures governing knowledge production, perpetuating global unequal resource access and biases. This research evaluates Sub-Saharan African progress in AI governance decolonization, focusing on indicators like AI governance institutions, national strategies, sovereignty prioritization, data protection regulations, and adherence to local data usage requirements. Results show limited progress, with only Rwanda notably responsive to decolonization among the ten countries evaluated; 80% are 'decolonization-aware', and one is 'decolonization-blind'. The paper provides a detailed analysis of each nation, offering recommendations for fostering decolonization, including stakeholder involvement, addressing inequalities, promoting ethical AI, supporting local innovation, building regional partnerships, capacity building, public awareness, and inclusive governance. This paper contributes to elucidating the challenges and opportunities associated with decolonization in SSA countries, thereby enriching the ongoing discourse on global AI governance.

The determinants of COVID-19 case reporting across Africa.

Background: According to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the case reporting and classify the African countries using the case reporting rates and the significant determinants. Methods: We used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 34 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries. Results: 21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central. Conclusion: Overall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection.

Understanding the impact of HIV on mpox transmission in the MSM population: A mathematical modeling study.

The recent mpox outbreak (in 2022-2023) has different clinical and epidemiological features compared with previous outbreaks of the disease. During this outbreak, sexual contact was believed to be the primary transmission route of the disease. In addition, the community of men having sex with men (MSM) was disproportionately affected by the outbreak. This population is also disproportionately affected by HIV infection. Given that both diseases can be transmitted sexually, the endemicity of HIV, and the high sexual behavior associated with the MSM community, it is essential to understand the effect of the two diseases spreading simultaneously in an MSM population. Particularly, we aim to understand the potential effects of HIV on an mpox outbreak in the MSM population. We develop a mechanistic mathematical model of HIV and mpox co-infection. Our model incorporates the dynamics of both diseases and considers HIV treatment with anti-retroviral therapy (ART). In addition, we consider a potential scenario where HIV infection increases susceptibility to mpox, and investigate the potential impact of this mechanism on mpox dynamics. Our analysis shows that HIV can facilitate the spread of mpox in an MSM population, and that HIV treatment with ART may not be sufficient to control the spread of mpox in the population. However, we showed that a moderate use of condoms or reduction in sexual contact in the population combined with ART is beneficial in controlling mpox transmission. Based on our analysis, it is evident that effective control of HIV, specifically through substantial ART use, moderate condom compliance, and reduction in sexual contact, is imperative for curtailing the transmission of mpox in an MSM population and mitigating the compounding impact of these intertwined epidemics.

Evaluating automatic annotation of lexicon-based models for stance detection of M-pox tweets from May 1st to Sep 5th, 2022.

Manually labeling data for supervised learning is time and energy consuming; therefore, lexicon-based models such as VADER and TextBlob are used to automatically label data. However, it is argued that automated labels do not have the accuracy required for training an efficient model. Although automated labeling is frequently used for stance detection, automated stance labels have not been properly evaluated, in the previous works. In this work, to assess the accuracy of VADER and TextBlob automated labels for stance analysis, we first manually label a Twitter, now X, dataset related to M-pox stance detection. We then fine-tune different transformer-based models on the hand-labeled M-pox dataset, and compare their accuracy before and after fine-tuning, with the accuracy of automated labeled data. Our results indicated that the fine-tuned models surpassed the accuracy of VADER and TextBlob automated labels by up to 38% and 72.5%, respectively. Topic modeling further shows that fine-tuning diminished the scope of misclassified tweets to specific sub-topics. We conclude that fine-tuning transformer models on hand-labeled data for stance detection, elevates the accuracy to a superior level that is significantly higher than automated stance detection labels. This study verifies that automated stance detection labels are not reliable for sensitive use-cases such as health-related purposes. Manually labeled data is more convenient for developing Natural Language Processing (NLP) models that study and analyze mass opinions and conversations on social media platforms, during crises such as pandemics and epidemics.

Assessment of bidirectional impact of stigmatization induced self-medication on COVID-19 and malaria transmissions using mathematical modeling: Nigeria as a case study.

The continual social and economic impact of infectious diseases on nations has maintained sustained attention on their control and treatment, of which self-medication has been one of the means employed by some individuals. Self-medication complicates the attempt of their control and treatment as it conflicts with some of the measures implemented by health authorities. Added to these complications is the stigmatization of individuals with some diseases in some jurisdictions. This study investigates the co-infection of COVID-19 and malaria and its related deaths and further highlights how self-medication and stigmatization add to the complexities of the fight against these two diseases using Nigeria as a study case. Using a mathematical model on COVID-19 and malaria co-infection, we address the question: to what degree does the impact of the interaction between COVID-19 and malaria amplify infections and deaths induced by both diseases via self-medication and stigmatization? We demonstrate that COVID-19 related self-medication due to misdiagnoses contributes substantially to the prevalence of disease. The control reproduction numbers for these diseases and quantification of model parameters uncertainties and sensitivities are presented.

Quantifying the basic reproduction number and underestimated fraction of Mpox cases worldwide at the onset of the outbreak.

In 2022, there was a global resurgence of mpox, with different clinical-epidemiological features compared with previous outbreaks. Sexual contact was hypothesized as the primary transmission route, and the community of men having sex with men (MSM) was disproportionately affected. Because of the stigma associated with sexually transmitted infections, the real burden of mpox could be masked. We quantified the basic reproduction number (R 0) and the underestimated fraction of mpox cases in 16 countries, from the onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-/low-risk populations) and two-route (sexual/non-sexual transmission) mathematical model. Machine learning (ML) was harnessed to identify underestimation determinants. Estimated R 0 ranged between 1.37 (Canada) and 3.68 (Germany). The underestimation rates for the high- and low-risk populations varied between 25-93% and 65-85%, respectively. The estimated total number of mpox cases, relative to the reported cases, is highest in Colombia (3.60) and lowest in Canada (1.08). In the ML analysis, two clusters of countries could be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and the importance of religion. Given the substantial mpox underestimation, surveillance should be enhanced, and country-specific campaigns against the stigmatization of MSM should be organized, leveraging community-based interventions.

Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework.

OBJECTIVE: To assess the impact of self-medication on the transmission dynamics of COVID-19 across different age groups, examine the interplay of vaccination and self-medication in disease spread, and identify the age group most prone to self-medication. METHODS: We developed an age-structured compartmentalized epidemiological model to track the early dynamics of COVID-19. Age-structured data from the Government of Gauteng, encompassing the reported cumulative number of cases and daily confirmed cases, were used to calibrate the model through a Markov Chain Monte Carlo (MCMC) framework. Subsequently, uncertainty and sensitivity analyses were conducted on the model parameters. RESULTS: We found that self-medication is predominant among the age group 15-64 (74.52%), followed by the age group 0-14 (34.02%), and then the age group 65+ (11.41%). The mean values of the basic reproduction number, the size of the first epidemic peak (the highest magnitude of the disease), and the time of the first epidemic peak (when the first highest magnitude occurs) are 4.16499, 241,715 cases, and 190.376 days, respectively. Moreover, we observed that self-medication among individuals aged 15-64 results in the highest spreading rate of COVID-19 at the onset of the outbreak and has the greatest impact on the first epidemic peak and its timing. CONCLUSION: Studies aiming to understand the dynamics of diseases in areas prone to self-medication should account for this practice. There is a need for a campaign against COVID-19-related self-medication, specifically targeting the active population (ages 15-64).

Models and data used to predict the abundance and distribution of Ixodes scapularis (blacklegged tick) in North America: a scoping review.

Tick-borne diseases (TBD) remain prevalent worldwide, and risk assessment of tick habitat suitability is crucial to prevent or reduce their burden. This scoping review provides a comprehensive survey of models and data used to predict I. scapularis distribution and abundance in North America. We identified 4661 relevant primary research articles published in English between January 1st, 2012, and July 18th, 2022, and selected 41 articles following full-text review. Models used data-driven and mechanistic modelling frameworks informed by diverse tick, hydroclimatic, and ecological variables. Predictions captured tick abundance (n = 14, 34.1%), distribution (n = 22, 53.6%) and both (n = 5, 12.1%). All studies used tick data, and many incorporated both hydroclimatic and ecological variables. Minimal host- and human-specific data were utilized. Biases related to data collection, protocols, and tick data quality affect completeness and representativeness of prediction models. Further research and collaboration are needed to improve prediction accuracy and develop effective strategies to reduce TBD.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Cross-sectional study to assess the impact of the COVID-19 pandemic on healthcare services and clinical admissions using statistical analysis and discovering hotspots in three regions of the Greater Toronto Area.

OBJECTIVES: The COVID-19 pandemic disrupted healthcare services, leading to the cancellation of non-urgent tests, screenings and procedures, a shift towards remote consultations, stalled childhood immunisations and clinic closures which had detrimental effects across the healthcare system. This study investigates the impact of the COVID-19 pandemic on clinical admissions and healthcare quality in the Peel, York and Toronto regions within the Greater Toronto Area (GTA). DESIGN: In a cross-sectional study, the negative impact of the pandemic on various healthcare sectors, including preventive and primary care (PPC), the emergency department (ED), alternative level of care (ALC) and imaging, procedures and surgeries is investigated. Study questions include assessing impairments caused by the COVID-19 pandemic and discovering hotspots and critical subregions that require special attention to recover. The measuring technique involves comparing the number of cases during the COVID-19 pandemic with before that, and determining the difference in percentage. Statistical analyses (Mann-Whitney U test, analysis of variance, Dunn's test) is used to evaluate sector-specific changes and inter-relationships. SETTING: This work uses primary data which were collected by the Black Creek Community Health Centre. The study population was from three regions of GTA, namely, the city of Toronto, York and Peel. For all health sectors, the sample size was large enough to have a statistical power of 0.95 to capture 1% variation in the number of cases during the COVID-19 pandemic compared with before that. RESULTS: All sectors experienced a significant decline in patient volume during the pandemic. ALC admissions surged in some areas, while IPS patients faced delays. Surgery waitlists increased by an average of 9.75%, and completed IPS procedures decreased in several subregions. CONCLUSIONS: The COVID-19 pandemic had a universally negative impact on healthcare sectors across various subregions. Identification of the hardest-hit subregions in each sector can assist health officials in crafting recovery policies.

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets.

BACKGROUND: The COVID-19 pandemic has highlighted gaps in the current handling of medical resource demand surges and the need for prioritizing scarce medical resources to mitigate the risk of health care facilities becoming overwhelmed. OBJECTIVE: During a health care emergency, such as the COVID-19 pandemic, the public often uses social media to express negative sentiment (eg, urgency, fear, and frustration) as a real-time response to the evolving crisis. The sentiment expressed in COVID-19 posts may provide valuable real-time information about the relative severity of medical resource demand in different regions of a country. In this study, Twitter (subsequently rebranded as X) sentiment analysis was used to investigate whether an increase in negative sentiment COVID-19 tweets corresponded to a greater demand for hospital intensive care unit (ICU) beds in specific regions of the United States, Brazil, and India. METHODS: Tweets were collected from a publicly available data set containing COVID-19 tweets with sentiment labels and geolocation information posted between February 1, 2020, and March 31, 2021. Regional medical resource shortage data were gathered from publicly available data sets reporting a time series of ICU bed demand across each country. Negative sentiment tweets were analyzed using the Granger causality test and convergent cross-mapping (CCM) analysis to assess the utility of the time series of negative sentiment tweets in forecasting ICU bed shortages. RESULTS: For the United States (30,742,934 negative sentiment tweets), the results of the Granger causality test (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a stochastic system) were significant (P<.05) for 14 (28%) of the 50 states that passed the augmented Dickey-Fuller test at lag 2, and the results of the CCM analysis (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a dynamic system) were significant (P<.05) for 46 (92%) of the 50 states. For Brazil (3,004,039 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (22%) of the 27 federative units, and the results of the CCM analysis were significant (P<.05) for 26 (96%) of the 27 federative units. For India (4,199,151 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (23%) of the 26 included regions (25 states and the national capital region of Delhi), and the results of the CCM analysis were significant (P<.05) for 26 (100%) of the 26 included regions. CONCLUSIONS: This study provides a novel approach for identifying the regions of high hospital bed demand during a health care emergency scenario by analyzing Twitter sentiment data. Leveraging analyses that take advantage of natural language processing-driven tweet extraction systems has the potential to be an effective method for the early detection of medical resource demand surges.

Using an innovative family-centered evidence toolkit to improve the livelihood of people with disabilities in Bamenda (Cameroon): a mixed-method study.

Background: Most of the disability-related scholarly literature focuses on high-income countries, whereas there is a lack of data concerning challenges (barriers and obstacles) and opportunities (participatory research and community engagement) in the Global South. Moreover, many frameworks for interventions for people with disabilities (PWDs) have been designed for resource-rich contexts, and little is known about their translatability to low- and middle-income countries (LMICs). Objective: The main objective of this study was to design and pilot an interventional approach based on an innovative framework aimed at improving the livelihood of PWDs in LMICs. Methodology: The present mixed-method study was conducted in Bamenda, North-West Region of Cameroon, through an intervention of household visits by community health workers using innovation and best practices informed by a systematic literature review and embedded into an evidence toolkit called the eBASE Family-Centered Evidence Toolkit for Disabilities (EFCETD), adapted from the WHO matrix and consisting of 43 questions across five categories (health, education, social wellbeing, empowerment, and livelihood). Out of 56 PWDs identified, 30 were randomly sampled, with an attrition of four participants. Three datasets (baseline, qualitative, and quantitative) were collected. The Washington Group tool, exploring the type of disability, gender, how long one has had the disability, their facility situation coupled with their coping strategies, and the context of livelihood, was used to design the questionnaire for baseline data collection. Qualitative data were collected through key informant interviews and focus group discussions analyzed with MAXQDA, while quantitative data were collected through the EFCETD and analyzed by means of descriptive statistics. Results: In total, 69.2% of PWDs were female individuals. Many PWDs were aged 10-20 years (57% of the sample size). Physical/motor disability was the most common type of disability recorded (84.6%). The mean percentile for education increased from 29.9% during the first visit to 70.2% during the last visit, while the mean percentile for health increased from 65.4 to 78.7% and the mean percentile for social wellbeing moved from 73.1 to 84.9%. The livelihood and empowerment standards increased from 16.3 to 37.2% and from 27.7 to 65.8%, respectively. Overall, the temporal trend was statistically significant (F = 35.11, p < 0.0001). The adjusted score increased from the baseline value of 45.02 ± 2.38 to 61.07 ± 2.25, 65.24 ± 2.67, and 68.46 ± 2.78, at 4, 8, and 12 months, respectively. Compared to the baseline, all timepoints were significantly different, indicating a significant impact of the intervention, which became stable after 4 months and was preserved until 12 months. Conclusion: PWDs faced many endeavors for sustainability and challenges resulting from a lack of inclusive policies and practices, leading to their exclusion from education, employment, and healthcare. Using implementation science approaches could bridge the gap and make policies and practices more effective.