Coronavirus pandemic in the South Asia region: Health policy and economy trade-off.

Background: The South Asian Association for Regional Cooperation (SAARC) covers Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. We conducted a comparative analysis of the trade-off between the health policies for the prevention of COVID-19 spread and the impact of these policies on the economies and livelihoods of the South Asia populations. Methods: We analyzed COVID-19 data on epidemiology, public health and health policy, health system capacity, and macroeconomic indicators from January 2020 to March 2021 to determine temporal trends by conducting joinpoint regression analysis using average weekly percent change (AWPC). Results: Bangladesh had the highest statistically significant AWPC for new COVID-19 cases (17.0; 95% CI = 7.7-27.1, P < 0.001), followed by the Maldives (12.9; 95% CI = 5.3-21.0, P < 0.001) and India (10.0; 95% CI = 8.4-11.5, P < 0.001). The AWPC for COVID-19 deaths was significant for India (6.5; 95% CI = 4.3-8.9, P < 0.001) and Bangladesh (6.1; 95% CI = 3.7-8.5, P < 0.001). Nepal (55.79%), and India (34.91%) had the second- and third-highest increase in unemployment, while Afghanistan (6.83%) and Pakistan (16.83%) had the lowest. The rate of change of real GDP had the highest decrease for Maldives (557.51%), and India (297.03%); Pakistan (46.46%) and Bangladesh (70.80%), however, had the lowest decrease. The government response stringency index for Pakistan had a see-saw pattern with a sharp decline followed by an increase in the government health policy restrictions that approximated the test-positivity trend. Conclusions: Unlike developed economies, the South Asian developing countries experienced a trade-off between health policy and their economies during the COVID-19 pandemic. South Asian countries (Nepal and India), with extended periods of lockdowns and a mismatch between temporal trends of government response stringency index and the test-positivity or disease incidence, had higher adverse economic effects, unemployment, and burden of COVID-19. Pakistan demonstrated targeted lockdowns with a rapid see-saw pattern of government health policy response that approximated the test-positivity trend and resulted in lesser adverse economic effects, unemployment, and burden of COVID-19.

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network.

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 inpatient 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 inpatients 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 involved new-variables creation and selection. Phase 3 and final phase applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor 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 deep neural network (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 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.