Background: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems. Methods: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR). Results: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare. Conclusion: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.

Background: Poor personal hygiene and inadequate sanitation practices among young children leads to communicable diseases. There remains a gap in the holistic assessment of school children's hygiene literacy, practices and effectiveness of school-based hygiene interventions in Pakistan. Therefore, a school-based intervention protocol has been designed to promote personal and environmental hygiene practices for primary school children. Towards improving children's hygiene behaviors, the study will also focus on enhancing mothers' hygiene knowledge and practices. Methods: Using quasi-experimental design with mixed methods data collection approaches, this study will be conducted in schools located in an urban squatter settlements in Pakistan. To assess primary grade children and their mothers' hygiene status, a survey will be held in the pre-intervention phase. This phase also includes qualitative exploration of key stakeholders (mothers, teachers, health & education authorities representatives') perceptions about the factors facilitating and impeding the adaption of hygiene behaviors among school children. In-depth guides and focus group discussion tools will be used for this purpose. This will be followed by multi-component intervention phase with behavior change strategies to improve children's and mothers' hygiene literacy and behaviors. The post-intervention phase will assess the intervention effectiveness in terms of enhancing hygiene knowledge and practices among school children and mothers, alongside exploration of mothers and teachers' insights into whether or not the intervention has been effective in improving hygiene practices among children. Paired t-test will be applied pre and post-intervention to measure the differences between the mothers and children's hygiene literacy and knowledge scores. Similar test will be performed to assess the differences in children's hygiene knowledge and practice scores, pre and post-intervention (< 50 = poor, 50-75 = good and > 75 = excellent). Thematic analysis will be carried out for qualitative data. Discussion: Multi-component intervention aimed at improving personal and environmental hygiene among primary school children offers an opportunity to design and test various behavioral change strategies at school and in home settings. The study findings will be significant in assessing the intervention's effectiveness in improving children's overall hygiene. Trial registration: Retrospectively registered with ClinicalTrials.gov (NCT03942523) on 5th May 2019.

Background The nextwave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a novel machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. Methods We collected clinical and laboratory data prospectively on all adult patients (≥18 years of age) that were admitted in the in-patient setting at Aga Khan University Hospital between February 2020 and September 2020 with a clinical diagnosis of COVID-19 infection. Only patients with a RT-PCR (reverse polymerase chain reaction) proven COVID-19 infection and complete medical records were included in this study. A Novel 3-phase machine learning framework was developed to predict mortality in the in-patients setting. Phase 1 included variable selection that was done using univariate and multivariate Cox-regression analysis; all variables that failed the regression analysis were excluded from the machine learning phase of the study. Phase 2 was involved in new-variables creation and selection. Phase 3 and final phase we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbour models, etc. The accuracy of these models were evaluated using test-set accuracy, sensitivity, specificity, positive predictive values, negative predictive values and area under the receiver-operating curves. Results After application of inclusion and exclusion criteria (n=)1214 patients were selected from a total of 1228 admitted patients. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our DNN (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the curve of the receiver-operator curve of 88.5. Conclusion Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

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BackgroundThe second wave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy.MethodsThe current Deep-Neo-V model is built on our previously statistically rigorous machine learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that evaluated statistically significant risk factors, generated new combined variables and then supply these risk factors to deep neural network to predict mortality in RT-PCR positive COVID-19 patients in the inpatient setting. We analyzed adult patients (≥18 years) admitted to the Aga Khan University Hospital, Pakistan with a working diagnosis of COVID-19 infection (n=1228). We excluded patients that were negative on COVID-19 on RT-PCR, had incomplete or missing health records. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we generated new variables and tested those statistically significant for mortality and in the third and final phase we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models and others.ResultsA total of 1228 cases were diagnosed as COVID-19 infection, we excluded 14 patients after the exclusion criteria and (n=)1214 patients were analyzed. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our DNN (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the curve of the receiver-operator curve of 88.5.ConclusionOur novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.