Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis.

Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.

Reading the geography of India's district-level fertility differentials: a spatial econometric approach.

India has gradually progressed into fertility transition over the last few decades. However, the timing and pace of this transition has varied notably in terms of both its geography and the demographic groups most affected by it. While much literature exists on the relationships between fertility level and its influence on demographic, economic, socio-cultural and policy-related factors, the potential spatial variations in the effects of these factors on the fertility level remain unaddressed. Using the most recent district-level census data (of 2011) for India, this nationwide study has identified plausible spatial dependencies and heterogeneities in the relationships between the district-wise Total Fertility Rates (TFRs) and their respective demographic, socioeconomic and cultural factors. After developing a geocoded database for 621 districts of India, spatial regression and Geographically Weighted Regression (GWR) models were used to decipher location-based relationships between the district-level TFR and its driving forces. The results revealed that the relationships between the district-level TFR and the considered selected predictors (percentage of Muslims, urbanization, caste group, female mean age at marriage, female education, females in the labour force, net migration, sex ratio at birth and exposure to mass media) were not spatially invariant in terms of their respective strength, magnitude and direction, and furthermore, these relationships were conspicuously place- and context-specific. This study suggests that such locality-based variations and their complexities cannot be explained simply by a single narrative of either socioeconomic advancement or government policy interventions. It therefore contributes to the ongoing debate on fertility research in India by highlighting the spatial dependence and heterogeneity of the impacts made by demographic, socioeconomic and cultural factors on local fertility levels. From a methodological perspective, the study also discerns that the GWR local model performs better, in terms of both model performance and prediction accuracy, compared with the conventional global model estimates.